Refactor g_km

[edelarua]

Pull Request

Fixes #1167

I'm not sure if the grob-related functions are used in other places - if so we may not be able to deprecate them currently. If/when we remove all of these functions we should be able to remove the dependencies on packages grid, gridExtra, and gtable.

[github-actions]

Code Coverage Summary

Filename                                   Stmts    Miss  Cover    Missing
---------------------------------------  -------  ------  -------  ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
R/abnormal_by_baseline.R                      65       0  100.00%
R/abnormal_by_marked.R                        55       5  90.91%   78-82
R/abnormal_by_worst_grade_worsen.R           116       3  97.41%   240-242
R/abnormal_by_worst_grade.R                   60       0  100.00%
R/abnormal.R                                  43       0  100.00%
R/analyze_variables.R                        162       3  98.15%   487, 511, 627
R/analyze_vars_in_cols.R                     176      33  81.25%   179, 202-207, 222, 236-237, 245-253, 259-265, 344-350
R/bland_altman.R                              92       1  98.91%   37
R/combination_function.R                       9       0  100.00%
R/compare_variables.R                         84       5  94.05%   130-134, 246, 305
R/control_incidence_rate.R                    10       0  100.00%
R/control_logistic.R                           7       0  100.00%
R/control_step.R                              23       1  95.65%   58
R/control_survival.R                          15       0  100.00%
R/count_cumulative.R                          50       1  98.00%   67
R/count_missed_doses.R                        34       0  100.00%
R/count_occurrences_by_grade.R               113       5  95.58%   101, 151-153, 156
R/count_occurrences.R                        115       1  99.13%   108
R/count_patients_events_in_cols.R             67       1  98.51%   53
R/count_patients_with_event.R                 47       0  100.00%
R/count_patients_with_flags.R                 58       4  93.10%   56-57, 62-63
R/count_values.R                              27       0  100.00%
R/cox_regression_inter.R                     154       0  100.00%
R/cox_regression.R                           161       0  100.00%
R/coxph.R                                    167       7  95.81%   191-195, 239, 254, 262, 268-269
R/d_pkparam.R                                406       0  100.00%
R/decorate_grob.R                            193      40  79.27%   241-272, 338-340, 351, 372-409
R/desctools_binom_diff.R                     621      64  89.69%   53, 88-89, 125-126, 129, 199, 223-232, 264, 266, 286, 290, 294, 298, 353, 356, 359, 362, 422, 430, 439, 444-447, 454, 457, 466, 469, 516-517, 519-520, 522-523, 525-526, 593, 604-616, 620, 663, 676, 680
R/df_explicit_na.R                            30       0  100.00%
R/estimate_multinomial_rsp.R                  50       1  98.00%   63
R/estimate_proportion.R                      205      12  94.15%   78-85, 89, 94, 315, 482, 588
R/fit_rsp_step.R                              36       0  100.00%
R/fit_survival_step.R                         36       0  100.00%
R/formatting_functions.R                     181       2  98.90%   145, 280
R/g_forest.R                                 585     429  26.67%   183-186, 189-192, 195-201, 204-207, 210-213, 240, 252-255, 260-261, 275, 277, 287-290, 335-338, 345, 414, 493-1042
R/g_km.R                                     295      57  80.68%   290-293, 312-314, 368-371, 405, 433, 437-480, 487-491
R/g_lineplot.R                               209      34  83.73%   173, 186, 215, 241-244, 320-327, 345-346, 352-362, 454, 460, 462, 504-505, 509-510
R/g_step.R                                    68       1  98.53%   109
R/g_waterfall.R                               47       0  100.00%
