From 88031017abe8b750b28fb940efd47ad463b70478 Mon Sep 17 00:00:00 2001 From: averissimo Date: Wed, 10 Apr 2024 09:51:46 +0000 Subject: [PATCH] [actions skip] Built site for teal.modules.general@1cf641f5a675b58700eeb8e792037aeafc65b87f --- main/404.html | 13 +++++++++++++ main/CODE_OF_CONDUCT.html | 13 +++++++++++++ main/CONTRIBUTING.html | 13 +++++++++++++ main/LICENSE-text.html | 13 +++++++++++++ main/SECURITY.html | 13 +++++++++++++ main/articles/index.html | 13 +++++++++++++ main/articles/teal-modules-general.html | 13 +++++++++++++ main/articles/using-association-plot.html | 13 +++++++++++++ main/articles/using-bivariate-plot.html | 13 +++++++++++++ main/articles/using-cross-table.html | 13 +++++++++++++ main/articles/using-data-table.html | 13 +++++++++++++ main/articles/using-outliers-module.html | 13 +++++++++++++ main/articles/using-regression-plots.html | 13 +++++++++++++ main/articles/using-response-plot.html | 13 +++++++++++++ main/articles/using-scatterplot-matrix.html | 13 +++++++++++++ main/articles/using-scatterplot.html | 13 +++++++++++++ main/authors.html | 13 +++++++++++++ main/index.html | 13 +++++++++++++ main/news/index.html | 13 +++++++++++++ main/pull_request_template.html | 13 +++++++++++++ main/reference/add_facet_labels.html | 13 +++++++++++++ main/reference/call_fun_dots.html | 13 +++++++++++++ main/reference/create_sparklines.html | 13 +++++++++++++ main/reference/establish_updating_selection.html | 13 +++++++++++++ main/reference/get_scatterplotmatrix_stats.html | 13 +++++++++++++ main/reference/include_css_files.html | 13 +++++++++++++ main/reference/index.html | 13 +++++++++++++ main/reference/is_tab_active_js.html | 13 +++++++++++++ main/reference/plot_var_summary.html | 13 +++++++++++++ main/reference/rADAE.html | 13 +++++++++++++ main/reference/rADLB.html | 13 +++++++++++++ main/reference/rADRS.html | 13 +++++++++++++ main/reference/rADSL.html | 13 +++++++++++++ main/reference/rADTTE.html | 13 +++++++++++++ main/reference/remove_outliers_from.html | 13 +++++++++++++ main/reference/render_single_tab.html | 13 +++++++++++++ main/reference/render_tab_header.html | 13 +++++++++++++ main/reference/render_tab_table.html | 13 +++++++++++++ main/reference/render_tabset_panel_content.html | 13 +++++++++++++ main/reference/shared_params.html | 13 +++++++++++++ main/reference/teal.modules.general.html | 13 +++++++++++++ main/reference/tm_a_pca.html | 13 +++++++++++++ main/reference/tm_a_regression.html | 13 +++++++++++++ main/reference/tm_data_table.html | 13 +++++++++++++ main/reference/tm_file_viewer.html | 13 +++++++++++++ main/reference/tm_front_page.html | 13 +++++++++++++ main/reference/tm_g_association.html | 13 +++++++++++++ main/reference/tm_g_bivariate.html | 13 +++++++++++++ main/reference/tm_g_distribution.html | 13 +++++++++++++ main/reference/tm_g_response.html | 13 +++++++++++++ main/reference/tm_g_scatterplot.html | 13 +++++++++++++ main/reference/tm_g_scatterplotmatrix.html | 13 +++++++++++++ main/reference/tm_missing_data.html | 13 +++++++++++++ main/reference/tm_outliers.html | 13 +++++++++++++ main/reference/tm_t_crosstable.html | 13 +++++++++++++ main/reference/tm_variable_browser.html | 13 +++++++++++++ main/reference/validate_input.html | 13 +++++++++++++ main/reference/var_missings_info.html | 13 +++++++++++++ main/reference/var_summary_table.html | 13 +++++++++++++ main/reference/variable_type_icons.html | 13 +++++++++++++ main/reference/varname_w_label.html | 13 +++++++++++++ main/search.json | 2 +- 62 files changed, 794 insertions(+), 1 deletion(-) diff --git a/main/404.html b/main/404.html index 83b79d918..5bb136ea2 100644 --- a/main/404.html +++ b/main/404.html @@ -57,6 +57,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/CODE_OF_CONDUCT.html b/main/CODE_OF_CONDUCT.html index 5a3763190..ea6ebe84b 100644 --- a/main/CODE_OF_CONDUCT.html +++ b/main/CODE_OF_CONDUCT.html @@ -41,6 +41,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/CONTRIBUTING.html b/main/CONTRIBUTING.html index 654303ab4..f005db1bc 100644 --- a/main/CONTRIBUTING.html +++ b/main/CONTRIBUTING.html @@ -41,6 +41,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/LICENSE-text.html b/main/LICENSE-text.html index 0f62729c4..9e417c4df 100644 --- a/main/LICENSE-text.html +++ b/main/LICENSE-text.html @@ -41,6 +41,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/SECURITY.html b/main/SECURITY.html index 4984e4401..652685d6d 100644 --- a/main/SECURITY.html +++ b/main/SECURITY.html @@ -41,6 +41,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/articles/index.html b/main/articles/index.html index 1164ce217..ae9167ebe 100644 --- a/main/articles/index.html +++ b/main/articles/index.html @@ -41,6 +41,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/articles/teal-modules-general.html b/main/articles/teal-modules-general.html index 08897837b..24ac568b2 100644 --- a/main/articles/teal-modules-general.html +++ b/main/articles/teal-modules-general.html @@ -59,6 +59,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/articles/using-association-plot.html b/main/articles/using-association-plot.html index aab6ff603..2e47b272b 100644 --- a/main/articles/using-association-plot.html +++ b/main/articles/using-association-plot.html @@ -59,6 +59,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/articles/using-bivariate-plot.html b/main/articles/using-bivariate-plot.html index cf86cb631..6a1ed447a 100644 --- a/main/articles/using-bivariate-plot.html +++ b/main/articles/using-bivariate-plot.html @@ -59,6 +59,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/articles/using-cross-table.html b/main/articles/using-cross-table.html index c8b9c4f29..4d39ec3a4 100644 --- a/main/articles/using-cross-table.html +++ b/main/articles/using-cross-table.html @@ -59,6 +59,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/articles/using-data-table.html b/main/articles/using-data-table.html index 72fb6f68c..3a5a05c4a 100644 --- a/main/articles/using-data-table.html +++ b/main/articles/using-data-table.html @@ -59,6 +59,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/articles/using-outliers-module.html b/main/articles/using-outliers-module.html index 380320785..fcdd6c7e3 100644 --- a/main/articles/using-outliers-module.html +++ b/main/articles/using-outliers-module.html @@ -59,6 +59,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/articles/using-regression-plots.html b/main/articles/using-regression-plots.html index 1aa0c4c47..0a6ba9795 100644 --- a/main/articles/using-regression-plots.html +++ b/main/articles/using-regression-plots.html @@ -59,6 +59,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/articles/using-response-plot.html b/main/articles/using-response-plot.html index 6f99eea65..5c5fac8d2 100644 --- a/main/articles/using-response-plot.html +++ b/main/articles/using-response-plot.html @@ -59,6 +59,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/articles/using-scatterplot-matrix.html b/main/articles/using-scatterplot-matrix.html index adaef45d1..877365cfe 100644 --- a/main/articles/using-scatterplot-matrix.html +++ b/main/articles/using-scatterplot-matrix.html @@ -59,6 +59,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/articles/using-scatterplot.html b/main/articles/using-scatterplot.html index db96b93c3..6d89a6929 100644 --- a/main/articles/using-scatterplot.html +++ b/main/articles/using-scatterplot.html @@ -59,6 +59,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/authors.html b/main/authors.html index cfc9f0a83..ea749a2ba 100644 --- a/main/authors.html +++ b/main/authors.html @@ -41,6 +41,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/index.html b/main/index.html index ecec883f3..6633118bd 100644 --- a/main/index.html +++ b/main/index.html @@ -65,6 +65,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/news/index.html b/main/news/index.html index f552305aa..1d4df9fba 100644 --- a/main/news/index.html +++ b/main/news/index.html @@ -41,6 +41,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/pull_request_template.html b/main/pull_request_template.html index f87ec407c..4372e20e2 100644 --- a/main/pull_request_template.html +++ b/main/pull_request_template.html @@ -41,6 +41,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/add_facet_labels.html b/main/reference/add_facet_labels.html index 2e7feb9ab..9ce9d976d 100644 --- a/main/reference/add_facet_labels.html +++ b/main/reference/add_facet_labels.html @@ -43,6 +43,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/call_fun_dots.html b/main/reference/call_fun_dots.html index 715ec67a1..3ff55b518 100644 --- a/main/reference/call_fun_dots.html +++ b/main/reference/call_fun_dots.html @@ -41,6 +41,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/create_sparklines.html b/main/reference/create_sparklines.html index 62a920613..f114db73b 100644 --- a/main/reference/create_sparklines.html +++ b/main/reference/create_sparklines.html @@ -45,6 +45,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/establish_updating_selection.html b/main/reference/establish_updating_selection.html index 58207ba72..38ad6af55 100644 --- a/main/reference/establish_updating_selection.html +++ b/main/reference/establish_updating_selection.html @@ -43,6 +43,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/get_scatterplotmatrix_stats.html b/main/reference/get_scatterplotmatrix_stats.html index 9af6af683..cdf8333ac 100644 --- a/main/reference/get_scatterplotmatrix_stats.html +++ b/main/reference/get_scatterplotmatrix_stats.html @@ -47,6 +47,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/include_css_files.html b/main/reference/include_css_files.html index 0f258b336..09f66bd68 100644 --- a/main/reference/include_css_files.html +++ b/main/reference/include_css_files.html @@ -45,6 +45,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/index.html b/main/reference/index.html index 350948181..18495d413 100644 --- a/main/reference/index.html +++ b/main/reference/index.html @@ -41,6 +41,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/is_tab_active_js.html b/main/reference/is_tab_active_js.html index 874974b5f..997b59ac1 100644 --- a/main/reference/is_tab_active_js.html +++ b/main/reference/is_tab_active_js.html @@ -41,6 +41,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/plot_var_summary.html b/main/reference/plot_var_summary.html index ff0541332..f24661659 100644 --- a/main/reference/plot_var_summary.html +++ b/main/reference/plot_var_summary.html @@ -41,6 +41,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/rADAE.html b/main/reference/rADAE.html index bd18235fd..339ebe26f 100644 --- a/main/reference/rADAE.html +++ b/main/reference/rADAE.html @@ -41,6 +41,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/rADLB.html b/main/reference/rADLB.html index 21a78fca2..56da2f634 100644 --- a/main/reference/rADLB.html +++ b/main/reference/rADLB.html @@ -41,6 +41,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/rADRS.html b/main/reference/rADRS.html index 211961936..d9fefedad 100644 --- a/main/reference/rADRS.html +++ b/main/reference/rADRS.html @@ -41,6 +41,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/rADSL.html b/main/reference/rADSL.html index 0555227a1..2b6d25ff2 100644 --- a/main/reference/rADSL.html +++ b/main/reference/rADSL.html @@ -41,6 +41,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/rADTTE.html b/main/reference/rADTTE.html index be214747a..aef4d411d 100644 --- a/main/reference/rADTTE.html +++ b/main/reference/rADTTE.html @@ -41,6 +41,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/remove_outliers_from.html b/main/reference/remove_outliers_from.html index 3a9ff8340..306c79aa4 100644 --- a/main/reference/remove_outliers_from.html +++ b/main/reference/remove_outliers_from.html @@ -41,6 +41,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/render_single_tab.html b/main/reference/render_single_tab.html index f3301858b..1f04b49fa 100644 --- a/main/reference/render_single_tab.html +++ b/main/reference/render_single_tab.html @@ -43,6 +43,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/render_tab_header.html b/main/reference/render_tab_header.html index 26281658c..fd2164a02 100644 --- a/main/reference/render_tab_header.html +++ b/main/reference/render_tab_header.html @@ -41,6 +41,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/render_tab_table.html b/main/reference/render_tab_table.html index 14f60fece..264720cfc 100644 --- a/main/reference/render_tab_table.html +++ b/main/reference/render_tab_table.html @@ -43,6 +43,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/render_tabset_panel_content.html b/main/reference/render_tabset_panel_content.html index 054bc3546..77b447c00 100644 --- a/main/reference/render_tabset_panel_content.html +++ b/main/reference/render_tabset_panel_content.html @@ -41,6 +41,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/shared_params.html b/main/reference/shared_params.html index 78fccdb6b..beabd5c86 100644 --- a/main/reference/shared_params.html +++ b/main/reference/shared_params.html @@ -43,6 +43,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/teal.modules.general.html b/main/reference/teal.modules.general.html index c8b0cb9a9..4782322bc 100644 --- a/main/reference/teal.modules.general.html +++ b/main/reference/teal.modules.general.html @@ -43,6 +43,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/tm_a_pca.html b/main/reference/tm_a_pca.html index 882575d9b..c619e420f 100644 --- a/main/reference/tm_a_pca.html +++ b/main/reference/tm_a_pca.html @@ -47,6 +47,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/tm_a_regression.html b/main/reference/tm_a_regression.html index e57ab1eff..31cd41758 100644 --- a/main/reference/tm_a_regression.html +++ b/main/reference/tm_a_regression.html @@ -47,6 +47,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/tm_data_table.html b/main/reference/tm_data_table.html index 3abf1d2e3..be3c47ce0 100644 --- a/main/reference/tm_data_table.html +++ b/main/reference/tm_data_table.html @@ -45,6 +45,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/tm_file_viewer.html b/main/reference/tm_file_viewer.html index db0806de7..a4e931d09 100644 --- a/main/reference/tm_file_viewer.html +++ b/main/reference/tm_file_viewer.html @@ -45,6 +45,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/tm_front_page.html b/main/reference/tm_front_page.html index cd965fa92..bf6ccbeb5 100644 --- a/main/reference/tm_front_page.html +++ b/main/reference/tm_front_page.html @@ -43,6 +43,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/tm_g_association.html b/main/reference/tm_g_association.html index 45f1ffbaa..7e010477c 100644 --- a/main/reference/tm_g_association.html +++ b/main/reference/tm_g_association.html @@ -45,6 +45,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/tm_g_bivariate.html b/main/reference/tm_g_bivariate.html index 3d991bf76..38c451be0 100644 --- a/main/reference/tm_g_bivariate.html +++ b/main/reference/tm_g_bivariate.html @@ -43,6 +43,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/tm_g_distribution.html b/main/reference/tm_g_distribution.html index 13a01e186..dea6a2b30 100644 --- a/main/reference/tm_g_distribution.html +++ b/main/reference/tm_g_distribution.html @@ -45,6 +45,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/tm_g_response.html b/main/reference/tm_g_response.html index 81dc0b625..e4160a408 100644 --- a/main/reference/tm_g_response.html +++ b/main/reference/tm_g_response.html @@ -51,6 +51,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/tm_g_scatterplot.html b/main/reference/tm_g_scatterplot.html index 0d71ed7a1..ee52c7391 100644 --- a/main/reference/tm_g_scatterplot.html +++ b/main/reference/tm_g_scatterplot.html @@ -47,6 +47,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/tm_g_scatterplotmatrix.html b/main/reference/tm_g_scatterplotmatrix.html index 52cd5959e..6ccc070e6 100644 --- a/main/reference/tm_g_scatterplotmatrix.html +++ b/main/reference/tm_g_scatterplotmatrix.html @@ -45,6 +45,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/tm_missing_data.html b/main/reference/tm_missing_data.html index 216e76590..be0643cf9 100644 --- a/main/reference/tm_missing_data.html +++ b/main/reference/tm_missing_data.html @@ -47,6 +47,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/tm_outliers.html b/main/reference/tm_outliers.html index 540cb310e..95bcfc6e2 100644 --- a/main/reference/tm_outliers.html +++ b/main/reference/tm_outliers.html @@ -45,6 +45,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/tm_t_crosstable.html b/main/reference/tm_t_crosstable.html index ac5dcd64d..7b6fbbe75 100644 --- a/main/reference/tm_t_crosstable.html +++ b/main/reference/tm_t_crosstable.html @@ -43,6 +43,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/tm_variable_browser.html b/main/reference/tm_variable_browser.html index 7a0a7c8d2..6dac69c16 100644 --- a/main/reference/tm_variable_browser.html +++ b/main/reference/tm_variable_browser.html @@ -43,6 +43,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/validate_input.html b/main/reference/validate_input.html index 617b182cc..cfea94ff4 100644 --- a/main/reference/validate_input.html +++ b/main/reference/validate_input.html @@ -41,6 +41,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/var_missings_info.html b/main/reference/var_missings_info.html index c09abfabd..9b2891f92 100644 --- a/main/reference/var_missings_info.html +++ b/main/reference/var_missings_info.html @@ -41,6 +41,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/var_summary_table.html b/main/reference/var_summary_table.html index f69a09067..ade0f479f 100644 --- a/main/reference/var_summary_table.html +++ b/main/reference/var_summary_table.html @@ -45,6 +45,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/variable_type_icons.html b/main/reference/variable_type_icons.html index 07f6798b8..7cacddaa1 100644 --- a/main/reference/variable_type_icons.html +++ b/main/reference/variable_type_icons.html @@ -41,6 +41,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/reference/varname_w_label.html b/main/reference/varname_w_label.html index 9e5fb17f1..f925880ac 100644 --- a/main/reference/varname_w_label.html +++ b/main/reference/varname_w_label.html @@ -41,6 +41,19 @@
  • Changelog
    • +
  • +
    main +latest-tag +release-candidate +v0.3.0-rc2 +v0.3.0-rc1 +v0.2.16 +v0.2.15 +v0.3.0
    • +
  • diff --git a/main/search.json b/main/search.json index b1522e247..318f0810a 100644 --- a/main/search.json +++ b/main/search.json @@ -1 +1 @@ -[{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement [INSERT CONTACT METHOD]. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.1, available https://www.contributor-covenant.org/version/2/1/code_of_conduct.html. Community Impact Guidelines inspired Mozilla’s code conduct enforcement ladder. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contribution Guidelines","title":"Contribution Guidelines","text":"🙏 Thank taking time contribute! input deeply valued, whether issue, pull request, even feedback, regardless size, content scope.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/CONTRIBUTING.html","id":"table-of-contents","dir":"","previous_headings":"","what":"Table of contents","title":"Contribution Guidelines","text":"👶 Getting started 📔 Code Conduct 🗃 License 📜 Issues 🚩 Pull requests 💻 Coding guidelines 🏆 Recognition model ❓ Questions","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/CONTRIBUTING.html","id":"getting-started","dir":"","previous_headings":"","what":"Getting started","title":"Contribution Guidelines","text":"Please refer project documentation brief introduction. Please also see articles within project documentation additional information.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contribution Guidelines","text":"Code Conduct governs project. Participants contributors expected follow rules outlined therein.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/CONTRIBUTING.html","id":"license","dir":"","previous_headings":"","what":"License","title":"Contribution Guidelines","text":"contributions covered project’s license.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/CONTRIBUTING.html","id":"issues","dir":"","previous_headings":"","what":"Issues","title":"Contribution Guidelines","text":"use GitHub track issues, feature requests, bugs. submitting new issue, please check issue already reported. issue already exists, please upvote existing issue 👍. new feature requests, please elaborate context benefit feature users, developers, relevant personas.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/CONTRIBUTING.html","id":"github-flow","dir":"","previous_headings":"Pull requests","what":"GitHub Flow","title":"Contribution Guidelines","text":"repository uses GitHub Flow model collaboration. submit pull request: Create branch Please see branch naming convention . don’t write access repository, please fork . Make changes Make sure code passes checks imposed GitHub Actions well documented well tested unit tests sufficiently covering changes introduced Create pull request (PR) pull request description, please link relevant issue (), provide detailed description change, include assumptions. Address review comments, Post approval Merge PR write access. Otherwise, reviewer merge PR behalf. Pat back Congratulations! 🎉 now official contributor project! grateful contribution.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/CONTRIBUTING.html","id":"branch-naming-convention","dir":"","previous_headings":"Pull requests","what":"Branch naming convention","title":"Contribution Guidelines","text":"Suppose changes related current issue current project; please name branch follows: _. Please use underscore (_) delimiter word separation. example, 420_fix_ui_bug suitable branch name change resolving UI-related bug reported issue number 420 current project. change affects multiple repositories, please name branches follows: __. example, 69_awesomeproject_fix_spelling_error reference issue 69 reported project awesomeproject aims resolve one spelling errors multiple (likely related) repositories.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/CONTRIBUTING.html","id":"monorepo-and-stageddependencies","dir":"","previous_headings":"Pull requests","what":"monorepo and staged.dependencies","title":"Contribution Guidelines","text":"Sometimes might need change upstream dependent package(s) able submit meaningful change. using staged.dependencies functionality simulate monorepo behavior. dependency configuration already specified project’s staged_dependencies.yaml file. need name feature branches appropriately. exception branch naming convention described . Please refer staged.dependencies package documentation details.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/CONTRIBUTING.html","id":"coding-guidelines","dir":"","previous_headings":"","what":"Coding guidelines","title":"Contribution Guidelines","text":"repository follows unified processes standards adopted maintainers ensure software development carried consistently within teams cohesively across repositories.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/CONTRIBUTING.html","id":"style-guide","dir":"","previous_headings":"Coding guidelines","what":"Style guide","title":"Contribution Guidelines","text":"repository follows standard tidyverse style guide uses lintr lint checks. Customized lint configurations available repository’s .lintr file.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/CONTRIBUTING.html","id":"dependency-management","dir":"","previous_headings":"Coding guidelines","what":"Dependency management","title":"Contribution Guidelines","text":"Lightweight right weight. repository follows tinyverse recommedations limiting dependencies minimum.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/CONTRIBUTING.html","id":"dependency-version-management","dir":"","previous_headings":"Coding guidelines","what":"Dependency version management","title":"Contribution Guidelines","text":"code compatible (!) historical versions given dependenct package, required specify minimal version DESCRIPTION file. particular: development version requires (imports) development version another package - required put abc (>= 1.2.3.9000).","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/CONTRIBUTING.html","id":"r--package-versions","dir":"","previous_headings":"Coding guidelines > Recommended development environment & tools","what":"R & package versions","title":"Contribution Guidelines","text":"continuously test packages newest R version along recent dependencies CRAN BioConductor. recommend working environment also set way. can find details R version packages used R CMD check GitHub Action execution log - step prints R sessionInfo(). discover bugs older R versions older set dependencies, please create relevant bug reports.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/CONTRIBUTING.html","id":"pre-commit","dir":"","previous_headings":"Coding guidelines > Recommended development environment & tools","what":"pre-commit","title":"Contribution Guidelines","text":"highly recommend use pre-commit tool combined R hooks pre-commit execute checks committing pushing changes. Pre-commit hooks already available repository’s .pre-commit-config.yaml file.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/CONTRIBUTING.html","id":"recognition-model","dir":"","previous_headings":"","what":"Recognition model","title":"Contribution Guidelines","text":"mentioned previously, contributions deeply valued appreciated. contribution data available part repository insights, recognize significant contribution hence add contributor package authors list, following rules enforced: Minimum 5% lines code authored* (determined git blame query) top 5 contributors terms number commits lines added lines removed* *Excluding auto-generated code, including limited roxygen comments renv.lock files. package maintainer also reserves right adjust criteria recognize contributions.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/CONTRIBUTING.html","id":"questions","dir":"","previous_headings":"","what":"Questions","title":"Contribution Guidelines","text":"questions regarding contribution guidelines, please contact package/repository maintainer.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/SECURITY.html","id":"reporting-security-issues","dir":"","previous_headings":"","what":"Reporting Security Issues","title":"Security Policy","text":"believe found security vulnerability repositories organization, please report us coordinated disclosure. Please report security vulnerabilities public GitHub issues, discussions, pull requests. Instead, please send email vulnerability.management[@]roche.com. Please include much information listed can help us better understand resolve issue: type issue (e.g., buffer overflow, SQL injection, cross-site scripting) Full paths source file(s) related manifestation issue location affected source code (tag/branch/commit direct URL) special configuration required reproduce issue Step--step instructions reproduce issue Proof--concept exploit code (possible) Impact issue, including attacker might exploit issue information help us triage report quickly.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/SECURITY.html","id":"data-security-standards-dss","dir":"","previous_headings":"","what":"Data Security Standards (DSS)","title":"Security Policy","text":"Please make sure reporting issues form bug, feature, pull request, sensitive information PII, PHI, PCI completely removed text attachments, including pictures videos.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/teal-modules-general.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Getting started with teal.modules.general","text":"teal extends shiny framework, enabling creation interactive GUI applications using R. shiny, tealfacilitate development extensive applications combining small, decoupled modules. teal.modules.general package consist collection modules essential developing teal applications. “general” sense intended functions modules fundamental. contrasts specialized focus clinical data found teal.modules.clinical package. modules teal.modules.general can used conjunction modules teal.modules.clinical / shiny modules build large teal / shiny app. concepts presented require knowledge core features teal, specifically launch teal application pass data . Therefore, highly recommended refer README file introductory vignette teal package. See also teal.modules.clinical’s README.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/teal-modules-general.html","id":"main-features","dir":"Articles","previous_headings":"","what":"Main features","title":"Getting started with teal.modules.general","text":"five areas data science teal.modules.general provides tools solutions (modules) : viewing data tabular formats visualizing data plots graphs viewing data files directory examining missing extreme values data performing data analysis See package functions / modules.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/teal-modules-general.html","id":"example-application","dir":"Articles","previous_headings":"","what":"Example application","title":"Getting started with teal.modules.general","text":"simple application featuring tm_variable_browser() module: Let’s break app pieces: 1: Load necessary libraries data. 2: Construct teal_data object containing serve source data teal app. teal_data encapsulates data app, also houses code required create data maintain reproducibility. , create empty teal_data object evaluate code produce data within teal_data object, code data stored together. Following , set datanames join_keys. 3: Initialize teal application specified data modules, case, module: tm_variable_browser, datasets:ADSL ADTTE. shiny::shinyApp() use ui server component initialize teal app. teal app, data modules decoupled. app : app developer specified data assigned data argument. app developer specified module assigned modules argument. init function took arguments returned list containing ui server object, can demonstrated running:","code":"# load libraries library(teal.modules.general) library(teal.widgets) # teal_data object data <- teal_data() data <- within(data, { ADSL <- rADSL ADTTE <- rADTTE }) datanames <- c(\"ADSL\", \"ADTTE\") datanames(data) <- datanames join_keys(data) <- default_cdisc_join_keys[datanames] # tm_variable_browser module tm_variable_browser_module <- tm_variable_browser( label = \"Variable browser\", ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Variable Browser Module\") ) ) # initialize the app app <- init( data = data, modules = modules(tm_variable_browser_module) ) shinyApp(app$ui, app$server) library(teal.modules.general) library(teal.widgets) data <- teal_data() data <- within(data, { ADSL <- rADSL ADTTE <- rADTTE }) datanames <- c(\"ADSL\", \"ADTTE\") datanames(data) <- datanames join_keys(data) <- default_cdisc_join_keys[datanames] tm_variable_browser_module <- tm_variable_browser( # module name to display in the GUI label = \"Variable browser\", # this argument takes a set of arguments to pass to ggplot2. # the arguments must have the same names as its ggplot2 counterpart, e.g. `subtitle` ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Variable Browser Module\") ) ) app <- init( data = data, modules = modules(tm_variable_browser_module) ) shinyApp(app$ui, app$server) class(app) ## [1] \"list\" names(app) ## [1] \"ui\" \"server\""},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-association-plot.html","id":"section-teal-application-to-use-association-plot-with-various-datasets-types","dir":"Articles","previous_headings":"","what":"teal application to use association plot with various datasets types","title":"Using association plot","text":"vignette guide four parts create teal application using various types datasets using association plot module tm_g_association(): Load libraries Create data sets Create app variable Run app","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-association-plot.html","id":"section-load-libraries","dir":"Articles","previous_headings":"teal application to use association plot with various datasets types","what":"1 - Load libraries","title":"Using association plot","text":"","code":"library(teal.modules.general) # used to create the app library(dplyr) # used to modify data sets"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-association-plot.html","id":"section-create-data-sets","dir":"Articles","previous_headings":"teal application to use association plot with various datasets types","what":"2 - Create data sets","title":"Using association plot","text":"Inside app 4 datasets used ADSL wide data set subject data ADRS long data set response data subjects different time points study ADTTE long data set time event data ADLB long data set lab measurements subject","code":"data <- teal_data() data <- within(data, { ADSL <- teal.modules.general::rADSL %>% mutate(TRTDUR = round(as.numeric(TRTEDTM - TRTSDTM), 1)) ADRS <- teal.modules.general::rADRS ADTTE <- teal.modules.general::rADTTE ADLB <- teal.modules.general::rADLB %>% mutate(CHGC = as.factor(case_when( CHG < 1 ~ \"N\", CHG > 1 ~ \"P\", TRUE ~ \"-\" ))) }) datanames <- c(\"ADSL\", \"ADRS\", \"ADTTE\", \"ADLB\") datanames(data) <- datanames join_keys(data) <- default_cdisc_join_keys[datanames]"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-association-plot.html","id":"section-create-an-app-variable","dir":"Articles","previous_headings":"teal application to use association plot with various datasets types","what":"3 - Create an app variable","title":"Using association plot","text":"important section. use teal::init() function create app. data handed using teal.data::teal_data(). app constructed multiple calls tm_g_association() using different combinations data sets.","code":"# configuration for a single wide dataset mod1 <- tm_g_association( label = \"Single wide dataset\", ref = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]]), selected = \"AGE\", fixed = FALSE ) ), vars = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADSL\"]]), selected = \"BMRKR1\", multiple = TRUE, fixed = FALSE ) ) ) # configuration for two wide datasets mod2 <- tm_g_association( label = \"Two wide datasets\", ref = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"SEX\", \"STRATA1\", \"RACE\")), selected = \"STRATA1\", multiple = FALSE, fixed = FALSE ) ), vars = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"SEX\", \"RACE\", \"COUNTRY\")), selected = c(\"AGE\", \"COUNTRY\", \"RACE\"), multiple = TRUE, fixed = FALSE ) ) ) # configuration for multiple long datasets mod3 <- tm_g_association( label = \"Multiple different long datasets\", ref = data_extract_spec( dataname = \"ADTTE\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADTTE\"]]), selected = \"AVAL\", multiple = FALSE, fixed = FALSE ), filter = filter_spec( label = \"Select endpoint:\", vars = \"PARAMCD\", choices = value_choices(data[[\"ADTTE\"]], \"PARAMCD\", \"PARAM\"), selected = c(\"PFS\", \"EFS\"), multiple = TRUE ) ), vars = data_extract_spec( dataname = \"ADRS\", reshape = TRUE, select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADRS\"]], c(\"AVALC\", \"BMRKR1\", \"BMRKR2\", \"ARM\")), selected = \"AVALC\", multiple = TRUE, fixed = FALSE ), filter = list( filter_spec( label = \"Select endpoints:\", vars = \"PARAMCD\", choices = value_choices(data[[\"ADRS\"]], \"PARAMCD\", \"PARAM\"), selected = \"BESRSPI\", multiple = TRUE ), filter_spec( label = \"Select endpoints:\", vars = \"AVISIT\", choices = levels(data[[\"ADRS\"]]$AVISIT), selected = \"SCREENING\", multiple = TRUE ) ) ) ) # configuration for wide and long datasets mod4 <- tm_g_association( label = \"Wide and long datasets\", ref = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"AVAL\", \"AVALC\")), selected = \"AVALC\", multiple = FALSE, fixed = FALSE, label = \"Selected variable:\" ), filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADRS\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADRS\"]]$PARAMCD), multiple = TRUE, label = \"Select response\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADRS\"]]$AVISIT), selected = levels(data[[\"ADRS\"]]$AVISIT), multiple = TRUE, label = \"Select visit:\" ) ) ), vars = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"SEX\", \"AGE\", \"RACE\", \"COUNTRY\", \"BMRKR1\", \"STRATA1\", \"ARM\")), selected = \"AGE\", multiple = TRUE, fixed = FALSE, label = \"Select variable:\" ) ) ) # configuration for the same long dataset (same subsets) mod5 <- tm_g_association( label = \"Same long datasets (same subsets)\", ref = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = variable_choices(data[[\"ADRS\"]]), selected = \"AVALC\", multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), vars = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = variable_choices(data[[\"ADRS\"]]), selected = \"PARAMCD\", multiple = TRUE, fixed = FALSE, label = \"Select variable:\" ) ) ) # configuration for the same long dataset (different subsets) mod6 <- tm_g_association( label = \"Same long datasets (different subsets)\", ref = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select lab:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE, label = \"Select visit:\" ) ), select = select_spec( choices = variable_choices(data[[\"ADLB\"]], c(\"AVAL\", \"CHG2\", \"PCHG2\")), selected = \"AVAL\", multiple = FALSE ) ), vars = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select labs:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE, label = \"Select visit:\" ) ), select = select_spec( choices = variable_choices(data[[\"ADLB\"]]), selected = \"STRATA1\", multiple = TRUE ) ) ) # initialize the app app <- init( data = data, modules = modules( # tm_g_association ---- modules( label = \"Association plot\", mod1, mod2, mod3, mod4, mod5, mod6 ) ) )"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-association-plot.html","id":"section-run-the-app","dir":"Articles","previous_headings":"teal application to use association plot with various datasets types","what":"4 - Run the app","title":"Using association plot","text":"simple shiny::shinyApp() call let run app. Note app displayed running code inside R session.","code":"shinyApp(app$ui, app$server, options = list(height = 1024, width = 1024))"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-bivariate-plot.html","id":"section-teal-application-to-use-bivariate-plot-with-various-datasets-types","dir":"Articles","previous_headings":"","what":"teal application to use bivariate plot with various datasets types","title":"Using bivariate plot","text":"vignette guide four parts create teal application using various types datasets using bivariate plot module tm_g_bivariate(): Load libraries Create data sets Create app variable Run app","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-bivariate-plot.html","id":"section-load-libraries","dir":"Articles","previous_headings":"teal application to use bivariate plot with various datasets types","what":"1 - Load libraries","title":"Using bivariate plot","text":"","code":"library(teal.modules.general) # used to create the app library(dplyr) # used to modify data sets"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-bivariate-plot.html","id":"section-create-data-sets","dir":"Articles","previous_headings":"teal application to use bivariate plot with various datasets types","what":"2 - Create data sets","title":"Using bivariate plot","text":"Inside app 4 datasets used ADSL wide data set subject data ADRS long data set response data subjects different time points study ADTTE long data set time event data ADLB long data set lab measurements subject","code":"data <- teal_data() data <- within(data, { ADSL <- teal.modules.general::rADSL %>% mutate(TRTDUR = round(as.numeric(TRTEDTM - TRTSDTM), 1)) ADRS <- teal.modules.general::rADRS ADTTE <- teal.modules.general::rADTTE ADLB <- teal.modules.general::rADLB %>% mutate(CHGC = as.factor(case_when( CHG < 1 ~ \"N\", CHG > 1 ~ \"P\", TRUE ~ \"-\" ))) }) datanames <- c(\"ADSL\", \"ADRS\", \"ADTTE\", \"ADLB\") datanames(data) <- datanames join_keys(data) <- default_cdisc_join_keys[datanames]"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-bivariate-plot.html","id":"section-create-an-app-variable","dir":"Articles","previous_headings":"teal application to use bivariate plot with various datasets types","what":"3 - Create an app variable","title":"Using bivariate plot","text":"important section. use teal::init() function create app. data handed using teal.data::teal_data(). app constructed multiple calls tm_g_bivariate() using different combinations data sets.","code":"# configuration for the single wide dataset mod1 <- tm_g_bivariate( label = \"Single wide dataset\", x = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]]), selected = \"BMRKR1\", fixed = FALSE ) ), y = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]]), selected = \"SEX\", multiple = FALSE, fixed = FALSE ) ), row_facet = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADSL\"]]), selected = NULL, multiple = FALSE, fixed = FALSE ) ), col_facet = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADSL\"]]), selected = NULL, multiple = FALSE, fixed = FALSE ) ) ) # configuration for the two wide datasets mod2 <- tm_g_bivariate( label = \"Two wide datasets\", x = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"BMRKR1\", \"AGE\", \"SEX\", \"STRATA1\", \"RACE\")), selected = c(\"BMRKR1\"), multiple = FALSE ) ), y = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADSL\"]], c(\"COUNTRY\", \"AGE\", \"RACE\")), selected = \"RACE\", multiple = FALSE ) ), row_facet = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]]), selected = NULL, multiple = FALSE, fixed = FALSE ) ), col_facet = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADSL\"]]), selected = NULL, multiple = FALSE, fixed = FALSE ) ) ) # configuration for the multiple different long datasets mod3 <- tm_g_bivariate( label = \"Multiple different long datasets\", x = data_extract_spec( dataname = \"ADRS\", filter = filter_spec( label = \"Select endpoints:\", vars = c(\"PARAMCD\", \"AVISIT\"), choices = value_choices(data[[\"ADRS\"]], c(\"PARAMCD\", \"AVISIT\"), c(\"PARAM\", \"AVISIT\")), selected = \"OVRINV - END OF INDUCTION\", multiple = TRUE ), select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"AVALC\", \"AVAL\")), selected = \"AVALC\", multiple = FALSE ) ), y = data_extract_spec( dataname = \"ADTTE\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADTTE\"]], c(\"AVAL\", \"CNSR\")), selected = \"AVAL\", multiple = FALSE, fixed = FALSE ), filter = filter_spec( label = \"Select endpoint:\", vars = c(\"PARAMCD\"), choices = value_choices(data[[\"ADTTE\"]], \"PARAMCD\", \"PARAM\"), selected = \"OS\", multiple = FALSE ) ), row_facet = data_extract_spec( dataname = \"ADRS\", filter = filter_spec( label = \"Select endpoints:\", vars = c(\"PARAMCD\", \"AVISIT\"), choices = value_choices(data[[\"ADRS\"]], c(\"PARAMCD\", \"AVISIT\"), c(\"PARAM\", \"AVISIT\")), selected = \"OVRINV - SCREENING\", multiple = TRUE ), select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADRS\"]], c(\"SEX\", \"RACE\", \"COUNTRY\", \"ARM\", \"PARAMCD\", \"AVISIT\")), selected = \"SEX\", multiple = FALSE, fixed = FALSE ) ), col_facet = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADSL\"]], c(\"SEX\", \"RACE\")), selected = NULL, multiple = FALSE, fixed = FALSE ) ), color_settings = TRUE, color = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"SEX\", \"RACE\", \"COUNTRY\")), selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), fill = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"SEX\", \"RACE\", \"COUNTRY\")), selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), size = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"BMRKR1\")), selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), plot_height = c(600, 200, 2000), ggtheme = \"gray\" ) # configuration for the wide and long datasets mod4 <- tm_g_bivariate( label = \"Wide and long datasets\", x = data_extract_spec( dataname = \"ADRS\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADRS\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADRS\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select response:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADRS\"]]$AVISIT), selected = levels(data[[\"ADRS\"]]$AVISIT)[1], multiple = FALSE, label = \"Select visit:\" ) ), select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"AVALC\", \"AVAL\")), selected = \"AVALC\", multiple = FALSE, label = \"Select variable:\" ) ), y = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"BMRKR1\", \"SEX\", \"AGE\", \"RACE\", \"COUNTRY\")), selected = \"BMRKR1\", multiple = FALSE, label = \"Select variable:\", fixed = FALSE ) ), row_facet = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"SEX\", \"RACE\", \"ARMCD\", \"PARAMCD\")), selected = \"SEX\", multiple = FALSE, label = \"Select variable:\" ) ), col_facet = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"SEX\", \"RACE\", \"ARMCD\", \"PARAMCD\", \"AVISIT\")), selected = \"ARMCD\", multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ) ) # configuration for the wide and multiple long datasets mod5 <- tm_g_bivariate( label = \"Wide and multiple long datasets\", x = data_extract_spec( dataname = \"ADRS\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADRS\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADRS\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select response:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADRS\"]]$AVISIT), selected = levels(data[[\"ADRS\"]]$AVISIT)[1], multiple = FALSE, label = \"Select visit:\" ) ), select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"AVALC\", \"AVAL\")), selected = \"AVALC\", multiple = FALSE, label = \"Select variable:\" ) ), y = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"BMRKR1\", \"SEX\", \"AGE\", \"RACE\", \"COUNTRY\")), selected = \"BMRKR1\", multiple = FALSE, fixed = FALSE ) ), row_facet = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select measurement:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE, label = \"Select visit:\" ) ), select = select_spec( choices = \"ARMCD\", selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), col_facet = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"SEX\", \"AGE\", \"RACE\", \"COUNTRY\")), selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), color_settings = TRUE, color = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"SEX\", \"RACE\", \"COUNTRY\")), selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), fill = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"SEX\", \"RACE\", \"COUNTRY\")), selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), size = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"BMRKR1\")), selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), plot_height = c(600, 200, 2000), ggtheme = \"gray\" ) # Configuration for the same long datasets (same subset) mod6 <- tm_g_bivariate( label = \"Same long datasets (same subset)\", x = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"AVALC\", \"AVAL\")), selected = \"AVALC\", multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), y = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"SEX\", \"RACE\", \"COUNTRY\", \"ARMCD\", \"BMRKR1\", \"BMRKR2\")), selected = \"BMRKR1\", multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), row_facet = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"AVISIT\", \"PARAMCD\")), selected = \"PARAMCD\", multiple = FALSE, label = \"Select variables:\" ) ), col_facet = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"AVISIT\", \"PARAMCD\")), selected = \"AVISIT\", multiple = FALSE, label = \"Select variables:\" ) ) ) # Configuration for the same datasets (different subsets) mod7 <- tm_g_bivariate( label = \"Same datasets (different subsets)\", x = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select lab:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE, label = \"Select visit:\" ) ), select = select_spec( choices = \"AVAL\", selected = \"AVAL\", multiple = FALSE, fixed = TRUE ) ), y = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select lab:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE, label = \"Select visit:\" ) ), select = select_spec( choices = \"AVAL\", selected = \"AVAL\", multiple = FALSE, fixed = TRUE ) ), use_density = FALSE, row_facet = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select lab:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE, label = \"Select category:\" ) ), select = select_spec( choices = variable_choices(data[[\"ADLB\"]], c(\"RACE\", \"SEX\", \"ARMCD\", \"ACTARMCD\")), selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), col_facet = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select lab:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE, label = \"Select category:\" ) ), select = select_spec( choices = variable_choices(data[[\"ADLB\"]], c(\"RACE\", \"SEX\", \"ARMCD\", \"ACTARMCD\")), selected = \"ARMCD\", multiple = FALSE, fixed = FALSE, label = \"Select variables:\" ) ), color_settings = TRUE, color = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"SEX\", \"RACE\", \"COUNTRY\")), selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), fill = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"SEX\", \"RACE\", \"COUNTRY\")), selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), size = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"BMRKR1\")), selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), plot_height = c(600, 200, 2000), ggtheme = \"gray\" ) # initialize the app app <- init( data = data, modules = modules( # tm_g_bivariate ------ modules( label = \"Bivariate plot\", mod1, mod2, mod3, mod4, mod5, mod6, mod7 ) ) )"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-bivariate-plot.html","id":"section-run-the-app","dir":"Articles","previous_headings":"teal application to use bivariate plot with various datasets types","what":"4 - Run the app","title":"Using bivariate plot","text":"simple shiny::shinyApp() call let run app. Note app displayed running code inside R session.","code":"shinyApp(app$ui, app$server, options = list(height = 1024, width = 1024))"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-cross-table.html","id":"section-teal-application-to-use-cross-table-with-various-datasets-types","dir":"Articles","previous_headings":"","what":"teal application to use cross table with various datasets types","title":"Using cross table","text":"vignette guide four parts create teal application using various types datasets using cross table module tm_t_crosstable(): Load libraries Create data sets Create app variable Run app","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-cross-table.html","id":"section-load-libraries","dir":"Articles","previous_headings":"teal application to use cross table with various datasets types","what":"1 - Load libraries","title":"Using cross table","text":"","code":"library(teal.modules.general) # used to create the app library(dplyr) # used to modify data sets"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-cross-table.html","id":"section-create-data-sets","dir":"Articles","previous_headings":"teal application to use cross table with various datasets types","what":"2 - Create data sets","title":"Using cross table","text":"Inside app 2 datasets used ADSL wide data set subject data ADLB long data set lab measurements subject","code":"data <- within(data, { ADSL <- teal.modules.general::rADSL ADLB <- teal.modules.general::rADLB %>% mutate(CHGC = as.factor(case_when( CHG < 1 ~ \"N\", CHG > 1 ~ \"P\", TRUE ~ \"-\" ))) }) datanames <- c(\"ADSL\", \"ADLB\") datanames(data) <- datanames join_keys(data) <- default_cdisc_join_keys[datanames]"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-cross-table.html","id":"section-create-an-app-variable","dir":"Articles","previous_headings":"teal application to use cross table with various datasets types","what":"3 - Create an app variable","title":"Using cross table","text":"important section. use teal::init() function