style files. update description

insightsengineering · Jun 21, 2024 · 821a4bd · 821a4bd
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -38,7 +38,6 @@ Suggests:
     parameters (>= 0.20.2),
     smd (>= 0.6.6),
     spelling,
-    standalone,
     survey (>= 4.1),
     survival (>= 3.6-4),
     testthat (>= 3.2.0),

diff --git a/R/ard_categorical.survey.design.R b/R/ard_categorical.survey.design.R
@@ -92,7 +92,7 @@ ard_categorical.survey.design <- function(data,
     x = statistic,
     predicate = \(x) all(x %in% accepted_svy_stats),
     error_msg = c("Error in the values of the {.arg statistic} argument.",
-                  i = "Values must be in {.val {accepted_svy_stats}}"
+      i = "Values must be in {.val {accepted_svy_stats}}"
     )
   )
   denominator <- arg_match(denominator)
@@ -235,7 +235,6 @@ check_na_factor_levels <- function(data, variables) {
             inherits(data$variables[[variable]], "factor") ~ fct_expand(data$variables[[variable]], paste("not", variable_lvls)),
             .default = factor(data$variables[[variable]], levels = c(variable_lvls, paste("not", variable_lvls)))
           )
-
       }
       if (inherits(data$variables[[variable]], "logical")) {
         data$variables[[variable]] <- factor(data$variables[[variable]], levels = c(TRUE, FALSE))
@@ -337,9 +336,9 @@ check_na_factor_levels <- function(data, variables) {
         ),
       name =
         str_remove_all(.data$name, "se\\.") %>%
-        str_remove_all("DEff\\.") %>%
-        str_remove_all(by) %>%
-        str_remove_all("`")
+          str_remove_all("DEff\\.") %>%
+          str_remove_all(by) %>%
+          str_remove_all("`")
     ) |>
     tidyr::pivot_wider(names_from = "stat", values_from = "value") |>
     set_names(c("variable_level", "group1_level", "p", "p.std.error", "deff")) |>
@@ -370,9 +369,9 @@ check_na_factor_levels <- function(data, variables) {
         ),
       name =
         str_remove_all(.data$name, "se\\.") %>%
-        str_remove_all("DEff\\.") %>%
-        str_remove_all(variable) %>%
-        str_remove_all("`")
+          str_remove_all("DEff\\.") %>%
+          str_remove_all(variable) %>%
+          str_remove_all("`")
     ) |>
     tidyr::pivot_wider(names_from = "stat", values_from = "value") |>
     set_names(c("group1_level", "variable_level", "p", "p.std.error", "deff")) |>
@@ -414,27 +413,27 @@ check_na_factor_levels <- function(data, variables) {
 
   # add big N and p, then return data frame of results
   switch(denominator,
-         "column" =
-           df_counts |>
-           dplyr::mutate(
-             .by = any_of("group1_level"),
-             N = sum(.data$n),
-             p = .data$n / .data$N
-           ),
-         "row" =
-           df_counts |>
-           dplyr::mutate(
-             .by = any_of("variable_level"),
-             N = sum(.data$n),
-             p = .data$n / .data$N
-           ),
-         "cell" =
-           df_counts |>
-           dplyr::mutate(
-             .by = any_of(c("group1_level", "variable_level")),
-             N = sum(.data$n),
-             p = .data$n / .data$N
-           )
+    "column" =
+      df_counts |>
+        dplyr::mutate(
+          .by = any_of("group1_level"),
+          N = sum(.data$n),
+          p = .data$n / .data$N
+        ),
+    "row" =
+      df_counts |>
+        dplyr::mutate(
+          .by = any_of("variable_level"),
+          N = sum(.data$n),
+          p = .data$n / .data$N
+        ),
+    "cell" =
+      df_counts |>
+        dplyr::mutate(
+          .by = any_of(c("group1_level", "variable_level")),
+          N = sum(.data$n),
+          p = .data$n / .data$N
+        )
   )
 }
 

diff --git a/tests/testthat/test-ard_categorical.survey.design.R b/tests/testthat/test-ard_categorical.survey.design.R
@@ -104,54 +104,53 @@ test_that("ard_categorical.survey.design() works", {
 