R/h_adsl_adlb_merge_using_worst_flag.R        73       0  100.00%
R/h_biomarkers_subgroups.R                    45       0  100.00%
R/h_cox_regression.R                         110       0  100.00%
R/h_km.R                                     509     352  30.84%   138, 190-195, 288, 369-1006, 1109-1120
R/h_logistic_regression.R                    468       3  99.36%   206-207, 276
R/h_map_for_count_abnormal.R                  54       0  100.00%
R/h_pkparam_sort.R                            15       0  100.00%
R/h_response_biomarkers_subgroups.R           90      12  86.67%   50-55, 107-112
R/h_response_subgroups.R                     178      18  89.89%   257-270, 329-334
R/h_stack_by_baskets.R                        64       1  98.44%   89
R/h_step.R                                   180       0  100.00%
R/h_survival_biomarkers_subgroups.R           88       6  93.18%   111-116
R/h_survival_duration_subgroups.R            207      18  91.30%   259-271, 336-341
R/imputation_rule.R                           17       2  88.24%   54-55
R/incidence_rate.R                            96       7  92.71%   44-51
R/individual_patient_plot.R                  133       0  100.00%
R/logistic_regression.R                      102       0  100.00%
R/missing_data.R                              21       3  85.71%   32, 66, 76
R/odds_ratio.R                               109       0  100.00%
R/prop_diff_test.R                            91       0  100.00%
R/prop_diff.R                                265      16  93.96%   62-65, 97, 282-289, 432, 492, 597
R/prune_occurrences.R                         57      10  82.46%   138-142, 188-192
R/response_biomarkers_subgroups.R             68       6  91.18%   189-194
R/response_subgroups.R                       192      10  94.79%   95-100, 276, 324-326
R/riskdiff.R                                  59       7  88.14%   102-105, 114, 124-125
R/rtables_access.R                            38       4  89.47%   159-162
R/score_occurrences.R                         20       1  95.00%   124
R/split_cols_by_groups.R                      49       0  100.00%
R/stat.R                                      59       3  94.92%   73-74, 129
R/summarize_ancova.R                         104       2  98.08%   172, 177
R/summarize_change.R                          30       0  100.00%
R/summarize_colvars.R                         10       0  100.00%
R/summarize_coxreg.R                         172       2  98.84%   203, 430
R/summarize_glm_count.R                      195      27  86.15%   206, 224-256, 301-302
R/summarize_num_patients.R                    93       5  94.62%   108-110, 244-245
R/summarize_patients_exposure_in_cols.R       96       1  98.96%   42
R/survival_biomarkers_subgroups.R             70       6  91.43%   112-117
R/survival_coxph_pairwise.R                   79      11  86.08%   45-46, 58-66
R/survival_duration_subgroups.R              191       6  96.86%   119-124
R/survival_time.R                             79       0  100.00%
R/survival_timepoint.R                       113       7  93.81%   120-126
R/utils_checkmate.R                           68       0  100.00%
R/utils_default_stats_formats_labels.R       116       1  99.14%   72
R/utils_factor.R                             109       2  98.17%   84, 302
R/utils_ggplot.R                             110       0  100.00%
R/utils_grid.R                               126       5  96.03%   161, 276-283
R/utils_rtables.R                            100       9  91.00%   39, 46, 51, 58-62, 403-404
R/utils_split_funs.R                          52       2  96.15%   81, 93
R/utils.R                                    141      10  92.91%   92, 94, 98, 118, 121, 124, 128, 137-138, 332
TOTAL                                      10383    1285  87.62%