create app. data handed using teal.data::teal_data(). app constructed multiple calls tm_t_crosstable() using different combinations data sets.","code":"# configuration for the single wide dataset mod1 <- tm_t_crosstable( label = \"Single wide dataset\", x = data_extract_spec( \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]]), selected = names(data[[\"ADSL\"]])[5], multiple = TRUE, fixed = FALSE, ordered = TRUE ) ), y = data_extract_spec( \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]]), selected = names(data[[\"ADSL\"]])[6], multiple = FALSE, fixed = FALSE ) ) ) # configuration for the same long datasets (different subsets) mod2 <- tm_t_crosstable( label = \"Same long datasets (different subsets)\", x = data_extract_spec( dataname = \"ADLB\", filter = filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE ), select = select_spec( choices = variable_choices(data[[\"ADLB\"]]), selected = \"AVISIT\", multiple = TRUE, fixed = FALSE, ordered = TRUE, label = \"Select variable:\" ) ), y = data_extract_spec( dataname = \"ADLB\", filter = filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE ), select = select_spec( choices = variable_choices(data[[\"ADLB\"]]), selected = \"LOQFL\", multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ) ) # initialize the app app <- init( data = data, modules = modules( modules( label = \"Cross table\", mod1, mod2 ) ) )"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-cross-table.html","id":"section-run-the-app","dir":"Articles","previous_headings":"teal application to use cross table with various datasets types","what":"4 - Run the app","title":"Using cross table","text":"simple shiny::shinyApp() call let run app. Note app displayed running code inside R session.","code":"shinyApp(app$ui, app$server, options = list(height = 1024, width = 1024))"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-data-table.html","id":"section-teal-application-to-display-data-table-with-various-datasets-types","dir":"Articles","previous_headings":"","what":"teal application to display data table with various datasets types","title":"Using data table","text":"vignette guide four parts create teal application using various types datasets using data table module tm_data_table(): Load libraries Create data sets Create app variable Run app","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-data-table.html","id":"section-load-libraries","dir":"Articles","previous_headings":"teal application to display data table with various datasets types","what":"1 - Load libraries","title":"Using data table","text":"","code":"library(teal.modules.general) # used to create the app"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-data-table.html","id":"section-create-data-sets","dir":"Articles","previous_headings":"teal application to display data table with various datasets types","what":"2 - Create data sets","title":"Using data table","text":"Inside app 3 datasets used ADSL wide data set subject data ADTTE long data set time event data ADLB long data set lab measurements subject","code":"data <- teal_data() data <- within(data, { ADSL <- teal.modules.general::rADSL ADTTE <- teal.modules.general::rADTTE ADLB <- teal.modules.general::rADLB }) datanames <- c(\"ADSL\", \"ADTTE\", \"ADLB\") datanames(data) <- datanames join_keys(data) <- default_cdisc_join_keys[datanames]"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-data-table.html","id":"section-create-an-app-variable","dir":"Articles","previous_headings":"teal application to display data table with various datasets types","what":"3 - Create an app variable","title":"Using data table","text":"important section. use teal::init() function create app. data handed using teal.data::teal_data(). app constructed multiple calls tm_data_table() using different combinations data sets.","code":"# configuration for the two-datasets example mod1 <- tm_data_table( label = \"Two datasets\", variables_selected = list( ADSL = c(\"STUDYID\", \"USUBJID\", \"SUBJID\", \"SITEID\", \"AGE\", \"SEX\"), ADTTE = c( \"STUDYID\", \"USUBJID\", \"SUBJID\", \"SITEID\", \"PARAM\", \"PARAMCD\", \"ARM\", \"ARMCD\", \"AVAL\", \"CNSR\" ) ) ) # configuration for the subsetting or changing order of datasets mod2 <- tm_data_table( label = \"Datasets order\", variables_selected = list( ADSL = c(\"STUDYID\", \"USUBJID\", \"SUBJID\", \"SITEID\", \"AGE\", \"SEX\"), ADLB = c( \"STUDYID\", \"USUBJID\", \"SUBJID\", \"SITEID\", \"PARAM\", \"PARAMCD\", \"AVISIT\", \"AVISITN\", \"AVAL\", \"CHG\" ) ), datasets_selected = c(\"ADTTE\", \"ADLB\", \"ADSL\") ) # configuration for the advanced usage of DT options and extensions mod3 <- tm_data_table( label = \"Advanced DT usage\", dt_args = list(extensions = c(\"Buttons\", \"ColReorder\", \"FixedHeader\")), dt_options = list( searching = FALSE, pageLength = 30, lengthMenu = c(5, 15, 25, 50, 100), scrollX = FALSE, dom = \"lBrtip\", buttons = c(\"copy\", \"csv\", \"excel\", \"pdf\", \"print\"), colReorder = TRUE, fixedHeader = TRUE ) ) # initialize the app app <- init( data = data, modules = modules( mod1, mod2, mod3 ) )"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-data-table.html","id":"section-run-the-app","dir":"Articles","previous_headings":"teal application to display data table with various datasets types","what":"4 - Run the app","title":"Using data table","text":"simple shiny::shinyApp() call let run app. Note app displayed running code inside R session.","code":"shinyApp(app$ui, app$server, options = list(height = 1024, width = 1024))"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-outliers-module.html","id":"section-teal-application-to-analyze-and-report-outliers-with-various-datasets-types-","dir":"Articles","previous_headings":"","what":"teal application to analyze and report outliers with various datasets types.","title":"Using outliers module","text":"vignette guide four parts create teal application using various types datasets using outliers module tm_outliers(): Load libraries Create data sets Create app variable Run app","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-outliers-module.html","id":"section-load-libraries","dir":"Articles","previous_headings":"teal application to analyze and report outliers with various datasets types.","what":"1 - Load libraries","title":"Using outliers module","text":"","code":"library(teal.modules.general) # used to create the app library(dplyr) # used to modify data sets"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-outliers-module.html","id":"section-create-data-sets","dir":"Articles","previous_headings":"teal application to analyze and report outliers with various datasets types.","what":"2 - Create data sets","title":"Using outliers module","text":"Inside app 3 datasets used ADSL wide data set subject data ADRS long data set response data subjects different time points study ADLB long data set lab measurements subject","code":"data <- teal_data() data <- within(data, { ADSL <- teal.modules.general::rADSL ADRS <- teal.modules.general::rADRS ADLB <- teal.modules.general::rADLB }) datanames <- c(\"ADSL\", \"ADRS\", \"ADLB\") datanames(data) <- datanames join_keys(data) <- default_cdisc_join_keys[datanames]"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-outliers-module.html","id":"section-create-an-app-variable","dir":"Articles","previous_headings":"teal application to analyze and report outliers with various datasets types.","what":"3 - Create an app variable","title":"Using outliers module","text":"important section. use teal::init() function create app. data handed using teal.data::teal_data(). app constructed multiple calls tm_outliers() using different combinations data sets.","code":"# configuration for the single wide dataset mod1 <- tm_outliers( label = \"Single wide dataset\", outlier_var = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"BMRKR1\")), selected = \"AGE\", fixed = FALSE ) ), categorical_var = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices( data[[\"ADSL\"]], subset = names(Filter(isTRUE, sapply(data[[\"ADSL\"]], is.factor))) ), selected = \"RACE\", multiple = FALSE, fixed = FALSE ) ) ) # configuration for the wide and long datasets mod2 <- tm_outliers( label = \"Wide and long datasets\", outlier_var = list( data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"BMRKR1\")), selected = \"AGE\", fixed = FALSE ) ), data_extract_spec( dataname = \"ADLB\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADLB\"]], c(\"AVAL\", \"CHG2\")), selected = \"AVAL\", multiple = FALSE, fixed = FALSE ) ) ), categorical_var = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices( data[[\"ADSL\"]], subset = names(Filter(isTRUE, sapply(data[[\"ADSL\"]], is.factor))) ), selected = \"RACE\", multiple = FALSE, fixed = FALSE ) ) ) # configuration for the multiple long datasets mod3 <- tm_outliers( label = \"Multiple long datasets\", outlier_var = list( data_extract_spec( dataname = \"ADRS\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADRS\"]], c(\"ADY\", \"EOSDY\")), selected = \"ADY\", fixed = FALSE ) ), data_extract_spec( dataname = \"ADLB\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADLB\"]], c(\"AVAL\", \"CHG2\")), selected = \"AVAL\", multiple = FALSE, fixed = FALSE ) ) ), categorical_var = list( data_extract_spec( dataname = \"ADRS\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADRS\"]], c(\"ARM\", \"ACTARM\")), selected = \"ARM\", multiple = FALSE, fixed = FALSE ) ), data_extract_spec( dataname = \"ADLB\", select = select_spec( label = \"Select variables:\", choices = variable_choices( data[[\"ADLB\"]], subset = names(Filter(isTRUE, sapply(data[[\"ADLB\"]], is.factor))) ), selected = \"RACE\", multiple = FALSE, fixed = FALSE ) ) ) ) # initialize the app app <- init( data = data, modules = modules( # tm_outliers ---- modules( label = \"Outliers module\", mod1, mod2, mod3 ) ) )"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-outliers-module.html","id":"section-run-the-app","dir":"Articles","previous_headings":"teal application to analyze and report outliers with various datasets types.","what":"4 - Run the app","title":"Using outliers module","text":"simple shiny::shinyApp() call let run app. Note app displayed running code inside R session.","code":"shinyApp(app$ui, app$server, options = list(height = 1024, width = 1024))"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-regression-plots.html","id":"section-teal-application-to-use-regression-plot-with-various-datasets-types","dir":"Articles","previous_headings":"","what":"teal application to use regression plot with various datasets types","title":"Using regression plots","text":"vignette guide four parts create teal application using various types datasets using regression plot module tm_a_regression(): Load libraries Create data sets Create app variable Run app","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-regression-plots.html","id":"section-load-libraries","dir":"Articles","previous_headings":"teal application to use regression plot with various datasets types","what":"1 - Load libraries","title":"Using regression plots","text":"","code":"library(teal.modules.general) # used to create the app library(dplyr) # used to modify data sets"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-regression-plots.html","id":"section-create-data-sets","dir":"Articles","previous_headings":"teal application to use regression plot with various datasets types","what":"2 - Create data sets","title":"Using regression plots","text":"Inside app 4 datasets used ADSL wide data set subject data ADRS long data set response data subjects different time points study ADTTE long data set time event data ADLB long data set lab measurements subject","code":"data <- teal_data() data <- within(data, { ADSL <- teal.modules.general::rADSL %>% mutate(TRTDUR = round(as.numeric(TRTEDTM - TRTSDTM), 1)) ADRS <- teal.modules.general::rADRS ADTTE <- teal.modules.general::rADTTE ADLB <- teal.modules.general::rADLB %>% mutate(CHGC = as.factor(case_when( CHG < 1 ~ \"N\", CHG > 1 ~ \"P\", TRUE ~ \"-\" ))) }) datanames <- c(\"ADSL\", \"ADRS\", \"ADTTE\", \"ADLB\") datanames(data) <- datanames join_keys(data) <- default_cdisc_join_keys[datanames]"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-regression-plots.html","id":"section-create-an-app-variable","dir":"Articles","previous_headings":"teal application to use regression plot with various datasets types","what":"3 - Create an app variable","title":"Using regression plots","text":"important section. use teal::init() function create app. data handed using teal.data::teal_data(). app constructed multiple calls tm_a_regression() using different combinations data sets.","code":"# configuration for the single wide dataset mod1 <- tm_a_regression( label = \"Single wide dataset\", response = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"BMRKR1\", \"BMRKR2\")), selected = \"BMRKR1\", multiple = FALSE, fixed = FALSE ) ), regressor = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"SEX\", \"RACE\")), selected = \"AGE\", multiple = TRUE, fixed = FALSE ) ) ) # configuration for the two wide datasets mod2 <- tm_a_regression( label = \"Two wide datasets\", default_plot_type = 2, response = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"BMRKR1\", \"BMRKR2\")), selected = \"BMRKR1\", multiple = FALSE, fixed = FALSE ) ), regressor = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"SEX\", \"RACE\")), selected = c(\"AGE\", \"RACE\"), multiple = TRUE, fixed = FALSE ) ) ) # configuration for the same long datasets (same subset) mod3 <- tm_a_regression( label = \"Same long datasets (same subset)\", default_plot_type = 2, response = data_extract_spec( dataname = \"ADTTE\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADTTE\"]], c(\"AVAL\", \"CNSR\")), selected = \"AVAL\", multiple = FALSE, fixed = FALSE ), filter = filter_spec( label = \"Select parameter:\", vars = \"PARAMCD\", choices = value_choices(data[[\"ADTTE\"]], \"PARAMCD\", \"PARAM\"), selected = \"PFS\", multiple = FALSE ) ), regressor = data_extract_spec( dataname = \"ADTTE\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADTTE\"]], c(\"AGE\", \"CNSR\", \"SEX\")), selected = c(\"AGE\", \"CNSR\", \"SEX\"), multiple = TRUE ), filter = filter_spec( label = \"Select parameter:\", vars = \"PARAMCD\", choices = value_choices(data[[\"ADTTE\"]], \"PARAMCD\", \"PARAM\"), selected = \"PFS\", multiple = FALSE ) ) ) # configuration for the wide and long datasets mod4 <- tm_a_regression( label = \"Wide and long datasets\", response = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[2], multiple = TRUE, label = \"Select measurement:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[2], multiple = TRUE, label = \"Select visit:\" ) ), select = select_spec( label = \"Select variable:\", choices = \"AVAL\", selected = \"AVAL\", multiple = FALSE, fixed = TRUE ) ), regressor = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADSL\"]], c(\"BMRKR1\", \"BMRKR2\", \"AGE\")), selected = \"AGE\", multiple = TRUE, fixed = FALSE ) ) ) # configuration for the same long datasets (different subsets) mod5 <- tm_a_regression( label = \"Same long datasets (different subsets)\", default_plot_type = 2, response = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = TRUE, label = \"Select lab:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = TRUE, label = \"Select visit:\" ) ), select = select_spec( choices = \"AVAL\", selected = \"AVAL\", multiple = FALSE, fixed = TRUE ) ), regressor = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select labs:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE, label = \"Select visit:\" ) ), select = select_spec( choices = variable_choices(data[[\"ADLB\"]], c(\"AVAL\", \"AGE\", \"BMRKR1\", \"BMRKR2\", \"SEX\", \"ARM\")), selected = c(\"AVAL\", \"BMRKR1\"), multiple = TRUE ) ) ) # initialize the app app <- init( data = data, modules = modules( modules( label = \"Regression plots\", mod1, mod2, mod3, mod4, mod5 ) ) )"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-regression-plots.html","id":"section-run-the-app","dir":"Articles","previous_headings":"teal application to use regression plot with various datasets types","what":"4 - Run the app","title":"Using regression plots","text":"simple shiny::shinyApp() call let run app. Note app displayed running code inside R session.","code":"shinyApp(app$ui, app$server, options = list(height = 1024, width = 1024))"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-response-plot.html","id":"section-teal-application-to-use-response-plot-with-various-datasets-types","dir":"Articles","previous_headings":"","what":"teal application to use response plot with various datasets types","title":"Using response plot","text":"vignette guide four parts create teal application using various types datasets using response plot module tm_g_response(): Load libraries Create data sets Create app variable Run app","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-response-plot.html","id":"section-load-libraries","dir":"Articles","previous_headings":"teal application to use response plot with various datasets types","what":"1 - Load libraries","title":"Using response plot","text":"","code":"library(teal.modules.general) # used to create the app library(dplyr) # used to modify data sets"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-response-plot.html","id":"section-create-data-sets","dir":"Articles","previous_headings":"teal application to use response plot with various datasets types","what":"2 - Create data sets","title":"Using response plot","text":"Inside app 4 datasets used ADSL wide data set subject data ADRS long data set response data subjects different time points study ADTTE long data set time event data ADLB long data set lab measurements subject","code":"data <- teal_data() data <- within(data, { ADSL <- teal.modules.general::rADSL %>% mutate(TRTDUR = round(as.numeric(TRTEDTM - TRTSDTM), 1)) ADRS <- teal.modules.general::rADRS ADTTE <- teal.modules.general::rADTTE ADLB <- teal.modules.general::rADLB %>% mutate(CHGC = as.factor(case_when( CHG < 1 ~ \"N\", CHG > 1 ~ \"P\", TRUE ~ \"-\" ))) }) datanames <- c(\"ADSL\", \"ADRS\", \"ADTTE\", \"ADLB\") datanames(data) <- datanames join_keys(data) <- default_cdisc_join_keys[datanames]"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-response-plot.html","id":"section-create-an-app-variable","dir":"Articles","previous_headings":"teal application to use response plot with various datasets types","what":"3 - Create an app variable","title":"Using response plot","text":"important section. use teal::init() function create app. data handed using teal.data::teal_data(). app constructed multiple calls tm_g_response() using different combinations data sets.","code":"# configuration for the single wide dataset mod1 <- tm_g_response( label = \"Single wide dataset\", response = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"BMRKR2\", \"ITTFL\", \"BEP01FL\")), selected = \"BMRKR2\", multiple = FALSE, fixed = FALSE ) ), x = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"SEX\", \"RACE\", \"COUNTRY\", \"ARMCD\", \"STRATA1\")), selected = \"ARMCD\", multiple = FALSE, fixed = FALSE ) ) ) # configuration for the two wide datasets mod2 <- tm_g_response( label = \"Two wide datasets\", response = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"BMRKR2\", \"ITTFL\", \"BEP01FL\")), selected = \"BMRKR2\", multiple = FALSE ) ), x = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = c(\"SEX\", \"COUNTRY\", \"RACE\", \"STRATA1\", \"ARMCD\"), selected = \"ARMCD\", multiple = FALSE ) ) ) # configuration for the multiple long datasets mod3 <- tm_g_response( label = \"Multiple long datasets\", response = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( label = \"Select parameter:\", vars = \"PARAMCD\", choices = levels(data[[\"ADLB\"]]$PARAMCD), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE ), filter_spec( label = \"Select visit:\", vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE ) ), select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADLB\"]], c(\"BMRKR2\", \"ITTFL\", \"BEP01FL\")), selected = \"BMRKR2\", multiple = FALSE ) ), x = data_extract_spec( dataname = \"ADRS\", filter = list( filter_spec( label = \"Select parameter:\", vars = \"PARAMCD\", choices = levels(data[[\"ADRS\"]]$PARAMCD), selected = levels(data[[\"ADRS\"]]$PARAMCD)[3], multiple = FALSE ), filter_spec( label = \"Select visit:\", vars = \"AVISIT\", choices = levels(data[[\"ADRS\"]]$AVISIT), selected = levels(data[[\"ADRS\"]]$AVISIT)[3], multiple = FALSE ) ), select = select_spec( choices = c(\"AVALC\", \"ITTFL\", \"BEP01FL\"), selected = \"AVALC\", multiple = FALSE, fixed = TRUE ) ), row_facet = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = \"SEX\", selected = NULL, multiple = FALSE ) ), col_facet = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"SEX\", \"COUNTRY\")), selected = NULL, multiple = FALSE ) ) ) # configuration for the wide and long dataset mod4 <- tm_g_response( label = \"Wide and long dataset\", response = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = levels(data[[\"ADLB\"]]$PARAMCD), selected = levels(data[[\"ADLB\"]]$PARAMCD)[2], multiple = TRUE, label = \"Select measurement:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[2], multiple = TRUE, label = \"Select visit:\" ) ), select = select_spec( choices = variable_choices(data[[\"ADLB\"]], c(\"BMRKR2\", \"ITTFL\", \"BEP01FL\")), selected = \"BMRKR2\", multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), x = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"ARMCD\", \"BMRKR1\", \"BMRKR2\", \"BEP01FL\")), selected = \"BMRKR2\", multiple = FALSE, fixed = FALSE ) ) ) # configuration for the same long datasets (same subsets) mod5 <- tm_g_response( label = \"Same long datasets (same subsets)\", response = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"BMRKR2\", \"AVALC\", \"BEP01FL\")), selected = \"AVALC\", multiple = FALSE, fixed = TRUE, label = \"Select variable:\" ) ), x = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"AVALC\", \"AGE\", \"SEX\", \"ARMCD\", \"STRATA1\")), selected = \"ARMCD\", multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), row_facet = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = \"PARAMCD\", selected = \"PARAMCD\", multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), col_facet = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = \"AVISIT\", selected = \"AVISIT\", multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ) ) # configuration for the same long datasets (different subsets) mod6 <- tm_g_response( label = \"Same long datasets (different subsets)\", response = data_extract_spec( dataname = \"ADLB\", filter = filter_spec( vars = \"PARAMCD\", choices = levels(data[[\"ADLB\"]]$PARAMCD), selected = levels(data[[\"ADLB\"]]$PARAMCD)[2], multiple = FALSE, label = \"Select lab:\" ), select = select_spec( choices = \"BMRKR2\", selected = \"BMRKR2\", multiple = FALSE, fixed = TRUE ) ), x = data_extract_spec( dataname = \"ADLB\", filter = filter_spec( vars = \"PARAMCD\", choices = levels(data[[\"ADLB\"]]$PARAMCD), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select lab:\" ), select = select_spec( choices = variable_choices(data[[\"ADLB\"]], c(\"AVISIT\", \"PARAMCD\", \"BEP01FL\")), selected = \"AVISIT\", multiple = FALSE, fixed = TRUE ) ), row_facet = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = levels(data[[\"ADLB\"]]$PARAMCD), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select lab:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE, label = \"Select visit:\" ) ), select = select_spec( choices = variable_choices(data[[\"ADLB\"]], c(\"SEX\", \"RACE\", \"ARMCD\")), selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select Variable\" ) ), col_facet = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = levels(data[[\"ADLB\"]]$PARAMCD), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select lab:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE, label = \"Select visit:\" ) ), select = select_spec( choices = variable_choices(data[[\"ADLB\"]], c(\"SEX\", \"RACE\", \"ARMCD\")), selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ) ) # initialize the app app <- init( data = data, modules = modules( modules( label = \"Response plot\", mod1, mod2, mod3, mod4, mod5, mod6 ) ) )"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-response-plot.html","id":"section-run-the-app","dir":"Articles","previous_headings":"teal application to use response plot with various datasets types","what":"4 - Run the app","title":"Using response plot","text":"simple shiny::shinyApp() call let run app. Note app displayed running code inside R session.","code":"shinyApp(app$ui, app$server, options = list(height = 1024, width = 1024))"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-scatterplot-matrix.html","id":"section-teal-application-to-use-scatter-plot-matrix-with-various-datasets-types","dir":"Articles","previous_headings":"","what":"teal application to use scatter plot matrix with various datasets types","title":"Using scatterplot matrix","text":"vignette guide four parts create teal application using various types datasets using scatter plot matrix module tm_g_scatterplotmatrix(): Load libraries Create data sets Create app variable Run app","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-scatterplot-matrix.html","id":"section-load-libraries","dir":"Articles","previous_headings":"teal application to use scatter plot matrix with various datasets types","what":"1 - Load libraries","title":"Using scatterplot matrix","text":"","code":"library(teal.modules.general) # used to create the app library(dplyr) # used to modify data sets"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-scatterplot-matrix.html","id":"section-create-data-sets","dir":"Articles","previous_headings":"teal application to use scatter plot matrix with various datasets types","what":"2 - Create data sets","title":"Using scatterplot matrix","text":"Inside app 4 datasets used ADSL wide data set subject data ADRS long data set response data subjects different time points study ADTTE long data set time event data ADLB long data set lab measurements subject","code":"data <- teal_data() data <- within(data, { ADSL <- teal.modules.general::rADSL %>% mutate(TRTDUR = round(as.numeric(TRTEDTM - TRTSDTM), 1)) ADRS <- teal.modules.general::rADRS ADTTE <- teal.modules.general::rADTTE ADLB <- teal.modules.general::rADLB %>% mutate(CHGC = as.factor(case_when( CHG < 1 ~ \"N\", CHG > 1 ~ \"P\", TRUE ~ \"-\" ))) }) datanames <- c(\"ADSL\", \"ADRS\", \"ADTTE\", \"ADLB\") datanames(data) <- datanames join_keys(data) <- default_cdisc_join_keys[datanames]"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-scatterplot-matrix.html","id":"section-create-an-app-variable","dir":"Articles","previous_headings":"teal application to use scatter plot matrix with various datasets types","what":"3 - Create an app variable","title":"Using scatterplot matrix","text":"important section. use teal::init() function create app. data handed using teal.data::teal_data(). app constructed multiple calls tm_g_scatterplotmatrix() using different combinations data sets.","code":"# configuration for the single wide dataset mod1 <- tm_g_scatterplotmatrix( label = \"Single wide dataset\", variables = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADSL\"]]), selected = c(\"AGE\", \"RACE\", \"SEX\", \"BMRKR1\", \"BMRKR2\"), multiple = TRUE, fixed = FALSE, ordered = TRUE ) ) ) # configuration for the one long datasets mod2 <- tm_g_scatterplotmatrix( \"One long dataset\", variables = data_extract_spec( dataname = \"ADTTE\", select = select_spec( choices = variable_choices(data[[\"ADTTE\"]], c(\"AVAL\", \"BMRKR1\", \"BMRKR2\")), selected = c(\"AVAL\", \"BMRKR1\", \"BMRKR2\"), multiple = TRUE, fixed = FALSE, ordered = TRUE, label = \"Select variables:\" ) ) ) # configuration for the two long datasets mod3 <- tm_g_scatterplotmatrix( label = \"Two long datasets\", variables = list( data_extract_spec( dataname = \"ADRS\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADRS\"]]), selected = c(\"AVAL\", \"AVALC\"), multiple = TRUE, fixed = FALSE, ordered = TRUE, ), filter = filter_spec( label = \"Select endpoints:\", vars = c(\"PARAMCD\", \"AVISIT\"), choices = value_choices(data[[\"ADRS\"]], c(\"PARAMCD\", \"AVISIT\"), c(\"PARAM\", \"AVISIT\")), selected = \"OVRINV - SCREENING\", multiple = FALSE ) ), data_extract_spec( dataname = \"ADTTE\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADTTE\"]]), selected = c(\"AVAL\", \"CNSR\"), multiple = TRUE, fixed = FALSE, ordered = TRUE ), filter = filter_spec( label = \"Select parameters:\", vars = \"PARAMCD\", choices = value_choices(data[[\"ADTTE\"]], \"PARAMCD\", \"PARAM\"), selected = \"OS\", multiple = TRUE ) ) ) ) # initialize the app app <- init( data = data, modules = modules( modules( label = \"Scatterplot matrix\", mod1, mod2, mod3 ) ) )"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-scatterplot-matrix.html","id":"section-run-the-app","dir":"Articles","previous_headings":"teal application to use scatter plot matrix with various datasets types","what":"4 - Run the app","title":"Using scatterplot matrix","text":"simple shiny::shinyApp() call let run app. Note app displayed running code inside R session.","code":"shinyApp(app$ui, app$server, options = list(height = 1024, width = 1024))"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-scatterplot.html","id":"section-teal-application-to-use-scatter-plot-with-various-datasets-types","dir":"Articles","previous_headings":"","what":"teal application to use scatter plot with various datasets types","title":"Using scatterplot","text":"vignette guide four parts create teal application using various types datasets using scatter plot module tm_g_scatterplot(): Load libraries Create data sets Create app variable Run app","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-scatterplot.html","id":"section-load-libraries","dir":"Articles","previous_headings":"teal application to use scatter plot with various datasets types","what":"1 - Load libraries","title":"Using scatterplot","text":"","code":"library(teal.modules.general) # used to create the app library(dplyr) # used to modify data sets"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-scatterplot.html","id":"section-create-data-sets","dir":"Articles","previous_headings":"teal application to use scatter plot with various datasets types","what":"2 - Create data sets","title":"Using scatterplot","text":"Inside app 4 datasets used ADSL wide data set subject data ADRS long data set response data subjects different time points study ADTTE long data set time event data ADLB long data set lab measurements subject","code":"data <- teal_data() data <- within(data, { ADSL <- teal.modules.general::rADSL %>% mutate(TRTDUR = round(as.numeric(TRTEDTM - TRTSDTM), 1)) ADRS <- teal.modules.general::rADRS ADTTE <- teal.modules.general::rADTTE ADLB <- teal.modules.general::rADLB %>% mutate(CHGC = as.factor(case_when( CHG < 1 ~ \"N\", CHG > 1 ~ \"P\", TRUE ~ \"-\" ))) }) datanames <- c(\"ADSL\", \"ADRS\", \"ADTTE\", \"ADLB\") datanames(data) <- datanames join_keys(data) <- default_cdisc_join_keys[datanames]"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-scatterplot.html","id":"section-create-an-app-variable","dir":"Articles","previous_headings":"teal application to use scatter plot with various datasets types","what":"3 - Create an app variable","title":"Using scatterplot","text":"important section. use teal::init() function create app. data handed using teal.data::teal_data(). app constructed multiple calls tm_g_scatterplot() using different combinations data sets.","code":"# configuration for the single wide datasets mod1 <- tm_g_scatterplot( label = \"Single wide dataset\", x = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"BMRKR1\", \"BMRKR2\")), selected = \"AGE\", multiple = FALSE, fixed = FALSE ) ), y = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"BMRKR1\", \"BMRKR2\")), selected = \"BMRKR1\", multiple = FALSE, fixed = FALSE ) ), color_by = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADSL\"]], c(\"RACE\", \"SEX\")), selected = NULL, multiple = TRUE, fixed = FALSE ) ) ) # configuration for the two wide datasets mod2 <- tm_g_scatterplot( label = \"Two wide datasets\", x = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"BMRKR1\", \"BMRKR2\")), selected = \"BMRKR1\", multiple = FALSE, fixed = FALSE ) ), y = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"SEX\")), selected = \"AGE\", multiple = FALSE, fixed = FALSE ) ), color_by = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"COUNTRY\", \"AGE\", \"RACE\")), selected = \"COUNTRY\", multiple = FALSE, fixed = FALSE ) ) ) # configuration for the different long datasets mod3 <- tm_g_scatterplot( label = \"Different long datasets\", x = data_extract_spec( dataname = \"ADRS\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADRS\"]]), selected = \"AVAL\", multiple = FALSE, fixed = FALSE ), filter = filter_spec( label = \"Select endpoint:\", vars = c(\"PARAMCD\", \"AVISIT\"), choices = value_choices(data[[\"ADRS\"]], c(\"PARAMCD\", \"AVISIT\"), c(\"PARAM\", \"AVISIT\")), selected = \"OVRINV - SCREENING\", multiple = FALSE ) ), y = data_extract_spec( dataname = \"ADTTE\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADTTE\"]]), selected = \"AVAL\", multiple = FALSE, fixed = FALSE ), filter = filter_spec( label = \"Select parameters:\", vars = c(\"PARAMCD\"), choices = value_choices(data[[\"ADTTE\"]], \"PARAMCD\", \"PARAM\"), selected = \"OS\", multiple = TRUE ) ), color_by = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"SEX\")), selected = \"AGE\", multiple = FALSE, fixed = FALSE ) ) ) # configuration for the wide and long datasets mod4 <- tm_g_scatterplot( label = \"Wide and long datasets\", x = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"SEX\", \"AGE\", \"BMRKR1\", \"COUNTRY\")), selected = \"AGE\", multiple = FALSE, fixed = FALSE ) ), y = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select measurement:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE, label = \"Select visit:\" ) ), select = select_spec( label = \"Selected variable:\", choices = \"AVAL\", selected = \"AVAL\", multiple = FALSE, fixed = TRUE ) ), color_by = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"SEX\", \"AGE\", \"RACE\", \"COUNTRY\")), selected = NULL, multiple = FALSE, fixed = FALSE ) ) ) # configuration for the same long datasets (same subsets) mod5 <- tm_g_scatterplot( label = \"Same long datasets (same subsets)\", x = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"AVAL\", \"BMRKR1\", \"BMRKR2\")), selected = \"AVAL\", multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), y = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"AVAL\", \"BMRKR1\", \"BMRKR2\")), selected = \"BMRKR1\", multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), color_by = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"AGE\", \"SEX\", \"RACE\")), selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ) ) # configuration for the same long datasets (different subsets) mod6 <- tm_g_scatterplot( label = \"Same long datasets (different subsets)\", x = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select lab:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE, label = \"Select visit:\" ) ), select = select_spec( choices = \"AVAL\", selected = \"AVAL\", multiple = FALSE, fixed = TRUE ) ), y = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select lab:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE, label = \"Select visit:\" ) ), select = select_spec( choices = \"AVAL\", selected = \"AVAL\", multiple = FALSE, fixed = TRUE ) ), color_by = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select lab:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE, label = \"Select visit:\" ) ), select = select_spec( choices = variable_choices(data[[\"ADLB\"]], c(\"RACE\", \"SEX\")), selected = \"SEX\", multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ) ) # initialize the app app <- init( data = data, modules = modules( modules( label = \"Scatterplot\", mod1, mod2, mod3, mod4, mod5, mod6 ) ) )"},{"path":"https://insightsengineering.github.io/teal.modules.general/articles/using-scatterplot.html","id":"section-run-the-app","dir":"Articles","previous_headings":"teal application to use scatter plot with various datasets types","what":"4 - Run the app","title":"Using scatterplot","text":"simple shiny::shinyApp() call let run app. Note app displayed running code inside R session.","code":"shinyApp(app$ui, app$server, options = list(height = 1024, width = 1024))"},{"path":"https://insightsengineering.github.io/teal.modules.general/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Dawid Kaledkowski. Author, maintainer. Pawel Rucki. Author. Mahmoud Hallal. Author. Ondrej Slama. Contributor. Maciej Nasinski. Author. Konrad Pagacz. Author. Nikolas Burkoff. Author. F. Hoffmann-La Roche AG. Copyright holder, funder.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Kaledkowski D, Rucki P, Hallal M, Nasinski M, Pagacz K, Burkoff N (2024). teal.modules.general: General Modules 'teal' Applications. R package version 0.3.0.9006, https://github.com/insightsengineering/teal.modules.general/, https://insightsengineering.github.io/teal.modules.general/.","code":"@Manual{, title = {teal.modules.general: General Modules for 'teal' Applications}, author = {Dawid Kaledkowski and Pawel Rucki and Mahmoud Hallal and Maciej Nasinski and Konrad Pagacz and Nikolas Burkoff}, year = {2024}, note = {R package version 0.3.0.9006, https://github.com/insightsengineering/teal.modules.general/}, url = {https://insightsengineering.github.io/teal.modules.general/}, }"},{"path":"https://insightsengineering.github.io/teal.modules.general/index.html","id":"tealmodulesgeneral","dir":"","previous_headings":"","what":"General Modules for teal Applications","title":"General Modules for teal Applications","text":"package contains set standard teal modules include applications. include modules : viewing data (including tm_variable_browser, tm_data_table, tm_file_viewer, …) visualizing data (tm_g_scatterplot, tm_g_association, …) understanding missing outlier values within data (tm_missing_data, tm_outliers, …) performing simple data analysis (tm_a_pca, tm_g_distribution, tm_a_regression, …). modules work CDISC data, independent datasets general relational data. Please see teal gallery TLG Catalog see examples teal apps modules package.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"General Modules for teal Applications","text":"Alternatively, might want use development version available r-universe.","code":"# stable versions install.packages('teal.modules.general') # install.packages(\"pak\") pak::pak(\"insightsengineering/teal.modules.general@*release\") # beta versions install.packages('teal.modules.general', repos = c('https://pharmaverse.r-universe.dev', getOption('repos'))) # install.packages(\"pak\") pak::pak(\"insightsengineering/teal.modules.general\")"},{"path":"https://insightsengineering.github.io/teal.modules.general/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"General Modules for teal Applications","text":"See package vignettes browseVignettes(package = \"teal.modules.general\") usage package visit Package Website.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/index.html","id":"getting-help","dir":"","previous_headings":"","what":"Getting help","title":"General Modules for teal Applications","text":"encounter bug feature request, please file issue. questions, discussions, staying date, please use teal channel pharmaverse slack workspace.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/index.html","id":"acknowledgment","dir":"","previous_headings":"","what":"Acknowledgment","title":"General Modules for teal Applications","text":"package result joint efforts many developers stakeholders. like thank everyone contributed far!","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/pull_request_template.html","id":null,"dir":"","previous_headings":"","what":"Pull Request","title":"Pull Request","text":"Fixes #nnn","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/add_facet_labels.html","id":null,"dir":"Reference","previous_headings":"","what":"Add labels for facets to a ggplot2 object — add_facet_labels","title":"Add labels for facets to a ggplot2 object — add_facet_labels","text":"Enhances ggplot2 plot adding labels describe faceting variables along x y axes.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/add_facet_labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add labels for facets to a ggplot2 object — add_facet_labels","text":"","code":"add_facet_labels(p, xfacet_label = NULL, yfacet_label = NULL)"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/add_facet_labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add labels for facets to a ggplot2 object — add_facet_labels","text":"p (ggplot2) object facet labels added. xfacet_label (character) Label facet along x-axis. NULL, label added. vector, labels joined \" & \". yfacet_label (character) Label facet along y-axis. Similar behavior xfacet_label.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/add_facet_labels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add labels for facets to a ggplot2 object — add_facet_labels","text":"Returns grid grob object (drawn grid.draw)","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/add_facet_labels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add labels for facets to a ggplot2 object — add_facet_labels","text":"","code":"library(ggplot2) library(grid) p <- ggplot(mtcars) + aes(x = mpg, y = disp) + geom_point() + facet_grid(gear ~ cyl) xfacet_label <- \"cylinders\" yfacet_label <- \"gear\" res <- add_facet_labels(p, xfacet_label, yfacet_label) grid.newpage() grid.draw(res) grid.newpage() grid.draw(add_facet_labels(p, xfacet_label = NULL, yfacet_label)) grid.newpage() grid.draw(add_facet_labels(p, xfacet_label, yfacet_label = NULL)) grid.newpage() grid.draw(add_facet_labels(p, xfacet_label = NULL, yfacet_label = NULL))"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/call_fun_dots.html","id":null,"dir":"Reference","previous_headings":"","what":"Call a function with a character vector for the ... argument — call_fun_dots","title":"Call a function with a character vector for the ... argument — call_fun_dots","text":"Call function character vector ... argument","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/call_fun_dots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Call a function with a character vector for the ... argument — call_fun_dots","text":"","code":"call_fun_dots(fun, str_args)"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/call_fun_dots.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Call a function with a character vector for the ... argument — call_fun_dots","text":"fun (character) Name function ... argument shall replaced values str_args. str_args (character) character vector function shall executed ","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/call_fun_dots.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Call a function with a character vector for the ... argument — call_fun_dots","text":"Value call fun arguments specified str_args.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/create_sparklines.html","id":null,"dir":"Reference","previous_headings":"","what":"S3 generic for sparkline widget HTML — create_sparklines","title":"S3 generic for sparkline widget HTML — create_sparklines","text":"Generates sparkline HTML code corresponding input array. numeric variables creates box plot, character factors - bar plot. Produces empty string variables types.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/create_sparklines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"S3 generic for sparkline widget HTML — create_sparklines","text":"","code":"create_sparklines(arr, width = 150, ...) # S3 method for logical create_sparklines(arr, ...) # S3 method for numeric create_sparklines(arr, width = 150, ...) # S3 method for character create_sparklines(arr, ...) # S3 method for factor create_sparklines(arr, width = 150, bar_spacing = 5, bar_width = 20, ...) # S3 method for Date create_sparklines(arr, width = 150, bar_spacing = 5, bar_width = 20, ...) # S3 method for POSIXct create_sparklines(arr, width = 150, bar_spacing = 5, bar_width = 20, ...) # S3 method for POSIXlt create_sparklines(arr, width = 150, bar_spacing = 5, bar_width = 20, ...) # S3 method for default create_sparklines(arr, width = 150, ...)"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/create_sparklines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"S3 generic for sparkline widget HTML — create_sparklines","text":"arr vector type length width numeric width sparkline widget (pixels) ... list additional options passed bar plots jquery.sparkline; see jquery.sparkline docs bar_spacing numeric spacing bars (pixels) bar_width numeric width bars (pixels)","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/create_sparklines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"S3 generic for sparkline widget HTML — create_sparklines","text":"Character string containing HTML code sparkline HTML widget.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/establish_updating_selection.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates observers updating the currently selected column — establish_updating_selection","title":"Creates observers updating the currently selected column — establish_updating_selection","text":"created observers update column currently selected left-hand side tabset panel.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/establish_updating_selection.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates observers updating the currently selected column — establish_updating_selection","text":"","code":"establish_updating_selection(datanames, input, plot_var, columns_names)"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/establish_updating_selection.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates observers updating the currently selected column — establish_updating_selection","text":"datanames (character) name dataset input (session$input) shiny session input plot_var (list) list containing currently selected dataset (tab) column names columns_names (environment) environment containing bindings dataset","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/establish_updating_selection.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Creates observers updating the currently selected column — establish_updating_selection","text":"Creates observer dataset (tab tabset panel).","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/get_scatterplotmatrix_stats.html","id":null,"dir":"Reference","previous_headings":"","what":"Get stats for x-y pairs in scatterplot matrix — get_scatterplotmatrix_stats","title":"Get stats for x-y pairs in scatterplot matrix — get_scatterplotmatrix_stats","text":"Uses stats::cor.test() per default numerical input variables converts results character vector. extended different stats different variable types needed. Meant called lattice::panel.text().","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/get_scatterplotmatrix_stats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get stats for x-y pairs in scatterplot matrix — get_scatterplotmatrix_stats","text":"","code":"get_scatterplotmatrix_stats( x, y, .f = stats::cor.test, .f_args = list(), round_stat = 2, round_pval = 4 )"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/get_scatterplotmatrix_stats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get stats for x-y pairs in scatterplot matrix — get_scatterplotmatrix_stats","text":"x, y (numeric) vectors data values. x y must length. .f (function) function accepts x y formula input ~ x + y. Default stats::cor.test. .f_args (list) arguments passed .f. round_stat (integer(1)) optional, number decimal places use rounding estimate. round_pval (integer(1)) optional, number decimal places use rounding p-value.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/get_scatterplotmatrix_stats.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get stats for x-y pairs in scatterplot matrix — get_scatterplotmatrix_stats","text":"Character stats. stats::cor.test() correlation coefficient p-value.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/get_scatterplotmatrix_stats.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get stats for x-y pairs in scatterplot matrix — get_scatterplotmatrix_stats","text":"Presently need use formula input stats::cor.test na.fail gets evaluated formula passed (see ).","code":"x = c(1,3,5,7,NA) y = c(3,6,7,8,1) stats::cor.test(x, y, na.action = \"na.fail\") stats::cor.test(~ x + y, na.action = \"na.fail\")"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/get_scatterplotmatrix_stats.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get stats for x-y pairs in scatterplot matrix — get_scatterplotmatrix_stats","text":"","code":"set.seed(1) x <- runif(25, 0, 1) y <- runif(25, 0, 1) x[c(3, 10, 18)] <- NA get_scatterplotmatrix_stats(x, y, .f = stats::cor.test, .f_args = list(method = \"pearson\")) #> [1] \"cor:0.11\\nP:0.6255\" get_scatterplotmatrix_stats(x, y, .f = stats::cor.test, .f_args = list( method = \"pearson\", na.action = na.fail )) #> [1] \"NA\""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/include_css_files.html","id":null,"dir":"Reference","previous_headings":"","what":"Include CSS files from /inst/css/ package directory to application header — include_css_files","title":"Include CSS files from /inst/css/ package directory to application header — include_css_files","text":"system.file used access files packages, work devtools. Therefore, redefine method package needed. Thus, export method","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/include_css_files.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Include CSS files from /inst/css/ package directory to application header — include_css_files","text":"","code":"include_css_files(pattern = \"*\")"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/include_css_files.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Include CSS files from /inst/css/ package directory to application header — include_css_files","text":"pattern (character) optional, regular expression match file names included.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/include_css_files.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Include CSS files from /inst/css/ package directory to application header — include_css_files","text":"HTML code includes CSS files.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/is_tab_active_js.html","id":null,"dir":"Reference","previous_headings":"","what":"JavaScript condition to check if a specific tab is active — is_tab_active_js","title":"JavaScript condition to check if a specific tab is active — is_tab_active_js","text":"JavaScript condition check specific tab active","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/is_tab_active_js.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"JavaScript condition to check if a specific tab is active — is_tab_active_js","text":"","code":"is_tab_active_js(id, name)"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/is_tab_active_js.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"JavaScript condition to check if a specific tab is active — is_tab_active_js","text":"id (character(1)) id tab panel tabs. name (character(1)) name tab.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/is_tab_active_js.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"JavaScript condition to check if a specific tab is active — is_tab_active_js","text":"JavaScript expression used shiny::conditionalPanel() determine specified tab active.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/plot_var_summary.