Fix a_summary() all-NA factor bug

[iaugusty]

Pull Request

Fixes #1431

[github-actions]

Unit Tests Summary

    1 files     85 suites   1m 26s ⏱️
  888 tests   879 ✅   9 💤 0 ❌
1 908 runs  1 208 ✅ 700 💤 0 ❌

Results for commit d785dc9.

♻️ This comment has been updated with latest results.

[github-actions]

Unit Test Performance Difference

Additional test case details

Test Suite	$Status$	Time on main	$±Time$	Test Case
analyze_variables	👶		$+0.05$	analyze_vars_works_with_all_NA_character_input
analyze_variables	👶		$+0.05$	analyze_vars_works_with_all_NA_factor_input

Results for commit 2a974f2

♻️ This comment has been updated with latest results.

[github-actions]

Code Coverage Summary

Filename                                   Stmts    Miss  Cover    Missing
---------------------------------------  -------  ------  -------  ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
R/abnormal_by_baseline.R                     101       3  97.03%   242, 244-245
R/abnormal_by_marked.R                        88       8  90.91%   94-98, 281, 283-284
R/abnormal_by_worst_grade.R                   94       3  96.81%   215, 217-218
R/abnormal_lab_worsen_by_baseline.R          159      10  93.71%   205-208, 213, 215-216, 459-461
R/abnormal.R                                  78       2  97.44%   222, 224
R/analyze_variables.R                        288       5  98.26%   586-589, 778
R/analyze_vars_in_cols.R                     178      14  92.13%   178, 221, 235-236, 238, 246-254
R/bland_altman.R                              92       1  98.91%   46
R/combination_function.R                       9       0  100.00%
R/compare_variables.R                         35       0  100.00%
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                         115       4  96.52%   74, 270-271, 273
R/count_missed_doses.R                        89       4  95.51%   206-209
R/count_occurrences_by_grade.R               169       8  95.27%   178, 386, 388, 465, 467, 469, 473-474
R/count_occurrences.R                        137      10  92.70%   119, 262-264, 330-332, 334, 338-339
R/count_patients_events_in_cols.R             67       1  98.51%   60
R/count_patients_with_event.R                 73       2  97.26%   220, 223
R/count_patients_with_flags.R                 93       2  97.85%   234, 236
R/count_values.R                              61       2  96.72%   193, 196
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, 238, 253, 261, 267-268
R/d_pkparam.R                                406       0  100.00%
R/decorate_grob.R                            113       0  100.00%
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                  86       4  95.35%   65, 212, 214-215
R/estimate_proportion.R                      240       7  97.08%   88, 99, 255, 257-258, 389, 553
R/fit_rsp_step.R                              36       0  100.00%
R/fit_survival_step.R                         36       0  100.00%
R/formatting_functions.R                     183       2  98.91%   141, 276
R/g_forest.R                                 585      60  89.74%   240, 252-255, 260-261, 275, 277, 287-290, 335-338, 345, 414, 501, 514, 518-519, 524-525, 538, 554, 601, 630, 705, 714, 720, 739, 794-814, 817, 828, 847, 902, 905, 1040-1045
R/g_ipp.R                                    133       0  100.00%
R/g_km.R                                     350      57  83.71%   285-288, 307-309, 363-366, 400, 428, 432-475, 482-486
R/g_lineplot.R                               261      22  91.57%   222, 397-404, 443-453, 562, 570
R/g_step.R                                    68       1  98.53%   108
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                    91      23  74.73%   40-42, 84-103
R/h_cox_regression.R                         110       0  100.00%
R/h_incidence_rate.R                          45       0  100.00%
R/h_km.R                                     509      41  91.94%   137, 189-194, 287, 378, 380-381, 392-394, 413, 420-421, 423-425, 433-435, 460, 465-468, 651-654, 1108-1119
R/h_logistic_regression.R                    468       3  99.36%   203-204, 273
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           77      12  84.42%   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           73       6  91.78%   111-116
R/h_survival_duration_subgroups.R            207      18  91.30%   259-271, 336-341
R/imputation_rule.R                           17       0  100.00%
R/incidence_rate.R                           103       7  93.20%   68-73, 242
R/logistic_regression.R                      102       0  100.00%
R/missing_data.R                              21       3  85.71%   32, 66, 76
R/odds_ratio.R                               157       4  97.45%   270-273
R/prop_diff_test.R                           144       2  98.61%   250, 252
R/prop_diff.R                                318      17  94.65%   71-74, 106, 299, 301, 387-394, 537, 702
R/prune_occurrences.R                         57       0  100.00%
R/response_biomarkers_subgroups.R            124      10  91.94%   88-91, 270-275
R/response_subgroups.R                       252      16  93.65%   100-105, 271-275, 280, 282-283, 310-311
R/riskdiff.R                                  65       4  93.85%   94-97
R/rtables_access.R                            38       0  100.00%
R/score_occurrences.R                         20       1  95.00%   124
R/split_cols_by_groups.R                      49       0  100.00%
R/stat.R                                      59       0  100.00%
R/summarize_ancova.R                         150       2  98.67%   327-328
R/summarize_change.R                          72       3  95.83%   175, 177-178
R/summarize_colvars.R                         13       1  92.31%   75
R/summarize_coxreg.R                         172       0  100.00%
R/summarize_glm_count.R                      269      10  96.28%   129-130, 202-203, 459-463, 596
R/summarize_num_patients.R                   121      10  91.74%   122-124, 244, 248, 252-253, 337-338, 340
R/summarize_patients_exposure_in_cols.R      155       7  95.48%   58, 232-233, 237, 357-358, 362
R/survival_biomarkers_subgroups.R            136      10  92.65%   117-122, 228-231
R/survival_coxph_pairwise.R                  124       5  95.97%   52-53, 248, 250-251
R/survival_duration_subgroups.R              250      15  94.00%   124-129, 268-273, 286, 288-289
R/survival_time.R                            120       1  99.17%   251
R/survival_timepoint.R                       153       2  98.69%   320, 322
R/utils_checkmate.R                           68       0  100.00%
R/utils_default_stats_formats_labels.R       196       0  100.00%
R/utils_factor.R                              87       1  98.85%   99
R/utils_ggplot.R                             110       0  100.00%
R/utils_grid.R                               126       5  96.03%   164, 279-286
R/utils_rtables.R                            125       9  92.80%   39, 46, 414-415, 537-541
R/utils_split_funs.R                          52       2  96.15%   82, 94
R/utils.R                                    143       7  95.10%   131, 134, 137, 141, 150-151, 345
TOTAL                                      11970     580  95.15%

