Removal of internal df_explicit_na

[Melkiades]

Fixes #1019

[github-actions]

Code Coverage Summary

Filename                                   Stmts    Miss  Cover    Missing
---------------------------------------  -------  ------  -------  -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
R/abnormal_by_baseline.R                      63       0  100.00%
R/abnormal_by_marked.R                        53       5  90.57%   115-119
R/abnormal_by_worst_grade_worsen.R           114       3  97.37%   233-235
R/abnormal_by_worst_grade.R                   38       0  100.00%
R/abnormal.R                                  41       0  100.00%
R/analyze_variables.R                        220       2  99.09%   273, 499
R/analyze_vars_in_cols.R                     120      22  81.67%   164, 188-193, 206, 219-225, 276-282
R/combination_function.R                       9       0  100.00%
R/compare_variables.R                        139       5  96.40%   126-127, 137, 241, 259
R/control_incidence_rate.R                    20       8  60.00%   32-35, 38-41
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                          48       1  97.92%   63
R/count_missed_doses.R                        32       0  100.00%
R/count_occurrences_by_grade.R                85       6  92.94%   156-158, 161, 176-177
R/count_occurrences.R                         62       1  98.39%   92
R/count_patients_events_in_cols.R             67       1  98.51%   62
R/count_patients_with_event.R                 34       0  100.00%
R/count_patients_with_flags.R                 45       4  91.11%   71-72, 77-78
R/count_values.R                              25       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                            169      40  76.33%   232-263, 323-325, 332, 353-390
R/desctools_binom_diff.R                     663      66  90.05%   52, 87-88, 128-129, 132, 211, 237-246, 285, 287, 307, 311, 315, 319, 375, 378, 381, 384, 445, 453, 465-466, 472-475, 483, 486, 495, 498, 546-547, 549-550, 552-553, 555-556, 626, 638-651, 656, 703, 716, 720
R/df_explicit_na.R                            30       0  100.00%
R/estimate_multinomial_rsp.R                  48       1  97.92%   60
R/estimate_proportion.R                      200      12  94.00%   75-82, 86, 91, 296, 463, 568
R/fit_rsp_step.R                              36       0  100.00%
R/fit_survival_step.R                         36       0  100.00%
R/formatting_functions.R                     115       2  98.26%   145, 155
R/g_forest.R                                 437      23  94.74%   197, 248-249, 316, 333-334, 339-340, 353, 369, 416, 447, 523, 532, 613-617, 627, 702, 705, 829
R/g_lineplot.R                               199      29  85.43%   160, 173, 201, 227-230, 307-314, 332-333, 339-349, 441, 449
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                    40       0  100.00%
R/h_cox_regression.R                         110       0  100.00%
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           75       0  100.00%
R/h_response_subgroups.R                     171      12  92.98%   257-270
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           79       0  100.00%
R/h_survival_duration_subgroups.R            200      12  94.00%   259-271
R/incidence_rate.R                            94       7  92.55%   53-60
R/individual_patient_plot.R                  133       0  100.00%
R/kaplan_meier_plot.R                        646      64  90.09%   226-229, 269-304, 313-317, 517, 704-706, 714-716, 748-749, 921, 1110, 1427-1438
R/logistic_regression.R                      101       0  100.00%
R/missing_data.R                              21       3  85.71%   32, 66, 76
R/odds_ratio.R                               107       0  100.00%
R/prop_diff_test.R                            89       0  100.00%
R/prop_diff.R                                261      16  93.87%   72-75, 107, 269-276, 415, 475, 580
R/prune_occurrences.R                         57      10  82.46%   138-142, 188-192
R/response_biomarkers_subgroups.R             60       0  100.00%
R/response_subgroups.R                       165       4  97.58%   266, 308-310
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                                      47       3  93.62%   73-74, 129
R/summarize_ancova.R                          96       1  98.96%   180
R/summarize_change.R                          28       0  100.00%
R/summarize_colvars.R                          6       0  100.00%
R/summarize_coxreg.R                         165       2  98.79%   198, 420
R/summarize_glm_count.R                      165       4  97.58%   158, 163, 207, 260
R/summarize_num_patients.R                    75       9  88.00%   103-105, 150-151, 218-223
R/summarize_patients_exposure_in_cols.R       97       1  98.97%   56
R/survival_biomarkers_subgroups.R             60       0  100.00%
R/survival_coxph_pairwise.R                   74       9  87.84%   59-67
R/survival_duration_subgroups.R              172       0  100.00%
R/survival_time.R                             48       0  100.00%
R/survival_timepoint.R                       118       7  94.07%   126-132
R/utils_checkmate.R                           68       0  100.00%
R/utils_factor.R                              87       1  98.85%   84
R/utils_grid.R                               111       5  95.50%   149, 258-264
R/utils_rtables.R                             86       7  91.86%   24, 31-35, 346-347
R/utils.R                                    137      10  92.70%   92, 94, 98, 118, 121, 124, 128, 137-138, 311
TOTAL                                       9206     436  95.26%

Diff against main

Filename                                  Stmts    Miss  Cover
--------------------------------------  -------  ------  --------
R/h_adsl_adlb_merge_using_worst_flag.R       -1       0  +100.00%
R/h_stack_by_baskets.R                       -1       0  -0.02%
TOTAL                                        -2       0  -0.00%

Results for commit: 30ac37a

Minimum allowed coverage is 80%

♻️ This comment has been updated with latest results

[github-actions]

Unit Tests Summary

       1 files    78 suites   1m 36s ⏱️
   743 tests 738 ✔️     5 💤 0 ❌
1 571 runs  984 ✔️ 587 💤 0 ❌

Results for commit 0d3bd4a.

♻️ This comment has been updated with latest results.

[edelarua]

Hi @Melkiades,

Could you add in a note so the user knows that df_explicit_na should be used during pre-processing? Specifically for h_stack_by_baskets the user should be aware that the na_level argument needs to match the na_level used in df_explicit_na, otherwise the argument does nothing.

Thanks!

[edelarua]

Approving to unblock since I will be OOO next week.

[shajoezhu]

Beniot also checked downstream. all is well. We are good to go! Thanks @Melkiades and @edelarua