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot variable — plot_var_summary","title":"Plot variable — plot_var_summary","text":"Creates summary plot statistics relevant data type.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/plot_var_summary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot variable — plot_var_summary","text":"","code":"plot_var_summary( var, var_lab, wrap_character = NULL, numeric_as_factor, display_density = is.numeric(var), remove_NA_hist = FALSE, outlier_definition, records_for_factor, ggplot2_args )"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/plot_var_summary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot variable — plot_var_summary","text":"var vector type plotted. numeric variables produces histogram density line, factors creates frequency plot var_lab text describing selected variable displayed plot wrap_character (numeric) number characters wrap text values var numeric_as_factor (logical) numeric variable treated factor display_density (logical) density estimation displayed numeric values remove_NA_hist (logical) NA values removed histogram factor like variables outlier_definition 0 outliers removed, otherwise outliers (outlier_definition*IQR /Q1/Q3 removed) records_for_factor (numeric) number factor levels >= value graph factors shown, list values ggplot2_args (ggplot2_args) object created teal.widgets::ggplot2_args() settings module plot. argument merged options variable teal.ggplot2_args default module setup. details see vignette: vignette(\"custom-ggplot2-arguments\", package = \"teal.widgets\")","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/plot_var_summary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot variable — plot_var_summary","text":"plot","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/rADAE.html","id":null,"dir":"Reference","previous_headings":"","what":"Random adverse events — rADAE","title":"Random adverse events — rADAE","text":"Random adverse events","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/rADAE.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Random adverse events — rADAE","text":"","code":"rADAE"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/rADAE.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Random adverse events — rADAE","text":"object class tbl_df (inherits tbl, data.frame) 1934 rows 92 columns.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/rADAE.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Random adverse events — rADAE","text":"internal","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/rADLB.html","id":null,"dir":"Reference","previous_headings":"","what":"Random lab analysis — rADLB","title":"Random lab analysis — rADLB","text":"Random lab analysis","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/rADLB.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Random lab analysis — rADLB","text":"","code":"rADLB"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/rADLB.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Random lab analysis — rADLB","text":"object class tbl_df (inherits tbl, data.frame) 8400 rows 102 columns.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/rADLB.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Random lab analysis — rADLB","text":"internal","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/rADRS.html","id":null,"dir":"Reference","previous_headings":"","what":"Random response — rADRS","title":"Random response — rADRS","text":"Random response","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/rADRS.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Random response — rADRS","text":"","code":"rADRS"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/rADRS.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Random response — rADRS","text":"object class tbl_df (inherits tbl, data.frame) 3200 rows 65 columns.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/rADRS.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Random response — rADRS","text":"internal","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/rADSL.html","id":null,"dir":"Reference","previous_headings":"","what":"Random patient listing — rADSL","title":"Random patient listing — rADSL","text":"Random patient listing","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/rADSL.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Random patient listing — rADSL","text":"","code":"rADSL"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/rADSL.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Random patient listing — rADSL","text":"object class tbl_df (inherits tbl, data.frame) 400 rows 55 columns.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/rADSL.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Random patient listing — rADSL","text":"internal","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/rADTTE.html","id":null,"dir":"Reference","previous_headings":"","what":"Random time to event analysis dataset — rADTTE","title":"Random time to event analysis dataset — rADTTE","text":"Random time event analysis dataset","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/rADTTE.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Random time to event analysis dataset — rADTTE","text":"","code":"rADTTE"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/rADTTE.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Random time to event analysis dataset — rADTTE","text":"object class tbl_df (inherits tbl, data.frame) 2000 rows 67 columns.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/rADTTE.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Random time to event analysis dataset — rADTTE","text":"internal","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/remove_outliers_from.html","id":null,"dir":"Reference","previous_headings":"","what":"Removes the outlier observation from an array — remove_outliers_from","title":"Removes the outlier observation from an array — remove_outliers_from","text":"Removes outlier observation array","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/remove_outliers_from.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Removes the outlier observation from an array — remove_outliers_from","text":"","code":"remove_outliers_from(var, outlier_definition)"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/remove_outliers_from.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Removes the outlier observation from an array — remove_outliers_from","text":"var (numeric) numeric vector outlier_definition (numeric) 0 outliers removed, otherwise outliers (outlier_definition*IQR /Q1/Q3) removed","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/remove_outliers_from.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Removes the outlier observation from an array — remove_outliers_from","text":"(numeric) vector without outlier values","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/render_single_tab.html","id":null,"dir":"Reference","previous_headings":"","what":"Renders a single tab in the left-hand side tabset panel — render_single_tab","title":"Renders a single tab in the left-hand side tabset panel — render_single_tab","text":"Renders single tab left-hand side tabset panel. rendered tab contains information one dataset many presented module.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/render_single_tab.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Renders a single tab in the left-hand side tabset panel — render_single_tab","text":"","code":"render_single_tab( dataset_name, parent_dataname, output, data, input, columns_names, plot_var )"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/render_single_tab.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Renders a single tab in the left-hand side tabset panel — render_single_tab","text":"dataset_name (character) name dataset contained rendered tab parent_dataname (character) name parent dataname filter variables output (session$output) shiny session output data (teal_data) object containing datasets input (session$input) shiny session input columns_names (environment) environment containing bindings dataset plot_var (list) list containing currently selected dataset (tab) column names","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/render_tab_header.html","id":null,"dir":"Reference","previous_headings":"","what":"Renders the text headlining a single tab in the left-hand side tabset panel — render_tab_header","title":"Renders the text headlining a single tab in the left-hand side tabset panel — render_tab_header","text":"Renders text headlining single tab left-hand side tabset panel","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/render_tab_header.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Renders the text headlining a single tab in the left-hand side tabset panel — render_tab_header","text":"","code":"render_tab_header(dataset_name, output, data)"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/render_tab_header.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Renders the text headlining a single tab in the left-hand side tabset panel — render_tab_header","text":"dataset_name (character) name dataset tab output (session$output) shiny session output data (teal_data) object containing datasets","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/render_tab_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Renders the table for a single dataset in the left-hand side tabset panel — render_tab_table","title":"Renders the table for a single dataset in the left-hand side tabset panel — render_tab_table","text":"table contains column names, column labels, small summary NA values sparkline (appropriate).","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/render_tab_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Renders the table for a single dataset in the left-hand side tabset panel — render_tab_table","text":"","code":"render_tab_table( dataset_name, parent_dataname, output, data, input, columns_names, plot_var )"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/render_tab_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Renders the table for a single dataset in the left-hand side tabset panel — render_tab_table","text":"dataset_name (character) name dataset parent_dataname (character) name parent dataname filter variables output (session$output) shiny session output data (teal_data) object containing datasets input (session$input) shiny session input columns_names (environment) environment containing bindings dataset plot_var (list) list containing currently selected dataset (tab) column names","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/render_tabset_panel_content.html","id":null,"dir":"Reference","previous_headings":"","what":"Renders the left-hand side tabset panel of the module — render_tabset_panel_content","title":"Renders the left-hand side tabset panel of the module — render_tabset_panel_content","text":"Renders left-hand side tabset panel module","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/render_tabset_panel_content.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Renders the left-hand side tabset panel of the module — render_tabset_panel_content","text":"","code":"render_tabset_panel_content( datanames, parent_dataname, output, data, input, columns_names, plot_var )"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/render_tabset_panel_content.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Renders the left-hand side tabset panel of the module — render_tabset_panel_content","text":"datanames (character) name dataset parent_dataname (character) name parent dataname filter variables output (session$output) shiny session output data (teal_data) object containing datasets input (session$input) shiny session input columns_names (environment) environment containing bindings dataset plot_var (list) list containing currently selected dataset (tab) column names","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/shared_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Shared parameters documentation — shared_params","title":"Shared parameters documentation — shared_params","text":"Defines common arguments shared across multiple functions package avoid repetition using inheritParams.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/shared_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Shared parameters documentation — shared_params","text":"plot_height (numeric) optional, specifies plot height three-element vector value, min, max intended use slider UI element. plot_width (numeric) optional, specifies plot width three-element vector value, min, max slider encoding plot width. rotate_xaxis_labels (logical) optional, whether rotate plot X axis labels. rotate default (FALSE). ggtheme (character) optional, ggplot2 theme used default. Defaults \"gray\". ggplot2_args (ggplot2_args) object created teal.widgets::ggplot2_args() settings module plot. argument merged options variable teal.ggplot2_args default module setup. details see vignette: vignette(\"custom-ggplot2-arguments\", package = \"teal.widgets\") basic_table_args (basic_table_args) object created teal.widgets::basic_table_args() settings module table. argument merged options variable teal.basic_table_args default module setup. details see vignette: vignette(\"custom-basic-table-arguments\", package = \"teal.widgets\") pre_output (shiny.tag) optional, text UI element displayed module's output, providing context title. text placed output put output context. example title. post_output (shiny.tag) optional, text UI element displayed module's output, adding context instructions. Elements like shiny::helpText() useful. alpha (integer(1) integer(3)) optional, specifies point opacity. length alpha one: plot points fixed opacity. length alpha three: plot points opacity dynamically adjusted based vector value, min, max. size (integer(1) integer(3)) optional, specifies point size. length size one: plot point sizes fixed size. length size three: plot points size dynamically adjusted based vector value, min, max.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/shared_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Shared parameters documentation — shared_params","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/teal.modules.general.html","id":null,"dir":"Reference","previous_headings":"","what":"teal.modules.general: General modules to add to a teal application — teal.modules.general","title":"teal.modules.general: General modules to add to a teal application — teal.modules.general","text":"modules package generic modules work data set (necessarily clinical trials data).","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/teal.modules.general.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"teal.modules.general: General modules to add to a teal application — teal.modules.general","text":"Maintainer: Dawid Kaledkowski dawid.kaledkowski@roche.com Authors: Pawel Rucki pawel.rucki@roche.com Mahmoud Hallal mahmoud.hallal@roche.com Maciej Nasinski Konrad Pagacz Nikolas Burkoff contributors: Ondrej Slama ondrej.slama@roche.com [contributor] F. Hoffmann-La Roche AG [copyright holder, funder]","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_a_pca.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: Principal component analysis — tm_a_pca","title":"teal module: Principal component analysis — tm_a_pca","text":"Module conducts principal component analysis (PCA) given dataset offers different ways visualizing outcomes, including elbow plot, circle plot, biplot, eigenvector plot. Additionally, enables dynamic customization plot aesthetics, opacity, size, font size, UI inputs.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_a_pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: Principal component analysis — tm_a_pca","text":"","code":"tm_a_pca( label = \"Principal Component Analysis\", dat, plot_height = c(600, 200, 2000), plot_width = NULL, ggtheme = c(\"gray\", \"bw\", \"linedraw\", \"light\", \"dark\", \"minimal\", \"classic\", \"void\"), ggplot2_args = teal.widgets::ggplot2_args(), rotate_xaxis_labels = FALSE, font_size = c(12, 8, 20), alpha = c(1, 0, 1), size = c(2, 1, 8), pre_output = NULL, post_output = NULL )"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_a_pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: Principal component analysis — tm_a_pca","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. dat (data_extract_spec list multiple data_extract_spec) specifying columns used compute PCA. plot_height (numeric) optional, specifies plot height three-element vector value, min, max intended use slider UI element. plot_width (numeric) optional, specifies plot width three-element vector value, min, max slider encoding plot width. ggtheme (character) optional, ggplot2 theme used default. Defaults \"gray\". ggplot2_args (ggplot2_args) optional, object created teal.widgets::ggplot2_args() settings plots named list ggplot2_args objects plot-specific settings. argument merged options variable teal.ggplot2_args default module setup. List names match following: c(\"default\", \"Elbow plot\", \"Circle plot\", \"Biplot\", \"Eigenvector plot\"). details see vignette: vignette(\"custom-ggplot2-arguments\", package = \"teal.widgets\"). rotate_xaxis_labels (logical) optional, whether rotate plot X axis labels. rotate default (FALSE). font_size (numeric) optional, specifies font size. controls font size plot titles, axis labels, legends. vector length == 1 font sizes fixed size. vector value, min, max allows dynamic adjustment. alpha (integer(1) integer(3)) optional, specifies point opacity. length alpha one: plot points fixed opacity. length alpha three: plot points opacity dynamically adjusted based vector value, min, max. size (integer(1) integer(3)) optional, specifies point size. length size one: plot point sizes fixed size. length size three: plot points size dynamically adjusted based vector value, min, max. pre_output (shiny.tag) optional, text UI element displayed module's output, providing context title. text placed output put output context. example title. post_output (shiny.tag) optional, text UI element displayed module's output, adding context instructions. Elements like shiny::helpText() useful.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_a_pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: Principal component analysis — tm_a_pca","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_a_pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: Principal component analysis — tm_a_pca","text":"","code":"library(teal.widgets) # general data example data <- teal_data() data <- within(data, { require(nestcolor) USArrests <- USArrests }) datanames(data) <- \"USArrests\" app <- init( data = data, modules = modules( tm_a_pca( \"PCA\", dat = data_extract_spec( dataname = \"USArrests\", select = select_spec( choices = variable_choices( data = data[[\"USArrests\"]], c(\"Murder\", \"Assault\", \"UrbanPop\", \"Rape\") ), selected = c(\"Murder\", \"Assault\"), multiple = TRUE ), filter = NULL ), ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by PCA Module\") ) ) ) ) #> Initializing tm_a_pca if (interactive()) { shinyApp(app$ui, app$server) } # CDISC data example data <- teal_data() data <- within(data, { require(nestcolor) ADSL <- rADSL }) datanames(data) <- \"ADSL\" join_keys(data) <- default_cdisc_join_keys[datanames(data)] app <- init( data = data, modules = modules( tm_a_pca( \"PCA\", dat = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices( data = data[[\"ADSL\"]], c(\"BMRKR1\", \"AGE\", \"EOSDY\") ), selected = c(\"BMRKR1\", \"AGE\"), multiple = TRUE ), filter = NULL ), ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by PCA Module\") ) ) ) ) #> Initializing tm_a_pca if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_a_regression.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: Scatterplot and regression analysis — tm_a_regression","title":"teal module: Scatterplot and regression analysis — tm_a_regression","text":"Module visualizing regression analysis, including scatterplots various regression diagnostics plots. allows users explore relationship set regressors response variable, visualize residuals, identify outliers.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_a_regression.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: Scatterplot and regression analysis — tm_a_regression","text":"","code":"tm_a_regression( label = \"Regression Analysis\", regressor, response, plot_height = c(600, 200, 2000), plot_width = NULL, alpha = c(1, 0, 1), size = c(2, 1, 8), ggtheme = c(\"gray\", \"bw\", \"linedraw\", \"light\", \"dark\", \"minimal\", \"classic\", \"void\"), ggplot2_args = teal.widgets::ggplot2_args(), pre_output = NULL, post_output = NULL, default_plot_type = 1, default_outlier_label = \"USUBJID\", label_segment_threshold = c(0.5, 0, 10) )"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_a_regression.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: Scatterplot and regression analysis — tm_a_regression","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. regressor (data_extract_spec list multiple data_extract_spec) Regressor variables incoming dataset filtering selecting. response (data_extract_spec list multiple data_extract_spec) Response variables incoming dataset filtering selecting. plot_height (numeric) optional, specifies plot height three-element vector value, min, max intended use slider UI element. plot_width (numeric) optional, specifies plot width three-element vector value, min, max slider encoding plot width. alpha (integer(1) integer(3)) optional, specifies point opacity. length alpha one: plot points fixed opacity. length alpha three: plot points opacity dynamically adjusted based vector value, min, max. size (integer(1) integer(3)) optional, specifies point size. length size one: plot point sizes fixed size. length size three: plot points size dynamically adjusted based vector value, min, max. ggtheme (character) optional, ggplot2 theme used default. Defaults \"gray\". ggplot2_args (ggplot2_args) optional, object created teal.widgets::ggplot2_args() settings plots named list ggplot2_args objects plot-specific settings. argument merged options variable teal.ggplot2_args default module setup. List names match following: c(\"default\", \"Response vs Regressor\", \"Residuals vs Fitted\", \"Scale-Location\", \"Cook's distance\", \"Residuals vs Leverage\"\", \"Cook's dist vs Leverage\"). details see vignette: vignette(\"custom-ggplot2-arguments\", package = \"teal.widgets\"). pre_output (shiny.tag) optional, text UI element displayed module's output, providing context title. text placed output put output context. example title. post_output (shiny.tag) optional, text UI element displayed module's output, adding context instructions. Elements like shiny::helpText() useful. default_plot_type (numeric) optional, defaults \"Response vs Regressor\". Response vs Regressor Residuals vs Fitted Normal Q-Q Scale-Location Cook's distance Residuals vs Leverage Cook's dist vs Leverage default_outlier_label (character) optional, default column selected label outliers. label_segment_threshold (numeric(1) numeric(3)) Minimum distance label point plot triggers creation line segment two. may happen label placed next point overlaps another label point. value used min.segment.length parameter ggrepel::geom_text_repel() function. can take following forms: numeric(1): Fixed value used minimum distance slider presented UI. numeric(3): slider presented UI (\"Plot settings\") adjust minimum distance dynamically. takes form c(value, min, max) passed value_min_max argument teal.widgets::optionalSliderInputValMinMax.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_a_regression.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: Scatterplot and regression analysis — tm_a_regression","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_a_regression.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"teal module: Scatterplot and regression analysis — tm_a_regression","text":"examples, please see vignette \"Using regression plots\" via vignette(\"using-regression-plots\", package = \"teal.modules.general\").","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_a_regression.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: Scatterplot and regression analysis — tm_a_regression","text":"","code":"# general data example library(teal.widgets) data <- teal_data() data <- within(data, { require(nestcolor) CO2 <- CO2 }) datanames(data) <- c(\"CO2\") app <- init( data = data, modules = modules( tm_a_regression( label = \"Regression\", response = data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variable:\", choices = \"uptake\", selected = \"uptake\", multiple = FALSE, fixed = TRUE ) ), regressor = data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"CO2\"]], c(\"conc\", \"Treatment\")), selected = \"conc\", multiple = TRUE, fixed = FALSE ) ), ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Regression Module\") ) ) ) ) #> Initializing tm_a_regression if (interactive()) { shinyApp(app$ui, app$server) } # CDISC data example library(teal.widgets) data <- teal_data() data <- within(data, { require(nestcolor) ADSL <- rADSL }) datanames(data) <- \"ADSL\" join_keys(data) <- default_cdisc_join_keys[datanames(data)] app <- init( data = data, modules = modules( tm_a_regression( label = \"Regression\", response = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = \"BMRKR1\", selected = \"BMRKR1\", multiple = FALSE, fixed = TRUE ) ), regressor = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"SEX\", \"RACE\")), selected = \"AGE\", multiple = TRUE, fixed = FALSE ) ), ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Regression Module\") ) ) ) ) #> Initializing tm_a_regression if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_data_table.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: Data table viewer — tm_data_table","title":"teal module: Data table viewer — tm_data_table","text":"Module provides dynamic interactive way view data.frames teal application. uses DT package display data tables paginated, searchable, sortable format, helps enhance data exploration analysis.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_data_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: Data table viewer — tm_data_table","text":"","code":"tm_data_table( label = \"Data Table\", variables_selected = list(), datasets_selected = character(0), dt_args = list(), dt_options = list(searching = FALSE, pageLength = 30, lengthMenu = c(5, 15, 30, 100), scrollX = TRUE), server_rendering = FALSE, pre_output = NULL, post_output = NULL )"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_data_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: Data table viewer — tm_data_table","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. variables_selected (named list) Character vectors variables (.e. columns) initially shown dataset. Names list elements correspond names datasets available app. entry specified dataset, first six variables dataset initially shown. datasets_selected (character) vector datasets shown order. Names vector correspond datasets names. vector length == 0 (default) datasets shown. Note: datasets data.frame class compatible. dt_args (named list) Additional arguments passed DT::datatable() (must include data options). dt_options (named list) options argument DT::datatable. default list(searching = FALSE, pageLength = 30, lengthMenu = c(5, 15, 30, 100), scrollX = TRUE) server_rendering (logical) data table rendered server side (see server argument DT::renderDataTable()) pre_output (shiny.tag) optional, text UI element displayed module's output, providing context title. text placed output put output context. example title. post_output (shiny.tag) optional, text UI element displayed module's output, adding context instructions. Elements like shiny::helpText() useful.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_data_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: Data table viewer — tm_data_table","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_data_table.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"teal module: Data table viewer — tm_data_table","text":"DT package option DT.TOJSON_ARGS show Inf NA data tables. Configure DT.TOJSON_ARGS option via options(DT.TOJSON_ARGS = list(na = \"string\")) running module. Note though sorting numeric columns NA/Inf lexicographic numerical.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_data_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: Data table viewer — tm_data_table","text":"","code":"# general data example data <- teal_data() data <- within(data, { require(nestcolor) iris <- iris }) datanames(data) <- c(\"iris\") app <- init( data = data, modules = modules( tm_data_table( variables_selected = list( iris = c(\"Sepal.Length\", \"Sepal.Width\", \"Petal.Length\", \"Petal.Width\", \"Species\") ), dt_args = list(caption = \"ADSL Table Caption\") ) ) ) #> Initializing tm_data_table if (interactive()) { shinyApp(app$ui, app$server) } # CDISC data example data <- teal_data() data <- within(data, { require(nestcolor) ADSL <- rADSL }) datanames(data) <- \"ADSL\" join_keys(data) <- default_cdisc_join_keys[datanames(data)] app <- init( data = data, modules = modules( tm_data_table( variables_selected = list(ADSL = c(\"STUDYID\", \"USUBJID\", \"SUBJID\", \"SITEID\", \"AGE\", \"SEX\")), dt_args = list(caption = \"ADSL Table Caption\") ) ) ) #> Initializing tm_data_table if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_file_viewer.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: File viewer — tm_file_viewer","title":"teal module: File viewer — tm_file_viewer","text":"file viewer module provides tool view static files. Supported formats include text formats, PDF, PNG APNG, JPEG SVG, WEBP, GIF BMP.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_file_viewer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: File viewer — tm_file_viewer","text":"","code":"tm_file_viewer( label = \"File Viewer Module\", input_path = list(`Current Working Directory` = \".\") )"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_file_viewer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: File viewer — tm_file_viewer","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. input_path (list) input paths, optional. element can : Paths can specified absolute paths relative running directory application. Default current working directory supplied.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_file_viewer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: File viewer — tm_file_viewer","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_file_viewer.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: File viewer — tm_file_viewer","text":"","code":"data <- teal_data() data <- within(data, { data <- data.frame(1) }) datanames(data) <- c(\"data\") app <- init( data = data, modules = modules( tm_file_viewer( input_path = list( folder = system.file(\"sample_files\", package = \"teal.modules.general\"), png = system.file(\"sample_files/sample_file.png\", package = \"teal.modules.general\"), txt = system.file(\"sample_files/sample_file.txt\", package = \"teal.modules.general\"), url = \"https://fda.gov/files/drugs/published/Portable-Document-Format-Specifications.pdf\" ) ) ) ) #> Initializing tm_file_viewer if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_front_page.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: Front page — tm_front_page","title":"teal module: Front page — tm_front_page","text":"Creates simple front page teal applications, displaying introductory text, tables, additional html shiny tags, footnotes.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_front_page.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: Front page — tm_front_page","text":"","code":"tm_front_page( label = \"Front page\", header_text = character(0), tables = list(), additional_tags = tagList(), footnotes = character(0), show_metadata = FALSE )"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_front_page.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: Front page — tm_front_page","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. header_text (character vector) text shown top module, element, named name shown first bold header followed value. first element's header displayed larger others. tables (named list data.frames) tables shown module. additional_tags (shiny.tag.list html) additional shiny tags html included table, example include image, tagList(tags$img(src = \"image.png\")) include html, HTML(\"html text \"). footnotes (character vector) text shown bottom module, element, named name shown first bold, followed value. show_metadata (logical) indicating whether metadata datasets available module.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_front_page.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: Front page — tm_front_page","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_front_page.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: Front page — tm_front_page","text":"","code":"data <- teal_data() data <- within(data, { require(nestcolor) ADSL <- rADSL attr(ADSL, \"metadata\") <- list(\"Author\" = \"NEST team\", \"data_source\" = \"synthetic data\") }) datanames(data) <- \"ADSL\" join_keys(data) <- default_cdisc_join_keys[datanames(data)] table_1 <- data.frame(Info = c(\"A\", \"B\"), Text = c(\"A\", \"B\")) table_2 <- data.frame(`Column 1` = c(\"C\", \"D\"), `Column 2` = c(5.5, 6.6), `Column 3` = c(\"A\", \"B\")) table_3 <- data.frame(Info = c(\"E\", \"F\"), Text = c(\"G\", \"H\")) table_input <- list( \"Table 1\" = table_1, \"Table 2\" = table_2, \"Table 3\" = table_3 ) app <- init( data = data, modules = modules( tm_front_page( header_text = c( \"Important information\" = \"It can go here.\", \"Other information\" = \"Can go here.\" ), tables = table_input, additional_tags = HTML(\"Additional HTML or shiny tags go here
    \"), footnotes = c(\"X\" = \"is the first footnote\", \"Y is the second footnote\"), show_metadata = TRUE ) ), header = tags$h1(\"Sample Application\"), footer = tags$p(\"Application footer\"), ) #> Initializing tm_front_page if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_association.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: Stack plots of variables and show association with reference variable — tm_g_association","title":"teal module: Stack plots of variables and show association with reference variable — tm_g_association","text":"Module provides functionality visualizing distribution variables association reference variable. supports configuring appearance plots, including themes whether show associations.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_association.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: Stack plots of variables and show association with reference variable — tm_g_association","text":"","code":"tm_g_association( label = \"Association\", ref, vars, show_association = TRUE, plot_height = c(600, 400, 5000), plot_width = NULL, distribution_theme = c(\"gray\", \"bw\", \"linedraw\", \"light\", \"dark\", \"minimal\", \"classic\", \"void\"), association_theme = c(\"gray\", \"bw\", \"linedraw\", \"light\", \"dark\", \"minimal\", \"classic\", \"void\"), pre_output = NULL, post_output = NULL, ggplot2_args = teal.widgets::ggplot2_args() )"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_association.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: Stack plots of variables and show association with reference variable — tm_g_association","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. ref (data_extract_spec list multiple data_extract_spec) Reference variable, must accepts data_extract_spec select_spec(multiple = FALSE) ensure single selection option. vars (data_extract_spec list multiple data_extract_spec) Variables associated reference variable. show_association (logical) optional, whether show association vars reference variable. Defaults TRUE. plot_height (numeric) optional, specifies plot height three-element vector value, min, max intended use slider UI element. plot_width (numeric) optional, specifies plot width three-element vector value, min, max slider encoding plot width. distribution_theme, association_theme (character) optional, ggplot2 themes used default. Default \"gray\". pre_output (shiny.tag) optional, text UI element displayed module's output, providing context title. text placed output put output context. example title. post_output (shiny.tag) optional, text UI element displayed module's output, adding context instructions. Elements like shiny::helpText() useful. ggplot2_args (ggplot2_args) optional, object created teal.widgets::ggplot2_args() settings plots named list ggplot2_args objects plot-specific settings. argument merged options variable teal.ggplot2_args default module setup. List names match following: c(\"default\", \"Bivariate1\", \"Bivariate2\"). details see vignette: vignette(\"custom-ggplot2-arguments\", package = \"teal.widgets\").","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_association.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: Stack plots of variables and show association with reference variable — tm_g_association","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_association.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"teal module: Stack plots of variables and show association with reference variable — tm_g_association","text":"examples, please see vignette \"Using association plot\" via vignette(\"using-association-plot\", package = \"teal.modules.general\").","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_association.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: Stack plots of variables and show association with reference variable — tm_g_association","text":"","code":"library(teal.widgets) # general data example data <- teal_data() data <- within(data, { require(nestcolor) CO2 <- CO2 factors <- names(Filter(isTRUE, vapply(CO2, is.factor, logical(1L)))) CO2[factors] <- lapply(CO2[factors], as.character) }) datanames(data) <- c(\"CO2\") app <- init( data = data, modules = modules( tm_g_association( ref = data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"CO2\"]], c(\"Plant\", \"Type\", \"Treatment\")), selected = \"Plant\", fixed = FALSE ) ), vars = data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"CO2\"]], c(\"Plant\", \"Type\", \"Treatment\")), selected = \"Treatment\", multiple = TRUE, fixed = FALSE ) ), ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Association Module\") ) ) ) ) #> Initializing tm_g_association if (interactive()) { shinyApp(app$ui, app$server) } # CDISC data example data <- teal_data() data <- within(data, { require(nestcolor) ADSL <- rADSL }) datanames(data) <- \"ADSL\" join_keys(data) <- default_cdisc_join_keys[datanames(data)] app <- init( data = data, modules = modules( tm_g_association( ref = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices( data[[\"ADSL\"]], c(\"SEX\", \"RACE\", \"COUNTRY\", \"ARM\", \"STRATA1\", \"STRATA2\", \"ITTFL\", \"BMRKR2\") ), selected = \"RACE\", fixed = FALSE ) ), vars = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices( data[[\"ADSL\"]], c(\"SEX\", \"RACE\", \"COUNTRY\", \"ARM\", \"STRATA1\", \"STRATA2\", \"ITTFL\", \"BMRKR2\") ), selected = \"BMRKR2\", multiple = TRUE, fixed = FALSE ) ), ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Association Module\") ) ) ) ) #> Initializing tm_g_association if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_bivariate.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: Univariate and bivariate visualizations — tm_g_bivariate","title":"teal module: Univariate and bivariate visualizations — tm_g_bivariate","text":"Module enables creation univariate bivariate plots, facilitating exploration data distributions relationships two variables.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_bivariate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: Univariate and bivariate visualizations — tm_g_bivariate","text":"","code":"tm_g_bivariate( label = \"Bivariate Plots\", x, y, row_facet = NULL, col_facet = NULL, facet = !is.null(row_facet) || !is.null(col_facet), color = NULL, fill = NULL, size = NULL, use_density = FALSE, color_settings = FALSE, free_x_scales = FALSE, free_y_scales = FALSE, plot_height = c(600, 200, 2000), plot_width = NULL, rotate_xaxis_labels = FALSE, swap_axes = FALSE, ggtheme = c(\"gray\", \"bw\", \"linedraw\", \"light\", \"dark\", \"minimal\", \"classic\", \"void\"), ggplot2_args = teal.widgets::ggplot2_args(), pre_output = NULL, post_output = NULL )"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_bivariate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: Univariate and bivariate visualizations — tm_g_bivariate","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. x (data_extract_spec list multiple data_extract_spec) Variable names selected plot along x-axis default. Can numeric, factor character. empty selections allowed. y (data_extract_spec list multiple data_extract_spec) Variable names selected plot along y-axis default. Can numeric, factor character. row_facet (data_extract_spec list multiple data_extract_spec) optional, specification data variable(s) use faceting rows. col_facet (data_extract_spec list multiple data_extract_spec) optional, specification data variable(s) use faceting columns. facet (logical) optional, specifies whether facet encodings ui elements toggled shown user default. Defaults TRUE either row_facet column_facet supplied. color (data_extract_spec list multiple data_extract_spec) optional, specification data variable(s) selected outline color inside coloring settings. applied color_settings set TRUE. fill (data_extract_spec list multiple data_extract_spec) optional, specification data variable(s) selected fill color inside coloring settings. applied color_settings set TRUE. size (data_extract_spec list multiple data_extract_spec) optional, specification data variable(s) selected size geom_point plots inside coloring settings. applied color_settings set TRUE. use_density (logical) optional, indicates whether plot density (TRUE) frequency (FALSE). Defaults frequency (FALSE). color_settings (logical) Whether coloring, filling size applied UI tool offered user. free_x_scales (logical) optional, whether X scaling shall changeable. allow scaling changed default (FALSE). free_y_scales (logical) optional, whether Y scaling shall changeable. allow scaling changed default (FALSE). plot_height (numeric) optional, specifies plot height three-element vector value, min, max intended use slider UI element. plot_width (numeric) optional, specifies plot width three-element vector value, min, max slider encoding plot width. rotate_xaxis_labels (logical) optional, whether rotate plot X axis labels. rotate default (FALSE). swap_axes (logical) optional, whether swap X Y axes. Defaults FALSE. ggtheme (character) optional, ggplot2 theme used default. Defaults \"gray\". ggplot2_args (ggplot2_args) object created teal.widgets::ggplot2_args() settings module plot. argument merged options variable teal.ggplot2_args default module setup. details see vignette: vignette(\"custom-ggplot2-arguments\", package = \"teal.widgets\") pre_output (shiny.tag) optional, text UI element displayed module's output, providing context title. text placed output put output context. example title. post_output (shiny.tag) optional, text UI element displayed module's output, adding context instructions. Elements like shiny::helpText() useful.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_bivariate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: Univariate and bivariate visualizations — tm_g_bivariate","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_bivariate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"teal module: Univariate and bivariate visualizations — tm_g_bivariate","text":"general module visualize 1 & 2 dimensional data.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_bivariate.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"teal module: Univariate and bivariate visualizations — tm_g_bivariate","text":"examples, please see vignette \"Using bivariate plot\" via vignette(\"using-bivariate-plot\", package = \"teal.modules.general\").","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_bivariate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: Univariate and bivariate visualizations — tm_g_bivariate","text":"","code":"library(teal.widgets) # general data example data <- teal_data() data <- within(data, { require(nestcolor) CO2 <- data.frame(CO2) }) datanames(data) <- c(\"CO2\") join_keys(data) <- default_cdisc_join_keys[datanames(data)] app <- init( data = data, modules = modules( tm_g_bivariate( x = data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"CO2\"]]), selected = \"conc\", fixed = FALSE ) ), y = data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"CO2\"]]), selected = \"uptake\", multiple = FALSE, fixed = FALSE ) ), row_facet = data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"CO2\"]]), selected = \"Type\", fixed = FALSE ) ), col_facet = data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"CO2\"]]), selected = \"Treatment\", fixed = FALSE ) ), ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Bivariate Module\") ) ) ) ) #> Initializing tm_g_bivariate if (interactive()) { shinyApp(app$ui, app$server) } # CDISC data example data <- teal_data() data <- within(data, { require(nestcolor) ADSL <- rADSL }) datanames(data) <- c(\"ADSL\") join_keys(data) <- default_cdisc_join_keys[datanames(data)] app <- init( data = data, modules = modules( tm_g_bivariate( x = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]]), selected = \"AGE\", fixed = FALSE ) ), y = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]]), selected = \"SEX\", multiple = FALSE, fixed = FALSE ) ), row_facet = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]]), selected = \"ARM\", fixed = FALSE ) ), col_facet = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]]), selected = \"COUNTRY\", fixed = FALSE ) ), ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Bivariate Module\") ) ) ) ) #> Initializing tm_g_bivariate if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_distribution.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: Distribution analysis — tm_g_distribution","title":"teal module: Distribution analysis — tm_g_distribution","text":"Module designed explore distribution single variable within given dataset. offers several tools, histograms, Q-Q plots, various statistical tests visually statistically analyze variable's distribution.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_distribution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: Distribution analysis — tm_g_distribution","text":"","code":"tm_g_distribution( label = \"Distribution Module\", dist_var, strata_var = NULL, group_var = NULL, freq = FALSE, ggtheme = c(\"gray\", \"bw\", \"linedraw\", \"light\", \"dark\", \"minimal\", \"classic\", \"void\"), ggplot2_args = teal.widgets::ggplot2_args(), bins = c(30L, 1L, 100L), plot_height = c(600, 200, 2000), plot_width = NULL, pre_output = NULL, post_output = NULL )"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_distribution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: Distribution analysis — tm_g_distribution","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. dist_var (data_extract_spec list multiple data_extract_spec) Variable(s) distribution analyzed. strata_var (data_extract_spec list multiple data_extract_spec) Categorical variable used split distribution analysis. group_var (data_extract_spec list multiple data_extract_spec) Variable used faceting plot multiple panels. freq (logical) optional, whether display frequency (TRUE) density (FALSE). Defaults density (FALSE). ggtheme (character) optional, ggplot2 theme used default. Defaults \"gray\". ggplot2_args (ggplot2_args) optional, object created teal.widgets::ggplot2_args() settings plots named list ggplot2_args objects plot-specific settings. argument merged options variable teal.ggplot2_args default module setup. List names match following: c(\"default\", \"Histogram\", \"QQplot\"). details see vignette: vignette(\"custom-ggplot2-arguments\", package = \"teal.widgets\"). bins (integer(1) integer(3)) optional, specifies number bins histogram. length bins one: histogram bins fixed size based bins provided. length bins three: histogram bins dynamically adjusted based vector value, min, max. Defaults c(30L, 1L, 100L). plot_height (numeric) optional, specifies plot height three-element vector value, min, max intended use slider UI element. plot_width (numeric) optional, specifies plot width three-element vector value, min, max slider encoding plot width. pre_output (shiny.tag) optional, text placed output put output context. example title. post_output (shiny.tag) optional, text placed output put output context. example shiny::helpText() elements useful.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_distribution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: Distribution analysis — tm_g_distribution","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_distribution.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: Distribution analysis — tm_g_distribution","text":"","code":"library(teal.widgets) # general data example data <- teal_data() data <- within(data, { iris <- iris }) datanames(data) <- \"iris\" app <- init( data = data, modules = list( tm_g_distribution( dist_var = data_extract_spec( dataname = \"iris\", select = select_spec(variable_choices(\"iris\"), \"Petal.Length\") ), ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Distribution Module\") ) ) ) ) #> Initializing tm_g_distribution #> Registered S3 methods overwritten by 'ggpp': #> method from #> heightDetails.titleGrob ggplot2 #> widthDetails.titleGrob ggplot2 #> Registered S3 method overwritten by 'ggpmisc': #> method from #> as.character.polynomial polynom if (interactive()) { shinyApp(app$ui, app$server) } # CDISC data example data <- teal_data() data <- within(data, { ADSL <- rADSL }) datanames(data) <- c(\"ADSL\") join_keys(data) <- default_cdisc_join_keys[datanames(data)] vars1 <- choices_selected( variable_choices(data[[\"ADSL\"]], c(\"ARM\", \"COUNTRY\", \"SEX\")), selected = NULL ) app <- init( data = data, modules = modules( tm_g_distribution( dist_var = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"BMRKR1\")), selected = \"BMRKR1\", multiple = FALSE, fixed = FALSE ) ), strata_var = data_extract_spec( dataname = \"ADSL\", filter = filter_spec( vars = vars1, multiple = TRUE ) ), group_var = data_extract_spec( dataname = \"ADSL\", filter = filter_spec( vars = vars1, multiple = TRUE ) ), ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Distribution Module\") ) ) ) ) #> Initializing tm_g_distribution if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_response.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: Response plot — tm_g_response","title":"teal module: Response plot — tm_g_response","text":"Generates response plot given response x variables. module allows users customize add annotations plot depending module's arguments. supports showing counts grouped variable facets (row / column), swapping coordinates, show count annotations displaying response plot frequency density.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_response.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: Response plot — tm_g_response","text":"","code":"tm_g_response( label = \"Response Plot\", response, x, row_facet = NULL, col_facet = NULL, coord_flip = FALSE, count_labels = TRUE, rotate_xaxis_labels = FALSE, freq = FALSE, plot_height = c(600, 400, 5000), plot_width = NULL, ggtheme = c(\"gray\", \"bw\", \"linedraw\", \"light\", \"dark\", \"minimal\", \"classic\", \"void\"), ggplot2_args = teal.widgets::ggplot2_args(), pre_output = NULL, post_output = NULL )"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_response.