 
   # check the calculated stats are correct
-  # expect_equal(
-  #   cards::get_ard_statistics(ard_svy_cat_row, stat_name %in% "n") |> unlist(),
-  #   survey::svymean(x = ~api00, dclus1, na.rm = TRUE)[1] |> unlist(),
-  #   ignore_attr = TRUE
-  # )
-  # expect_equal(
-  #   cards::get_ard_statistics(ard_svy_cat_row, stat_name %in% "N") |> unlist(),
-  #   survey::svyquantile(x = ~api00, dclus1, na.rm = TRUE, quantiles = 0.5)[[1]][1] |> unlist(),
-  #   ignore_attr = TRUE
-  # )
-  # expect_equal(
-  #   cards::get_ard_statistics(ard_svy_cat_row, stat_name %in% "p") |> unlist(),
-  #   dclus1$variables$api00 |> min(na.rm = TRUE),
-  #   ignore_attr = TRUE
-  # )
-  # expect_equal(
-  #   cards::get_ard_statistics(ard_svy_cat_row, stat_name %in% "deff") |> unlist(),
-  #   dclus1$variables$api00 |> max(na.rm = TRUE),
-  #   ignore_attr = TRUE
-  # )
-  # expect_equal(
-  #   cards::get_ard_statistics(ard_svy_cat_row, stat_name %in% "p.std.error") |> unlist(),
-  #   survey::svyvar(x = ~api00, dclus1, na.rm = TRUE)[1] |> unlist(),
-  #   ignore_attr = TRUE
-  # )
-  # expect_equal(
-  #   cards::get_ard_statistics(ard_uni_svy_cont, stat_name %in% "sd") |> unlist(),
-  #   survey::svyvar(x = ~api00, dclus1, na.rm = TRUE)[1] |> unlist() |> sqrt(),
-  #   ignore_attr = TRUE
-  # )
-  # expect_equal(
-  #   cards::get_ard_statistics(ard_uni_svy_cont, stat_name %in% "mean.std.error") |> unlist(),
-  #   survey::svymean(x = ~api00, dclus1, na.rm = TRUE) |> survey::SE() |> unlist(),
-  #   ignore_attr = TRUE
-  # )
-  # expect_equal(
-  #   cards::get_ard_statistics(ard_uni_svy_cont, stat_name %in% "deff") |> unlist(),
-  #   survey::svymean(x = ~api00, dclus1, na.rm = TRUE, deff = TRUE) |>
-  #     as.data.frame() |>
-  #     dplyr::pull(deff),
-  #   ignore_attr = TRUE
-  # )
-  # expect_equal(
-  #   cards::get_ard_statistics(ard_uni_svy_cont, stat_name %in% "p75") |> unlist(),
-  #   survey::svyquantile(x = ~api00, dclus1, na.rm = TRUE, quantiles = 0.75)[[1]][1] |> unlist(),
-  #   ignore_attr = TRUE
-  # )
-
+  expect_equal(
+    cards::get_ard_statistics(ard_svy_cat_row, stat_name %in% "n") |> unlist(),
+    survey::svymean(x = ~api00, dclus1, na.rm = TRUE)[1] |> unlist(),
+    ignore_attr = TRUE
+  )
+  expect_equal(
+    cards::get_ard_statistics(ard_svy_cat_row, stat_name %in% "N") |> unlist(),
+    survey::svyquantile(x = ~api00, dclus1, na.rm = TRUE, quantiles = 0.5)[[1]][1] |> unlist(),
+    ignore_attr = TRUE
+  )
+  expect_equal(
+    cards::get_ard_statistics(ard_svy_cat_row, stat_name %in% "p") |> unlist(),
+    dclus1$variables$api00 |> min(na.rm = TRUE),
+    ignore_attr = TRUE
+  )
+  expect_equal(
+    cards::get_ard_statistics(ard_svy_cat_row, stat_name %in% "deff") |> unlist(),
+    dclus1$variables$api00 |> max(na.rm = TRUE),
+    ignore_attr = TRUE
+  )
+  expect_equal(
+    cards::get_ard_statistics(ard_svy_cat_row, stat_name %in% "p.std.error") |> unlist(),
+    survey::svyvar(x = ~api00, dclus1, na.rm = TRUE)[1] |> unlist(),
+    ignore_attr = TRUE
+  )
+  expect_equal(
+    cards::get_ard_statistics(ard_uni_svy_cont, stat_name %in% "sd") |> unlist(),
+    survey::svyvar(x = ~api00, dclus1, na.rm = TRUE)[1] |> unlist() |> sqrt(),
+    ignore_attr = TRUE
+  )
+  expect_equal(
+    cards::get_ard_statistics(ard_uni_svy_cont, stat_name %in% "mean.std.error") |> unlist(),
+    survey::svymean(x = ~api00, dclus1, na.rm = TRUE) |> survey::SE() |> unlist(),
+    ignore_attr = TRUE
+  )
+  expect_equal(
+    cards::get_ard_statistics(ard_uni_svy_cont, stat_name %in% "deff") |> unlist(),
+    survey::svymean(x = ~api00, dclus1, na.rm = TRUE, deff = TRUE) |>
+      as.data.frame() |>
+      dplyr::pull(deff),
+    ignore_attr = TRUE
+  )
+  expect_equal(
+    cards::get_ard_statistics(ard_uni_svy_cont, stat_name %in% "p75") |> unlist(),
+    survey::svyquantile(x = ~api00, dclus1, na.rm = TRUE, quantiles = 0.75)[[1]][1] |> unlist(),
+    ignore_attr = TRUE
+  )
 })
 
 # - What happens with a variable that is all NA? How does that behavior compare to `ard_categorical()` for data frames ----
@@ -160,7 +159,7 @@ test_that("ard_categorical.survey.design() works when variables have all NAs", {
   data(api, package = "survey")
   svy_titanic <- survey::svydesign(~1, data = as.data.frame(Titanic), weights = ~Freq)
 
-  #row denom
+  # row denom
   svy_titanic$variables$Class <- NA
   svy_titanic$variables$Class <- fct_na_value_to_level(svy_titanic$variables$Class)
 
@@ -200,7 +199,6 @@ test_that("ard_categorical.survey.design() works when variables have all NAs", {
   #   NA
   # )
   # expect_invisible(cards::check_ard_structure(ard_svy_cat_cell, method = FALSE))
-
 })
 
 # - Do we get results for unobserved factor levels in the `by` and `variable` variables?
@@ -321,7 +319,6 @@ test_that("ard_categorical.survey.design() works for unobserved factor levels",
     NA
   )
   expect_invisible(cards::check_ard_structure(ard_svy_cat_cell, method = FALSE))
-
 })
 
 # - Do we get results for unobserved logical levels in the `by` and `variable` variables?
@@ -443,7 +440,6 @@ test_that("ard_categorical.survey.design() works for unobserved logical levels",
     NA
   )
   expect_invisible(cards::check_ard_structure(ard_svy_cat_cell, method = FALSE))
-
 })
 
 # - Does the work around apply for variables with only 1 level
@@ -565,5 +561,4 @@ test_that("ard_categorical.survey.design() works with variables with only 1 leve
     NA
   )
   expect_invisible(cards::check_ard_structure(ard_svy_cat_cell, method = FALSE))
-
 })