Diff against main

Filename             Stmts    Miss  Cover
-----------------  -------  ------  --------
R/decorate_grob.R      +20       0  +2.40%
R/g_forest.R             0      +1  -0.17%
R/g_km.R              +295     +57  +80.68%
R/h_km.R              +509    +352  +30.84%
R/utils_ggplot.R       +38       0  +100.00%
R/utils_grid.R         +15       0  +0.54%
TOTAL                 +877    +410  -3.05%

Results for commit: 69e1632

Minimum allowed coverage is 80%

♻️ This comment has been updated with latest results

[github-actions]

Unit Tests Summary

    1 files     83 suites   1m 7s ⏱️
  822 tests   790 ✅  32 💤 0 ❌
1 738 runs  1 071 ✅ 667 💤 0 ❌

Results for commit 69e1632.

♻️ This comment has been updated with latest results.

[github-actions]

Unit Test Performance Difference

Test Suite	$Status$	Time on main	$±Time$	$±Tests$	$±Skipped$	$±Failures$	$±Errors$
g_km	💔	$0.08$	$+4.81$	$+3$	$+3$	$0$	$0$
h_km	👶		$+0.40$	$+28$	$+13$	$0$	$0$
kaplan_meier_plot	💀	$1.90$	$-1.90$	$-27$	$-11$	$0$	$0$

Additional test case details

Test Suite	$Status$	Time on main	$±Time$	Test Case
g_km	👶		$+0.49$	g_km_as_list_argument_works
g_km	👶		$+0.65$	g_km_works_with_custom_arguments
g_km	👶		$+0.62$	g_km_works_with_title_footnotes_and_annotation
h_km	👶		$+0.02$	control_coxph_annot_works_with_default_settings
h_km	👶		$+0.02$	control_surv_med_annot_works_with_default_settings
h_km	👶		$+0.02$	h_data_plot_adds_rows_that_have_time_0_and_estimate_1
h_km	👶		$+0.03$	h_data_plot_respects_the_ordering_of_the_arm_variable_factor_levels
h_km	👶		$+0.02$	h_data_plot_works_as_expected
h_km	👶		$+0.08$	h_grob_coxph_returns_error_when_only_one_arm
h_km	👶		$+0.03$	h_tbl_coxph_pairwise_estimates_HR_CI_and_pvalue
h_km	👶		$+0.02$	h_tbl_median_surv_estimates_median_survival_time_with_CI
h_km	👶		$+0.02$	h_xticks_returns_error_when_xticks_non_numeric
h_km	👶		$+0.02$	h_xticks_works_with_default_settings
h_km	👶		$+0.01$	h_xticks_works_with_max_time_only
h_km	👶		$+0.02$	h_xticks_works_with_xticks_number
h_km	👶		$+0.03$	h_xticks_works_with_xticks_number_when_max_time_is_not_NULL
h_km	👶		$+0.02$	h_xticks_works_with_xticks_numeric
h_km	👶		$+0.03$	h_xticks_works_with_xticks_numeric_when_max_time_is_not_NULL
kaplan_meier_plot	💀	$0.67$	$-0.67$	g_km_works_with_custom_settings
kaplan_meier_plot	💀	$0.42$	$-0.42$	g_km_works_with_default_settings
kaplan_meier_plot	💀	$0.48$	$-0.48$	g_km_works_with_title_footnotes_and_annotation
kaplan_meier_plot	💀	$0.03$	$-0.03$	h_data_plot_adds_rows_that_have_time_0_and_estimate_1
kaplan_meier_plot	💀	$0.04$	$-0.04$	h_data_plot_respects_the_ordering_of_the_arm_variable_factor_levels
kaplan_meier_plot	💀	$0.02$	$-0.02$	h_data_plot_works_as_expected
kaplan_meier_plot	💀	$0.05$	$-0.05$	h_grob_coxph_returns_error_when_only_one_arm
kaplan_meier_plot	💀	$0.04$	$-0.04$	h_tbl_coxph_pairwise_estimates_HR_CI_and_pvalue
kaplan_meier_plot	💀	$0.02$	$-0.02$	h_tbl_median_surv_estimates_median_survival_time_with_CI
kaplan_meier_plot	💀	$0.02$	$-0.02$	h_xticks_returns_error_when_xticks_non_numeric
kaplan_meier_plot	💀	$0.02$	$-0.02$	h_xticks_works_with_default_settings
kaplan_meier_plot	💀	$0.01$	$-0.01$	h_xticks_works_with_max_time_only
kaplan_meier_plot	💀	$0.02$	$-0.02$	h_xticks_works_with_xticks_number
kaplan_meier_plot	💀	$0.02$	$-0.02$	h_xticks_works_with_xticks_number_when_max_time_is_not_NULL
kaplan_meier_plot	💀	$0.02$	$-0.02$	h_xticks_works_with_xticks_numeric
kaplan_meier_plot	💀	$0.02$	$-0.02$	h_xticks_works_with_xticks_numeric_when_max_time_is_not_NULL
utils_ggplot	👶		$+0.03$	df2gg_works_as_expected

Results for commit f817de3

♻️ This comment has been updated with latest results.

[Melkiades]

Pull Request

Fixes #1167

I'm not sure if the grob-related functions are used in other places - if so we may not be able to deprecate them currently. If/when we remove all of these functions we should be able to remove the dependencies on packages grid, gridExtra, and gtable.

Talking with Kartik yesterday, I saw that grob is used to produce reports in teal.report. It may be useful then to move towards removing this dep also there, as it is not maintained (right?). @kartikeyakirar fyi

[shajoezhu]

looks really great! thanks @edelarua