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: Response plot — tm_g_response","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. response (data_extract_spec list multiple data_extract_spec) variable use response. can define one fixed column setting fixed = TRUE inside select_spec. data_extract_spec must allow multiple selection case. x (data_extract_spec list multiple data_extract_spec) Specifies variable use X-axis response plot. Allow user select multiple columns data allowed teal. data_extract_spec must allow multiple selection case. row_facet (data_extract_spec list multiple data_extract_spec) optional specification data variable(s) use faceting rows. col_facet (data_extract_spec list multiple data_extract_spec) optional specification data variable(s) use faceting columns. coord_flip (logical(1)) Indicates whether flip coordinates x response. default value FALSE show x variable x-axis response variable y-axis. count_labels (logical(1)) Indicates whether show count labels. Defaults TRUE. rotate_xaxis_labels (logical) optional, whether rotate plot X axis labels. rotate default (FALSE). freq (logical(1)) Indicates whether display frequency (TRUE) density (FALSE). Defaults density (FALSE). plot_height (numeric) optional, specifies plot height three-element vector value, min, max intended use slider UI element. plot_width (numeric) optional, specifies plot width three-element vector value, min, max slider encoding plot width. ggtheme (character) optional, ggplot2 theme used default. Defaults \"gray\". ggplot2_args (ggplot2_args) object created teal.widgets::ggplot2_args() settings module plot. argument merged options variable teal.ggplot2_args default module setup. details see vignette: vignette(\"custom-ggplot2-arguments\", package = \"teal.widgets\") pre_output (shiny.tag) optional, text UI element displayed module's output, providing context title. text placed output put output context. example title. post_output (shiny.tag) optional, text UI element displayed module's output, adding context instructions. Elements like shiny::helpText() useful.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_response.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: Response plot — tm_g_response","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_response.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"teal module: Response plot — tm_g_response","text":"examples, please see vignette \"Using response plot\" via vignette(\"using-response-plot\", package = \"teal.modules.general\").","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_response.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: Response plot — tm_g_response","text":"","code":"# general data example library(teal.widgets) data <- teal_data() data <- within(data, { require(nestcolor) mtcars <- mtcars for (v in c(\"cyl\", \"vs\", \"am\", \"gear\")) { mtcars[[v]] <- as.factor(mtcars[[v]]) } }) datanames(data) <- \"mtcars\" app <- init( data = data, modules = modules( tm_g_response( label = \"Response Plots\", response = data_extract_spec( dataname = \"mtcars\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"mtcars\"]], c(\"cyl\", \"gear\")), selected = \"cyl\", multiple = FALSE, fixed = FALSE ) ), x = data_extract_spec( dataname = \"mtcars\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"mtcars\"]], c(\"vs\", \"am\")), selected = \"vs\", multiple = FALSE, fixed = FALSE ) ), ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Response Module\") ) ) ) ) #> Initializing tm_g_response if (interactive()) { shinyApp(app$ui, app$server) } # CDISC data example library(teal.widgets) data <- teal_data() data <- within(data, { require(nestcolor) ADSL <- rADSL }) datanames(data) <- c(\"ADSL\") join_keys(data) <- default_cdisc_join_keys[datanames(data)] app <- init( data = data, modules = modules( tm_g_response( label = \"Response Plots\", response = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"BMRKR2\", \"COUNTRY\")), selected = \"BMRKR2\", multiple = FALSE, fixed = FALSE ) ), x = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"SEX\", \"RACE\")), selected = \"RACE\", multiple = FALSE, fixed = FALSE ) ), ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Response Module\") ) ) ) ) #> Initializing tm_g_response if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_scatterplot.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: Scatterplot — tm_g_scatterplot","title":"teal module: Scatterplot — tm_g_scatterplot","text":"Generates customizable scatterplot using ggplot2. module allows users select variables x y axes, color size encodings, faceting options, . supports log transformations, trend line additions, dynamic adjustments point opacity size UI controls.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_scatterplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: Scatterplot — tm_g_scatterplot","text":"","code":"tm_g_scatterplot( label = \"Scatterplot\", x, y, color_by = NULL, size_by = NULL, row_facet = NULL, col_facet = NULL, plot_height = c(600, 200, 2000), plot_width = NULL, alpha = c(1, 0, 1), shape = shape_names, size = c(5, 1, 15), max_deg = 5L, rotate_xaxis_labels = FALSE, ggtheme = c(\"gray\", \"bw\", \"linedraw\", \"light\", \"dark\", \"minimal\", \"classic\", \"void\"), pre_output = NULL, post_output = NULL, table_dec = 4, ggplot2_args = teal.widgets::ggplot2_args() )"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_scatterplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: Scatterplot — tm_g_scatterplot","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. x (data_extract_spec list multiple data_extract_spec) Specifies variable names selected plot along x-axis default. y (data_extract_spec list multiple data_extract_spec) Specifies variable names selected plot along y-axis default. color_by (data_extract_spec list multiple data_extract_spec) optional, defines color encoding. NULL color encoding option displayed. size_by (data_extract_spec list multiple data_extract_spec) optional, defines point size encoding. NULL size encoding option displayed. row_facet (data_extract_spec list multiple data_extract_spec) optional, specifies variable(s) faceting rows. col_facet (data_extract_spec list multiple data_extract_spec) optional, specifies variable(s) faceting columns. plot_height (numeric) optional, specifies plot height three-element vector value, min, max intended use slider UI element. plot_width (numeric) optional, specifies plot width three-element vector value, min, max slider encoding plot width. alpha (integer(1) integer(3)) optional, specifies point opacity. length alpha one: plot points fixed opacity. length alpha three: plot points opacity dynamically adjusted based vector value, min, max. shape (character) optional, character vector names shape, e.g. c(\"triangle\", \"square\", \"circle\"). defaults shape_names. complete list vignette(\"ggplot2-specs\", package=\"ggplot2\"). size (integer(1) integer(3)) optional, specifies point size. length size one: plot point sizes fixed size. length size three: plot points size dynamically adjusted based vector value, min, max. max_deg (integer) optional, maximum degree polynomial trend line. Must less 1. rotate_xaxis_labels (logical) optional, whether rotate plot X axis labels. rotate default (FALSE). ggtheme (character) optional, ggplot2 theme used default. Defaults \"gray\". pre_output (shiny.tag) optional, text UI element displayed module's output, providing context title. text placed output put output context. example title. post_output (shiny.tag) optional, text UI element displayed module's output, adding context instructions. Elements like shiny::helpText() useful. table_dec (integer) optional, number decimal places used round numeric values table. ggplot2_args (ggplot2_args) object created teal.widgets::ggplot2_args() settings module plot. argument merged options variable teal.ggplot2_args default module setup. details see vignette: vignette(\"custom-ggplot2-arguments\", package = \"teal.widgets\")","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_scatterplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: Scatterplot — tm_g_scatterplot","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_scatterplot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"teal module: Scatterplot — tm_g_scatterplot","text":"examples, please see vignette \"Using scatterplot\" via vignette(\"using-scatterplot\", package = \"teal.modules.general\").","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_scatterplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: Scatterplot — tm_g_scatterplot","text":"","code":"library(teal.widgets) # general data example data <- teal_data() data <- within(data, { require(nestcolor) CO2 <- CO2 }) datanames(data) <- \"CO2\" app <- init( data = data, modules = modules( tm_g_scatterplot( label = \"Scatterplot Choices\", x = data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"CO2\"]], c(\"conc\", \"uptake\")), selected = \"conc\", multiple = FALSE, fixed = FALSE ) ), y = data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"CO2\"]], c(\"conc\", \"uptake\")), selected = \"uptake\", multiple = FALSE, fixed = FALSE ) ), color_by = data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variable:\", choices = variable_choices( data[[\"CO2\"]], c(\"Plant\", \"Type\", \"Treatment\", \"conc\", \"uptake\") ), selected = NULL, multiple = FALSE, fixed = FALSE ) ), size_by = data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"CO2\"]], c(\"conc\", \"uptake\")), selected = \"uptake\", multiple = FALSE, fixed = FALSE ) ), row_facet = data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"CO2\"]], c(\"Plant\", \"Type\", \"Treatment\")), selected = NULL, multiple = FALSE, fixed = FALSE ) ), col_facet = data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"CO2\"]], c(\"Plant\", \"Type\", \"Treatment\")), selected = NULL, multiple = FALSE, fixed = FALSE ) ), ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Scatterplot Module\") ) ) ) ) #> Initializing tm_g_scatterplot if (interactive()) { shinyApp(app$ui, app$server) } # CDISC data example data <- teal_data() data <- within(data, { require(nestcolor) ADSL <- rADSL }) datanames(data) <- c(\"ADSL\") join_keys(data) <- default_cdisc_join_keys[datanames(data)] app <- init( data = data, modules = modules( tm_g_scatterplot( label = \"Scatterplot Choices\", x = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"BMRKR1\", \"BMRKR2\")), selected = \"AGE\", multiple = FALSE, fixed = FALSE ) ), y = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"BMRKR1\", \"BMRKR2\")), selected = \"BMRKR1\", multiple = FALSE, fixed = FALSE ) ), color_by = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices( data[[\"ADSL\"]], c(\"AGE\", \"BMRKR1\", \"BMRKR2\", \"RACE\", \"REGION1\") ), selected = NULL, multiple = FALSE, fixed = FALSE ) ), size_by = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"BMRKR1\")), selected = \"AGE\", multiple = FALSE, fixed = FALSE ) ), row_facet = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"BMRKR2\", \"RACE\", \"REGION1\")), selected = NULL, multiple = FALSE, fixed = FALSE ) ), col_facet = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"BMRKR2\", \"RACE\", \"REGION1\")), selected = NULL, multiple = FALSE, fixed = FALSE ) ), ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Scatterplot Module\") ) ) ) ) #> Initializing tm_g_scatterplot if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_scatterplotmatrix.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: Scatterplot matrix — tm_g_scatterplotmatrix","title":"teal module: Scatterplot matrix — tm_g_scatterplotmatrix","text":"Generates scatterplot matrix selected variables datasets. plot within matrix represents relationship two variables, providing overview correlations distributions across selected data.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_scatterplotmatrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: Scatterplot matrix — tm_g_scatterplotmatrix","text":"","code":"tm_g_scatterplotmatrix( label = \"Scatterplot Matrix\", variables, plot_height = c(600, 200, 2000), plot_width = NULL, pre_output = NULL, post_output = NULL )"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_scatterplotmatrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: Scatterplot matrix — tm_g_scatterplotmatrix","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. variables (data_extract_spec list multiple data_extract_spec) Specifies plotting variables incoming dataset filtering selecting. case data_extract_spec use select_spec(..., ordered = TRUE) plot elements rendered according selection order. plot_height (numeric) optional, specifies plot height three-element vector value, min, max intended use slider UI element. plot_width (numeric) optional, specifies plot width three-element vector value, min, max slider encoding plot width. pre_output (shiny.tag) optional, text UI element displayed module's output, providing context title. text placed output put output context. example title. post_output (shiny.tag) optional, text UI element displayed module's output, adding context instructions. Elements like shiny::helpText() useful.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_scatterplotmatrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: Scatterplot matrix — tm_g_scatterplotmatrix","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_scatterplotmatrix.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"teal module: Scatterplot matrix — tm_g_scatterplotmatrix","text":"examples, please see vignette \"Using scatterplot matrix\" via vignette(\"using-scatterplot-matrix\", package = \"teal.modules.general\").","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_g_scatterplotmatrix.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: Scatterplot matrix — tm_g_scatterplotmatrix","text":"","code":"# general data example data <- teal_data() data <- within(data, { countries <- data.frame( id = c(\"DE\", \"FR\", \"IT\", \"ES\", \"PT\", \"GR\", \"NL\", \"BE\", \"LU\", \"AT\"), government = factor( c(2, 2, 2, 1, 2, 2, 1, 1, 1, 2), labels = c(\"Monarchy\", \"Republic\") ), language_family = factor( c(1, 3, 3, 3, 3, 2, 1, 1, 3, 1), labels = c(\"Germanic\", \"Hellenic\", \"Romance\") ), population = c(83, 67, 60, 47, 10, 11, 17, 11, 0.6, 9), area = c(357, 551, 301, 505, 92, 132, 41, 30, 2.6, 83), gdp = c(3.4, 2.7, 2.1, 1.4, 0.3, 0.2, 0.7, 0.5, 0.1, 0.4), debt = c(2.1, 2.3, 2.4, 2.6, 2.3, 2.4, 2.3, 2.4, 2.3, 2.4) ) sales <- data.frame( id = 1:50, country_id = sample( c(\"DE\", \"FR\", \"IT\", \"ES\", \"PT\", \"GR\", \"NL\", \"BE\", \"LU\", \"AT\"), size = 50, replace = TRUE ), year = sort(sample(2010:2020, 50, replace = TRUE)), venue = sample(c(\"small\", \"medium\", \"large\", \"online\"), 50, replace = TRUE), cancelled = sample(c(TRUE, FALSE), 50, replace = TRUE), quantity = rnorm(50, 100, 20), costs = rnorm(50, 80, 20), profit = rnorm(50, 20, 10) ) }) datanames(data) <- c(\"countries\", \"sales\") join_keys(data) <- join_keys( join_key(\"countries\", \"countries\", \"id\"), join_key(\"sales\", \"sales\", \"id\"), join_key(\"countries\", \"sales\", c(\"id\" = \"country_id\")) ) app <- init( data = data, modules = modules( tm_g_scatterplotmatrix( label = \"Scatterplot matrix\", variables = list( data_extract_spec( dataname = \"countries\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"countries\"]]), selected = c(\"area\", \"gdp\", \"debt\"), multiple = TRUE, ordered = TRUE, fixed = FALSE ) ), data_extract_spec( dataname = \"sales\", filter = filter_spec( label = \"Select variable:\", vars = \"country_id\", choices = value_choices(data[[\"sales\"]], \"country_id\"), selected = c(\"DE\", \"FR\", \"IT\", \"ES\", \"PT\", \"GR\", \"NL\", \"BE\", \"LU\", \"AT\"), multiple = TRUE ), select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"sales\"]], c(\"quantity\", \"costs\", \"profit\")), selected = c(\"quantity\", \"costs\", \"profit\"), multiple = TRUE, ordered = TRUE, fixed = FALSE ) ) ) ) ) ) #> Initializing tm_g_scatterplotmatrix if (interactive()) { shinyApp(app$ui, app$server) } # CDISC data example data <- teal_data() data <- within(data, { ADSL <- rADSL ADRS <- rADRS }) datanames(data) <- c(\"ADSL\", \"ADRS\") join_keys(data) <- default_cdisc_join_keys[datanames(data)] app <- init( data = data, modules = modules( tm_g_scatterplotmatrix( label = \"Scatterplot matrix\", variables = list( data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADSL\"]]), selected = c(\"AGE\", \"RACE\", \"SEX\"), multiple = TRUE, ordered = TRUE, fixed = FALSE ) ), data_extract_spec( dataname = \"ADRS\", filter = filter_spec( label = \"Select endpoints:\", vars = c(\"PARAMCD\", \"AVISIT\"), choices = value_choices(data[[\"ADRS\"]], c(\"PARAMCD\", \"AVISIT\"), c(\"PARAM\", \"AVISIT\")), selected = \"INVET - END OF INDUCTION\", multiple = TRUE ), select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADRS\"]]), selected = c(\"AGE\", \"AVAL\", \"ADY\"), multiple = TRUE, ordered = TRUE, fixed = FALSE ) ) ) ) ) ) #> Initializing tm_g_scatterplotmatrix if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_missing_data.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: Missing data analysis — tm_missing_data","title":"teal module: Missing data analysis — tm_missing_data","text":"module analyzes missing data data.frames help users explore missing observations gain insights completeness data. useful clinical data analysis within context CDISC standards adaptable general data analysis purposes.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_missing_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: Missing data analysis — tm_missing_data","text":"","code":"tm_missing_data( label = \"Missing data\", plot_height = c(600, 400, 5000), plot_width = NULL, parent_dataname = \"ADSL\", ggtheme = c(\"classic\", \"gray\", \"bw\", \"linedraw\", \"light\", \"dark\", \"minimal\", \"void\"), ggplot2_args = list(`Combinations Hist` = teal.widgets::ggplot2_args(labs = list(caption = NULL)), `Combinations Main` = teal.widgets::ggplot2_args(labs = list(title = NULL))), pre_output = NULL, post_output = NULL )"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_missing_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: Missing data analysis — tm_missing_data","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. plot_height (numeric) optional, specifies plot height three-element vector value, min, max intended use slider UI element. plot_width (numeric) optional, specifies plot width three-element vector value, min, max slider encoding plot width. parent_dataname (character(1)) Specifies parent dataset name. Default ADSL CDISC data. provided exists, enables additional analysis \"subject\". non-CDISC data, parameter can ignored. ggtheme (character) optional, specifies default ggplot2 theme plots. Defaults classic. ggplot2_args (ggplot2_args) optional, object created teal.widgets::ggplot2_args() settings plots named list ggplot2_args objects plot-specific settings. argument merged options variable teal.ggplot2_args default module setup. List names match following: c(\"default\", \"Summary Obs\", \"Summary Patients\", \"Combinations Main\", \"Combinations Hist\", \"Subject\"). details see vignette: vignette(\"custom-ggplot2-arguments\", package = \"teal.widgets\"). pre_output (shiny.tag) optional, text UI element displayed module's output, providing context title. text placed output put output context. example title. post_output (shiny.tag) optional, text UI element displayed module's output, adding context instructions. Elements like shiny::helpText() useful.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_missing_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: Missing data analysis — tm_missing_data","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_missing_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: Missing data analysis — tm_missing_data","text":"","code":"library(teal.widgets) # module specification used in apps below tm_missing_data_module <- tm_missing_data( ggplot2_args = list( \"Combinations Hist\" = ggplot2_args( labs = list(subtitle = \"Plot produced by Missing Data Module\", caption = NULL) ), \"Combinations Main\" = ggplot2_args(labs = list(title = NULL)) ) ) #> Initializing tm_missing_data # general example data data <- teal_data() data <- within(data, { require(nestcolor) add_nas <- function(x) { x[sample(seq_along(x), floor(length(x) * runif(1, .05, .17)))] <- NA x } iris <- iris mtcars <- mtcars iris[] <- lapply(iris, add_nas) mtcars[] <- lapply(mtcars, add_nas) mtcars[[\"cyl\"]] <- as.factor(mtcars[[\"cyl\"]]) mtcars[[\"gear\"]] <- as.factor(mtcars[[\"gear\"]]) }) datanames(data) <- c(\"iris\", \"mtcars\") app <- init( data = data, modules = modules(tm_missing_data_module) ) #> Warning: 'package:nestcolor' may not be available when loading if (interactive()) { shinyApp(app$ui, app$server) } # CDISC example data data <- teal_data() data <- within(data, { require(nestcolor) ADSL <- rADSL ADRS <- rADRS }) datanames(data) <- c(\"ADSL\", \"ADRS\") join_keys(data) <- default_cdisc_join_keys[datanames(data)] app <- init( data = data, modules = modules(tm_missing_data_module) ) if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_outliers.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: Outliers analysis — tm_outliers","title":"teal module: Outliers analysis — tm_outliers","text":"Module analyze identify outliers using different methods IQR, Z-score, Percentiles, offers visualizations including box plots, density plots, cumulative distribution plots help interpret outliers.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_outliers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: Outliers analysis — tm_outliers","text":"","code":"tm_outliers( label = \"Outliers Module\", outlier_var, categorical_var = NULL, ggtheme = c(\"gray\", \"bw\", \"linedraw\", \"light\", \"dark\", \"minimal\", \"classic\", \"void\"), ggplot2_args = teal.widgets::ggplot2_args(), plot_height = c(600, 200, 2000), plot_width = NULL, pre_output = NULL, post_output = NULL )"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_outliers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: Outliers analysis — tm_outliers","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. outlier_var (data_extract_spec list multiple data_extract_spec) Specifies variable(s) analyzed outliers. categorical_var (data_extract_spec list multiple data_extract_spec) optional, specifies categorical variable(s) split selected outlier variables . ggtheme (character) optional, ggplot2 theme used default. Defaults \"gray\". ggplot2_args (ggplot2_args) optional, object created teal.widgets::ggplot2_args() settings plots named list ggplot2_args objects plot-specific settings. argument merged options variable teal.ggplot2_args default module setup. List names match following: c(\"default\", \"Boxplot\",\"Density Plot\",\"Cumulative Distribution Plot\"). details see vignette: vignette(\"custom-ggplot2-arguments\", package = \"teal.widgets\"). plot_height (numeric) optional, specifies plot height three-element vector value, min, max intended use slider UI element. plot_width (numeric) optional, specifies plot width three-element vector value, min, max slider encoding plot width. pre_output (shiny.tag) optional, text UI element displayed module's output, providing context title. text placed output put output context. example title. post_output (shiny.tag) optional, text UI element displayed module's output, adding context instructions. Elements like shiny::helpText() useful.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_outliers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: Outliers analysis — tm_outliers","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_outliers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: Outliers analysis — tm_outliers","text":"","code":"library(teal.widgets) # general data example data <- teal_data() data <- within(data, { CO2 <- CO2 CO2[[\"primary_key\"]] <- seq_len(nrow(CO2)) }) datanames(data) <- \"CO2\" join_keys(data) <- join_keys(join_key(\"CO2\", \"CO2\", \"primary_key\")) vars <- choices_selected(variable_choices(data[[\"CO2\"]], c(\"Plant\", \"Type\", \"Treatment\"))) app <- init( data = data, modules = modules( tm_outliers( outlier_var = list( data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"CO2\"]], c(\"conc\", \"uptake\")), selected = \"uptake\", multiple = FALSE, fixed = FALSE ) ) ), categorical_var = list( data_extract_spec( dataname = \"CO2\", filter = filter_spec( vars = vars, choices = value_choices(data[[\"CO2\"]], vars$selected), selected = value_choices(data[[\"CO2\"]], vars$selected), multiple = TRUE ) ) ), ggplot2_args = list( ggplot2_args( labs = list(subtitle = \"Plot generated by Outliers Module\") ) ) ) ) ) #> Initializing tm_outliers if (interactive()) { shinyApp(app$ui, app$server) } # CDISC data example data <- teal_data() data <- within(data, { ADSL <- rADSL }) datanames(data) <- \"ADSL\" join_keys(data) <- default_cdisc_join_keys[datanames(data)] fact_vars_adsl <- names(Filter(isTRUE, sapply(data[[\"ADSL\"]], is.factor))) vars <- choices_selected(variable_choices(data[[\"ADSL\"]], fact_vars_adsl)) app <- init( data = data, modules = modules( tm_outliers( outlier_var = list( data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"BMRKR1\")), selected = \"AGE\", multiple = FALSE, fixed = FALSE ) ) ), categorical_var = list( data_extract_spec( dataname = \"ADSL\", filter = filter_spec( vars = vars, choices = value_choices(data[[\"ADSL\"]], vars$selected), selected = value_choices(data[[\"ADSL\"]], vars$selected), multiple = TRUE ) ) ), ggplot2_args = list( ggplot2_args( labs = list(subtitle = \"Plot generated by Outliers Module\") ) ) ) ) ) #> Initializing tm_outliers if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_t_crosstable.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: Cross-table — tm_t_crosstable","title":"teal module: Cross-table — tm_t_crosstable","text":"Generates simple cross-table two variables dataset custom options showing percentages sub-totals.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_t_crosstable.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: Cross-table — tm_t_crosstable","text":"","code":"tm_t_crosstable( label = \"Cross Table\", x, y, show_percentage = TRUE, show_total = TRUE, pre_output = NULL, post_output = NULL, basic_table_args = teal.widgets::basic_table_args() )"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_t_crosstable.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: Cross-table — tm_t_crosstable","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. x (data_extract_spec list multiple data_extract_spec) Object available choices pre-selected option variable X - row values. case data_extract_spec use select_spec(..., ordered = TRUE) table elements rendered according selection order. y (data_extract_spec list multiple data_extract_spec) Object available choices pre-selected option variable Y - column values. data_extract_spec must allow multiple selection case. show_percentage (logical(1)) Indicates whether show percentages (relevant x factor). Defaults TRUE. show_total (logical(1)) Indicates whether show total column. Defaults TRUE. pre_output (shiny.tag) optional, text UI element displayed module's output, providing context title. text placed output put output context. example title. post_output (shiny.tag) optional, text UI element displayed module's output, adding context instructions. Elements like shiny::helpText() useful. basic_table_args (basic_table_args) object created teal.widgets::basic_table_args() settings module table. argument merged options variable teal.basic_table_args default module setup. details see vignette: vignette(\"custom-basic-table-arguments\", package = \"teal.widgets\")","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_t_crosstable.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: Cross-table — tm_t_crosstable","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_t_crosstable.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"teal module: Cross-table — tm_t_crosstable","text":"examples, please see vignette \"Using cross table\" via vignette(\"using-cross-table\", package = \"teal.modules.general\").","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_t_crosstable.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: Cross-table — tm_t_crosstable","text":"","code":"# general data example library(teal.widgets) data <- teal_data() data <- within(data, { mtcars <- mtcars for (v in c(\"cyl\", \"vs\", \"am\", \"gear\")) { mtcars[[v]] <- as.factor(mtcars[[v]]) } mtcars[[\"primary_key\"]] <- seq_len(nrow(mtcars)) }) datanames(data) <- \"mtcars\" join_keys(data) <- join_keys(join_key(\"mtcars\", \"mtcars\", \"primary_key\")) app <- init( data = data, modules = modules( tm_t_crosstable( label = \"Cross Table\", x = data_extract_spec( dataname = \"mtcars\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"mtcars\"]], c(\"cyl\", \"vs\", \"am\", \"gear\")), selected = c(\"cyl\", \"gear\"), multiple = TRUE, ordered = TRUE, fixed = FALSE ) ), y = data_extract_spec( dataname = \"mtcars\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"mtcars\"]], c(\"cyl\", \"vs\", \"am\", \"gear\")), selected = \"vs\", multiple = FALSE, fixed = FALSE ) ), basic_table_args = basic_table_args( subtitles = \"Table generated by Crosstable Module\" ) ) ) ) #> Initializing tm_t_crosstable if (interactive()) { shinyApp(app$ui, app$server) } # CDISC data example library(teal.widgets) data <- teal_data() data <- within(data, { ADSL <- rADSL }) datanames(data) <- \"ADSL\" join_keys(data) <- default_cdisc_join_keys[datanames(data)] app <- init( data = data, modules = modules( tm_t_crosstable( label = \"Cross Table\", x = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], subset = function(data) { idx <- !vapply(data, inherits, logical(1), c(\"Date\", \"POSIXct\", \"POSIXlt\")) return(names(data)[idx]) }), selected = \"COUNTRY\", multiple = TRUE, ordered = TRUE, fixed = FALSE ) ), y = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], subset = function(data) { idx <- vapply(data, is.factor, logical(1)) return(names(data)[idx]) }), selected = \"SEX\", multiple = FALSE, fixed = FALSE ) ), basic_table_args = basic_table_args( subtitles = \"Table generated by Crosstable Module\" ) ) ) ) #> Initializing tm_t_crosstable if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_variable_browser.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: Variable browser — tm_variable_browser","title":"teal module: Variable browser — tm_variable_browser","text":"Module provides provides detailed summary visualization variable distributions data.frame objects, interactive features customize analysis.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_variable_browser.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: Variable browser — tm_variable_browser","text":"","code":"tm_variable_browser( label = \"Variable Browser\", datasets_selected = character(0), parent_dataname = \"ADSL\", pre_output = NULL, post_output = NULL, ggplot2_args = teal.widgets::ggplot2_args() )"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_variable_browser.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: Variable browser — tm_variable_browser","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. datasets_selected (character) vector datasets shown, order. Names must correspond datasets names. vector length zero (default) datasets shown. Note: data.frame objects compatible; using types cause error. parent_dataname (character(1)) string specifying parent dataset. exists datasets_selectedthen extra checkbox shown allow users show variables datasets exist dataname. typically used remove ADSL columns CDISC data. non CDISC data can ignored. Defaults \"ADSL\". pre_output (shiny.tag) optional, text UI element displayed module's output, providing context title. text placed output put output context. example title. post_output (shiny.tag) optional, text UI element displayed module's output, adding context instructions. Elements like shiny::helpText() useful. ggplot2_args (ggplot2_args) object created teal.widgets::ggplot2_args() settings module plot. argument merged options variable teal.ggplot2_args default module setup. details see vignette: vignette(\"custom-ggplot2-arguments\", package = \"teal.widgets\")","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_variable_browser.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: Variable browser — tm_variable_browser","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_variable_browser.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"teal module: Variable browser — tm_variable_browser","text":"Numeric columns fewer 30 distinct values can treated either discrete continuous checkbox allowing users switch treated(< 6 unique values default discrete, otherwise continuous).","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/tm_variable_browser.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: Variable browser — tm_variable_browser","text":"","code":"library(teal.widgets) # Module specification used in apps below tm_variable_browser_module <- tm_variable_browser( label = \"Variable browser\", ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Variable Browser Module\") ) ) #> Initializing tm_variable_browser # general data example data <- teal_data() data <- within(data, { iris <- iris mtcars <- mtcars women <- women faithful <- faithful CO2 <- CO2 }) datanames(data) <- c(\"iris\", \"mtcars\", \"women\", \"faithful\", \"CO2\") app <- init( data = data, modules = modules(tm_variable_browser_module) ) if (interactive()) { shinyApp(app$ui, app$server) } # CDISC example data data <- teal_data() data <- within(data, { ADSL <- rADSL ADTTE <- rADTTE }) datanames(data) <- c(\"ADSL\", \"ADTTE\") join_keys(data) <- default_cdisc_join_keys[datanames(data)] app <- init( data = data, modules = modules(tm_variable_browser_module) ) if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/validate_input.html","id":null,"dir":"Reference","previous_headings":"","what":"Validates the variable browser inputs — validate_input","title":"Validates the variable browser inputs — validate_input","text":"Validates variable browser inputs","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/validate_input.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validates the variable browser inputs — validate_input","text":"","code":"validate_input(input, plot_var, data)"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/validate_input.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validates the variable browser inputs — validate_input","text":"input (session$input) shiny session input plot_var (list) list data frame array variable names data (teal_data) datasets passed module","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/validate_input.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Validates the variable browser inputs — validate_input","text":"logical TRUE validations pass; shiny validation error otherwise","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/var_missings_info.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize NAs. — var_missings_info","title":"Summarize NAs. — var_missings_info","text":"Summarizes occurrence missing values vector.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/var_missings_info.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize NAs. — var_missings_info","text":"","code":"var_missings_info(x)"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/var_missings_info.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize NAs. — var_missings_info","text":"x vector type length","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/var_missings_info.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize NAs. — var_missings_info","text":"Character string describing NA occurrence.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/var_summary_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarizes variable — var_summary_table","title":"Summarizes variable — var_summary_table","text":"Creates html summary statistics relevant data type. numeric values returns central tendency measures, factor returns level counts, Date date range, just number levels.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/var_summary_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarizes variable — var_summary_table","text":"","code":"var_summary_table(x, numeric_as_factor, dt_rows, outlier_definition)"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/var_summary_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarizes variable — var_summary_table","text":"x vector type numeric_as_factor logical numeric variable treated factor dt_rows numeric current/latest DT page length outlier_definition 0 outliers removed, otherwise outliers (outlier_definition*IQR /Q1/Q3 removed)","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/var_summary_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarizes variable — var_summary_table","text":"text simple statistics.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/variable_type_icons.html","id":null,"dir":"Reference","previous_headings":"","what":"Get icons to represent variable types in dataset — variable_type_icons","title":"Get icons to represent variable types in dataset — variable_type_icons","text":"Get icons represent variable types dataset","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/variable_type_icons.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get icons to represent variable types in dataset — variable_type_icons","text":"","code":"variable_type_icons(var_type)"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/variable_type_icons.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get icons to represent variable types in dataset — variable_type_icons","text":"var_type (character) R internal types (classes).","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/variable_type_icons.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get icons to represent variable types in dataset — variable_type_icons","text":"(character) vector HTML icons corresponding data type column.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/varname_w_label.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate a string for a variable including its label — varname_w_label","title":"Generate a string for a variable including its label — varname_w_label","text":"Generate string variable including label","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/varname_w_label.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate a string for a variable including its label — varname_w_label","text":"","code":"varname_w_label( var_names, dataset, wrap_width = 80, prefix = NULL, suffix = NULL )"},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/varname_w_label.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate a string for a variable including its label — varname_w_label","text":"var_names (character) Name variable extract labels . dataset (dataset) Name analysis dataset. wrap_width (numeric) Number characters wrap original label . Defaults 80. prefix, suffix (character) String paste beginning/end variable name label.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/reference/varname_w_label.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate a string for a variable including its label — varname_w_label","text":"(character) String variable name label.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"tealmodulesgeneral-030","dir":"Changelog","previous_headings":"","what":"teal.modules.general 0.3.0","title":"teal.modules.general 0.3.0","text":"CRAN release: 2024-03-05","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"enhancements-0-3-0","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"teal.modules.general 0.3.0","text":"Updated package docs vignettes new way specifying data teal::init(). data argument accept teal_data object","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"bug-fixes-0-3-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"teal.modules.general 0.3.0","text":"Outlier labels longer appear bounds tm_a_regression. Fixed bug tm_outliers changing selected variable cause popup.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"miscellaneous-0-3-0","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"teal.modules.general 0.3.0","text":"Removed teal.slice dependencies. Specified minimal version package dependencies.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"breaking-changes-0-2-16","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"teal.modules.general 0.2.16","text":"Replaced chunks simpler qenv class. Replaced datasets argument containing FilteredData new arguments data (tdata object) filter_panel_api (FilterPanelAPI).","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"enhancements-0-2-16","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"teal.modules.general 0.2.16","text":"Added parent_dataname argument tm_variable_browser tm_missing_data allow specification parent dataset modules. Improved UI labels plot panel title tm_g_association. Added inputs tm_variable_browser module text size plot theme. Forced ggplot theme always selected modules. Updated encodings input checks use shinyvalidate::InputValidator instead shiny::validate better UI experience.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"bug-fixes-0-2-16","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"teal.modules.general 0.2.16","text":"Fixed bug tm_g_scatterplot selected x y facets . Fixed bug tm_g_distribution plot theoretical distribution newer ggplot2 version. Fixed bug tm_g_bivariate adding lines checkbox available one x y deselected. Fixed bug tm_variable_browser changing filters reset selected variable first list.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"miscellaneous-0-2-16","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"teal.modules.general 0.2.16","text":"Removed scda package dependency examples. Replaced deprecated ggplot2 functions ..count.., ..density.. ..prop... Version bump forcats dependency.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"enhancements-0-2-15","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"teal.modules.general 0.2.15","text":"Added teal.reporter functionality modules. Implemented nestcolor examples, refactored tm_a_pca tm_missing_data allow using nestcolor. Added log transformation options tm_g_scatterplot. Added server_rendering flag tm_data_table control whether table rendered server client side.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"bug-fixes-0-2-15","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"teal.modules.general 0.2.15","text":"Fixed overflow wide tm_a_pca tables. Fixed join type functionality tm_t_crosstable. Fixed bug tm_missing_data selecting variables missings. Fixed bug tm_missing_data using any_na.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"miscellaneous-0-2-15","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"teal.modules.general 0.2.15","text":"Moved packages used one module Imports Suggests DESCRIPTION file. Moved magrittr package Depends Imports DESCRIPTION file.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"enhancements-0-2-14","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"teal.modules.general 0.2.14","text":"New teal module tm_front_page simplify creating front page teal apps. Added slider widget control font size label tm_g_scatterplot. Output integers without decimal places selecting points tm_g_scatterplot. Improved names code chunks shown Debug Info. Improved validation message number regressors big tm_a_regression.R.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"bug-fixes-0-2-14","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"teal.modules.general 0.2.14","text":"Fixed wrong validation tm_a_pca. Fixed crash deselecting categorical factor tm_outliers.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"miscellaneous-0-2-14","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"teal.modules.general 0.2.14","text":"Added template pkgdown site. Updated package authors.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"enhancements-0-2-13","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"teal.modules.general 0.2.13","text":"Rewrote modules use moduleServer updated call plot_with_settings_srv changes teal.devel. Fixed tracking selection order data_extract_ui. selectors can return ordered selection one specifies ordered = TRUE select_spec. Switched order tm_missing_data combination plot show data descending order. Changed variable levels output tm_missing_data allow numerical sorting.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"miscellaneous-0-2-13","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"teal.modules.general 0.2.13","text":"Removed unneeded n row tm_t_crosstable. Replaced calls teal::root_modules teal::modules following deprecation teal::root_modules. Adjusted package imports take account changes teal framework. Added “Getting started teal.modules.general” vignette. Updated README file. Moved ggmosaic package Depends Imports DESCRIPTION file.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"enhancements-0-2-12","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"teal.modules.general 0.2.12","text":"Added support logging logger package added info level logs upon initialization module. Added support custom arguments ggplot2::labs ggplot2::theme plot based modules. Added support custom arguments rtables::basic_table tm_t_crosstable. Updated tm_outliers, tm_g_scatterplotmatrix, tm_g_association, tm_t_crosstable modules adopt new teal.transform::data_merge_srv teal.transform::data_extract_multiple_srv modules. Distinguished bars representing NA plot depicting counts tm_variable_browser different color fill. Modified summary statistics table numeric columns tm_variable_browser reflect plot outliers removed. Added option remove missing values tm_variable_browser histogram factor like variables. Added ability sort Variable Type tm_variable_browser separating variable type icons column. Updated Grouped Subject tab tm_missing_data module present data way Summary tab . Added support NA level grouping variable variable levels table tm_missing_data module. Added informative labels level grouping variable variable levels table tm_missing_data module. Added checkbox tm_g_scatterplot toggle option free x y axis scales whenever faceting arguments provided. Used browser-side processing tm_data_table Buttons extension download full table. Added example advanced usage DT module.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"bug-fixes-0-2-12","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"teal.modules.general 0.2.12","text":"Fixed error tm_variable_browser selected column logical(1). Fixed bugs modules’ vignette examples.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"miscellaneous-0-2-12","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"teal.modules.general 0.2.12","text":"Added R version requirement R >= 3.6. Removed input_id argument teal.transform::data_merge_module calls modules. Refactored defunct teal.devel::data_extract_input replacement teal.transform::data_extract_ui. Updated teal.transform::data_merge_srv use dplyr::inner_join instead dplyr::left_join tm_outliers module. Removed overlay statistics table tm_g_distribution module. Removed dependency test.nest package. Removed dependency utils.nest package replaced functions equivalents checkmate package.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"new-features-0-2-11","dir":"Changelog","previous_headings":"","what":"New features","title":"teal.modules.general 0.2.11","text":"new module, tm_file_viewer, added visualization static files. new module, tm_g_distribution, added distribution analysis.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"bug-fixes-0-2-11","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"teal.modules.general 0.2.11","text":"Fixed bar plot order factor variables tm_variable_browser.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"tm_variable_browser-0-2-11","dir":"Changelog","previous_headings":"Enhancements","what":"tm_variable_browser","title":"teal.modules.general 0.2.11","text":"Added sparkline support Date/POSIXct/POSIXlt variable types. Fixed histograms contain least two bars.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"tm_outliers-0-2-11","dir":"Changelog","previous_headings":"Enhancements","what":"tm_outliers","title":"teal.modules.general 0.2.11","text":"Added filter_spec support. Simplified returned R code. Support select_spec categorical variables.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"tm_g_scatterplotmatrix-0-2-11","dir":"Changelog","previous_headings":"Enhancements","what":"tm_g_scatterplotmatrix","title":"teal.modules.general 0.2.11","text":"Added ordered select input support. Decoupled dataset list variables list parameters.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"tm_g_association-0-2-11","dir":"Changelog","previous_headings":"Enhancements","what":"tm_g_association","title":"teal.modules.general 0.2.11","text":"Added ordered select input support.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"tm_t_crosstable-0-2-11","dir":"Changelog","previous_headings":"Enhancements","what":"tm_t_crosstable","title":"teal.modules.general 0.2.11","text":"Added ordered select input support.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"tm_g_scatterplot-0-2-11","dir":"Changelog","previous_headings":"Enhancements","what":"tm_g_scatterplot","title":"teal.modules.general 0.2.11","text":"Modified trend line feature scatterplot display raw equation equal actual y value actual x values plugged .","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"miscellaneous-0-2-11","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"teal.modules.general 0.2.11","text":"Updated LICENCE README new package references. Added error_on_lint: TRUE .lintr. Updated quantile type = 2 argument tm_variable_browser line STREAM.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"enhancements-0-2-10","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"teal.modules.general 0.2.10","text":"Refactored internal code modules optimize performance. Refactored tm_g_scatterplot compute trend line statistics using ggpmisc::stat_poly_eq simplify code labels group plot. Refactored tm_g_scatterplot enable filtering via Encoding Panel using new filter_spec functionality. Added support logical variables tm_variable_browser. Updated tm_outliers handle non-CDISC datasets. Updated UI tm_variable_browser factor character variables 30 levels. Updated sparklines remove NA values numeric variables. Added checkbox display number observations plot tm_g_scatterplot. Added validation statements tm_g_scatterplot handling users choosing one facet variable. Improved performance tm_variable_browser efficient sparkline generation. Added html code type datasets labels inside tm_missing_data. Improved styling tool tips tm_a_regression, tm_missing_data tm_variable_browser.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"enhancements-0-2-9","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"teal.modules.general 0.2.9","text":"Added download, enlarge resize graph options tm_variable_browser module. Added download expand options table tm_t_crosstable. Enabled selection custom datasets tm_variable_browser datasets_selected argument. Allowed dataset variables used grouping variables variable levels tab tm_missing_data, just selected displayed rows table. Enabled brushing points NA categories facetting tm_g_scatterplot.