Diff against main

Filename                 Stmts    Miss  Cover
---------------------  -------  ------  -------
R/analyze_variables.R       +1       0  +0.01%
TOTAL                       +1       0  +0.00%

Results for commit: d785dc9

Minimum allowed coverage is 80%

♻️ This comment has been updated with latest results

[edelarua]

Hi @iaugusty,

Thanks for the PR! I have updated your solution to simplify it a bit. All tests are passing but feel free to double check that this still works for you.

[edelarua]

This should be good to go after a final review by @iaugusty

[Melkiades]

This should be good to go after a final review by @iaugusty

Thanks Emily for helping us finding a neat solution! :)

[shajoezhu]

Thanks for the fix! @edelarua . @iaugusty can you please add tests to scda.test, we will merge this PR after the snapshot test is introduced. Thanks!

[iaugusty]

@edelarua indeed more elegant solution, thanks
@shajoezhu I'll take a look on how to add a test for this in scda.test

[iaugusty]

@shajoezhu could you provide some guidance on how to include a test for this scenario in scda.test, shall I eg include it as a special variant situation in eg test-table_dmt01.R, or would you prefer a separate program for a simple situation with this scenario? Any naming convention I need to follow, or any other guidance documentation you have would be helpfull.

[shajoezhu]

hi @iaugusty

Please do the following

1. at https://github.com/insightsengineering/scda.test, create a branch and raise PR, consider using example from, but the test case should demonstrate what you would like to do for this example, https://github.com/insightsengineering/scda.test/blob/main/tests/testthat/test-table_dmt01.R
2. for your test, we can give it a name say test-table_jnj_tb1.R we can discuss with better naming in the future.
3. change https://github.com/insightsengineering/scda.test/blob/main/DESCRIPTION#L50 to insightsengineering/tern@1431-bug-s_summaryfactor
4. change https://github.com/insightsengineering/scda.test/blob/main/DESCRIPTION#L28 to tern (>= 0.9.9.9002)

[shajoezhu]

addressed in https://github.com/insightsengineering/scda.test/pull/196/files#diff-a0973dd1ba424a01606d79f9b532714c009ac1e3950824bb8918217bb6c03b9f, good to go. Thanks @iaugusty