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"bug-fixes-0-2-9","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"teal.modules.general 0.2.9","text":"Added persistence number entries displayed data tables. Removed ‘treat variable factor’ checkbox tm_variable_browser graph shown. Fixed DT length reset tm_variable_browser “Show parent dataset variables” checkbox tick.","code":""},{"path":[]},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"tm_outliers-0-2-8","dir":"Changelog","previous_headings":"New Module","what":"tm_outliers","title":"teal.modules.general 0.2.8","text":"Added new module tm_outliers analyze outliers datasets.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"tm_g_scatterplot-0-2-8","dir":"Changelog","previous_headings":"Enhancements","what":"tm_g_scatterplot","title":"teal.modules.general 0.2.8","text":"Added max_deg optional argument scatterplot allow users choose maximum smoothing degree trend line. Added column row facetting functionality scatterplot. Added possibility specify type join tm_t_crosstable. Incorporate missings resulting non inner join.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"bug-fixes-0-2-8","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"teal.modules.general 0.2.8","text":"Fixed edge case errors tm_missing_data caused input dataset categorical variables data.frame instead tibble. Fixed tm_a_pca biplot plot coloring factor/character/numeric < 6 values variable. Fixed typo outlier definition tm_variable_browser.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"miscellaneous-0-2-8","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"teal.modules.general 0.2.8","text":"Sparklines longer shown numeric variables 100000 rows. Colors legend plot. Replaced \"Show variables ADSL\" checkbox general \"Show parent dataset variables\". Capitalize default module labels. outputs tm_missing_data conditional data CDISC data. Added pre_output post_output arguments tm_data_table, tm_missing_data, tm_variable_browser. Fixed issue tm_data_table selecting “show distinct rows” variable names non-alphanumeric characters.","code":""},{"path":[]},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"tm_variable_browser-0-2-7","dir":"Changelog","previous_headings":"Enhancements","what":"tm_variable_browser","title":"teal.modules.general 0.2.7","text":"Introduced sparklines quick, inline variable summaries. Added option remove outliers. Histograms statistics tables now displayed variables type Date, POSIXct POSIXlt. Character variable summary statistics tables now displayed decreasing order occurrences instead alphabetical. Display number non-missing rows n statistics table numeric variables. Added ability treat numeric variables categorical. tm_variable_browser shows 30 50 levels divided 2 columns character/factor variables 30 unique levels.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"tm_a_regression-0-2-7","dir":"Changelog","previous_headings":"Enhancements","what":"tm_a_regression","title":"teal.modules.general 0.2.7","text":"Added support outlier labels tm_a_regression provided additional optional argument, default_outlier_label can used specify default column used label outliers.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"tm_g_scatterplot-0-2-7","dir":"Changelog","previous_headings":"Enhancements","what":"tm_g_scatterplot","title":"teal.modules.general 0.2.7","text":"Implemented trend line. Added marginal density plots. Added option display rug plot axes. Added shape point color arguments. latter users colourpicker::colourInput. Added size_by variable scatterplot allowed point size mapped numeric variable.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"tm_missing_data-0-2-7","dir":"Changelog","previous_headings":"Enhancements","what":"tm_missing_data","title":"teal.modules.general 0.2.7","text":"Added bar chart missing data combination plot. Removed keys missing data combinations plot selected.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"tm_g_bivariate-0-2-7","dir":"Changelog","previous_headings":"Enhancements","what":"tm_g_bivariate","title":"teal.modules.general 0.2.7","text":"Bivariate plots two continuous variables now allow adding lines. Show labels x axis selecting categorical variable fixed issues deselecting variables datasets encodings panel (including addition meaningful warnings).","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"tm_a_pca-0-2-7","dir":"Changelog","previous_headings":"Enhancements","what":"tm_a_pca","title":"teal.modules.general 0.2.7","text":"Updated include Plot settings Plot specific settings.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"miscellaneous-0-2-7","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"teal.modules.general 0.2.7","text":"Integrated is_single_dataset argument data_extract_input function calls simplify encodings panel UI. Moved code argument cdisc_dataset (cdisc_data) examples vignettes. Require ggmosaic version >= 0.3.0.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"tealmodulesgeneral-026","dir":"Changelog","previous_headings":"","what":"teal.modules.general 0.2.6","title":"teal.modules.general 0.2.6","text":"Adds additional plot settings themes tm_g_association, tm_g_response, tm_g_scatterplot, well point size opacity settings tm_g_association tm_g_scatterplot. Replace pickerInput optionalSelectInput tm_data_table. Adds new facet parameter tm_g_bivariate specify whether facet encodings elements visible user default. Replace plot_with_height module new plot_with_settings module. Missing data module now uses chunks get reproducible R code. New theme missing data module new label positioning. tm_data_table: allow developers pass arguments DT::dataTable use pickerInput select variables. tm_g_response new argument count_labels. Counts might assessed frequency plot . Moved missing data module summary plot footer elements tool tip encodings panels. Replaced base plots tm_a_regression ggplot2. Added optional slider adjust width plot_with_settings. Handle Inf data gracefully. tm_variable_browser now outputs summary table factors missing values.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"tealmodulesgeneral-025","dir":"Changelog","previous_headings":"","what":"teal.modules.general 0.2.5","title":"teal.modules.general 0.2.5","text":"Adds ggplot call inside chunks modules. Pass dataname get_rcode_srv. Uses utils function standardize plot label generation. Improved handling variable labels. Removed “Add filter variable” button variable browser module. Scatterplot matrix module now automatically converts characters factors message.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"tealmodulesgeneral-024","dir":"Changelog","previous_headings":"","what":"teal.modules.general 0.2.4","title":"teal.modules.general 0.2.4","text":"New PCA module. Documentation fix cross table module. Display variable labels drop-menu data table (#393). Optional subsetting ordering datasets tm_data_table. Use teal.code::chunks_push_data_merge include merge code reproducibility code. consistent coloring behavior bivariate plots. Updates missing data module. Updated graph axis labels following modules: bivariate plot, missing data, response plot, regression module, scatterplot, scatterplot matrix. Simplify scatterplot matrix display plot within one dataset .","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"tealmodulesgeneral-023","dir":"Changelog","previous_headings":"","what":"teal.modules.general 0.2.3","title":"teal.modules.general 0.2.3","text":"Fixed naming tm_variable_browser. Correlation added Scatter Plot Matrix. Opacity slider added tm_bivariate. Fixes due bug teal.devel#313. Performance enhancements missing data module. Fix bug reactivity scatterplot matrix module.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"tealmodulesgeneral-022","dir":"Changelog","previous_headings":"","what":"teal.modules.general 0.2.2","title":"teal.modules.general 0.2.2","text":"New module summarize missing data. Refactor variable browser module. Correct display density / frequency option bivariate plot.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"tealmodulesgeneral-021","dir":"Changelog","previous_headings":"","what":"teal.modules.general 0.2.1","title":"teal.modules.general 0.2.1","text":"Fix magrittr loading reproducible code.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"tealmodulesgeneral-020","dir":"Changelog","previous_headings":"","what":"teal.modules.general 0.2.0","title":"teal.modules.general 0.2.0","text":"Refactor functions data extract data merge. Create sample_app.R demonstrating teal.modules.general modules. Rename tm_table tm_cross_table. Include show R code modules. Merge tm_cross_table tm_t_percentage_table one module.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/news/index.html","id":"tealmodulesgeneral-010","dir":"Changelog","previous_headings":"","what":"teal.modules.general 0.1.0","title":"teal.modules.general 0.1.0","text":"Initial release.","code":""}] +[{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/main/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement [INSERT CONTACT METHOD]. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.1, available https://www.contributor-covenant.org/version/2/1/code_of_conduct.html. Community Impact Guidelines inspired Mozilla’s code conduct enforcement ladder. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contribution Guidelines","title":"Contribution Guidelines","text":"🙏 Thank taking time contribute! input deeply valued, whether issue, pull request, even feedback, regardless size, content scope.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/CONTRIBUTING.html","id":"table-of-contents","dir":"","previous_headings":"","what":"Table of contents","title":"Contribution Guidelines","text":"👶 Getting started 📔 Code Conduct 🗃 License 📜 Issues 🚩 Pull requests 💻 Coding guidelines 🏆 Recognition model ❓ Questions","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/CONTRIBUTING.html","id":"getting-started","dir":"","previous_headings":"","what":"Getting started","title":"Contribution Guidelines","text":"Please refer project documentation brief introduction. Please also see articles within project documentation additional information.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contribution Guidelines","text":"Code Conduct governs project. Participants contributors expected follow rules outlined therein.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/CONTRIBUTING.html","id":"license","dir":"","previous_headings":"","what":"License","title":"Contribution Guidelines","text":"contributions covered project’s license.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/CONTRIBUTING.html","id":"issues","dir":"","previous_headings":"","what":"Issues","title":"Contribution Guidelines","text":"use GitHub track issues, feature requests, bugs. submitting new issue, please check issue already reported. issue already exists, please upvote existing issue 👍. new feature requests, please elaborate context benefit feature users, developers, relevant personas.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/main/CONTRIBUTING.html","id":"github-flow","dir":"","previous_headings":"Pull requests","what":"GitHub Flow","title":"Contribution Guidelines","text":"repository uses GitHub Flow model collaboration. submit pull request: Create branch Please see branch naming convention . don’t write access repository, please fork . Make changes Make sure code passes checks imposed GitHub Actions well documented well tested unit tests sufficiently covering changes introduced Create pull request (PR) pull request description, please link relevant issue (), provide detailed description change, include assumptions. Address review comments, Post approval Merge PR write access. Otherwise, reviewer merge PR behalf. Pat back Congratulations! 🎉 now official contributor project! grateful contribution.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/CONTRIBUTING.html","id":"branch-naming-convention","dir":"","previous_headings":"Pull requests","what":"Branch naming convention","title":"Contribution Guidelines","text":"Suppose changes related current issue current project; please name branch follows: _. Please use underscore (_) delimiter word separation. example, 420_fix_ui_bug suitable branch name change resolving UI-related bug reported issue number 420 current project. change affects multiple repositories, please name branches follows: __. example, 69_awesomeproject_fix_spelling_error reference issue 69 reported project awesomeproject aims resolve one spelling errors multiple (likely related) repositories.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/CONTRIBUTING.html","id":"monorepo-and-stageddependencies","dir":"","previous_headings":"Pull requests","what":"monorepo and staged.dependencies","title":"Contribution Guidelines","text":"Sometimes might need change upstream dependent package(s) able submit meaningful change. using staged.dependencies functionality simulate monorepo behavior. dependency configuration already specified project’s staged_dependencies.yaml file. need name feature branches appropriately. exception branch naming convention described . Please refer staged.dependencies package documentation details.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/CONTRIBUTING.html","id":"coding-guidelines","dir":"","previous_headings":"","what":"Coding guidelines","title":"Contribution Guidelines","text":"repository follows unified processes standards adopted maintainers ensure software development carried consistently within teams cohesively across repositories.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/CONTRIBUTING.html","id":"style-guide","dir":"","previous_headings":"Coding guidelines","what":"Style guide","title":"Contribution Guidelines","text":"repository follows standard tidyverse style guide uses lintr lint checks. Customized lint configurations available repository’s .lintr file.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/CONTRIBUTING.html","id":"dependency-management","dir":"","previous_headings":"Coding guidelines","what":"Dependency management","title":"Contribution Guidelines","text":"Lightweight right weight. repository follows tinyverse recommedations limiting dependencies minimum.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/CONTRIBUTING.html","id":"dependency-version-management","dir":"","previous_headings":"Coding guidelines","what":"Dependency version management","title":"Contribution Guidelines","text":"code compatible (!) historical versions given dependenct package, required specify minimal version DESCRIPTION file. particular: development version requires (imports) development version another package - required put abc (>= 1.2.3.9000).","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/main/CONTRIBUTING.html","id":"r--package-versions","dir":"","previous_headings":"Coding guidelines > Recommended development environment & tools","what":"R & package versions","title":"Contribution Guidelines","text":"continuously test packages newest R version along recent dependencies CRAN BioConductor. recommend working environment also set way. can find details R version packages used R CMD check GitHub Action execution log - step prints R sessionInfo(). discover bugs older R versions older set dependencies, please create relevant bug reports.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/CONTRIBUTING.html","id":"pre-commit","dir":"","previous_headings":"Coding guidelines > Recommended development environment & tools","what":"pre-commit","title":"Contribution Guidelines","text":"highly recommend use pre-commit tool combined R hooks pre-commit execute checks committing pushing changes. Pre-commit hooks already available repository’s .pre-commit-config.yaml file.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/CONTRIBUTING.html","id":"recognition-model","dir":"","previous_headings":"","what":"Recognition model","title":"Contribution Guidelines","text":"mentioned previously, contributions deeply valued appreciated. contribution data available part repository insights, recognize significant contribution hence add contributor package authors list, following rules enforced: Minimum 5% lines code authored* (determined git blame query) top 5 contributors terms number commits lines added lines removed* *Excluding auto-generated code, including limited roxygen comments renv.lock files. package maintainer also reserves right adjust criteria recognize contributions.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/CONTRIBUTING.html","id":"questions","dir":"","previous_headings":"","what":"Questions","title":"Contribution Guidelines","text":"questions regarding contribution guidelines, please contact package/repository maintainer.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/main/SECURITY.html","id":"reporting-security-issues","dir":"","previous_headings":"","what":"Reporting Security Issues","title":"Security Policy","text":"believe found security vulnerability repositories organization, please report us coordinated disclosure. Please report security vulnerabilities public GitHub issues, discussions, pull requests. Instead, please send email vulnerability.management[@]roche.com. Please include much information listed can help us better understand resolve issue: type issue (e.g., buffer overflow, SQL injection, cross-site scripting) Full paths source file(s) related manifestation issue location affected source code (tag/branch/commit direct URL) special configuration required reproduce issue Step--step instructions reproduce issue Proof--concept exploit code (possible) Impact issue, including attacker might exploit issue information help us triage report quickly.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/SECURITY.html","id":"data-security-standards-dss","dir":"","previous_headings":"","what":"Data Security Standards (DSS)","title":"Security Policy","text":"Please make sure reporting issues form bug, feature, pull request, sensitive information PII, PHI, PCI completely removed text attachments, including pictures videos.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/teal-modules-general.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Getting started with teal.modules.general","text":"teal extends shiny framework, enabling creation interactive GUI applications using R. shiny, tealfacilitate development extensive applications combining small, decoupled modules. teal.modules.general package consist collection modules essential developing teal applications. “general” sense intended functions modules fundamental. contrasts specialized focus clinical data found teal.modules.clinical package. modules teal.modules.general can used conjunction modules teal.modules.clinical / shiny modules build large teal / shiny app. concepts presented require knowledge core features teal, specifically launch teal application pass data . Therefore, highly recommended refer README file introductory vignette teal package. See also teal.modules.clinical’s README.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/teal-modules-general.html","id":"main-features","dir":"Articles","previous_headings":"","what":"Main features","title":"Getting started with teal.modules.general","text":"five areas data science teal.modules.general provides tools solutions (modules) : viewing data tabular formats visualizing data plots graphs viewing data files directory examining missing extreme values data performing data analysis See package functions / modules.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/teal-modules-general.html","id":"example-application","dir":"Articles","previous_headings":"","what":"Example application","title":"Getting started with teal.modules.general","text":"simple application featuring tm_variable_browser() module: Let’s break app pieces: 1: Load necessary libraries data. 2: Construct teal_data object containing serve source data teal app. teal_data encapsulates data app, also houses code required create data maintain reproducibility. , create empty teal_data object evaluate code produce data within teal_data object, code data stored together. Following , set datanames join_keys. 3: Initialize teal application specified data modules, case, module: tm_variable_browser, datasets:ADSL ADTTE. shiny::shinyApp() use ui server component initialize teal app. teal app, data modules decoupled. app : app developer specified data assigned data argument. app developer specified module assigned modules argument. init function took arguments returned list containing ui server object, can demonstrated running:","code":"# load libraries library(teal.modules.general) library(teal.widgets) # teal_data object data <- teal_data() data <- within(data, { ADSL <- rADSL ADTTE <- rADTTE }) datanames <- c(\"ADSL\", \"ADTTE\") datanames(data) <- datanames join_keys(data) <- default_cdisc_join_keys[datanames] # tm_variable_browser module tm_variable_browser_module <- tm_variable_browser( label = \"Variable browser\", ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Variable Browser Module\") ) ) # initialize the app app <- init( data = data, modules = modules(tm_variable_browser_module) ) shinyApp(app$ui, app$server) library(teal.modules.general) library(teal.widgets) data <- teal_data() data <- within(data, { ADSL <- rADSL ADTTE <- rADTTE }) datanames <- c(\"ADSL\", \"ADTTE\") datanames(data) <- datanames join_keys(data) <- default_cdisc_join_keys[datanames] tm_variable_browser_module <- tm_variable_browser( # module name to display in the GUI label = \"Variable browser\", # this argument takes a set of arguments to pass to ggplot2. # the arguments must have the same names as its ggplot2 counterpart, e.g. `subtitle` ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Variable Browser Module\") ) ) app <- init( data = data, modules = modules(tm_variable_browser_module) ) shinyApp(app$ui, app$server) class(app) ## [1] \"list\" names(app) ## [1] \"ui\" \"server\""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-association-plot.html","id":"section-teal-application-to-use-association-plot-with-various-datasets-types","dir":"Articles","previous_headings":"","what":"teal application to use association plot with various datasets types","title":"Using association plot","text":"vignette guide four parts create teal application using various types datasets using association plot module tm_g_association(): Load libraries Create data sets Create app variable Run app","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-association-plot.html","id":"section-load-libraries","dir":"Articles","previous_headings":"teal application to use association plot with various datasets types","what":"1 - Load libraries","title":"Using association plot","text":"","code":"library(teal.modules.general) # used to create the app library(dplyr) # used to modify data sets"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-association-plot.html","id":"section-create-data-sets","dir":"Articles","previous_headings":"teal application to use association plot with various datasets types","what":"2 - Create data sets","title":"Using association plot","text":"Inside app 4 datasets used ADSL wide data set subject data ADRS long data set response data subjects different time points study ADTTE long data set time event data ADLB long data set lab measurements subject","code":"data <- teal_data() data <- within(data, { ADSL <- teal.modules.general::rADSL %>% mutate(TRTDUR = round(as.numeric(TRTEDTM - TRTSDTM), 1)) ADRS <- teal.modules.general::rADRS ADTTE <- teal.modules.general::rADTTE ADLB <- teal.modules.general::rADLB %>% mutate(CHGC = as.factor(case_when( CHG < 1 ~ \"N\", CHG > 1 ~ \"P\", TRUE ~ \"-\" ))) }) datanames <- c(\"ADSL\", \"ADRS\", \"ADTTE\", \"ADLB\") datanames(data) <- datanames join_keys(data) <- default_cdisc_join_keys[datanames]"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-association-plot.html","id":"section-create-an-app-variable","dir":"Articles","previous_headings":"teal application to use association plot with various datasets types","what":"3 - Create an app variable","title":"Using association plot","text":"important section. use teal::init() function create app. data handed using teal.data::teal_data(). app constructed multiple calls tm_g_association() using different combinations data sets.","code":"# configuration for a single wide dataset mod1 <- tm_g_association( label = \"Single wide dataset\", ref = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]]), selected = \"AGE\", fixed = FALSE ) ), vars = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADSL\"]]), selected = \"BMRKR1\", multiple = TRUE, fixed = FALSE ) ) ) # configuration for two wide datasets mod2 <- tm_g_association( label = \"Two wide datasets\", ref = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"SEX\", \"STRATA1\", \"RACE\")), selected = \"STRATA1\", multiple = FALSE, fixed = FALSE ) ), vars = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"SEX\", \"RACE\", \"COUNTRY\")), selected = c(\"AGE\", \"COUNTRY\", \"RACE\"), multiple = TRUE, fixed = FALSE ) ) ) # configuration for multiple long datasets mod3 <- tm_g_association( label = \"Multiple different long datasets\", ref = data_extract_spec( dataname = \"ADTTE\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADTTE\"]]), selected = \"AVAL\", multiple = FALSE, fixed = FALSE ), filter = filter_spec( label = \"Select endpoint:\", vars = \"PARAMCD\", choices = value_choices(data[[\"ADTTE\"]], \"PARAMCD\", \"PARAM\"), selected = c(\"PFS\", \"EFS\"), multiple = TRUE ) ), vars = data_extract_spec( dataname = \"ADRS\", reshape = TRUE, select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADRS\"]], c(\"AVALC\", \"BMRKR1\", \"BMRKR2\", \"ARM\")), selected = \"AVALC\", multiple = TRUE, fixed = FALSE ), filter = list( filter_spec( label = \"Select endpoints:\", vars = \"PARAMCD\", choices = value_choices(data[[\"ADRS\"]], \"PARAMCD\", \"PARAM\"), selected = \"BESRSPI\", multiple = TRUE ), filter_spec( label = \"Select endpoints:\", vars = \"AVISIT\", choices = levels(data[[\"ADRS\"]]$AVISIT), selected = \"SCREENING\", multiple = TRUE ) ) ) ) # configuration for wide and long datasets mod4 <- tm_g_association( label = \"Wide and long datasets\", ref = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"AVAL\", \"AVALC\")), selected = \"AVALC\", multiple = FALSE, fixed = FALSE, label = \"Selected variable:\" ), filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADRS\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADRS\"]]$PARAMCD), multiple = TRUE, label = \"Select response\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADRS\"]]$AVISIT), selected = levels(data[[\"ADRS\"]]$AVISIT), multiple = TRUE, label = \"Select visit:\" ) ) ), vars = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"SEX\", \"AGE\", \"RACE\", \"COUNTRY\", \"BMRKR1\", \"STRATA1\", \"ARM\")), selected = \"AGE\", multiple = TRUE, fixed = FALSE, label = \"Select variable:\" ) ) ) # configuration for the same long dataset (same subsets) mod5 <- tm_g_association( label = \"Same long datasets (same subsets)\", ref = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = variable_choices(data[[\"ADRS\"]]), selected = \"AVALC\", multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), vars = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = variable_choices(data[[\"ADRS\"]]), selected = \"PARAMCD\", multiple = TRUE, fixed = FALSE, label = \"Select variable:\" ) ) ) # configuration for the same long dataset (different subsets) mod6 <- tm_g_association( label = \"Same long datasets (different subsets)\", ref = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select lab:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE, label = \"Select visit:\" ) ), select = select_spec( choices = variable_choices(data[[\"ADLB\"]], c(\"AVAL\", \"CHG2\", \"PCHG2\")), selected = \"AVAL\", multiple = FALSE ) ), vars = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select labs:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE, label = \"Select visit:\" ) ), select = select_spec( choices = variable_choices(data[[\"ADLB\"]]), selected = \"STRATA1\", multiple = TRUE ) ) ) # initialize the app app <- init( data = data, modules = modules( # tm_g_association ---- modules( label = \"Association plot\", mod1, mod2, mod3, mod4, mod5, mod6 ) ) )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-association-plot.html","id":"section-run-the-app","dir":"Articles","previous_headings":"teal application to use association plot with various datasets types","what":"4 - Run the app","title":"Using association plot","text":"simple shiny::shinyApp() call let run app. Note app displayed running code inside R session.","code":"shinyApp(app$ui, app$server, options = list(height = 1024, width = 1024))"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-bivariate-plot.html","id":"section-teal-application-to-use-bivariate-plot-with-various-datasets-types","dir":"Articles","previous_headings":"","what":"teal application to use bivariate plot with various datasets types","title":"Using bivariate plot","text":"vignette guide four parts create teal application using various types datasets using bivariate plot module tm_g_bivariate(): Load libraries Create data sets Create app variable Run app","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-bivariate-plot.html","id":"section-load-libraries","dir":"Articles","previous_headings":"teal application to use bivariate plot with various datasets types","what":"1 - Load libraries","title":"Using bivariate plot","text":"","code":"library(teal.modules.general) # used to create the app library(dplyr) # used to modify data sets"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-bivariate-plot.html","id":"section-create-data-sets","dir":"Articles","previous_headings":"teal application to use bivariate plot with various datasets types","what":"2 - Create data sets","title":"Using bivariate plot","text":"Inside app 4 datasets used ADSL wide data set subject data ADRS long data set response data subjects different time points study ADTTE long data set time event data ADLB long data set lab measurements subject","code":"data <- teal_data() data <- within(data, { ADSL <- teal.modules.general::rADSL %>% mutate(TRTDUR = round(as.numeric(TRTEDTM - TRTSDTM), 1)) ADRS <- teal.modules.general::rADRS ADTTE <- teal.modules.general::rADTTE ADLB <- teal.modules.general::rADLB %>% mutate(CHGC = as.factor(case_when( CHG < 1 ~ \"N\", CHG > 1 ~ \"P\", TRUE ~ \"-\" ))) }) datanames <- c(\"ADSL\", \"ADRS\", \"ADTTE\", \"ADLB\") datanames(data) <- datanames join_keys(data) <- default_cdisc_join_keys[datanames]"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-bivariate-plot.html","id":"section-create-an-app-variable","dir":"Articles","previous_headings":"teal application to use bivariate plot with various datasets types","what":"3 - Create an app variable","title":"Using bivariate plot","text":"important section. use teal::init() function create app. data handed using teal.data::teal_data(). app constructed multiple calls tm_g_bivariate() using different combinations data sets.","code":"# configuration for the single wide dataset mod1 <- tm_g_bivariate( label = \"Single wide dataset\", x = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]]), selected = \"BMRKR1\", fixed = FALSE ) ), y = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]]), selected = \"SEX\", multiple = FALSE, fixed = FALSE ) ), row_facet = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADSL\"]]), selected = NULL, multiple = FALSE, fixed = FALSE ) ), col_facet = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADSL\"]]), selected = NULL, multiple = FALSE, fixed = FALSE ) ) ) # configuration for the two wide datasets mod2 <- tm_g_bivariate( label = \"Two wide datasets\", x = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"BMRKR1\", \"AGE\", \"SEX\", \"STRATA1\", \"RACE\")), selected = c(\"BMRKR1\"), multiple = FALSE ) ), y = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADSL\"]], c(\"COUNTRY\", \"AGE\", \"RACE\")), selected = \"RACE\", multiple = FALSE ) ), row_facet = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]]), selected = NULL, multiple = FALSE, fixed = FALSE ) ), col_facet = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADSL\"]]), selected = NULL, multiple = FALSE, fixed = FALSE ) ) ) # configuration for the multiple different long datasets mod3 <- tm_g_bivariate( label = \"Multiple different long datasets\", x = data_extract_spec( dataname = \"ADRS\", filter = filter_spec( label = \"Select endpoints:\", vars = c(\"PARAMCD\", \"AVISIT\"), choices = value_choices(data[[\"ADRS\"]], c(\"PARAMCD\", \"AVISIT\"), c(\"PARAM\", \"AVISIT\")), selected = \"OVRINV - END OF INDUCTION\", multiple = TRUE ), select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"AVALC\", \"AVAL\")), selected = \"AVALC\", multiple = FALSE ) ), y = data_extract_spec( dataname = \"ADTTE\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADTTE\"]], c(\"AVAL\", \"CNSR\")), selected = \"AVAL\", multiple = FALSE, fixed = FALSE ), filter = filter_spec( label = \"Select endpoint:\", vars = c(\"PARAMCD\"), choices = value_choices(data[[\"ADTTE\"]], \"PARAMCD\", \"PARAM\"), selected = \"OS\", multiple = FALSE ) ), row_facet = data_extract_spec( dataname = \"ADRS\", filter = filter_spec( label = \"Select endpoints:\", vars = c(\"PARAMCD\", \"AVISIT\"), choices = value_choices(data[[\"ADRS\"]], c(\"PARAMCD\", \"AVISIT\"), c(\"PARAM\", \"AVISIT\")), selected = \"OVRINV - SCREENING\", multiple = TRUE ), select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADRS\"]], c(\"SEX\", \"RACE\", \"COUNTRY\", \"ARM\", \"PARAMCD\", \"AVISIT\")), selected = \"SEX\", multiple = FALSE, fixed = FALSE ) ), col_facet = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADSL\"]], c(\"SEX\", \"RACE\")), selected = NULL, multiple = FALSE, fixed = FALSE ) ), color_settings = TRUE, color = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"SEX\", \"RACE\", \"COUNTRY\")), selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), fill = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"SEX\", \"RACE\", \"COUNTRY\")), selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), size = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"BMRKR1\")), selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), plot_height = c(600, 200, 2000), ggtheme = \"gray\" ) # configuration for the wide and long datasets mod4 <- tm_g_bivariate( label = \"Wide and long datasets\", x = data_extract_spec( dataname = \"ADRS\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADRS\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADRS\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select response:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADRS\"]]$AVISIT), selected = levels(data[[\"ADRS\"]]$AVISIT)[1], multiple = FALSE, label = \"Select visit:\" ) ), select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"AVALC\", \"AVAL\")), selected = \"AVALC\", multiple = FALSE, label = \"Select variable:\" ) ), y = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"BMRKR1\", \"SEX\", \"AGE\", \"RACE\", \"COUNTRY\")), selected = \"BMRKR1\", multiple = FALSE, label = \"Select variable:\", fixed = FALSE ) ), row_facet = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"SEX\", \"RACE\", \"ARMCD\", \"PARAMCD\")), selected = \"SEX\", multiple = FALSE, label = \"Select variable:\" ) ), col_facet = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"SEX\", \"RACE\", \"ARMCD\", \"PARAMCD\", \"AVISIT\")), selected = \"ARMCD\", multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ) ) # configuration for the wide and multiple long datasets mod5 <- tm_g_bivariate( label = \"Wide and multiple long datasets\", x = data_extract_spec( dataname = \"ADRS\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADRS\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADRS\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select response:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADRS\"]]$AVISIT), selected = levels(data[[\"ADRS\"]]$AVISIT)[1], multiple = FALSE, label = \"Select visit:\" ) ), select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"AVALC\", \"AVAL\")), selected = \"AVALC\", multiple = FALSE, label = \"Select variable:\" ) ), y = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"BMRKR1\", \"SEX\", \"AGE\", \"RACE\", \"COUNTRY\")), selected = \"BMRKR1\", multiple = FALSE, fixed = FALSE ) ), row_facet = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select measurement:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE, label = \"Select visit:\" ) ), select = select_spec( choices = \"ARMCD\", selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), col_facet = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"SEX\", \"AGE\", \"RACE\", \"COUNTRY\")), selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), color_settings = TRUE, color = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"SEX\", \"RACE\", \"COUNTRY\")), selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), fill = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"SEX\", \"RACE\", \"COUNTRY\")), selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), size = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"BMRKR1\")), selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), plot_height = c(600, 200, 2000), ggtheme = \"gray\" ) # Configuration for the same long datasets (same subset) mod6 <- tm_g_bivariate( label = \"Same long datasets (same subset)\", x = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"AVALC\", \"AVAL\")), selected = \"AVALC\", multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), y = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"SEX\", \"RACE\", \"COUNTRY\", \"ARMCD\", \"BMRKR1\", \"BMRKR2\")), selected = \"BMRKR1\", multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), row_facet = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"AVISIT\", \"PARAMCD\")), selected = \"PARAMCD\", multiple = FALSE, label = \"Select variables:\" ) ), col_facet = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"AVISIT\", \"PARAMCD\")), selected = \"AVISIT\", multiple = FALSE, label = \"Select variables:\" ) ) ) # Configuration for the same datasets (different subsets) mod7 <- tm_g_bivariate( label = \"Same datasets (different subsets)\", x = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select lab:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE, label = \"Select visit:\" ) ), select = select_spec( choices = \"AVAL\", selected = \"AVAL\", multiple = FALSE, fixed = TRUE ) ), y = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select lab:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE, label = \"Select visit:\" ) ), select = select_spec( choices = \"AVAL\", selected = \"AVAL\", multiple = FALSE, fixed = TRUE ) ), use_density = FALSE, row_facet = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select lab:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE, label = \"Select category:\" ) ), select = select_spec( choices = variable_choices(data[[\"ADLB\"]], c(\"RACE\", \"SEX\", \"ARMCD\", \"ACTARMCD\")), selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), col_facet = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select lab:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE, label = \"Select category:\" ) ), select = select_spec( choices = variable_choices(data[[\"ADLB\"]], c(\"RACE\", \"SEX\", \"ARMCD\", \"ACTARMCD\")), selected = \"ARMCD\", multiple = FALSE, fixed = FALSE, label = \"Select variables:\" ) ), color_settings = TRUE, color = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"SEX\", \"RACE\", \"COUNTRY\")), selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), fill = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"SEX\", \"RACE\", \"COUNTRY\")), selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), size = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"BMRKR1\")), selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), plot_height = c(600, 200, 2000), ggtheme = \"gray\" ) # initialize the app app <- init( data = data, modules = modules( # tm_g_bivariate ------ modules( label = \"Bivariate plot\", mod1, mod2, mod3, mod4, mod5, mod6, mod7 ) ) )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-bivariate-plot.html","id":"section-run-the-app","dir":"Articles","previous_headings":"teal application to use bivariate plot with various datasets types","what":"4 - Run the app","title":"Using bivariate plot","text":"simple shiny::shinyApp() call let run app. Note app displayed running code inside R session.","code":"shinyApp(app$ui, app$server, options = list(height = 1024, width = 1024))"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-cross-table.html","id":"section-teal-application-to-use-cross-table-with-various-datasets-types","dir":"Articles","previous_headings":"","what":"teal application to use cross table with various datasets types","title":"Using cross table","text":"vignette guide four parts create teal application using various types datasets using cross table module tm_t_crosstable(): Load libraries Create data sets Create app variable Run app","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-cross-table.html","id":"section-load-libraries","dir":"Articles","previous_headings":"teal application to use cross table with various datasets types","what":"1 - Load libraries","title":"Using cross table","text":"","code":"library(teal.modules.general) # used to create the app library(dplyr) # used to modify data sets"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-cross-table.html","id":"section-create-data-sets","dir":"Articles","previous_headings":"teal application to use cross table with various datasets types","what":"2 - Create data sets","title":"Using cross table","text":"Inside app 2 datasets used ADSL wide data set subject data ADLB long data set lab measurements subject","code":"data <- within(data, { ADSL <- teal.modules.general::rADSL ADLB <- teal.modules.general::rADLB %>% mutate(CHGC = as.factor(case_when( CHG < 1 ~ \"N\", CHG > 1 ~ \"P\", TRUE ~ \"-\" ))) }) datanames <- c(\"ADSL\", \"ADLB\") datanames(data) <- datanames join_keys(data) <- default_cdisc_join_keys[datanames]"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-cross-table.html","id":"section-create-an-app-variable","dir":"Articles","previous_headings":"teal application to use cross table with various datasets types","what":"3 - Create an app variable","title":"Using cross table","text":"important section. use teal::init() function create app. data handed using teal.data::teal_data(). app constructed multiple calls tm_t_crosstable() using different combinations data sets.","code":"# configuration for the single wide dataset mod1 <- tm_t_crosstable( label = \"Single wide dataset\", x = data_extract_spec( \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]]), selected = names(data[[\"ADSL\"]])[5], multiple = TRUE, fixed = FALSE, ordered = TRUE ) ), y = data_extract_spec( \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]]), selected = names(data[[\"ADSL\"]])[6], multiple = FALSE, fixed = FALSE ) ) ) # configuration for the same long datasets (different subsets) mod2 <- tm_t_crosstable( label = \"Same long datasets (different subsets)\", x = data_extract_spec( dataname = \"ADLB\", filter = filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE ), select = select_spec( choices = variable_choices(data[[\"ADLB\"]]), selected = \"AVISIT\", multiple = TRUE, fixed = FALSE, ordered = TRUE, label = \"Select variable:\" ) ), y = data_extract_spec( dataname = \"ADLB\", filter = filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE ), select = select_spec( choices = variable_choices(data[[\"ADLB\"]]), selected = \"LOQFL\", multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ) ) # initialize the app app <- init( data = data, modules = modules( modules( label = \"Cross table\", mod1, mod2 ) ) )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-cross-table.html","id":"section-run-the-app","dir":"Articles","previous_headings":"teal application to use cross table with various datasets types","what":"4 - Run the app","title":"Using cross table","text":"simple shiny::shinyApp() call let run app. Note app displayed running code inside R session.","code":"shinyApp(app$ui, app$server, options = list(height = 1024, width = 1024))"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-data-table.html","id":"section-teal-application-to-display-data-table-with-various-datasets-types","dir":"Articles","previous_headings":"","what":"teal application to display data table with various datasets types","title":"Using data table","text":"vignette guide four parts create teal application using various types datasets using data table module tm_data_table(): Load libraries Create data sets Create app variable Run app","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-data-table.html","id":"section-load-libraries","dir":"Articles","previous_headings":"teal application to display data table with various datasets types","what":"1 - Load libraries","title":"Using data table","text":"","code":"library(teal.modules.general) # used to create the app"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-data-table.html","id":"section-create-data-sets","dir":"Articles","previous_headings":"teal application to display data table with various datasets types","what":"2 - Create data sets","title":"Using data table","text":"Inside app 3 datasets used ADSL wide data set subject data ADTTE long data set time event data ADLB long data set lab measurements subject","code":"data <- teal_data() data <- within(data, { ADSL <- teal.modules.general::rADSL ADTTE <- teal.modules.general::rADTTE ADLB <- teal.modules.general::rADLB }) datanames <- c(\"ADSL\", \"ADTTE\", \"ADLB\") datanames(data) <- datanames join_keys(data) <- default_cdisc_join_keys[datanames]"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-data-table.html","id":"section-create-an-app-variable","dir":"Articles","previous_headings":"teal application to display data table with various datasets types","what":"3 - Create an app variable","title":"Using data table","text":"important section. use teal::init() function create app. data handed using teal.data::teal_data(). app constructed multiple calls tm_data_table() using different combinations data sets.","code":"# configuration for the two-datasets example mod1 <- tm_data_table( label = \"Two datasets\", variables_selected = list( ADSL = c(\"STUDYID\", \"USUBJID\", \"SUBJID\", \"SITEID\", \"AGE\", \"SEX\"), ADTTE = c( \"STUDYID\", \"USUBJID\", \"SUBJID\", \"SITEID\", \"PARAM\", \"PARAMCD\", \"ARM\", \"ARMCD\", \"AVAL\", \"CNSR\" ) ) ) # configuration for the subsetting or changing order of datasets mod2 <- tm_data_table( label = \"Datasets order\", variables_selected = list( ADSL = c(\"STUDYID\", \"USUBJID\", \"SUBJID\", \"SITEID\", \"AGE\", \"SEX\"), ADLB = c( \"STUDYID\", \"USUBJID\", \"SUBJID\", \"SITEID\", \"PARAM\", \"PARAMCD\", \"AVISIT\", \"AVISITN\", \"AVAL\", \"CHG\" ) ), datasets_selected = c(\"ADTTE\", \"ADLB\", \"ADSL\") ) # configuration for the advanced usage of DT options and extensions mod3 <- tm_data_table( label = \"Advanced DT usage\", dt_args = list(extensions = c(\"Buttons\", \"ColReorder\", \"FixedHeader\")), dt_options = list( searching = FALSE, pageLength = 30, lengthMenu = c(5, 15, 25, 50, 100), scrollX = FALSE, dom = \"lBrtip\", buttons = c(\"copy\", \"csv\", \"excel\", \"pdf\", \"print\"), colReorder = TRUE, fixedHeader = TRUE ) ) # initialize the app app <- init( data = data, modules = modules( mod1, mod2, mod3 ) )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-data-table.html","id":"section-run-the-app","dir":"Articles","previous_headings":"teal application to display data table with various datasets types","what":"4 - Run the app","title":"Using data table","text":"simple shiny::shinyApp() call let run app. Note app displayed running code inside R session.","code":"shinyApp(app$ui, app$server, options = list(height = 1024, width = 1024))"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-outliers-module.html","id":"section-teal-application-to-analyze-and-report-outliers-with-various-datasets-types-","dir":"Articles","previous_headings":"","what":"teal application to analyze and report outliers with various datasets types.","title":"Using outliers module","text":"vignette guide four parts create teal application using various types datasets using outliers module tm_outliers(): Load libraries Create data sets Create app variable Run app","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-outliers-module.html","id":"section-load-libraries","dir":"Articles","previous_headings":"teal application to analyze and report outliers with various datasets types.","what":"1 - Load libraries","title":"Using outliers module","text":"","code":"library(teal.modules.general) # used to create the app library(dplyr) # used to modify data sets"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-outliers-module.html","id":"section-create-data-sets","dir":"Articles","previous_headings":"teal application to analyze and report outliers with various datasets types.","what":"2 - Create data sets","title":"Using outliers module","text":"Inside app 3 datasets used ADSL wide data set subject data ADRS long data set response data subjects different time points study ADLB long data set lab measurements subject","code":"data <- teal_data() data <- within(data, { ADSL <- teal.modules.general::rADSL ADRS <- teal.modules.general::rADRS ADLB <- teal.modules.general::rADLB }) datanames <- c(\"ADSL\", \"ADRS\", \"ADLB\") datanames(data) <- datanames join_keys(data) <- default_cdisc_join_keys[datanames]"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-outliers-module.html","id":"section-create-an-app-variable","dir":"Articles","previous_headings":"teal application to analyze and report outliers with various datasets types.","what":"3 - Create an app variable","title":"Using outliers module","text":"important section. use teal::init() function create app. data handed using teal.data::teal_data(). app constructed multiple calls tm_outliers() using different combinations data sets.","code":"# configuration for the single wide dataset mod1 <- tm_outliers( label = \"Single wide dataset\", outlier_var = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"BMRKR1\")), selected = \"AGE\", fixed = FALSE ) ), categorical_var = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices( data[[\"ADSL\"]], subset = names(Filter(isTRUE, sapply(data[[\"ADSL\"]], is.factor))) ), selected = \"RACE\", multiple = FALSE, fixed = FALSE ) ) ) # configuration for the wide and long datasets mod2 <- tm_outliers( label = \"Wide and long datasets\", outlier_var = list( data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"BMRKR1\")), selected = \"AGE\", fixed = FALSE ) ), data_extract_spec( dataname = \"ADLB\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADLB\"]], c(\"AVAL\", \"CHG2\")), selected = \"AVAL\", multiple = FALSE, fixed = FALSE ) ) ), categorical_var = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices( data[[\"ADSL\"]], subset = names(Filter(isTRUE, sapply(data[[\"ADSL\"]], is.factor))) ), selected = \"RACE\", multiple = FALSE, fixed = FALSE ) ) ) # configuration for the multiple long datasets mod3 <- tm_outliers( label = \"Multiple long datasets\", outlier_var = list( data_extract_spec( dataname = \"ADRS\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADRS\"]], c(\"ADY\", \"EOSDY\")), selected = \"ADY\", fixed = FALSE ) ), data_extract_spec( dataname = \"ADLB\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADLB\"]], c(\"AVAL\", \"CHG2\")), selected = \"AVAL\", multiple = FALSE, fixed = FALSE ) ) ), categorical_var = list( data_extract_spec( dataname = \"ADRS\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADRS\"]], c(\"ARM\", \"ACTARM\")), selected = \"ARM\", multiple = FALSE, fixed = FALSE ) ), data_extract_spec( dataname = \"ADLB\", select = select_spec( label = \"Select variables:\", choices = variable_choices( data[[\"ADLB\"]], subset = names(Filter(isTRUE, sapply(data[[\"ADLB\"]], is.factor))) ), selected = \"RACE\", multiple = FALSE, fixed = FALSE ) ) ) ) # initialize the app app <- init( data = data, modules = modules( # tm_outliers ---- modules( label = \"Outliers module\", mod1, mod2, mod3 ) ) )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-outliers-module.html","id":"section-run-the-app","dir":"Articles","previous_headings":"teal application to analyze and report outliers with various datasets types.","what":"4 - Run the app","title":"Using outliers module","text":"simple shiny::shinyApp() call let run app. Note app displayed running code inside R session.","code":"shinyApp(app$ui, app$server, options = list(height = 1024, width = 1024))"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-regression-plots.html","id":"section-teal-application-to-use-regression-plot-with-various-datasets-types","dir":"Articles","previous_headings":"","what":"teal application to use regression plot with various datasets types","title":"Using regression plots","text":"vignette guide four parts create teal application using various types datasets using regression plot module tm_a_regression(): Load libraries Create data sets Create app variable Run app","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-regression-plots.html","id":"section-load-libraries","dir":"Articles","previous_headings":"teal application to use regression plot with various datasets types","what":"1 - Load libraries","title":"Using regression plots","text":"","code":"library(teal.modules.general) # used to create the app library(dplyr) # used to modify data sets"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-regression-plots.html","id":"section-create-data-sets","dir":"Articles","previous_headings":"teal application to use regression plot with various datasets types","what":"2 - Create data sets","title":"Using regression plots","text":"Inside app 4 datasets used ADSL wide data set subject data ADRS long data set response data subjects different time points study ADTTE long data set time event data ADLB long data set lab measurements subject","code":"data <- teal_data() data <- within(data, { ADSL <- teal.modules.general::rADSL %>% mutate(TRTDUR = round(as.numeric(TRTEDTM - TRTSDTM), 1)) ADRS <- teal.modules.general::rADRS ADTTE <- teal.modules.general::rADTTE ADLB <- teal.modules.general::rADLB %>% mutate(CHGC = as.factor(case_when( CHG < 1 ~ \"N\", CHG > 1 ~ \"P\", TRUE ~ \"-\" ))) }) datanames <- c(\"ADSL\", \"ADRS\", \"ADTTE\", \"ADLB\") datanames(data) <- datanames join_keys(data) <- default_cdisc_join_keys[datanames]"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-regression-plots.html","id":"section-create-an-app-variable","dir":"Articles","previous_headings":"teal application to use regression plot with various datasets types","what":"3 - Create an app variable","title":"Using regression plots","text":"important section. use teal::init() function create app. data handed using teal.data::teal_data(). app constructed multiple calls tm_a_regression() using different combinations data sets.","code":"# configuration for the single wide dataset mod1 <- tm_a_regression( label = \"Single wide dataset\", response = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"BMRKR1\", \"BMRKR2\")), selected = \"BMRKR1\", multiple = FALSE, fixed = FALSE ) ), regressor = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"SEX\", \"RACE\")), selected = \"AGE\", multiple = TRUE, fixed = FALSE ) ) ) # configuration for the two wide datasets mod2 <- tm_a_regression( label = \"Two wide datasets\", default_plot_type = 2, response = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"BMRKR1\", \"BMRKR2\")), selected = \"BMRKR1\", multiple = FALSE, fixed = FALSE ) ), regressor = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"SEX\", \"RACE\")), selected = c(\"AGE\", \"RACE\"), multiple = TRUE, fixed = FALSE ) ) ) # configuration for the same long datasets (same subset) mod3 <- tm_a_regression( label = \"Same long datasets (same subset)\", default_plot_type = 2, response = data_extract_spec( dataname = \"ADTTE\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADTTE\"]], c(\"AVAL\", \"CNSR\")), selected = \"AVAL\", multiple = FALSE, fixed = FALSE ), filter = filter_spec( label = \"Select parameter:\", vars = \"PARAMCD\", choices = value_choices(data[[\"ADTTE\"]], \"PARAMCD\", \"PARAM\"), selected = \"PFS\", multiple = FALSE ) ), regressor = data_extract_spec( dataname = \"ADTTE\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADTTE\"]], c(\"AGE\", \"CNSR\", \"SEX\")), selected = c(\"AGE\", \"CNSR\", \"SEX\"), multiple = TRUE ), filter = filter_spec( label = \"Select parameter:\", vars = \"PARAMCD\", choices = value_choices(data[[\"ADTTE\"]], \"PARAMCD\", \"PARAM\"), selected = \"PFS\", multiple = FALSE ) ) ) # configuration for the wide and long datasets mod4 <- tm_a_regression( label = \"Wide and long datasets\", response = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[2], multiple = TRUE, label = \"Select measurement:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[2], multiple = TRUE, label = \"Select visit:\" ) ), select = select_spec( label = \"Select variable:\", choices = \"AVAL\", selected = \"AVAL\", multiple = FALSE, fixed = TRUE ) ), regressor = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADSL\"]], c(\"BMRKR1\", \"BMRKR2\", \"AGE\")), selected = \"AGE\", multiple = TRUE, fixed = FALSE ) ) ) # configuration for the same long datasets (different subsets) mod5 <- tm_a_regression( label = \"Same long datasets (different subsets)\", default_plot_type = 2, response = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = TRUE, label = \"Select lab:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = TRUE, label = \"Select visit:\" ) ), select = select_spec( choices = \"AVAL\", selected = \"AVAL\", multiple = FALSE, fixed = TRUE ) ), regressor = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select labs:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE, label = \"Select visit:\" ) ), select = select_spec( choices = variable_choices(data[[\"ADLB\"]], c(\"AVAL\", \"AGE\", \"BMRKR1\", \"BMRKR2\", \"SEX\", \"ARM\")), selected = c(\"AVAL\", \"BMRKR1\"), multiple = TRUE ) ) ) # initialize the app app <- init( data = data, modules = modules( modules( label = \"Regression plots\", mod1, mod2, mod3, mod4, mod5 ) ) )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-regression-plots.html","id":"section-run-the-app","dir":"Articles","previous_headings":"teal application to use regression plot with various datasets types","what":"4 - Run the app","title":"Using regression plots","text":"simple shiny::shinyApp() call let run app. Note app displayed running code inside R session.","code":"shinyApp(app$ui, app$server, options = list(height = 1024, width = 1024))"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-response-plot.html","id":"section-teal-application-to-use-response-plot-with-various-datasets-types","dir":"Articles","previous_headings":"","what":"teal application to use response plot with various datasets types","title":"Using response plot","text":"vignette guide four parts create teal application using various types datasets using response plot module tm_g_response(): Load libraries Create data sets Create app variable Run app","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-response-plot.html","id":"section-load-libraries","dir":"Articles","previous_headings":"teal application to use response plot with various datasets types","what":"1 - Load libraries","title":"Using response plot","text":"","code":"library(teal.modules.general) # used to create the app library(dplyr) # used to modify data sets"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-response-plot.html","id":"section-create-data-sets","dir":"Articles","previous_headings":"teal application to use response plot with various datasets types","what":"2 - Create data sets","title":"Using response plot","text":"Inside app 4 datasets used ADSL wide data set subject data ADRS long data set response data subjects different time points study ADTTE long data set time event data ADLB long data set lab measurements subject","code":"data <- teal_data() data <- within(data, { ADSL <- teal.modules.general::rADSL %>% mutate(TRTDUR = round(as.numeric(TRTEDTM - TRTSDTM), 1)) ADRS <- teal.modules.general::rADRS ADTTE <- teal.modules.general::rADTTE ADLB <- teal.modules.general::rADLB %>% mutate(CHGC = as.factor(case_when( CHG < 1 ~ \"N\", CHG > 1 ~ \"P\", TRUE ~ \"-\" ))) }) datanames <- c(\"ADSL\", \"ADRS\", \"ADTTE\", \"ADLB\") datanames(data) <- datanames join_keys(data) <- default_cdisc_join_keys[datanames]"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-response-plot.html","id":"section-create-an-app-variable","dir":"Articles","previous_headings":"teal application to use response plot with various datasets types","what":"3 - Create an app variable","title":"Using response plot","text":"important section. use teal::init() function create app. data handed using teal.data::teal_data(). app constructed multiple calls tm_g_response() using different combinations data sets.","code":"# configuration for the single wide dataset mod1 <- tm_g_response( label = \"Single wide dataset\", response = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"BMRKR2\", \"ITTFL\", \"BEP01FL\")), selected = \"BMRKR2\", multiple = FALSE, fixed = FALSE ) ), x = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"SEX\", \"RACE\", \"COUNTRY\", \"ARMCD\", \"STRATA1\")), selected = \"ARMCD\", multiple = FALSE, fixed = FALSE ) ) ) # configuration for the two wide datasets mod2 <- tm_g_response( label = \"Two wide datasets\", response = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"BMRKR2\", \"ITTFL\", \"BEP01FL\")), selected = \"BMRKR2\", multiple = FALSE ) ), x = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = c(\"SEX\", \"COUNTRY\", \"RACE\", \"STRATA1\", \"ARMCD\"), selected = \"ARMCD\", multiple = FALSE ) ) ) # configuration for the multiple long datasets mod3 <- tm_g_response( label = \"Multiple long datasets\", response = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( label = \"Select parameter:\", vars = \"PARAMCD\", choices = levels(data[[\"ADLB\"]]$PARAMCD), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE ), filter_spec( label = \"Select visit:\", vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE ) ), select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADLB\"]], c(\"BMRKR2\", \"ITTFL\", \"BEP01FL\")), selected = \"BMRKR2\", multiple = FALSE ) ), x = data_extract_spec( dataname = \"ADRS\", filter = list( filter_spec( label = \"Select parameter:\", vars = \"PARAMCD\", choices = levels(data[[\"ADRS\"]]$PARAMCD), selected = levels(data[[\"ADRS\"]]$PARAMCD)[3], multiple = FALSE ), filter_spec( label = \"Select visit:\", vars = \"AVISIT\", choices = levels(data[[\"ADRS\"]]$AVISIT), selected = levels(data[[\"ADRS\"]]$AVISIT)[3], multiple = FALSE ) ), select = select_spec( choices = c(\"AVALC\", \"ITTFL\", \"BEP01FL\"), selected = \"AVALC\", multiple = FALSE, fixed = TRUE ) ), row_facet = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = \"SEX\", selected = NULL, multiple = FALSE ) ), col_facet = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"SEX\", \"COUNTRY\")), selected = NULL, multiple = FALSE ) ) ) # configuration for the wide and long dataset mod4 <- tm_g_response( label = \"Wide and long dataset\", response = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = levels(data[[\"ADLB\"]]$PARAMCD), selected = levels(data[[\"ADLB\"]]$PARAMCD)[2], multiple = TRUE, label = \"Select measurement:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[2], multiple = TRUE, label = \"Select visit:\" ) ), select = select_spec( choices = variable_choices(data[[\"ADLB\"]], c(\"BMRKR2\", \"ITTFL\", \"BEP01FL\")), selected = \"BMRKR2\", multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), x = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"ARMCD\", \"BMRKR1\", \"BMRKR2\", \"BEP01FL\")), selected = \"BMRKR2\", multiple = FALSE, fixed = FALSE ) ) ) # configuration for the same long datasets (same subsets) mod5 <- tm_g_response( label = \"Same long datasets (same subsets)\", response = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"BMRKR2\", \"AVALC\", \"BEP01FL\")), selected = \"AVALC\", multiple = FALSE, fixed = TRUE, label = \"Select variable:\" ) ), x = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"AVALC\", \"AGE\", \"SEX\", \"ARMCD\", \"STRATA1\")), selected = \"ARMCD\", multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), row_facet = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = \"PARAMCD\", selected = \"PARAMCD\", multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), col_facet = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = \"AVISIT\", selected = \"AVISIT\", multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ) ) # configuration for the same long datasets (different subsets) mod6 <- tm_g_response( label = \"Same long datasets (different subsets)\", response = data_extract_spec( dataname = \"ADLB\", filter = filter_spec( vars = \"PARAMCD\", choices = levels(data[[\"ADLB\"]]$PARAMCD), selected = levels(data[[\"ADLB\"]]$PARAMCD)[2], multiple = FALSE, label = \"Select lab:\" ), select = select_spec( choices = \"BMRKR2\", selected = \"BMRKR2\", multiple = FALSE, fixed = TRUE ) ), x = data_extract_spec( dataname = \"ADLB\", filter = filter_spec( vars = \"PARAMCD\", choices = levels(data[[\"ADLB\"]]$PARAMCD), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select lab:\" ), select = select_spec( choices = variable_choices(data[[\"ADLB\"]], c(\"AVISIT\", \"PARAMCD\", \"BEP01FL\")), selected = \"AVISIT\", multiple = FALSE, fixed = TRUE ) ), row_facet = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = levels(data[[\"ADLB\"]]$PARAMCD), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select lab:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE, label = \"Select visit:\" ) ), select = select_spec( choices = variable_choices(data[[\"ADLB\"]], c(\"SEX\", \"RACE\", \"ARMCD\")), selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select Variable\" ) ), col_facet = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = levels(data[[\"ADLB\"]]$PARAMCD), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select lab:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE, label = \"Select visit:\" ) ), select = select_spec( choices = variable_choices(data[[\"ADLB\"]], c(\"SEX\", \"RACE\", \"ARMCD\")), selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ) ) # initialize the app app <- init( data = data, modules = modules( modules( label = \"Response plot\", mod1, mod2, mod3, mod4, mod5, mod6 ) ) )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-response-plot.html","id":"section-run-the-app","dir":"Articles","previous_headings":"teal application to use response plot with various datasets types","what":"4 - Run the app","title":"Using response plot","text":"simple shiny::shinyApp() call let run app. Note app displayed running code inside R session.","code":"shinyApp(app$ui, app$server, options = list(height = 1024, width = 1024))"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-scatterplot-matrix.html","id":"section-teal-application-to-use-scatter-plot-matrix-with-various-datasets-types","dir":"Articles","previous_headings":"","what":"teal application to use scatter plot matrix with various datasets types","title":"Using scatterplot matrix","text":"vignette guide four parts create teal application using various types datasets using scatter plot matrix module tm_g_scatterplotmatrix(): Load libraries Create data sets Create app variable Run app","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-scatterplot-matrix.html","id":"section-load-libraries","dir":"Articles","previous_headings":"teal application to use scatter plot matrix with various datasets types","what":"1 - Load libraries","title":"Using scatterplot matrix","text":"","code":"library(teal.modules.general) # used to create the app library(dplyr) # used to modify data sets"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-scatterplot-matrix.html","id":"section-create-data-sets","dir":"Articles","previous_headings":"teal application to use scatter plot matrix with various datasets types","what":"2 - Create data sets","title":"Using scatterplot matrix","text":"Inside app 4 datasets used ADSL wide data set subject data ADRS long data set response data subjects different time points study ADTTE long data set time event data ADLB long data set lab measurements subject","code":"data <- teal_data() data <- within(data, { ADSL <- teal.modules.general::rADSL %>% mutate(TRTDUR = round(as.numeric(TRTEDTM - TRTSDTM), 1)) ADRS <- teal.modules.general::rADRS ADTTE <- teal.modules.general::rADTTE ADLB <- teal.modules.general::rADLB %>% mutate(CHGC = as.factor(case_when( CHG < 1 ~ \"N\", CHG > 1 ~ \"P\", TRUE ~ \"-\" ))) }) datanames <- c(\"ADSL\", \"ADRS\", \"ADTTE\", \"ADLB\") datanames(data) <- datanames join_keys(data) <- default_cdisc_join_keys[datanames]"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-scatterplot-matrix.html","id":"section-create-an-app-variable","dir":"Articles","previous_headings":"teal application to use scatter plot matrix with various datasets types","what":"3 - Create an app variable","title":"Using scatterplot matrix","text":"important section. use teal::init() function create app. data handed using teal.data::teal_data(). app constructed multiple calls tm_g_scatterplotmatrix() using different combinations data sets.","code":"# configuration for the single wide dataset mod1 <- tm_g_scatterplotmatrix( label = \"Single wide dataset\", variables = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADSL\"]]), selected = c(\"AGE\", \"RACE\", \"SEX\", \"BMRKR1\", \"BMRKR2\"), multiple = TRUE, fixed = FALSE, ordered = TRUE ) ) ) # configuration for the one long datasets mod2 <- tm_g_scatterplotmatrix( \"One long dataset\", variables = data_extract_spec( dataname = \"ADTTE\", select = select_spec( choices = variable_choices(data[[\"ADTTE\"]], c(\"AVAL\", \"BMRKR1\", \"BMRKR2\")), selected = c(\"AVAL\", \"BMRKR1\", \"BMRKR2\"), multiple = TRUE, fixed = FALSE, ordered = TRUE, label = \"Select variables:\" ) ) ) # configuration for the two long datasets mod3 <- tm_g_scatterplotmatrix( label = \"Two long datasets\", variables = list( data_extract_spec( dataname = \"ADRS\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADRS\"]]), selected = c(\"AVAL\", \"AVALC\"), multiple = TRUE, fixed = FALSE, ordered = TRUE, ), filter = filter_spec( label = \"Select endpoints:\", vars = c(\"PARAMCD\", \"AVISIT\"), choices = value_choices(data[[\"ADRS\"]], c(\"PARAMCD\", \"AVISIT\"), c(\"PARAM\", \"AVISIT\")), selected = \"OVRINV - SCREENING\", multiple = FALSE ) ), data_extract_spec( dataname = \"ADTTE\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADTTE\"]]), selected = c(\"AVAL\", \"CNSR\"), multiple = TRUE, fixed = FALSE, ordered = TRUE ), filter = filter_spec( label = \"Select parameters:\", vars = \"PARAMCD\", choices = value_choices(data[[\"ADTTE\"]], \"PARAMCD\", \"PARAM\"), selected = \"OS\", multiple = TRUE ) ) ) ) # initialize the app app <- init( data = data, modules = modules( modules( label = \"Scatterplot matrix\", mod1, mod2, mod3 ) ) )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-scatterplot-matrix.html","id":"section-run-the-app","dir":"Articles","previous_headings":"teal application to use scatter plot matrix with various datasets types","what":"4 - Run the app","title":"Using scatterplot matrix","text":"simple shiny::shinyApp() call let run app. Note app displayed running code inside R session.","code":"shinyApp(app$ui, app$server, options = list(height = 1024, width = 1024))"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-scatterplot.html","id":"section-teal-application-to-use-scatter-plot-with-various-datasets-types","dir":"Articles","previous_headings":"","what":"teal application to use scatter plot with various datasets types","title":"Using scatterplot","text":"vignette guide four parts create teal application using various types datasets using scatter plot module tm_g_scatterplot(): Load libraries Create data sets Create app variable Run app","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-scatterplot.html","id":"section-load-libraries","dir":"Articles","previous_headings":"teal application to use scatter plot with various datasets types","what":"1 - Load libraries","title":"Using scatterplot","text":"","code":"library(teal.modules.general) # used to create the app library(dplyr) # used to modify data sets"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-scatterplot.html","id":"section-create-data-sets","dir":"Articles","previous_headings":"teal application to use scatter plot with various datasets types","what":"2 - Create data sets","title":"Using scatterplot","text":"Inside app 4 datasets used ADSL wide data set subject data ADRS long data set response data subjects different time points study ADTTE long data set time event data ADLB long data set lab measurements subject","code":"data <- teal_data() data <- within(data, { ADSL <- teal.modules.general::rADSL %>% mutate(TRTDUR = round(as.numeric(TRTEDTM - TRTSDTM), 1)) ADRS <- teal.modules.general::rADRS ADTTE <- teal.modules.general::rADTTE ADLB <- teal.modules.general::rADLB %>% mutate(CHGC = as.factor(case_when( CHG < 1 ~ \"N\", CHG > 1 ~ \"P\", TRUE ~ \"-\" ))) }) datanames <- c(\"ADSL\", \"ADRS\", \"ADTTE\", \"ADLB\") datanames(data) <- datanames join_keys(data) <- default_cdisc_join_keys[datanames]"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-scatterplot.html","id":"section-create-an-app-variable","dir":"Articles","previous_headings":"teal application to use scatter plot with various datasets types","what":"3 - Create an app variable","title":"Using scatterplot","text":"important section. use teal::init() function create app. data handed using teal.data::teal_data(). app constructed multiple calls tm_g_scatterplot() using different combinations data sets.","code":"# configuration for the single wide datasets mod1 <- tm_g_scatterplot( label = \"Single wide dataset\", x = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"BMRKR1\", \"BMRKR2\")), selected = \"AGE\", multiple = FALSE, fixed = FALSE ) ), y = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"BMRKR1\", \"BMRKR2\")), selected = \"BMRKR1\", multiple = FALSE, fixed = FALSE ) ), color_by = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADSL\"]], c(\"RACE\", \"SEX\")), selected = NULL, multiple = TRUE, fixed = FALSE ) ) ) # configuration for the two wide datasets mod2 <- tm_g_scatterplot( label = \"Two wide datasets\", x = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"BMRKR1\", \"BMRKR2\")), selected = \"BMRKR1\", multiple = FALSE, fixed = FALSE ) ), y = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"SEX\")), selected = \"AGE\", multiple = FALSE, fixed = FALSE ) ), color_by = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"COUNTRY\", \"AGE\", \"RACE\")), selected = \"COUNTRY\", multiple = FALSE, fixed = FALSE ) ) ) # configuration for the different long datasets mod3 <- tm_g_scatterplot( label = \"Different long datasets\", x = data_extract_spec( dataname = \"ADRS\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADRS\"]]), selected = \"AVAL\", multiple = FALSE, fixed = FALSE ), filter = filter_spec( label = \"Select endpoint:\", vars = c(\"PARAMCD\", \"AVISIT\"), choices = value_choices(data[[\"ADRS\"]], c(\"PARAMCD\", \"AVISIT\"), c(\"PARAM\", \"AVISIT\")), selected = \"OVRINV - SCREENING\", multiple = FALSE ) ), y = data_extract_spec( dataname = \"ADTTE\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADTTE\"]]), selected = \"AVAL\", multiple = FALSE, fixed = FALSE ), filter = filter_spec( label = \"Select parameters:\", vars = c(\"PARAMCD\"), choices = value_choices(data[[\"ADTTE\"]], \"PARAMCD\", \"PARAM\"), selected = \"OS\", multiple = TRUE ) ), color_by = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"SEX\")), selected = \"AGE\", multiple = FALSE, fixed = FALSE ) ) ) # configuration for the wide and long datasets mod4 <- tm_g_scatterplot( label = \"Wide and long datasets\", x = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"SEX\", \"AGE\", \"BMRKR1\", \"COUNTRY\")), selected = \"AGE\", multiple = FALSE, fixed = FALSE ) ), y = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select measurement:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE, label = \"Select visit:\" ) ), select = select_spec( label = \"Selected variable:\", choices = \"AVAL\", selected = \"AVAL\", multiple = FALSE, fixed = TRUE ) ), color_by = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"SEX\", \"AGE\", \"RACE\", \"COUNTRY\")), selected = NULL, multiple = FALSE, fixed = FALSE ) ) ) # configuration for the same long datasets (same subsets) mod5 <- tm_g_scatterplot( label = \"Same long datasets (same subsets)\", x = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"AVAL\", \"BMRKR1\", \"BMRKR2\")), selected = \"AVAL\", multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), y = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"AVAL\", \"BMRKR1\", \"BMRKR2\")), selected = \"BMRKR1\", multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ), color_by = data_extract_spec( dataname = \"ADRS\", select = select_spec( choices = variable_choices(data[[\"ADRS\"]], c(\"AGE\", \"SEX\", \"RACE\")), selected = NULL, multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ) ) # configuration for the same long datasets (different subsets) mod6 <- tm_g_scatterplot( label = \"Same long datasets (different subsets)\", x = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select lab:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE, label = \"Select visit:\" ) ), select = select_spec( choices = \"AVAL\", selected = \"AVAL\", multiple = FALSE, fixed = TRUE ) ), y = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select lab:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE, label = \"Select visit:\" ) ), select = select_spec( choices = \"AVAL\", selected = \"AVAL\", multiple = FALSE, fixed = TRUE ) ), color_by = data_extract_spec( dataname = \"ADLB\", filter = list( filter_spec( vars = \"PARAMCD\", choices = value_choices(data[[\"ADLB\"]], \"PARAMCD\", \"PARAM\"), selected = levels(data[[\"ADLB\"]]$PARAMCD)[1], multiple = FALSE, label = \"Select lab:\" ), filter_spec( vars = \"AVISIT\", choices = levels(data[[\"ADLB\"]]$AVISIT), selected = levels(data[[\"ADLB\"]]$AVISIT)[1], multiple = FALSE, label = \"Select visit:\" ) ), select = select_spec( choices = variable_choices(data[[\"ADLB\"]], c(\"RACE\", \"SEX\")), selected = \"SEX\", multiple = FALSE, fixed = FALSE, label = \"Select variable:\" ) ) ) # initialize the app app <- init( data = data, modules = modules( modules( label = \"Scatterplot\", mod1, mod2, mod3, mod4, mod5, mod6 ) ) )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/articles/using-scatterplot.html","id":"section-run-the-app","dir":"Articles","previous_headings":"teal application to use scatter plot with various datasets types","what":"4 - Run the app","title":"Using scatterplot","text":"simple shiny::shinyApp() call let run app. Note app displayed running code inside R session.","code":"shinyApp(app$ui, app$server, options = list(height = 1024, width = 1024))"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Dawid Kaledkowski. Author, maintainer. Pawel Rucki. Author. Mahmoud Hallal. Author. Ondrej Slama. Contributor. Maciej Nasinski. Author. Konrad Pagacz. Author. Nikolas Burkoff. Author. F. Hoffmann-La Roche AG. Copyright holder, funder.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Kaledkowski D, Rucki P, Hallal M, Nasinski M, Pagacz K, Burkoff N (2024). teal.modules.general: General Modules 'teal' Applications. R package version 0.3.0.9006, https://github.com/insightsengineering/teal.modules.general/, https://insightsengineering.github.io/teal.modules.general/main/.","code":"@Manual{, title = {teal.modules.general: General Modules for 'teal' Applications}, author = {Dawid Kaledkowski and Pawel Rucki and Mahmoud Hallal and Maciej Nasinski and Konrad Pagacz and Nikolas Burkoff}, year = {2024}, note = {R package version 0.3.0.9006, https://github.com/insightsengineering/teal.modules.general/}, url = {https://insightsengineering.github.io/teal.modules.general/main/}, }"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/index.html","id":"tealmodulesgeneral","dir":"","previous_headings":"","what":"General Modules for teal Applications","title":"General Modules for teal Applications","text":"package contains set standard teal modules include applications. include modules : viewing data (including tm_variable_browser, tm_data_table, tm_file_viewer, …) visualizing data (tm_g_scatterplot, tm_g_association, …) understanding missing outlier values within data (tm_missing_data, tm_outliers, …) performing simple data analysis (tm_a_pca, tm_g_distribution, tm_a_regression, …). modules work CDISC data, independent datasets general relational data. Please see teal gallery TLG Catalog see examples teal apps modules package.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"General Modules for teal Applications","text":"Alternatively, might want use development version available r-universe.","code":"# stable versions install.packages('teal.modules.general') # install.packages(\"pak\") pak::pak(\"insightsengineering/teal.modules.general@*release\") # beta versions install.packages('teal.modules.general', repos = c('https://pharmaverse.r-universe.dev', getOption('repos'))) # install.packages(\"pak\") pak::pak(\"insightsengineering/teal.modules.general\")"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"General Modules for teal Applications","text":"See package vignettes browseVignettes(package = \"teal.modules.general\") usage package visit Package Website.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/index.html","id":"getting-help","dir":"","previous_headings":"","what":"Getting help","title":"General Modules for teal Applications","text":"encounter bug feature request, please file issue. questions, discussions, staying date, please use teal channel pharmaverse slack workspace.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/index.html","id":"acknowledgment","dir":"","previous_headings":"","what":"Acknowledgment","title":"General Modules for teal Applications","text":"package result joint efforts many developers stakeholders. like thank everyone contributed far!","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/main/pull_request_template.html","id":null,"dir":"","previous_headings":"","what":"Pull Request","title":"Pull Request","text":"Fixes #nnn","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/add_facet_labels.html","id":null,"dir":"Reference","previous_headings":"","what":"Add labels for facets to a ggplot2 object — add_facet_labels","title":"Add labels for facets to a ggplot2 object — add_facet_labels","text":"Enhances ggplot2 plot adding labels describe faceting variables along x y axes.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/add_facet_labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add labels for facets to a ggplot2 object — add_facet_labels","text":"","code":"add_facet_labels(p, xfacet_label = NULL, yfacet_label = NULL)"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/add_facet_labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add labels for facets to a ggplot2 object — add_facet_labels","text":"p (ggplot2) object facet labels added. xfacet_label (character) Label facet along x-axis. NULL, label added. vector, labels joined \" & \". yfacet_label (character) Label facet along y-axis. Similar behavior xfacet_label.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/add_facet_labels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add labels for facets to a ggplot2 object — add_facet_labels","text":"Returns grid grob object (drawn grid.draw)","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/add_facet_labels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add labels for facets to a ggplot2 object — add_facet_labels","text":"","code":"library(ggplot2) library(grid) p <- ggplot(mtcars) + aes(x = mpg, y = disp) + geom_point() + facet_grid(gear ~ cyl) xfacet_label <- \"cylinders\" yfacet_label <- \"gear\" res <- add_facet_labels(p, xfacet_label, yfacet_label) grid.newpage() grid.draw(res) grid.newpage() grid.draw(add_facet_labels(p, xfacet_label = NULL, yfacet_label)) grid.newpage() grid.draw(add_facet_labels(p, xfacet_label, yfacet_label = NULL)) grid.newpage() grid.draw(add_facet_labels(p, xfacet_label = NULL, yfacet_label = NULL))"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/call_fun_dots.html","id":null,"dir":"Reference","previous_headings":"","what":"Call a function with a character vector for the ... argument — call_fun_dots","title":"Call a function with a character vector for the ... argument — call_fun_dots","text":"Call function character vector ... argument","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/call_fun_dots.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Call a function with a character vector for the ... argument — call_fun_dots","text":"","code":"call_fun_dots(fun, str_args)"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/call_fun_dots.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Call a function with a character vector for the ... argument — call_fun_dots","text":"fun (character) Name function ... argument shall replaced values str_args. str_args (character) character vector function shall executed ","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/call_fun_dots.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Call a function with a character vector for the ... argument — call_fun_dots","text":"Value call fun arguments specified str_args.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/create_sparklines.html","id":null,"dir":"Reference","previous_headings":"","what":"S3 generic for sparkline widget HTML — create_sparklines","title":"S3 generic for sparkline widget HTML — create_sparklines","text":"Generates sparkline HTML code corresponding input array. numeric variables creates box plot, character factors - bar plot. Produces empty string variables types.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/create_sparklines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"S3 generic for sparkline widget HTML — create_sparklines","text":"","code":"create_sparklines(arr, width = 150, ...) # S3 method for logical create_sparklines(arr, ...) # S3 method for numeric create_sparklines(arr, width = 150, ...) # S3 method for character create_sparklines(arr, ...) # S3 method for factor create_sparklines(arr, width = 150, bar_spacing = 5, bar_width = 20, ...) # S3 method for Date create_sparklines(arr, width = 150, bar_spacing = 5, bar_width = 20, ...) # S3 method for POSIXct create_sparklines(arr, width = 150, bar_spacing = 5, bar_width = 20, ...) # S3 method for POSIXlt create_sparklines(arr, width = 150, bar_spacing = 5, bar_width = 20, ...) # S3 method for default create_sparklines(arr, width = 150, ...)"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/create_sparklines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"S3 generic for sparkline widget HTML — create_sparklines","text":"arr vector type length width numeric width sparkline widget (pixels) ... list additional options passed bar plots jquery.sparkline; see jquery.sparkline docs bar_spacing numeric spacing bars (pixels) bar_width numeric width bars (pixels)","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/create_sparklines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"S3 generic for sparkline widget HTML — create_sparklines","text":"Character string containing HTML code sparkline HTML widget.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/establish_updating_selection.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates observers updating the currently selected column — establish_updating_selection","title":"Creates observers updating the currently selected column — establish_updating_selection","text":"created observers update column currently selected left-hand side tabset panel.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/establish_updating_selection.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates observers updating the currently selected column — establish_updating_selection","text":"","code":"establish_updating_selection(datanames, input, plot_var, columns_names)"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/establish_updating_selection.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates observers updating the currently selected column — establish_updating_selection","text":"datanames (character) name dataset input (session$input) shiny session input plot_var (list) list containing currently selected dataset (tab) column names columns_names (environment) environment containing bindings dataset","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/establish_updating_selection.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"Creates observers updating the currently selected column — establish_updating_selection","text":"Creates observer dataset (tab tabset panel).","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/get_scatterplotmatrix_stats.html","id":null,"dir":"Reference","previous_headings":"","what":"Get stats for x-y pairs in scatterplot matrix — get_scatterplotmatrix_stats","title":"Get stats for x-y pairs in scatterplot matrix — get_scatterplotmatrix_stats","text":"Uses stats::cor.test() per default numerical input variables converts results character vector. extended different stats different variable types needed. Meant called lattice::panel.text().","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/get_scatterplotmatrix_stats.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get stats for x-y pairs in scatterplot matrix — get_scatterplotmatrix_stats","text":"","code":"get_scatterplotmatrix_stats( x, y, .f = stats::cor.test, .f_args = list(), round_stat = 2, round_pval = 4 )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/get_scatterplotmatrix_stats.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get stats for x-y pairs in scatterplot matrix — get_scatterplotmatrix_stats","text":"x, y (numeric) vectors data values. x y must length. .f (function) function accepts x y formula input ~ x + y. Default stats::cor.test. .f_args (list) arguments passed .f. round_stat (integer(1)) optional, number decimal places use rounding estimate. round_pval (integer(1)) optional, number decimal places use rounding p-value.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/get_scatterplotmatrix_stats.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get stats for x-y pairs in scatterplot matrix — get_scatterplotmatrix_stats","text":"Character stats. stats::cor.test() correlation coefficient p-value.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/get_scatterplotmatrix_stats.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get stats for x-y pairs in scatterplot matrix — get_scatterplotmatrix_stats","text":"Presently need use formula input stats::cor.test na.fail gets evaluated formula passed (see ).","code":"x = c(1,3,5,7,NA) y = c(3,6,7,8,1) stats::cor.test(x, y, na.action = \"na.fail\") stats::cor.test(~ x + y, na.action = \"na.fail\")"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/get_scatterplotmatrix_stats.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get stats for x-y pairs in scatterplot matrix — get_scatterplotmatrix_stats","text":"","code":"set.seed(1) x <- runif(25, 0, 1) y <- runif(25, 0, 1) x[c(3, 10, 18)] <- NA get_scatterplotmatrix_stats(x, y, .f = stats::cor.test, .f_args = list(method = \"pearson\")) #> [1] \"cor:0.11\\nP:0.6255\" get_scatterplotmatrix_stats(x, y, .f = stats::cor.test, .f_args = list( method = \"pearson\", na.action = na.fail )) #> [1] \"NA\""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/include_css_files.html","id":null,"dir":"Reference","previous_headings":"","what":"Include CSS files from /inst/css/ package directory to application header — include_css_files","title":"Include CSS files from /inst/css/ package directory to application header — include_css_files","text":"system.file used access files packages, work devtools. Therefore, redefine method package needed. Thus, export method","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/include_css_files.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Include CSS files from /inst/css/ package directory to application header — include_css_files","text":"","code":"include_css_files(pattern = \"*\")"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/include_css_files.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Include CSS files from /inst/css/ package directory to application header — include_css_files","text":"pattern (character) optional, regular expression match file names included.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/include_css_files.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Include CSS files from /inst/css/ package directory to application header — include_css_files","text":"HTML code includes CSS files.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/is_tab_active_js.html","id":null,"dir":"Reference","previous_headings":"","what":"JavaScript condition to check if a specific tab is active — is_tab_active_js","title":"JavaScript condition to check if a specific tab is active — is_tab_active_js","text":"JavaScript condition check specific tab active","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/is_tab_active_js.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"JavaScript condition to check if a specific tab is active — is_tab_active_js","text":"","code":"is_tab_active_js(id, name)"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/is_tab_active_js.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"JavaScript condition to check if a specific tab is active — is_tab_active_js","text":"id (character(1)) id tab panel tabs. name (character(1)) name tab.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/is_tab_active_js.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"JavaScript condition to check if a specific tab is active — is_tab_active_js","text":"JavaScript expression used shiny::conditionalPanel() determine specified tab active.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/plot_var_summary.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot variable — plot_var_summary","title":"Plot variable — plot_var_summary","text":"Creates summary plot statistics relevant data type.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/plot_var_summary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot variable — plot_var_summary","text":"","code":"plot_var_summary( var, var_lab, wrap_character = NULL, numeric_as_factor, display_density = is.numeric(var), remove_NA_hist = FALSE, outlier_definition, records_for_factor, ggplot2_args )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/plot_var_summary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot variable — plot_var_summary","text":"var vector type plotted. numeric variables produces histogram density line, factors creates frequency plot var_lab text describing selected variable displayed plot wrap_character (numeric) number characters wrap text values var numeric_as_factor (logical) numeric variable treated factor display_density (logical) density estimation displayed numeric values remove_NA_hist (logical) NA values removed histogram factor like variables outlier_definition 0 outliers removed, otherwise outliers (outlier_definition*IQR /Q1/Q3 removed) records_for_factor (numeric) number factor levels >= value graph factors shown, list values ggplot2_args (ggplot2_args) object created teal.widgets::ggplot2_args() settings module plot. argument merged options variable teal.ggplot2_args default module setup. details see vignette: vignette(\"custom-ggplot2-arguments\", package = \"teal.widgets\")","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/plot_var_summary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot variable — plot_var_summary","text":"plot","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/rADAE.html","id":null,"dir":"Reference","previous_headings":"","what":"Random adverse events — rADAE","title":"Random adverse events — rADAE","text":"Random adverse events","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/rADAE.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Random adverse events — rADAE","text":"","code":"rADAE"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/rADAE.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Random adverse events — rADAE","text":"object class tbl_df (inherits tbl, data.frame) 1934 rows 92 columns.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/rADAE.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Random adverse events — rADAE","text":"internal","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/rADLB.html","id":null,"dir":"Reference","previous_headings":"","what":"Random lab analysis — rADLB","title":"Random lab analysis — rADLB","text":"Random lab analysis","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/rADLB.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Random lab analysis — rADLB","text":"","code":"rADLB"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/rADLB.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Random lab analysis — rADLB","text":"object class tbl_df (inherits tbl, data.frame) 8400 rows 102 columns.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/rADLB.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Random lab analysis — rADLB","text":"internal","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/rADRS.html","id":null,"dir":"Reference","previous_headings":"","what":"Random response — rADRS","title":"Random response — rADRS","text":"Random response","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/rADRS.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Random response — rADRS","text":"","code":"rADRS"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/rADRS.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Random response — rADRS","text":"object class tbl_df (inherits tbl, data.frame) 3200 rows 65 columns.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/rADRS.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Random response — rADRS","text":"internal","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/rADSL.html","id":null,"dir":"Reference","previous_headings":"","what":"Random patient listing — rADSL","title":"Random patient listing — rADSL","text":"Random patient listing","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/rADSL.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Random patient listing — rADSL","text":"","code":"rADSL"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/rADSL.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Random patient listing — rADSL","text":"object class tbl_df (inherits tbl, data.frame) 400 rows 55 columns.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/rADSL.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Random patient listing — rADSL","text":"internal","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/rADTTE.html","id":null,"dir":"Reference","previous_headings":"","what":"Random time to event analysis dataset — rADTTE","title":"Random time to event analysis dataset — rADTTE","text":"Random time event analysis dataset","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/rADTTE.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Random time to event analysis dataset — rADTTE","text":"","code":"rADTTE"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/rADTTE.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Random time to event analysis dataset — rADTTE","text":"object class tbl_df (inherits tbl, data.frame) 2000 rows 67 columns.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/rADTTE.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Random time to event analysis dataset — rADTTE","text":"internal","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/remove_outliers_from.html","id":null,"dir":"Reference","previous_headings":"","what":"Removes the outlier observation from an array — remove_outliers_from","title":"Removes the outlier observation from an array — remove_outliers_from","text":"Removes outlier observation array","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/remove_outliers_from.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Removes the outlier observation from an array — remove_outliers_from","text":"","code":"remove_outliers_from(var, outlier_definition)"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/remove_outliers_from.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Removes the outlier observation from an array — remove_outliers_from","text":"var (numeric) numeric vector outlier_definition (numeric) 0 outliers removed, otherwise outliers (outlier_definition*IQR /Q1/Q3) removed","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/remove_outliers_from.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Removes the outlier observation from an array — remove_outliers_from","text":"(numeric) vector without outlier values","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/render_single_tab.html","id":null,"dir":"Reference","previous_headings":"","what":"Renders a single tab in the left-hand side tabset panel — render_single_tab","title":"Renders a single tab in the left-hand side tabset panel — render_single_tab","text":"Renders single tab left-hand side tabset panel. rendered tab contains information one dataset many presented module.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/render_single_tab.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Renders a single tab in the left-hand side tabset panel — render_single_tab","text":"","code":"render_single_tab( dataset_name, parent_dataname, output, data, input, columns_names, plot_var )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/render_single_tab.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Renders a single tab in the left-hand side tabset panel — render_single_tab","text":"dataset_name (character) name dataset contained rendered tab parent_dataname (character) name parent dataname filter variables output (session$output) shiny session output data (teal_data) object containing datasets input (session$input) shiny session input columns_names (environment) environment containing bindings dataset plot_var (list) list containing currently selected dataset (tab) column names","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/render_tab_header.html","id":null,"dir":"Reference","previous_headings":"","what":"Renders the text headlining a single tab in the left-hand side tabset panel — render_tab_header","title":"Renders the text headlining a single tab in the left-hand side tabset panel — render_tab_header","text":"Renders text headlining single tab left-hand side tabset panel","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/render_tab_header.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Renders the text headlining a single tab in the left-hand side tabset panel — render_tab_header","text":"","code":"render_tab_header(dataset_name, output, data)"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/render_tab_header.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Renders the text headlining a single tab in the left-hand side tabset panel — render_tab_header","text":"dataset_name (character) name dataset tab output (session$output) shiny session output data (teal_data) object containing datasets","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/render_tab_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Renders the table for a single dataset in the left-hand side tabset panel — render_tab_table","title":"Renders the table for a single dataset in the left-hand side tabset panel — render_tab_table","text":"table contains column names, column labels, small summary NA values sparkline (appropriate).","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/render_tab_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Renders the table for a single dataset in the left-hand side tabset panel — render_tab_table","text":"","code":"render_tab_table( dataset_name, parent_dataname, output, data, input, columns_names, plot_var )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/render_tab_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Renders the table for a single dataset in the left-hand side tabset panel — render_tab_table","text":"dataset_name (character) name dataset parent_dataname (character) name parent dataname filter variables output (session$output) shiny session output data (teal_data) object containing datasets input (session$input) shiny session input columns_names (environment) environment containing bindings dataset plot_var (list) list containing currently selected dataset (tab) column names","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/render_tabset_panel_content.html","id":null,"dir":"Reference","previous_headings":"","what":"Renders the left-hand side tabset panel of the module — render_tabset_panel_content","title":"Renders the left-hand side tabset panel of the module — render_tabset_panel_content","text":"Renders left-hand side tabset panel module","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/render_tabset_panel_content.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Renders the left-hand side tabset panel of the module — render_tabset_panel_content","text":"","code":"render_tabset_panel_content( datanames, parent_dataname, output, data, input, columns_names, plot_var )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/render_tabset_panel_content.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Renders the left-hand side tabset panel of the module — render_tabset_panel_content","text":"datanames (character) name dataset parent_dataname (character) name parent dataname filter variables output (session$output) shiny session output data (teal_data) object containing datasets input (session$input) shiny session input columns_names (environment) environment containing bindings dataset plot_var (list) list containing currently selected dataset (tab) column names","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/shared_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Shared parameters documentation — shared_params","title":"Shared parameters documentation — shared_params","text":"Defines common arguments shared across multiple functions package avoid repetition using inheritParams.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/shared_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Shared parameters documentation — shared_params","text":"plot_height (numeric) optional, specifies plot height three-element vector value, min, max intended use slider UI element. plot_width (numeric) optional, specifies plot width three-element vector value, min, max slider encoding plot width. rotate_xaxis_labels (logical) optional, whether rotate plot X axis labels. rotate default (FALSE). ggtheme (character) optional, ggplot2 theme used default. Defaults \"gray\". ggplot2_args (ggplot2_args) object created teal.widgets::ggplot2_args() settings module plot. argument merged options variable teal.ggplot2_args default module setup. details see vignette: vignette(\"custom-ggplot2-arguments\", package = \"teal.widgets\") basic_table_args (basic_table_args) object created teal.widgets::basic_table_args() settings module table. argument merged options variable teal.basic_table_args default module setup. details see vignette: vignette(\"custom-basic-table-arguments\", package = \"teal.widgets\") pre_output (shiny.tag) optional, text UI element displayed module's output, providing context title. text placed output put output context. example title. post_output (shiny.tag) optional, text UI element displayed module's output, adding context instructions. Elements like shiny::helpText() useful. alpha (integer(1) integer(3)) optional, specifies point opacity. length alpha one: plot points fixed opacity. length alpha three: plot points opacity dynamically adjusted based vector value, min, max. size (integer(1) integer(3)) optional, specifies point size. length size one: plot point sizes fixed size. length size three: plot points size dynamically adjusted based vector value, min, max.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/shared_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Shared parameters documentation — shared_params","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/teal.modules.general.html","id":null,"dir":"Reference","previous_headings":"","what":"teal.modules.general: General modules to add to a teal application — teal.modules.general","title":"teal.modules.general: General modules to add to a teal application — teal.modules.general","text":"modules package generic modules work data set (necessarily clinical trials data).","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/teal.modules.general.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"teal.modules.general: General modules to add to a teal application — teal.modules.general","text":"Maintainer: Dawid Kaledkowski dawid.kaledkowski@roche.com Authors: Pawel Rucki pawel.rucki@roche.com Mahmoud Hallal mahmoud.hallal@roche.com Maciej Nasinski Konrad Pagacz Nikolas Burkoff contributors: Ondrej Slama ondrej.slama@roche.com [contributor] F. Hoffmann-La Roche AG [copyright holder, funder]","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_a_pca.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: Principal component analysis — tm_a_pca","title":"teal module: Principal component analysis — tm_a_pca","text":"Module conducts principal component analysis (PCA) given dataset offers different ways visualizing outcomes, including elbow plot, circle plot, biplot, eigenvector plot. Additionally, enables dynamic customization plot aesthetics, opacity, size, font size, UI inputs.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_a_pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: Principal component analysis — tm_a_pca","text":"","code":"tm_a_pca( label = \"Principal Component Analysis\", dat, plot_height = c(600, 200, 2000), plot_width = NULL, ggtheme = c(\"gray\", \"bw\", \"linedraw\", \"light\", \"dark\", \"minimal\", \"classic\", \"void\"), ggplot2_args = teal.widgets::ggplot2_args(), rotate_xaxis_labels = FALSE, font_size = c(12, 8, 20), alpha = c(1, 0, 1), size = c(2, 1, 8), pre_output = NULL, post_output = NULL )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_a_pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: Principal component analysis — tm_a_pca","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. dat (data_extract_spec list multiple data_extract_spec) specifying columns used compute PCA. plot_height (numeric) optional, specifies plot height three-element vector value, min, max intended use slider UI element. plot_width (numeric) optional, specifies plot width three-element vector value, min, max slider encoding plot width. ggtheme (character) optional, ggplot2 theme used default. Defaults \"gray\". ggplot2_args (ggplot2_args) optional, object created teal.widgets::ggplot2_args() settings plots named list ggplot2_args objects plot-specific settings. argument merged options variable teal.ggplot2_args default module setup. List names match following: c(\"default\", \"Elbow plot\", \"Circle plot\", \"Biplot\", \"Eigenvector plot\"). details see vignette: vignette(\"custom-ggplot2-arguments\", package = \"teal.widgets\"). rotate_xaxis_labels (logical) optional, whether rotate plot X axis labels. rotate default (FALSE). font_size (numeric) optional, specifies font size. controls font size plot titles, axis labels, legends. vector length == 1 font sizes fixed size. vector value, min, max allows dynamic adjustment. alpha (integer(1) integer(3)) optional, specifies point opacity. length alpha one: plot points fixed opacity. length alpha three: plot points opacity dynamically adjusted based vector value, min, max. size (integer(1) integer(3)) optional, specifies point size. length size one: plot point sizes fixed size. length size three: plot points size dynamically adjusted based vector value, min, max. pre_output (shiny.tag) optional, text UI element displayed module's output, providing context title. text placed output put output context. example title. post_output (shiny.tag) optional, text UI element displayed module's output, adding context instructions. Elements like shiny::helpText() useful.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_a_pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: Principal component analysis — tm_a_pca","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_a_pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: Principal component analysis — tm_a_pca","text":"","code":"library(teal.widgets) # general data example data <- teal_data() data <- within(data, { require(nestcolor) USArrests <- USArrests }) datanames(data) <- \"USArrests\" app <- init( data = data, modules = modules( tm_a_pca( \"PCA\", dat = data_extract_spec( dataname = \"USArrests\", select = select_spec( choices = variable_choices( data = data[[\"USArrests\"]], c(\"Murder\", \"Assault\", \"UrbanPop\", \"Rape\") ), selected = c(\"Murder\", \"Assault\"), multiple = TRUE ), filter = NULL ), ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by PCA Module\") ) ) ) ) #> Initializing tm_a_pca if (interactive()) { shinyApp(app$ui, app$server) } # CDISC data example data <- teal_data() data <- within(data, { require(nestcolor) ADSL <- rADSL }) datanames(data) <- \"ADSL\" join_keys(data) <- default_cdisc_join_keys[datanames(data)] app <- init( data = data, modules = modules( tm_a_pca( \"PCA\", dat = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices( data = data[[\"ADSL\"]], c(\"BMRKR1\", \"AGE\", \"EOSDY\") ), selected = c(\"BMRKR1\", \"AGE\"), multiple = TRUE ), filter = NULL ), ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by PCA Module\") ) ) ) ) #> Initializing tm_a_pca if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_a_regression.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: Scatterplot and regression analysis — tm_a_regression","title":"teal module: Scatterplot and regression analysis — tm_a_regression","text":"Module visualizing regression analysis, including scatterplots various regression diagnostics plots. allows users explore relationship set regressors response variable, visualize residuals, identify outliers.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_a_regression.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: Scatterplot and regression analysis — tm_a_regression","text":"","code":"tm_a_regression( label = \"Regression Analysis\", regressor, response, plot_height = c(600, 200, 2000), plot_width = NULL, alpha = c(1, 0, 1), size = c(2, 1, 8), ggtheme = c(\"gray\", \"bw\", \"linedraw\", \"light\", \"dark\", \"minimal\", \"classic\", \"void\"), ggplot2_args = teal.widgets::ggplot2_args(), pre_output = NULL, post_output = NULL, default_plot_type = 1, default_outlier_label = \"USUBJID\", label_segment_threshold = c(0.5, 0, 10) )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_a_regression.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: Scatterplot and regression analysis — tm_a_regression","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. regressor (data_extract_spec list multiple data_extract_spec) Regressor variables incoming dataset filtering selecting. response (data_extract_spec list multiple data_extract_spec) Response variables incoming dataset filtering selecting. plot_height (numeric) optional, specifies plot height three-element vector value, min, max intended use slider UI element. plot_width (numeric) optional, specifies plot width three-element vector value, min, max slider encoding plot width. alpha (integer(1) integer(3)) optional, specifies point opacity. length alpha one: plot points fixed opacity. length alpha three: plot points opacity dynamically adjusted based vector value, min, max. size (integer(1) integer(3)) optional, specifies point size. length size one: plot point sizes fixed size. length size three: plot points size dynamically adjusted based vector value, min, max. ggtheme (character) optional, ggplot2 theme used default. Defaults \"gray\". ggplot2_args (ggplot2_args) optional, object created teal.widgets::ggplot2_args() settings plots named list ggplot2_args objects plot-specific settings. argument merged options variable teal.ggplot2_args default module setup. List names match following: c(\"default\", \"Response vs Regressor\", \"Residuals vs Fitted\", \"Scale-Location\", \"Cook's distance\", \"Residuals vs Leverage\"\", \"Cook's dist vs Leverage\"). details see vignette: vignette(\"custom-ggplot2-arguments\", package = \"teal.widgets\"). pre_output (shiny.tag) optional, text UI element displayed module's output, providing context title. text placed output put output context. example title. post_output (shiny.tag) optional, text UI element displayed module's output, adding context instructions. Elements like shiny::helpText() useful. default_plot_type (numeric) optional, defaults \"Response vs Regressor\". Response vs Regressor Residuals vs Fitted Normal Q-Q Scale-Location Cook's distance Residuals vs Leverage Cook's dist vs Leverage default_outlier_label (character) optional, default column selected label outliers. label_segment_threshold (numeric(1) numeric(3)) Minimum distance label point plot triggers creation line segment two. may happen label placed next point overlaps another label point. value used min.segment.length parameter ggrepel::geom_text_repel() function. can take following forms: numeric(1): Fixed value used minimum distance slider presented UI. numeric(3): slider presented UI (\"Plot settings\") adjust minimum distance dynamically. takes form c(value, min, max) passed value_min_max argument teal.widgets::optionalSliderInputValMinMax.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_a_regression.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: Scatterplot and regression analysis — tm_a_regression","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_a_regression.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"teal module: Scatterplot and regression analysis — tm_a_regression","text":"examples, please see vignette \"Using regression plots\" via vignette(\"using-regression-plots\", package = \"teal.modules.general\").","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_a_regression.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: Scatterplot and regression analysis — tm_a_regression","text":"","code":"# general data example library(teal.widgets) data <- teal_data() data <- within(data, { require(nestcolor) CO2 <- CO2 }) datanames(data) <- c(\"CO2\") app <- init( data = data, modules = modules( tm_a_regression( label = \"Regression\", response = data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variable:\", choices = \"uptake\", selected = \"uptake\", multiple = FALSE, fixed = TRUE ) ), regressor = data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"CO2\"]], c(\"conc\", \"Treatment\")), selected = \"conc\", multiple = TRUE, fixed = FALSE ) ), ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Regression Module\") ) ) ) ) #> Initializing tm_a_regression if (interactive()) { shinyApp(app$ui, app$server) } # CDISC data example library(teal.widgets) data <- teal_data() data <- within(data, { require(nestcolor) ADSL <- rADSL }) datanames(data) <- \"ADSL\" join_keys(data) <- default_cdisc_join_keys[datanames(data)] app <- init( data = data, modules = modules( tm_a_regression( label = \"Regression\", response = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = \"BMRKR1\", selected = \"BMRKR1\", multiple = FALSE, fixed = TRUE ) ), regressor = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"SEX\", \"RACE\")), selected = \"AGE\", multiple = TRUE, fixed = FALSE ) ), ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Regression Module\") ) ) ) ) #> Initializing tm_a_regression if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_data_table.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: Data table viewer — tm_data_table","title":"teal module: Data table viewer — tm_data_table","text":"Module provides dynamic interactive way view data.frames teal application. uses DT package display data tables paginated, searchable, sortable format, helps enhance data exploration analysis.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_data_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: Data table viewer — tm_data_table","text":"","code":"tm_data_table( label = \"Data Table\", variables_selected = list(), datasets_selected = character(0), dt_args = list(), dt_options = list(searching = FALSE, pageLength = 30, lengthMenu = c(5, 15, 30, 100), scrollX = TRUE), server_rendering = FALSE, pre_output = NULL, post_output = NULL )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_data_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: Data table viewer — tm_data_table","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. variables_selected (named list) Character vectors variables (.e. columns) initially shown dataset. Names list elements correspond names datasets available app. entry specified dataset, first six variables dataset initially shown. datasets_selected (character) vector datasets shown order. Names vector correspond datasets names. vector length == 0 (default) datasets shown. Note: datasets data.frame class compatible. dt_args (named list) Additional arguments passed DT::datatable() (must include data options). dt_options (named list) options argument DT::datatable. default list(searching = FALSE, pageLength = 30, lengthMenu = c(5, 15, 30, 100), scrollX = TRUE) server_rendering (logical) data table rendered server side (see server argument DT::renderDataTable()) pre_output (shiny.tag) optional, text UI element displayed module's output, providing context title. text placed output put output context. example title. post_output (shiny.tag) optional, text UI element displayed module's output, adding context instructions. Elements like shiny::helpText() useful.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_data_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: Data table viewer — tm_data_table","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_data_table.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"teal module: Data table viewer — tm_data_table","text":"DT package option DT.TOJSON_ARGS show Inf NA data tables. Configure DT.TOJSON_ARGS option via options(DT.TOJSON_ARGS = list(na = \"string\")) running module. Note though sorting numeric columns NA/Inf lexicographic numerical.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_data_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: Data table viewer — tm_data_table","text":"","code":"# general data example data <- teal_data() data <- within(data, { require(nestcolor) iris <- iris }) datanames(data) <- c(\"iris\") app <- init( data = data, modules = modules( tm_data_table( variables_selected = list( iris = c(\"Sepal.Length\", \"Sepal.Width\", \"Petal.Length\", \"Petal.Width\", \"Species\") ), dt_args = list(caption = \"ADSL Table Caption\") ) ) ) #> Initializing tm_data_table if (interactive()) { shinyApp(app$ui, app$server) } # CDISC data example data <- teal_data() data <- within(data, { require(nestcolor) ADSL <- rADSL }) datanames(data) <- \"ADSL\" join_keys(data) <- default_cdisc_join_keys[datanames(data)] app <- init( data = data, modules = modules( tm_data_table( variables_selected = list(ADSL = c(\"STUDYID\", \"USUBJID\", \"SUBJID\", \"SITEID\", \"AGE\", \"SEX\")), dt_args = list(caption = \"ADSL Table Caption\") ) ) ) #> Initializing tm_data_table if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_file_viewer.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: File viewer — tm_file_viewer","title":"teal module: File viewer — tm_file_viewer","text":"file viewer module provides tool view static files. Supported formats include text formats, PDF, PNG APNG, JPEG SVG, WEBP, GIF BMP.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_file_viewer.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: File viewer — tm_file_viewer","text":"","code":"tm_file_viewer( label = \"File Viewer Module\", input_path = list(`Current Working Directory` = \".\") )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_file_viewer.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: File viewer — tm_file_viewer","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. input_path (list) input paths, optional. element can : Paths can specified absolute paths relative running directory application. Default current working directory supplied.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_file_viewer.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: File viewer — tm_file_viewer","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_file_viewer.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: File viewer — tm_file_viewer","text":"","code":"data <- teal_data() data <- within(data, { data <- data.frame(1) }) datanames(data) <- c(\"data\") app <- init( data = data, modules = modules( tm_file_viewer( input_path = list( folder = system.file(\"sample_files\", package = \"teal.modules.general\"), png = system.file(\"sample_files/sample_file.png\", package = \"teal.modules.general\"), txt = system.file(\"sample_files/sample_file.txt\", package = \"teal.modules.general\"), url = \"https://fda.gov/files/drugs/published/Portable-Document-Format-Specifications.pdf\" ) ) ) ) #> Initializing tm_file_viewer if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_front_page.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: Front page — tm_front_page","title":"teal module: Front page — tm_front_page","text":"Creates simple front page teal applications, displaying introductory text, tables, additional html shiny tags, footnotes.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_front_page.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: Front page — tm_front_page","text":"","code":"tm_front_page( label = \"Front page\", header_text = character(0), tables = list(), additional_tags = tagList(), footnotes = character(0), show_metadata = FALSE )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_front_page.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: Front page — tm_front_page","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. header_text (character vector) text shown top module, element, named name shown first bold header followed value. first element's header displayed larger others. tables (named list data.frames) tables shown module. additional_tags (shiny.tag.list html) additional shiny tags html included table, example include image, tagList(tags$img(src = \"image.png\")) include html, HTML(\"html text \"). footnotes (character vector) text shown bottom module, element, named name shown first bold, followed value. show_metadata (logical) indicating whether metadata datasets available module.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_front_page.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: Front page — tm_front_page","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_front_page.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: Front page — tm_front_page","text":"","code":"data <- teal_data() data <- within(data, { require(nestcolor) ADSL <- rADSL attr(ADSL, \"metadata\") <- list(\"Author\" = \"NEST team\", \"data_source\" = \"synthetic data\") }) datanames(data) <- \"ADSL\" join_keys(data) <- default_cdisc_join_keys[datanames(data)] table_1 <- data.frame(Info = c(\"A\", \"B\"), Text = c(\"A\", \"B\")) table_2 <- data.frame(`Column 1` = c(\"C\", \"D\"), `Column 2` = c(5.5, 6.6), `Column 3` = c(\"A\", \"B\")) table_3 <- data.frame(Info = c(\"E\", \"F\"), Text = c(\"G\", \"H\")) table_input <- list( \"Table 1\" = table_1, \"Table 2\" = table_2, \"Table 3\" = table_3 ) app <- init( data = data, modules = modules( tm_front_page( header_text = c( \"Important information\" = \"It can go here.\", \"Other information\" = \"Can go here.\" ), tables = table_input, additional_tags = HTML(\"Additional HTML or shiny tags go here
    \"), footnotes = c(\"X\" = \"is the first footnote\", \"Y is the second footnote\"), show_metadata = TRUE ) ), header = tags$h1(\"Sample Application\"), footer = tags$p(\"Application footer\"), ) #> Initializing tm_front_page if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_association.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: Stack plots of variables and show association with reference variable — tm_g_association","title":"teal module: Stack plots of variables and show association with reference variable — tm_g_association","text":"Module provides functionality visualizing distribution variables association reference variable. supports configuring appearance plots, including themes whether show associations.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_association.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: Stack plots of variables and show association with reference variable — tm_g_association","text":"","code":"tm_g_association( label = \"Association\", ref, vars, show_association = TRUE, plot_height = c(600, 400, 5000), plot_width = NULL, distribution_theme = c(\"gray\", \"bw\", \"linedraw\", \"light\", \"dark\", \"minimal\", \"classic\", \"void\"), association_theme = c(\"gray\", \"bw\", \"linedraw\", \"light\", \"dark\", \"minimal\", \"classic\", \"void\"), pre_output = NULL, post_output = NULL, ggplot2_args = teal.widgets::ggplot2_args() )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_association.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: Stack plots of variables and show association with reference variable — tm_g_association","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. ref (data_extract_spec list multiple data_extract_spec) Reference variable, must accepts data_extract_spec select_spec(multiple = FALSE) ensure single selection option. vars (data_extract_spec list multiple data_extract_spec) Variables associated reference variable. show_association (logical) optional, whether show association vars reference variable. Defaults TRUE. plot_height (numeric) optional, specifies plot height three-element vector value, min, max intended use slider UI element. plot_width (numeric) optional, specifies plot width three-element vector value, min, max slider encoding plot width. distribution_theme, association_theme (character) optional, ggplot2 themes used default. Default \"gray\". pre_output (shiny.tag) optional, text UI element displayed module's output, providing context title. text placed output put output context. example title. post_output (shiny.tag) optional, text UI element displayed module's output, adding context instructions. Elements like shiny::helpText() useful. ggplot2_args (ggplot2_args) optional, object created teal.widgets::ggplot2_args() settings plots named list ggplot2_args objects plot-specific settings. argument merged options variable teal.ggplot2_args default module setup. List names match following: c(\"default\", \"Bivariate1\", \"Bivariate2\"). details see vignette: vignette(\"custom-ggplot2-arguments\", package = \"teal.widgets\").","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_association.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: Stack plots of variables and show association with reference variable — tm_g_association","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_association.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"teal module: Stack plots of variables and show association with reference variable — tm_g_association","text":"examples, please see vignette \"Using association plot\" via vignette(\"using-association-plot\", package = \"teal.modules.general\").","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_association.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: Stack plots of variables and show association with reference variable — tm_g_association","text":"","code":"library(teal.widgets) # general data example data <- teal_data() data <- within(data, { require(nestcolor) CO2 <- CO2 factors <- names(Filter(isTRUE, vapply(CO2, is.factor, logical(1L)))) CO2[factors] <- lapply(CO2[factors], as.character) }) datanames(data) <- c(\"CO2\") app <- init( data = data, modules = modules( tm_g_association( ref = data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"CO2\"]], c(\"Plant\", \"Type\", \"Treatment\")), selected = \"Plant\", fixed = FALSE ) ), vars = data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"CO2\"]], c(\"Plant\", \"Type\", \"Treatment\")), selected = \"Treatment\", multiple = TRUE, fixed = FALSE ) ), ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Association Module\") ) ) ) ) #> Initializing tm_g_association if (interactive()) { shinyApp(app$ui, app$server) } # CDISC data example data <- teal_data() data <- within(data, { require(nestcolor) ADSL <- rADSL }) datanames(data) <- \"ADSL\" join_keys(data) <- default_cdisc_join_keys[datanames(data)] app <- init( data = data, modules = modules( tm_g_association( ref = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices( data[[\"ADSL\"]], c(\"SEX\", \"RACE\", \"COUNTRY\", \"ARM\", \"STRATA1\", \"STRATA2\", \"ITTFL\", \"BMRKR2\") ), selected = \"RACE\", fixed = FALSE ) ), vars = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices( data[[\"ADSL\"]], c(\"SEX\", \"RACE\", \"COUNTRY\", \"ARM\", \"STRATA1\", \"STRATA2\", \"ITTFL\", \"BMRKR2\") ), selected = \"BMRKR2\", multiple = TRUE, fixed = FALSE ) ), ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Association Module\") ) ) ) ) #> Initializing tm_g_association if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_bivariate.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: Univariate and bivariate visualizations — tm_g_bivariate","title":"teal module: Univariate and bivariate visualizations — tm_g_bivariate","text":"Module enables creation univariate bivariate plots, facilitating exploration data distributions relationships two variables.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_bivariate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: Univariate and bivariate visualizations — tm_g_bivariate","text":"","code":"tm_g_bivariate( label = \"Bivariate Plots\", x, y, row_facet = NULL, col_facet = NULL, facet = !is.null(row_facet) || !is.null(col_facet), color = NULL, fill = NULL, size = NULL, use_density = FALSE, color_settings = FALSE, free_x_scales = FALSE, free_y_scales = FALSE, plot_height = c(600, 200, 2000), plot_width = NULL, rotate_xaxis_labels = FALSE, swap_axes = FALSE, ggtheme = c(\"gray\", \"bw\", \"linedraw\", \"light\", \"dark\", \"minimal\", \"classic\", \"void\"), ggplot2_args = teal.widgets::ggplot2_args(), pre_output = NULL, post_output = NULL )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_bivariate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: Univariate and bivariate visualizations — tm_g_bivariate","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. x (data_extract_spec list multiple data_extract_spec) Variable names selected plot along x-axis default. Can numeric, factor character. empty selections allowed. y (data_extract_spec list multiple data_extract_spec) Variable names selected plot along y-axis default. Can numeric, factor character. row_facet (data_extract_spec list multiple data_extract_spec) optional, specification data variable(s) use faceting rows. col_facet (data_extract_spec list multiple data_extract_spec) optional, specification data variable(s) use faceting columns. facet (logical) optional, specifies whether facet encodings ui elements toggled shown user default. Defaults TRUE either row_facet column_facet supplied. color (data_extract_spec list multiple data_extract_spec) optional, specification data variable(s) selected outline color inside coloring settings. applied color_settings set TRUE. fill (data_extract_spec list multiple data_extract_spec) optional, specification data variable(s) selected fill color inside coloring settings. applied color_settings set TRUE. size (data_extract_spec list multiple data_extract_spec) optional, specification data variable(s) selected size geom_point plots inside coloring settings. applied color_settings set TRUE. use_density (logical) optional, indicates whether plot density (TRUE) frequency (FALSE). Defaults frequency (FALSE). color_settings (logical) Whether coloring, filling size applied UI tool offered user. free_x_scales (logical) optional, whether X scaling shall changeable. allow scaling changed default (FALSE). free_y_scales (logical) optional, whether Y scaling shall changeable. allow scaling changed default (FALSE). plot_height (numeric) optional, specifies plot height three-element vector value, min, max intended use slider UI element. plot_width (numeric) optional, specifies plot width three-element vector value, min, max slider encoding plot width. rotate_xaxis_labels (logical) optional, whether rotate plot X axis labels. rotate default (FALSE). swap_axes (logical) optional, whether swap X Y axes. Defaults FALSE. ggtheme (character) optional, ggplot2 theme used default. Defaults \"gray\". ggplot2_args (ggplot2_args) object created teal.widgets::ggplot2_args() settings module plot. argument merged options variable teal.ggplot2_args default module setup. details see vignette: vignette(\"custom-ggplot2-arguments\", package = \"teal.widgets\") pre_output (shiny.tag) optional, text UI element displayed module's output, providing context title. text placed output put output context. example title. post_output (shiny.tag) optional, text UI element displayed module's output, adding context instructions. Elements like shiny::helpText() useful.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_bivariate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: Univariate and bivariate visualizations — tm_g_bivariate","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_bivariate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"teal module: Univariate and bivariate visualizations — tm_g_bivariate","text":"general module visualize 1 & 2 dimensional data.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_bivariate.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"teal module: Univariate and bivariate visualizations — tm_g_bivariate","text":"examples, please see vignette \"Using bivariate plot\" via vignette(\"using-bivariate-plot\", package = \"teal.modules.general\").","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_bivariate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: Univariate and bivariate visualizations — tm_g_bivariate","text":"","code":"library(teal.widgets) # general data example data <- teal_data() data <- within(data, { require(nestcolor) CO2 <- data.frame(CO2) }) datanames(data) <- c(\"CO2\") join_keys(data) <- default_cdisc_join_keys[datanames(data)] app <- init( data = data, modules = modules( tm_g_bivariate( x = data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"CO2\"]]), selected = \"conc\", fixed = FALSE ) ), y = data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"CO2\"]]), selected = \"uptake\", multiple = FALSE, fixed = FALSE ) ), row_facet = data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"CO2\"]]), selected = \"Type\", fixed = FALSE ) ), col_facet = data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"CO2\"]]), selected = \"Treatment\", fixed = FALSE ) ), ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Bivariate Module\") ) ) ) ) #> Initializing tm_g_bivariate if (interactive()) { shinyApp(app$ui, app$server) } # CDISC data example data <- teal_data() data <- within(data, { require(nestcolor) ADSL <- rADSL }) datanames(data) <- c(\"ADSL\") join_keys(data) <- default_cdisc_join_keys[datanames(data)] app <- init( data = data, modules = modules( tm_g_bivariate( x = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]]), selected = \"AGE\", fixed = FALSE ) ), y = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]]), selected = \"SEX\", multiple = FALSE, fixed = FALSE ) ), row_facet = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]]), selected = \"ARM\", fixed = FALSE ) ), col_facet = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]]), selected = \"COUNTRY\", fixed = FALSE ) ), ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Bivariate Module\") ) ) ) ) #> Initializing tm_g_bivariate if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_distribution.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: Distribution analysis — tm_g_distribution","title":"teal module: Distribution analysis — tm_g_distribution","text":"Module designed explore distribution single variable within given dataset. offers several tools, histograms, Q-Q plots, various statistical tests visually statistically analyze variable's distribution.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_distribution.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: Distribution analysis — tm_g_distribution","text":"","code":"tm_g_distribution( label = \"Distribution Module\", dist_var, strata_var = NULL, group_var = NULL, freq = FALSE, ggtheme = c(\"gray\", \"bw\", \"linedraw\", \"light\", \"dark\", \"minimal\", \"classic\", \"void\"), ggplot2_args = teal.widgets::ggplot2_args(), bins = c(30L, 1L, 100L), plot_height = c(600, 200, 2000), plot_width = NULL, pre_output = NULL, post_output = NULL )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_distribution.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: Distribution analysis — tm_g_distribution","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. dist_var (data_extract_spec list multiple data_extract_spec) Variable(s) distribution analyzed. strata_var (data_extract_spec list multiple data_extract_spec) Categorical variable used split distribution analysis. group_var (data_extract_spec list multiple data_extract_spec) Variable used faceting plot multiple panels. freq (logical) optional, whether display frequency (TRUE) density (FALSE). Defaults density (FALSE). ggtheme (character) optional, ggplot2 theme used default. Defaults \"gray\". ggplot2_args (ggplot2_args) optional, object created teal.widgets::ggplot2_args() settings plots named list ggplot2_args objects plot-specific settings. argument merged options variable teal.ggplot2_args default module setup. List names match following: c(\"default\", \"Histogram\", \"QQplot\"). details see vignette: vignette(\"custom-ggplot2-arguments\", package = \"teal.widgets\"). bins (integer(1) integer(3)) optional, specifies number bins histogram. length bins one: histogram bins fixed size based bins provided. length bins three: histogram bins dynamically adjusted based vector value, min, max. Defaults c(30L, 1L, 100L). plot_height (numeric) optional, specifies plot height three-element vector value, min, max intended use slider UI element. plot_width (numeric) optional, specifies plot width three-element vector value, min, max slider encoding plot width. pre_output (shiny.tag) optional, text placed output put output context. example title. post_output (shiny.tag) optional, text placed output put output context. example shiny::helpText() elements useful.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_distribution.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: Distribution analysis — tm_g_distribution","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_distribution.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: Distribution analysis — tm_g_distribution","text":"","code":"library(teal.widgets) # general data example data <- teal_data() data <- within(data, { iris <- iris }) datanames(data) <- \"iris\" app <- init( data = data, modules = list( tm_g_distribution( dist_var = data_extract_spec( dataname = \"iris\", select = select_spec(variable_choices(\"iris\"), \"Petal.Length\") ), ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Distribution Module\") ) ) ) ) #> Initializing tm_g_distribution #> Registered S3 methods overwritten by 'ggpp': #> method from #> heightDetails.titleGrob ggplot2 #> widthDetails.titleGrob ggplot2 #> Registered S3 method overwritten by 'ggpmisc': #> method from #> as.character.polynomial polynom if (interactive()) { shinyApp(app$ui, app$server) } # CDISC data example data <- teal_data() data <- within(data, { ADSL <- rADSL }) datanames(data) <- c(\"ADSL\") join_keys(data) <- default_cdisc_join_keys[datanames(data)] vars1 <- choices_selected( variable_choices(data[[\"ADSL\"]], c(\"ARM\", \"COUNTRY\", \"SEX\")), selected = NULL ) app <- init( data = data, modules = modules( tm_g_distribution( dist_var = data_extract_spec( dataname = \"ADSL\", select = select_spec( choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"BMRKR1\")), selected = \"BMRKR1\", multiple = FALSE, fixed = FALSE ) ), strata_var = data_extract_spec( dataname = \"ADSL\", filter = filter_spec( vars = vars1, multiple = TRUE ) ), group_var = data_extract_spec( dataname = \"ADSL\", filter = filter_spec( vars = vars1, multiple = TRUE ) ), ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Distribution Module\") ) ) ) ) #> Initializing tm_g_distribution if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_response.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: Response plot — tm_g_response","title":"teal module: Response plot — tm_g_response","text":"Generates response plot given response x variables. module allows users customize add annotations plot depending module's arguments. supports showing counts grouped variable facets (row / column), swapping coordinates, show count annotations displaying response plot frequency density.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_response.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: Response plot — tm_g_response","text":"","code":"tm_g_response( label = \"Response Plot\", response, x, row_facet = NULL, col_facet = NULL, coord_flip = FALSE, count_labels = TRUE, rotate_xaxis_labels = FALSE, freq = FALSE, plot_height = c(600, 400, 5000), plot_width = NULL, ggtheme = c(\"gray\", \"bw\", \"linedraw\", \"light\", \"dark\", \"minimal\", \"classic\", \"void\"), ggplot2_args = teal.widgets::ggplot2_args(), pre_output = NULL, post_output = NULL )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_response.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: Response plot — tm_g_response","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. response (data_extract_spec list multiple data_extract_spec) variable use response. can define one fixed column setting fixed = TRUE inside select_spec. data_extract_spec must allow multiple selection case. x (data_extract_spec list multiple data_extract_spec) Specifies variable use X-axis response plot. Allow user select multiple columns data allowed teal. data_extract_spec must allow multiple selection case. row_facet (data_extract_spec list multiple data_extract_spec) optional specification data variable(s) use faceting rows. col_facet (data_extract_spec list multiple data_extract_spec) optional specification data variable(s) use faceting columns. coord_flip (logical(1)) Indicates whether flip coordinates x response. default value FALSE show x variable x-axis response variable y-axis. count_labels (logical(1)) Indicates whether show count labels. Defaults TRUE. rotate_xaxis_labels (logical) optional, whether rotate plot X axis labels. rotate default (FALSE). freq (logical(1)) Indicates whether display frequency (TRUE) density (FALSE). Defaults density (FALSE). plot_height (numeric) optional, specifies plot height three-element vector value, min, max intended use slider UI element. plot_width (numeric) optional, specifies plot width three-element vector value, min, max slider encoding plot width. ggtheme (character) optional, ggplot2 theme used default. Defaults \"gray\". ggplot2_args (ggplot2_args) object created teal.widgets::ggplot2_args() settings module plot. argument merged options variable teal.ggplot2_args default module setup. details see vignette: vignette(\"custom-ggplot2-arguments\", package = \"teal.widgets\") pre_output (shiny.tag) optional, text UI element displayed module's output, providing context title. text placed output put output context. example title. post_output (shiny.tag) optional, text UI element displayed module's output, adding context instructions. Elements like shiny::helpText() useful.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_response.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: Response plot — tm_g_response","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_response.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"teal module: Response plot — tm_g_response","text":"examples, please see vignette \"Using response plot\" via vignette(\"using-response-plot\", package = \"teal.modules.general\").","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_response.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: Response plot — tm_g_response","text":"","code":"# general data example library(teal.widgets) data <- teal_data() data <- within(data, { require(nestcolor) mtcars <- mtcars for (v in c(\"cyl\", \"vs\", \"am\", \"gear\")) { mtcars[[v]] <- as.factor(mtcars[[v]]) } }) datanames(data) <- \"mtcars\" app <- init( data = data, modules = modules( tm_g_response( label = \"Response Plots\", response = data_extract_spec( dataname = \"mtcars\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"mtcars\"]], c(\"cyl\", \"gear\")), selected = \"cyl\", multiple = FALSE, fixed = FALSE ) ), x = data_extract_spec( dataname = \"mtcars\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"mtcars\"]], c(\"vs\", \"am\")), selected = \"vs\", multiple = FALSE, fixed = FALSE ) ), ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Response Module\") ) ) ) ) #> Initializing tm_g_response if (interactive()) { shinyApp(app$ui, app$server) } # CDISC data example library(teal.widgets) data <- teal_data() data <- within(data, { require(nestcolor) ADSL <- rADSL }) datanames(data) <- c(\"ADSL\") join_keys(data) <- default_cdisc_join_keys[datanames(data)] app <- init( data = data, modules = modules( tm_g_response( label = \"Response Plots\", response = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"BMRKR2\", \"COUNTRY\")), selected = \"BMRKR2\", multiple = FALSE, fixed = FALSE ) ), x = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"SEX\", \"RACE\")), selected = \"RACE\", multiple = FALSE, fixed = FALSE ) ), ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Response Module\") ) ) ) ) #> Initializing tm_g_response if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_scatterplot.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: Scatterplot — tm_g_scatterplot","title":"teal module: Scatterplot — tm_g_scatterplot","text":"Generates customizable scatterplot using ggplot2. module allows users select variables x y axes, color size encodings, faceting options, . supports log transformations, trend line additions, dynamic adjustments point opacity size UI controls.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_scatterplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: Scatterplot — tm_g_scatterplot","text":"","code":"tm_g_scatterplot( label = \"Scatterplot\", x, y, color_by = NULL, size_by = NULL, row_facet = NULL, col_facet = NULL, plot_height = c(600, 200, 2000), plot_width = NULL, alpha = c(1, 0, 1), shape = shape_names, size = c(5, 1, 15), max_deg = 5L, rotate_xaxis_labels = FALSE, ggtheme = c(\"gray\", \"bw\", \"linedraw\", \"light\", \"dark\", \"minimal\", \"classic\", \"void\"), pre_output = NULL, post_output = NULL, table_dec = 4, ggplot2_args = teal.widgets::ggplot2_args() )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_scatterplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: Scatterplot — tm_g_scatterplot","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. x (data_extract_spec list multiple data_extract_spec) Specifies variable names selected plot along x-axis default. y (data_extract_spec list multiple data_extract_spec) Specifies variable names selected plot along y-axis default. color_by (data_extract_spec list multiple data_extract_spec) optional, defines color encoding. NULL color encoding option displayed. size_by (data_extract_spec list multiple data_extract_spec) optional, defines point size encoding. NULL size encoding option displayed. row_facet (data_extract_spec list multiple data_extract_spec) optional, specifies variable(s) faceting rows. col_facet (data_extract_spec list multiple data_extract_spec) optional, specifies variable(s) faceting columns. plot_height (numeric) optional, specifies plot height three-element vector value, min, max intended use slider UI element. plot_width (numeric) optional, specifies plot width three-element vector value, min, max slider encoding plot width. alpha (integer(1) integer(3)) optional, specifies point opacity. length alpha one: plot points fixed opacity. length alpha three: plot points opacity dynamically adjusted based vector value, min, max. shape (character) optional, character vector names shape, e.g. c(\"triangle\", \"square\", \"circle\"). defaults shape_names. complete list vignette(\"ggplot2-specs\", package=\"ggplot2\"). size (integer(1) integer(3)) optional, specifies point size. length size one: plot point sizes fixed size. length size three: plot points size dynamically adjusted based vector value, min, max. max_deg (integer) optional, maximum degree polynomial trend line. Must less 1. rotate_xaxis_labels (logical) optional, whether rotate plot X axis labels. rotate default (FALSE). ggtheme (character) optional, ggplot2 theme used default. Defaults \"gray\". pre_output (shiny.tag) optional, text UI element displayed module's output, providing context title. text placed output put output context. example title. post_output (shiny.tag) optional, text UI element displayed module's output, adding context instructions. Elements like shiny::helpText() useful. table_dec (integer) optional, number decimal places used round numeric values table. ggplot2_args (ggplot2_args) object created teal.widgets::ggplot2_args() settings module plot. argument merged options variable teal.ggplot2_args default module setup. details see vignette: vignette(\"custom-ggplot2-arguments\", package = \"teal.widgets\")","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_scatterplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: Scatterplot — tm_g_scatterplot","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_scatterplot.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"teal module: Scatterplot — tm_g_scatterplot","text":"examples, please see vignette \"Using scatterplot\" via vignette(\"using-scatterplot\", package = \"teal.modules.general\").","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_scatterplot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: Scatterplot — tm_g_scatterplot","text":"","code":"library(teal.widgets) # general data example data <- teal_data() data <- within(data, { require(nestcolor) CO2 <- CO2 }) datanames(data) <- \"CO2\" app <- init( data = data, modules = modules( tm_g_scatterplot( label = \"Scatterplot Choices\", x = data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"CO2\"]], c(\"conc\", \"uptake\")), selected = \"conc\", multiple = FALSE, fixed = FALSE ) ), y = data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"CO2\"]], c(\"conc\", \"uptake\")), selected = \"uptake\", multiple = FALSE, fixed = FALSE ) ), color_by = data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variable:\", choices = variable_choices( data[[\"CO2\"]], c(\"Plant\", \"Type\", \"Treatment\", \"conc\", \"uptake\") ), selected = NULL, multiple = FALSE, fixed = FALSE ) ), size_by = data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"CO2\"]], c(\"conc\", \"uptake\")), selected = \"uptake\", multiple = FALSE, fixed = FALSE ) ), row_facet = data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"CO2\"]], c(\"Plant\", \"Type\", \"Treatment\")), selected = NULL, multiple = FALSE, fixed = FALSE ) ), col_facet = data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"CO2\"]], c(\"Plant\", \"Type\", \"Treatment\")), selected = NULL, multiple = FALSE, fixed = FALSE ) ), ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Scatterplot Module\") ) ) ) ) #> Initializing tm_g_scatterplot if (interactive()) { shinyApp(app$ui, app$server) } # CDISC data example data <- teal_data() data <- within(data, { require(nestcolor) ADSL <- rADSL }) datanames(data) <- c(\"ADSL\") join_keys(data) <- default_cdisc_join_keys[datanames(data)] app <- init( data = data, modules = modules( tm_g_scatterplot( label = \"Scatterplot Choices\", x = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"BMRKR1\", \"BMRKR2\")), selected = \"AGE\", multiple = FALSE, fixed = FALSE ) ), y = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"BMRKR1\", \"BMRKR2\")), selected = \"BMRKR1\", multiple = FALSE, fixed = FALSE ) ), color_by = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices( data[[\"ADSL\"]], c(\"AGE\", \"BMRKR1\", \"BMRKR2\", \"RACE\", \"REGION1\") ), selected = NULL, multiple = FALSE, fixed = FALSE ) ), size_by = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"BMRKR1\")), selected = \"AGE\", multiple = FALSE, fixed = FALSE ) ), row_facet = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"BMRKR2\", \"RACE\", \"REGION1\")), selected = NULL, multiple = FALSE, fixed = FALSE ) ), col_facet = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"BMRKR2\", \"RACE\", \"REGION1\")), selected = NULL, multiple = FALSE, fixed = FALSE ) ), ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Scatterplot Module\") ) ) ) ) #> Initializing tm_g_scatterplot if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_scatterplotmatrix.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: Scatterplot matrix — tm_g_scatterplotmatrix","title":"teal module: Scatterplot matrix — tm_g_scatterplotmatrix","text":"Generates scatterplot matrix selected variables datasets. plot within matrix represents relationship two variables, providing overview correlations distributions across selected data.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_scatterplotmatrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: Scatterplot matrix — tm_g_scatterplotmatrix","text":"","code":"tm_g_scatterplotmatrix( label = \"Scatterplot Matrix\", variables, plot_height = c(600, 200, 2000), plot_width = NULL, pre_output = NULL, post_output = NULL )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_scatterplotmatrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: Scatterplot matrix — tm_g_scatterplotmatrix","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. variables (data_extract_spec list multiple data_extract_spec) Specifies plotting variables incoming dataset filtering selecting. case data_extract_spec use select_spec(..., ordered = TRUE) plot elements rendered according selection order. plot_height (numeric) optional, specifies plot height three-element vector value, min, max intended use slider UI element. plot_width (numeric) optional, specifies plot width three-element vector value, min, max slider encoding plot width. pre_output (shiny.tag) optional, text UI element displayed module's output, providing context title. text placed output put output context. example title. post_output (shiny.tag) optional, text UI element displayed module's output, adding context instructions. Elements like shiny::helpText() useful.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_scatterplotmatrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: Scatterplot matrix — tm_g_scatterplotmatrix","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_scatterplotmatrix.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"teal module: Scatterplot matrix — tm_g_scatterplotmatrix","text":"examples, please see vignette \"Using scatterplot matrix\" via vignette(\"using-scatterplot-matrix\", package = \"teal.modules.general\").","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_g_scatterplotmatrix.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: Scatterplot matrix — tm_g_scatterplotmatrix","text":"","code":"# general data example data <- teal_data() data <- within(data, { countries <- data.frame( id = c(\"DE\", \"FR\", \"IT\", \"ES\", \"PT\", \"GR\", \"NL\", \"BE\", \"LU\", \"AT\"), government = factor( c(2, 2, 2, 1, 2, 2, 1, 1, 1, 2), labels = c(\"Monarchy\", \"Republic\") ), language_family = factor( c(1, 3, 3, 3, 3, 2, 1, 1, 3, 1), labels = c(\"Germanic\", \"Hellenic\", \"Romance\") ), population = c(83, 67, 60, 47, 10, 11, 17, 11, 0.6, 9), area = c(357, 551, 301, 505, 92, 132, 41, 30, 2.6, 83), gdp = c(3.4, 2.7, 2.1, 1.4, 0.3, 0.2, 0.7, 0.5, 0.1, 0.4), debt = c(2.1, 2.3, 2.4, 2.6, 2.3, 2.4, 2.3, 2.4, 2.3, 2.4) ) sales <- data.frame( id = 1:50, country_id = sample( c(\"DE\", \"FR\", \"IT\", \"ES\", \"PT\", \"GR\", \"NL\", \"BE\", \"LU\", \"AT\"), size = 50, replace = TRUE ), year = sort(sample(2010:2020, 50, replace = TRUE)), venue = sample(c(\"small\", \"medium\", \"large\", \"online\"), 50, replace = TRUE), cancelled = sample(c(TRUE, FALSE), 50, replace = TRUE), quantity = rnorm(50, 100, 20), costs = rnorm(50, 80, 20), profit = rnorm(50, 20, 10) ) }) datanames(data) <- c(\"countries\", \"sales\") join_keys(data) <- join_keys( join_key(\"countries\", \"countries\", \"id\"), join_key(\"sales\", \"sales\", \"id\"), join_key(\"countries\", \"sales\", c(\"id\" = \"country_id\")) ) app <- init( data = data, modules = modules( tm_g_scatterplotmatrix( label = \"Scatterplot matrix\", variables = list( data_extract_spec( dataname = \"countries\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"countries\"]]), selected = c(\"area\", \"gdp\", \"debt\"), multiple = TRUE, ordered = TRUE, fixed = FALSE ) ), data_extract_spec( dataname = \"sales\", filter = filter_spec( label = \"Select variable:\", vars = \"country_id\", choices = value_choices(data[[\"sales\"]], \"country_id\"), selected = c(\"DE\", \"FR\", \"IT\", \"ES\", \"PT\", \"GR\", \"NL\", \"BE\", \"LU\", \"AT\"), multiple = TRUE ), select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"sales\"]], c(\"quantity\", \"costs\", \"profit\")), selected = c(\"quantity\", \"costs\", \"profit\"), multiple = TRUE, ordered = TRUE, fixed = FALSE ) ) ) ) ) ) #> Initializing tm_g_scatterplotmatrix if (interactive()) { shinyApp(app$ui, app$server) } # CDISC data example data <- teal_data() data <- within(data, { ADSL <- rADSL ADRS <- rADRS }) datanames(data) <- c(\"ADSL\", \"ADRS\") join_keys(data) <- default_cdisc_join_keys[datanames(data)] app <- init( data = data, modules = modules( tm_g_scatterplotmatrix( label = \"Scatterplot matrix\", variables = list( data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADSL\"]]), selected = c(\"AGE\", \"RACE\", \"SEX\"), multiple = TRUE, ordered = TRUE, fixed = FALSE ) ), data_extract_spec( dataname = \"ADRS\", filter = filter_spec( label = \"Select endpoints:\", vars = c(\"PARAMCD\", \"AVISIT\"), choices = value_choices(data[[\"ADRS\"]], c(\"PARAMCD\", \"AVISIT\"), c(\"PARAM\", \"AVISIT\")), selected = \"INVET - END OF INDUCTION\", multiple = TRUE ), select = select_spec( label = \"Select variables:\", choices = variable_choices(data[[\"ADRS\"]]), selected = c(\"AGE\", \"AVAL\", \"ADY\"), multiple = TRUE, ordered = TRUE, fixed = FALSE ) ) ) ) ) ) #> Initializing tm_g_scatterplotmatrix if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_missing_data.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: Missing data analysis — tm_missing_data","title":"teal module: Missing data analysis — tm_missing_data","text":"module analyzes missing data data.frames help users explore missing observations gain insights completeness data. useful clinical data analysis within context CDISC standards adaptable general data analysis purposes.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_missing_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: Missing data analysis — tm_missing_data","text":"","code":"tm_missing_data( label = \"Missing data\", plot_height = c(600, 400, 5000), plot_width = NULL, parent_dataname = \"ADSL\", ggtheme = c(\"classic\", \"gray\", \"bw\", \"linedraw\", \"light\", \"dark\", \"minimal\", \"void\"), ggplot2_args = list(`Combinations Hist` = teal.widgets::ggplot2_args(labs = list(caption = NULL)), `Combinations Main` = teal.widgets::ggplot2_args(labs = list(title = NULL))), pre_output = NULL, post_output = NULL )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_missing_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: Missing data analysis — tm_missing_data","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. plot_height (numeric) optional, specifies plot height three-element vector value, min, max intended use slider UI element. plot_width (numeric) optional, specifies plot width three-element vector value, min, max slider encoding plot width. parent_dataname (character(1)) Specifies parent dataset name. Default ADSL CDISC data. provided exists, enables additional analysis \"subject\". non-CDISC data, parameter can ignored. ggtheme (character) optional, specifies default ggplot2 theme plots. Defaults classic. ggplot2_args (ggplot2_args) optional, object created teal.widgets::ggplot2_args() settings plots named list ggplot2_args objects plot-specific settings. argument merged options variable teal.ggplot2_args default module setup. List names match following: c(\"default\", \"Summary Obs\", \"Summary Patients\", \"Combinations Main\", \"Combinations Hist\", \"Subject\"). details see vignette: vignette(\"custom-ggplot2-arguments\", package = \"teal.widgets\"). pre_output (shiny.tag) optional, text UI element displayed module's output, providing context title. text placed output put output context. example title. post_output (shiny.tag) optional, text UI element displayed module's output, adding context instructions. Elements like shiny::helpText() useful.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_missing_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: Missing data analysis — tm_missing_data","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_missing_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: Missing data analysis — tm_missing_data","text":"","code":"library(teal.widgets) # module specification used in apps below tm_missing_data_module <- tm_missing_data( ggplot2_args = list( \"Combinations Hist\" = ggplot2_args( labs = list(subtitle = \"Plot produced by Missing Data Module\", caption = NULL) ), \"Combinations Main\" = ggplot2_args(labs = list(title = NULL)) ) ) #> Initializing tm_missing_data # general example data data <- teal_data() data <- within(data, { require(nestcolor) add_nas <- function(x) { x[sample(seq_along(x), floor(length(x) * runif(1, .05, .17)))] <- NA x } iris <- iris mtcars <- mtcars iris[] <- lapply(iris, add_nas) mtcars[] <- lapply(mtcars, add_nas) mtcars[[\"cyl\"]] <- as.factor(mtcars[[\"cyl\"]]) mtcars[[\"gear\"]] <- as.factor(mtcars[[\"gear\"]]) }) datanames(data) <- c(\"iris\", \"mtcars\") app <- init( data = data, modules = modules(tm_missing_data_module) ) #> Warning: 'package:nestcolor' may not be available when loading if (interactive()) { shinyApp(app$ui, app$server) } # CDISC example data data <- teal_data() data <- within(data, { require(nestcolor) ADSL <- rADSL ADRS <- rADRS }) datanames(data) <- c(\"ADSL\", \"ADRS\") join_keys(data) <- default_cdisc_join_keys[datanames(data)] app <- init( data = data, modules = modules(tm_missing_data_module) ) if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_outliers.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: Outliers analysis — tm_outliers","title":"teal module: Outliers analysis — tm_outliers","text":"Module analyze identify outliers using different methods IQR, Z-score, Percentiles, offers visualizations including box plots, density plots, cumulative distribution plots help interpret outliers.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_outliers.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: Outliers analysis — tm_outliers","text":"","code":"tm_outliers( label = \"Outliers Module\", outlier_var, categorical_var = NULL, ggtheme = c(\"gray\", \"bw\", \"linedraw\", \"light\", \"dark\", \"minimal\", \"classic\", \"void\"), ggplot2_args = teal.widgets::ggplot2_args(), plot_height = c(600, 200, 2000), plot_width = NULL, pre_output = NULL, post_output = NULL )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_outliers.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: Outliers analysis — tm_outliers","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. outlier_var (data_extract_spec list multiple data_extract_spec) Specifies variable(s) analyzed outliers. categorical_var (data_extract_spec list multiple data_extract_spec) optional, specifies categorical variable(s) split selected outlier variables . ggtheme (character) optional, ggplot2 theme used default. Defaults \"gray\". ggplot2_args (ggplot2_args) optional, object created teal.widgets::ggplot2_args() settings plots named list ggplot2_args objects plot-specific settings. argument merged options variable teal.ggplot2_args default module setup. List names match following: c(\"default\", \"Boxplot\",\"Density Plot\",\"Cumulative Distribution Plot\"). details see vignette: vignette(\"custom-ggplot2-arguments\", package = \"teal.widgets\"). plot_height (numeric) optional, specifies plot height three-element vector value, min, max intended use slider UI element. plot_width (numeric) optional, specifies plot width three-element vector value, min, max slider encoding plot width. pre_output (shiny.tag) optional, text UI element displayed module's output, providing context title. text placed output put output context. example title. post_output (shiny.tag) optional, text UI element displayed module's output, adding context instructions. Elements like shiny::helpText() useful.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_outliers.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: Outliers analysis — tm_outliers","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_outliers.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: Outliers analysis — tm_outliers","text":"","code":"library(teal.widgets) # general data example data <- teal_data() data <- within(data, { CO2 <- CO2 CO2[[\"primary_key\"]] <- seq_len(nrow(CO2)) }) datanames(data) <- \"CO2\" join_keys(data) <- join_keys(join_key(\"CO2\", \"CO2\", \"primary_key\")) vars <- choices_selected(variable_choices(data[[\"CO2\"]], c(\"Plant\", \"Type\", \"Treatment\"))) app <- init( data = data, modules = modules( tm_outliers( outlier_var = list( data_extract_spec( dataname = \"CO2\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"CO2\"]], c(\"conc\", \"uptake\")), selected = \"uptake\", multiple = FALSE, fixed = FALSE ) ) ), categorical_var = list( data_extract_spec( dataname = \"CO2\", filter = filter_spec( vars = vars, choices = value_choices(data[[\"CO2\"]], vars$selected), selected = value_choices(data[[\"CO2\"]], vars$selected), multiple = TRUE ) ) ), ggplot2_args = list( ggplot2_args( labs = list(subtitle = \"Plot generated by Outliers Module\") ) ) ) ) ) #> Initializing tm_outliers if (interactive()) { shinyApp(app$ui, app$server) } # CDISC data example data <- teal_data() data <- within(data, { ADSL <- rADSL }) datanames(data) <- \"ADSL\" join_keys(data) <- default_cdisc_join_keys[datanames(data)] fact_vars_adsl <- names(Filter(isTRUE, sapply(data[[\"ADSL\"]], is.factor))) vars <- choices_selected(variable_choices(data[[\"ADSL\"]], fact_vars_adsl)) app <- init( data = data, modules = modules( tm_outliers( outlier_var = list( data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], c(\"AGE\", \"BMRKR1\")), selected = \"AGE\", multiple = FALSE, fixed = FALSE ) ) ), categorical_var = list( data_extract_spec( dataname = \"ADSL\", filter = filter_spec( vars = vars, choices = value_choices(data[[\"ADSL\"]], vars$selected), selected = value_choices(data[[\"ADSL\"]], vars$selected), multiple = TRUE ) ) ), ggplot2_args = list( ggplot2_args( labs = list(subtitle = \"Plot generated by Outliers Module\") ) ) ) ) ) #> Initializing tm_outliers if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_t_crosstable.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: Cross-table — tm_t_crosstable","title":"teal module: Cross-table — tm_t_crosstable","text":"Generates simple cross-table two variables dataset custom options showing percentages sub-totals.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_t_crosstable.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: Cross-table — tm_t_crosstable","text":"","code":"tm_t_crosstable( label = \"Cross Table\", x, y, show_percentage = TRUE, show_total = TRUE, pre_output = NULL, post_output = NULL, basic_table_args = teal.widgets::basic_table_args() )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_t_crosstable.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: Cross-table — tm_t_crosstable","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. x (data_extract_spec list multiple data_extract_spec) Object available choices pre-selected option variable X - row values. case data_extract_spec use select_spec(..., ordered = TRUE) table elements rendered according selection order. y (data_extract_spec list multiple data_extract_spec) Object available choices pre-selected option variable Y - column values. data_extract_spec must allow multiple selection case. show_percentage (logical(1)) Indicates whether show percentages (relevant x factor). Defaults TRUE. show_total (logical(1)) Indicates whether show total column. Defaults TRUE. pre_output (shiny.tag) optional, text UI element displayed module's output, providing context title. text placed output put output context. example title. post_output (shiny.tag) optional, text UI element displayed module's output, adding context instructions. Elements like shiny::helpText() useful. basic_table_args (basic_table_args) object created teal.widgets::basic_table_args() settings module table. argument merged options variable teal.basic_table_args default module setup. details see vignette: vignette(\"custom-basic-table-arguments\", package = \"teal.widgets\")","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_t_crosstable.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: Cross-table — tm_t_crosstable","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_t_crosstable.html","id":"note","dir":"Reference","previous_headings":"","what":"Note","title":"teal module: Cross-table — tm_t_crosstable","text":"examples, please see vignette \"Using cross table\" via vignette(\"using-cross-table\", package = \"teal.modules.general\").","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_t_crosstable.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: Cross-table — tm_t_crosstable","text":"","code":"# general data example library(teal.widgets) data <- teal_data() data <- within(data, { mtcars <- mtcars for (v in c(\"cyl\", \"vs\", \"am\", \"gear\")) { mtcars[[v]] <- as.factor(mtcars[[v]]) } mtcars[[\"primary_key\"]] <- seq_len(nrow(mtcars)) }) datanames(data) <- \"mtcars\" join_keys(data) <- join_keys(join_key(\"mtcars\", \"mtcars\", \"primary_key\")) app <- init( data = data, modules = modules( tm_t_crosstable( label = \"Cross Table\", x = data_extract_spec( dataname = \"mtcars\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"mtcars\"]], c(\"cyl\", \"vs\", \"am\", \"gear\")), selected = c(\"cyl\", \"gear\"), multiple = TRUE, ordered = TRUE, fixed = FALSE ) ), y = data_extract_spec( dataname = \"mtcars\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"mtcars\"]], c(\"cyl\", \"vs\", \"am\", \"gear\")), selected = \"vs\", multiple = FALSE, fixed = FALSE ) ), basic_table_args = basic_table_args( subtitles = \"Table generated by Crosstable Module\" ) ) ) ) #> Initializing tm_t_crosstable if (interactive()) { shinyApp(app$ui, app$server) } # CDISC data example library(teal.widgets) data <- teal_data() data <- within(data, { ADSL <- rADSL }) datanames(data) <- \"ADSL\" join_keys(data) <- default_cdisc_join_keys[datanames(data)] app <- init( data = data, modules = modules( tm_t_crosstable( label = \"Cross Table\", x = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], subset = function(data) { idx <- !vapply(data, inherits, logical(1), c(\"Date\", \"POSIXct\", \"POSIXlt\")) return(names(data)[idx]) }), selected = \"COUNTRY\", multiple = TRUE, ordered = TRUE, fixed = FALSE ) ), y = data_extract_spec( dataname = \"ADSL\", select = select_spec( label = \"Select variable:\", choices = variable_choices(data[[\"ADSL\"]], subset = function(data) { idx <- vapply(data, is.factor, logical(1)) return(names(data)[idx]) }), selected = \"SEX\", multiple = FALSE, fixed = FALSE ) ), basic_table_args = basic_table_args( subtitles = \"Table generated by Crosstable Module\" ) ) ) ) #> Initializing tm_t_crosstable if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_variable_browser.html","id":null,"dir":"Reference","previous_headings":"","what":"teal module: Variable browser — tm_variable_browser","title":"teal module: Variable browser — tm_variable_browser","text":"Module provides provides detailed summary visualization variable distributions data.frame objects, interactive features customize analysis.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_variable_browser.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"teal module: Variable browser — tm_variable_browser","text":"","code":"tm_variable_browser( label = \"Variable Browser\", datasets_selected = character(0), parent_dataname = \"ADSL\", pre_output = NULL, post_output = NULL, ggplot2_args = teal.widgets::ggplot2_args() )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_variable_browser.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"teal module: Variable browser — tm_variable_browser","text":"label (character(1)) Label shown navigation item module module group. modules() defaults \"root\". See Details. datasets_selected (character) vector datasets shown, order. Names must correspond datasets names. vector length zero (default) datasets shown. Note: data.frame objects compatible; using types cause error. parent_dataname (character(1)) string specifying parent dataset. exists datasets_selectedthen extra checkbox shown allow users show variables datasets exist dataname. typically used remove ADSL columns CDISC data. non CDISC data can ignored. Defaults \"ADSL\". pre_output (shiny.tag) optional, text UI element displayed module's output, providing context title. text placed output put output context. example title. post_output (shiny.tag) optional, text UI element displayed module's output, adding context instructions. Elements like shiny::helpText() useful. ggplot2_args (ggplot2_args) object created teal.widgets::ggplot2_args() settings module plot. argument merged options variable teal.ggplot2_args default module setup. details see vignette: vignette(\"custom-ggplot2-arguments\", package = \"teal.widgets\")","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_variable_browser.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"teal module: Variable browser — tm_variable_browser","text":"Object class teal_module used teal applications.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_variable_browser.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"teal module: Variable browser — tm_variable_browser","text":"Numeric columns fewer 30 distinct values can treated either discrete continuous checkbox allowing users switch treated(< 6 unique values default discrete, otherwise continuous).","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/tm_variable_browser.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"teal module: Variable browser — tm_variable_browser","text":"","code":"library(teal.widgets) # Module specification used in apps below tm_variable_browser_module <- tm_variable_browser( label = \"Variable browser\", ggplot2_args = ggplot2_args( labs = list(subtitle = \"Plot generated by Variable Browser Module\") ) ) #> Initializing tm_variable_browser # general data example data <- teal_data() data <- within(data, { iris <- iris mtcars <- mtcars women <- women faithful <- faithful CO2 <- CO2 }) datanames(data) <- c(\"iris\", \"mtcars\", \"women\", \"faithful\", \"CO2\") app <- init( data = data, modules = modules(tm_variable_browser_module) ) if (interactive()) { shinyApp(app$ui, app$server) } # CDISC example data data <- teal_data() data <- within(data, { ADSL <- rADSL ADTTE <- rADTTE }) datanames(data) <- c(\"ADSL\", \"ADTTE\") join_keys(data) <- default_cdisc_join_keys[datanames(data)] app <- init( data = data, modules = modules(tm_variable_browser_module) ) if (interactive()) { shinyApp(app$ui, app$server) }"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/validate_input.html","id":null,"dir":"Reference","previous_headings":"","what":"Validates the variable browser inputs — validate_input","title":"Validates the variable browser inputs — validate_input","text":"Validates variable browser inputs","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/validate_input.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validates the variable browser inputs — validate_input","text":"","code":"validate_input(input, plot_var, data)"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/validate_input.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validates the variable browser inputs — validate_input","text":"input (session$input) shiny session input plot_var (list) list data frame array variable names data (teal_data) datasets passed module","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/validate_input.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Validates the variable browser inputs — validate_input","text":"logical TRUE validations pass; shiny validation error otherwise","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/var_missings_info.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarize NAs. — var_missings_info","title":"Summarize NAs. — var_missings_info","text":"Summarizes occurrence missing values vector.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/var_missings_info.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarize NAs. — var_missings_info","text":"","code":"var_missings_info(x)"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/var_missings_info.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarize NAs. — var_missings_info","text":"x vector type length","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/var_missings_info.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarize NAs. — var_missings_info","text":"Character string describing NA occurrence.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/var_summary_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Summarizes variable — var_summary_table","title":"Summarizes variable — var_summary_table","text":"Creates html summary statistics relevant data type. numeric values returns central tendency measures, factor returns level counts, Date date range, just number levels.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/var_summary_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summarizes variable — var_summary_table","text":"","code":"var_summary_table(x, numeric_as_factor, dt_rows, outlier_definition)"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/var_summary_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summarizes variable — var_summary_table","text":"x vector type numeric_as_factor logical numeric variable treated factor dt_rows numeric current/latest DT page length outlier_definition 0 outliers removed, otherwise outliers (outlier_definition*IQR /Q1/Q3 removed)","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/var_summary_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summarizes variable — var_summary_table","text":"text simple statistics.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/variable_type_icons.html","id":null,"dir":"Reference","previous_headings":"","what":"Get icons to represent variable types in dataset — variable_type_icons","title":"Get icons to represent variable types in dataset — variable_type_icons","text":"Get icons represent variable types dataset","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/variable_type_icons.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get icons to represent variable types in dataset — variable_type_icons","text":"","code":"variable_type_icons(var_type)"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/variable_type_icons.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get icons to represent variable types in dataset — variable_type_icons","text":"var_type (character) R internal types (classes).","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/variable_type_icons.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get icons to represent variable types in dataset — variable_type_icons","text":"(character) vector HTML icons corresponding data type column.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/varname_w_label.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate a string for a variable including its label — varname_w_label","title":"Generate a string for a variable including its label — varname_w_label","text":"Generate string variable including label","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/varname_w_label.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate a string for a variable including its label — varname_w_label","text":"","code":"varname_w_label( var_names, dataset, wrap_width = 80, prefix = NULL, suffix = NULL )"},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/varname_w_label.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate a string for a variable including its label — varname_w_label","text":"var_names (character) Name variable extract labels . dataset (dataset) Name analysis dataset. wrap_width (numeric) Number characters wrap original label . Defaults 80. prefix, suffix (character) String paste beginning/end variable name label.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/reference/varname_w_label.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate a string for a variable including its label — varname_w_label","text":"(character) String variable name label.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"tealmodulesgeneral-030","dir":"Changelog","previous_headings":"","what":"teal.modules.general 0.3.0","title":"teal.modules.general 0.3.0","text":"CRAN release: 2024-03-05","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"enhancements-0-3-0","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"teal.modules.general 0.3.0","text":"Updated package docs vignettes new way specifying data teal::init(). data argument accept teal_data object","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"bug-fixes-0-3-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"teal.modules.general 0.3.0","text":"Outlier labels longer appear bounds tm_a_regression. Fixed bug tm_outliers changing selected variable cause popup.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"miscellaneous-0-3-0","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"teal.modules.general 0.3.0","text":"Removed teal.slice dependencies. Specified minimal version package dependencies.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"breaking-changes-0-2-16","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"teal.modules.general 0.2.16","text":"Replaced chunks simpler qenv class. Replaced datasets argument containing FilteredData new arguments data (tdata object) filter_panel_api (FilterPanelAPI).","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"enhancements-0-2-16","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"teal.modules.general 0.2.16","text":"Added parent_dataname argument tm_variable_browser tm_missing_data allow specification parent dataset modules. Improved UI labels plot panel title tm_g_association. Added inputs tm_variable_browser module text size plot theme. Forced ggplot theme always selected modules. Updated encodings input checks use shinyvalidate::InputValidator instead shiny::validate better UI experience.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"bug-fixes-0-2-16","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"teal.modules.general 0.2.16","text":"Fixed bug tm_g_scatterplot selected x y facets . Fixed bug tm_g_distribution plot theoretical distribution newer ggplot2 version. Fixed bug tm_g_bivariate adding lines checkbox available one x y deselected. Fixed bug tm_variable_browser changing filters reset selected variable first list.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"miscellaneous-0-2-16","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"teal.modules.general 0.2.16","text":"Removed scda package dependency examples. Replaced deprecated ggplot2 functions ..count.., ..density.. ..prop... Version bump forcats dependency.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"enhancements-0-2-15","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"teal.modules.general 0.2.15","text":"Added teal.reporter functionality modules. Implemented nestcolor examples, refactored tm_a_pca tm_missing_data allow using nestcolor. Added log transformation options tm_g_scatterplot. Added server_rendering flag tm_data_table control whether table rendered server client side.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"bug-fixes-0-2-15","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"teal.modules.general 0.2.15","text":"Fixed overflow wide tm_a_pca tables. Fixed join type functionality tm_t_crosstable. Fixed bug tm_missing_data selecting variables missings. Fixed bug tm_missing_data using any_na.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"miscellaneous-0-2-15","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"teal.modules.general 0.2.15","text":"Moved packages used one module Imports Suggests DESCRIPTION file. Moved magrittr package Depends Imports DESCRIPTION file.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"enhancements-0-2-14","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"teal.modules.general 0.2.14","text":"New teal module tm_front_page simplify creating front page teal apps. Added slider widget control font size label tm_g_scatterplot. Output integers without decimal places selecting points tm_g_scatterplot. Improved names code chunks shown Debug Info. Improved validation message number regressors big tm_a_regression.R.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"bug-fixes-0-2-14","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"teal.modules.general 0.2.14","text":"Fixed wrong validation tm_a_pca. Fixed crash deselecting categorical factor tm_outliers.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"miscellaneous-0-2-14","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"teal.modules.general 0.2.14","text":"Added template pkgdown site. Updated package authors.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"enhancements-0-2-13","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"teal.modules.general 0.2.13","text":"Rewrote modules use moduleServer updated call plot_with_settings_srv changes teal.devel. Fixed tracking selection order data_extract_ui. selectors can return ordered selection one specifies ordered = TRUE select_spec. Switched order tm_missing_data combination plot show data descending order. Changed variable levels output tm_missing_data allow numerical sorting.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"miscellaneous-0-2-13","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"teal.modules.general 0.2.13","text":"Removed unneeded n row tm_t_crosstable. Replaced calls teal::root_modules teal::modules following deprecation teal::root_modules. Adjusted package imports take account changes teal framework. Added “Getting started teal.modules.general” vignette. Updated README file. Moved ggmosaic package Depends Imports DESCRIPTION file.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"enhancements-0-2-12","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"teal.modules.general 0.2.12","text":"Added support logging logger package added info level logs upon initialization module. Added support custom arguments ggplot2::labs ggplot2::theme plot based modules. Added support custom arguments rtables::basic_table tm_t_crosstable. Updated tm_outliers, tm_g_scatterplotmatrix, tm_g_association, tm_t_crosstable modules adopt new teal.transform::data_merge_srv teal.transform::data_extract_multiple_srv modules. Distinguished bars representing NA plot depicting counts tm_variable_browser different color fill. Modified summary statistics table numeric columns tm_variable_browser reflect plot outliers removed. Added option remove missing values tm_variable_browser histogram factor like variables. Added ability sort Variable Type tm_variable_browser separating variable type icons column. Updated Grouped Subject tab tm_missing_data module present data way Summary tab . Added support NA level grouping variable variable levels table tm_missing_data module. Added informative labels level grouping variable variable levels table tm_missing_data module. Added checkbox tm_g_scatterplot toggle option free x y axis scales whenever faceting arguments provided. Used browser-side processing tm_data_table Buttons extension download full table. Added example advanced usage DT module.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"bug-fixes-0-2-12","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"teal.modules.general 0.2.12","text":"Fixed error tm_variable_browser selected column logical(1). Fixed bugs modules’ vignette examples.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"miscellaneous-0-2-12","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"teal.modules.general 0.2.12","text":"Added R version requirement R >= 3.6. Removed input_id argument teal.transform::data_merge_module calls modules. Refactored defunct teal.devel::data_extract_input replacement teal.transform::data_extract_ui. Updated teal.transform::data_merge_srv use dplyr::inner_join instead dplyr::left_join tm_outliers module. Removed overlay statistics table tm_g_distribution module. Removed dependency test.nest package. Removed dependency utils.nest package replaced functions equivalents checkmate package.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"new-features-0-2-11","dir":"Changelog","previous_headings":"","what":"New features","title":"teal.modules.general 0.2.11","text":"new module, tm_file_viewer, added visualization static files. new module, tm_g_distribution, added distribution analysis.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"bug-fixes-0-2-11","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"teal.modules.general 0.2.11","text":"Fixed bar plot order factor variables tm_variable_browser.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"tm_variable_browser-0-2-11","dir":"Changelog","previous_headings":"Enhancements","what":"tm_variable_browser","title":"teal.modules.general 0.2.11","text":"Added sparkline support Date/POSIXct/POSIXlt variable types. Fixed histograms contain least two bars.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"tm_outliers-0-2-11","dir":"Changelog","previous_headings":"Enhancements","what":"tm_outliers","title":"teal.modules.general 0.2.11","text":"Added filter_spec support. Simplified returned R code. Support select_spec categorical variables.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"tm_g_scatterplotmatrix-0-2-11","dir":"Changelog","previous_headings":"Enhancements","what":"tm_g_scatterplotmatrix","title":"teal.modules.general 0.2.11","text":"Added ordered select input support. Decoupled dataset list variables list parameters.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"tm_g_association-0-2-11","dir":"Changelog","previous_headings":"Enhancements","what":"tm_g_association","title":"teal.modules.general 0.2.11","text":"Added ordered select input support.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"tm_t_crosstable-0-2-11","dir":"Changelog","previous_headings":"Enhancements","what":"tm_t_crosstable","title":"teal.modules.general 0.2.11","text":"Added ordered select input support.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"tm_g_scatterplot-0-2-11","dir":"Changelog","previous_headings":"Enhancements","what":"tm_g_scatterplot","title":"teal.modules.general 0.2.11","text":"Modified trend line feature scatterplot display raw equation equal actual y value actual x values plugged .","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"miscellaneous-0-2-11","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"teal.modules.general 0.2.11","text":"Updated LICENCE README new package references. Added error_on_lint: TRUE .lintr. Updated quantile type = 2 argument tm_variable_browser line STREAM.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"enhancements-0-2-10","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"teal.modules.general 0.2.10","text":"Refactored internal code modules optimize performance. Refactored tm_g_scatterplot compute trend line statistics using ggpmisc::stat_poly_eq simplify code labels group plot. Refactored tm_g_scatterplot enable filtering via Encoding Panel using new filter_spec functionality. Added support logical variables tm_variable_browser. Updated tm_outliers handle non-CDISC datasets. Updated UI tm_variable_browser factor character variables 30 levels. Updated sparklines remove NA values numeric variables. Added checkbox display number observations plot tm_g_scatterplot. Added validation statements tm_g_scatterplot handling users choosing one facet variable. Improved performance tm_variable_browser efficient sparkline generation. Added html code type datasets labels inside tm_missing_data. Improved styling tool tips tm_a_regression, tm_missing_data tm_variable_browser.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"enhancements-0-2-9","dir":"Changelog","previous_headings":"","what":"Enhancements","title":"teal.modules.general 0.2.9","text":"Added download, enlarge resize graph options tm_variable_browser module. Added download expand options table tm_t_crosstable. Enabled selection custom datasets tm_variable_browser datasets_selected argument. Allowed dataset variables used grouping variables variable levels tab tm_missing_data, just selected displayed rows table. Enabled brushing points NA categories facetting tm_g_scatterplot.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"bug-fixes-0-2-9","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"teal.modules.general 0.2.9","text":"Added persistence number entries displayed data tables. Removed ‘treat variable factor’ checkbox tm_variable_browser graph shown. Fixed DT length reset tm_variable_browser “Show parent dataset variables” checkbox tick.","code":""},{"path":[]},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"tm_outliers-0-2-8","dir":"Changelog","previous_headings":"New Module","what":"tm_outliers","title":"teal.modules.general 0.2.8","text":"Added new module tm_outliers analyze outliers datasets.","code":""},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"tm_g_scatterplot-0-2-8","dir":"Changelog","previous_headings":"Enhancements","what":"tm_g_scatterplot","title":"teal.modules.general 0.2.8","text":"Added max_deg optional argument scatterplot allow users choose maximum smoothing degree trend line. Added column row facetting functionality scatterplot. Added possibility specify type join tm_t_crosstable. Incorporate missings resulting non inner join.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"bug-fixes-0-2-8","dir":"Changelog","previous_headings":"","what":"Bug Fixes","title":"teal.modules.general 0.2.8","text":"Fixed edge case errors tm_missing_data caused input dataset categorical variables data.frame instead tibble. Fixed tm_a_pca biplot plot coloring factor/character/numeric < 6 values variable. Fixed typo outlier definition tm_variable_browser.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"miscellaneous-0-2-8","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"teal.modules.general 0.2.8","text":"Sparklines longer shown numeric variables 100000 rows. Colors legend plot. Replaced \"Show variables ADSL\" checkbox general \"Show parent dataset variables\". Capitalize default module labels. outputs tm_missing_data conditional data CDISC data. Added pre_output post_output arguments tm_data_table, tm_missing_data, tm_variable_browser. Fixed issue tm_data_table selecting “show distinct rows” variable names non-alphanumeric characters.","code":""},{"path":[]},{"path":[]},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"tm_variable_browser-0-2-7","dir":"Changelog","previous_headings":"Enhancements","what":"tm_variable_browser","title":"teal.modules.general 0.2.7","text":"Introduced sparklines quick, inline variable summaries. Added option remove outliers. Histograms statistics tables now displayed variables type Date, POSIXct POSIXlt. Character variable summary statistics tables now displayed decreasing order occurrences instead alphabetical. Display number non-missing rows n statistics table numeric variables. Added ability treat numeric variables categorical. tm_variable_browser shows 30 50 levels divided 2 columns character/factor variables 30 unique levels.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"tm_a_regression-0-2-7","dir":"Changelog","previous_headings":"Enhancements","what":"tm_a_regression","title":"teal.modules.general 0.2.7","text":"Added support outlier labels tm_a_regression provided additional optional argument, default_outlier_label can used specify default column used label outliers.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"tm_g_scatterplot-0-2-7","dir":"Changelog","previous_headings":"Enhancements","what":"tm_g_scatterplot","title":"teal.modules.general 0.2.7","text":"Implemented trend line. Added marginal density plots. Added option display rug plot axes. Added shape point color arguments. latter users colourpicker::colourInput. Added size_by variable scatterplot allowed point size mapped numeric variable.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"tm_missing_data-0-2-7","dir":"Changelog","previous_headings":"Enhancements","what":"tm_missing_data","title":"teal.modules.general 0.2.7","text":"Added bar chart missing data combination plot. Removed keys missing data combinations plot selected.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"tm_g_bivariate-0-2-7","dir":"Changelog","previous_headings":"Enhancements","what":"tm_g_bivariate","title":"teal.modules.general 0.2.7","text":"Bivariate plots two continuous variables now allow adding lines. Show labels x axis selecting categorical variable fixed issues deselecting variables datasets encodings panel (including addition meaningful warnings).","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"tm_a_pca-0-2-7","dir":"Changelog","previous_headings":"Enhancements","what":"tm_a_pca","title":"teal.modules.general 0.2.7","text":"Updated include Plot settings Plot specific settings.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"miscellaneous-0-2-7","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"teal.modules.general 0.2.7","text":"Integrated is_single_dataset argument data_extract_input function calls simplify encodings panel UI. Moved code argument cdisc_dataset (cdisc_data) examples vignettes. Require ggmosaic version >= 0.3.0.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"tealmodulesgeneral-026","dir":"Changelog","previous_headings":"","what":"teal.modules.general 0.2.6","title":"teal.modules.general 0.2.6","text":"Adds additional plot settings themes tm_g_association, tm_g_response, tm_g_scatterplot, well point size opacity settings tm_g_association tm_g_scatterplot. Replace pickerInput optionalSelectInput tm_data_table. Adds new facet parameter tm_g_bivariate specify whether facet encodings elements visible user default. Replace plot_with_height module new plot_with_settings module. Missing data module now uses chunks get reproducible R code. New theme missing data module new label positioning. tm_data_table: allow developers pass arguments DT::dataTable use pickerInput select variables. tm_g_response new argument count_labels. Counts might assessed frequency plot . Moved missing data module summary plot footer elements tool tip encodings panels. Replaced base plots tm_a_regression ggplot2. Added optional slider adjust width plot_with_settings. Handle Inf data gracefully. tm_variable_browser now outputs summary table factors missing values.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"tealmodulesgeneral-025","dir":"Changelog","previous_headings":"","what":"teal.modules.general 0.2.5","title":"teal.modules.general 0.2.5","text":"Adds ggplot call inside chunks modules. Pass dataname get_rcode_srv. Uses utils function standardize plot label generation. Improved handling variable labels. Removed “Add filter variable” button variable browser module. Scatterplot matrix module now automatically converts characters factors message.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"tealmodulesgeneral-024","dir":"Changelog","previous_headings":"","what":"teal.modules.general 0.2.4","title":"teal.modules.general 0.2.4","text":"New PCA module. Documentation fix cross table module. Display variable labels drop-menu data table (#393). Optional subsetting ordering datasets tm_data_table. Use teal.code::chunks_push_data_merge include merge code reproducibility code. consistent coloring behavior bivariate plots. Updates missing data module. Updated graph axis labels following modules: bivariate plot, missing data, response plot, regression module, scatterplot, scatterplot matrix. Simplify scatterplot matrix display plot within one dataset .","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"tealmodulesgeneral-023","dir":"Changelog","previous_headings":"","what":"teal.modules.general 0.2.3","title":"teal.modules.general 0.2.3","text":"Fixed naming tm_variable_browser. Correlation added Scatter Plot Matrix. Opacity slider added tm_bivariate. Fixes due bug teal.devel#313. Performance enhancements missing data module. Fix bug reactivity scatterplot matrix module.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"tealmodulesgeneral-022","dir":"Changelog","previous_headings":"","what":"teal.modules.general 0.2.2","title":"teal.modules.general 0.2.2","text":"New module summarize missing data. Refactor variable browser module. Correct display density / frequency option bivariate plot.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"tealmodulesgeneral-021","dir":"Changelog","previous_headings":"","what":"teal.modules.general 0.2.1","title":"teal.modules.general 0.2.1","text":"Fix magrittr loading reproducible code.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"tealmodulesgeneral-020","dir":"Changelog","previous_headings":"","what":"teal.modules.general 0.2.0","title":"teal.modules.general 0.2.0","text":"Refactor functions data extract data merge. Create sample_app.R demonstrating teal.modules.general modules. Rename tm_table tm_cross_table. Include show R code modules. Merge tm_cross_table tm_t_percentage_table one module.","code":""},{"path":"https://insightsengineering.github.io/teal.modules.general/main/news/index.html","id":"tealmodulesgeneral-010","dir":"Changelog","previous_headings":"","what":"teal.modules.general 0.1.0","title":"teal.modules.general 0.1.0","text":"Initial release.","code":""}]