Add helper function to pre-process ADLB for count_abnormal_by_worst_grade

[edelarua]

Pull Request

Fixes #1102

[github-actions]

Unit Tests Summary

       1 files       81 suites   1m 55s ⏱️
   800 tests    777 ✔️   23 💤 0 ❌
1 696 runs  1 052 ✔️ 644 💤 0 ❌

Results for commit e3548eb.

♻️ This comment has been updated with latest results.

[github-actions]

Code Coverage Summary

Filename                                   Stmts    Miss  Cover    Missing
---------------------------------------  -------  ------  -------  ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
R/abnormal_by_baseline.R                      67       2  97.01%   78-79
R/abnormal_by_marked.R                        54       5  90.74%   117-121
R/abnormal_by_worst_grade_worsen.R           115       3  97.39%   235-237
R/abnormal_by_worst_grade.R                   59       0  100.00%
R/abnormal.R                                  42       0  100.00%
R/analyze_variables.R                        190      10  94.74%   489-490, 506, 530, 686-687, 692-693, 711-712
R/analyze_vars_in_cols.R                     178      35  80.34%   168-169, 184, 207-212, 227, 241-242, 250-258, 264-270, 349-355
R/combination_function.R                       9       0  100.00%
R/compare_variables.R                        124      17  86.29%   130-134, 246, 324-333, 387-388, 394
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                          49       1  97.96%   65
R/count_missed_doses.R                        33       0  100.00%
R/count_occurrences_by_grade.R               105       4  96.19%   158-160, 163
R/count_occurrences.R                        113       1  99.12%   94
R/count_patients_events_in_cols.R             68       1  98.53%   65
R/count_patients_with_event.R                 46       0  100.00%
R/count_patients_with_flags.R                 57       4  92.98%   73-74, 79-80
R/count_values.R                              26       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                            173      40  76.88%   235-266, 326-328, 339, 360-397
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                  49       1  97.96%   62
R/estimate_proportion.R                      201      12  94.03%   77-84, 88, 93, 300, 467, 572
R/fit_rsp_step.R                              36       0  100.00%
R/fit_survival_step.R                         36       0  100.00%
R/formatting_functions.R                     181       3  98.34%   145, 155, 280
R/g_forest.R                                 438      21  95.21%   199, 319, 336-337, 342-343, 356, 372, 419, 450, 526, 535, 616-620, 630, 705, 708, 832
R/g_lineplot.R                               206      34  83.50%   168, 181, 210, 236-239, 315-322, 340-341, 347-357, 449, 455, 457, 499-500, 504-505
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                    42       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                  57       2  96.49%   77-78
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                        67       3  95.52%   68-69, 95
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/imputation_rule.R                           17       2  88.24%   54-55
R/incidence_rate.R                            95       7  92.63%   55-62
R/individual_patient_plot.R                  133       0  100.00%
R/kaplan_meier_plot.R                        688      65  90.55%   236-239, 279-314, 323-327, 538, 725-727, 735-737, 769-770, 943-946, 1169, 1495-1506
R/logistic_regression.R                      102       0  100.00%
R/missing_data.R                              21       3  85.71%   32, 66, 76
R/odds_ratio.R                               108       0  100.00%
R/prop_diff_test.R                            90       0  100.00%
R/prop_diff.R                                262      16  93.89%   74-77, 109, 273-280, 419, 479, 584
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%   268, 312-314
R/riskdiff.R                                  52       7  86.54%   88-91, 100, 110-111
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                          97       1  98.97%   182
R/summarize_change.R                          29       0  100.00%
R/summarize_colvars.R                         12       2  83.33%   72-73
R/summarize_coxreg.R                         174       6  96.55%   198-199, 206, 342-343, 436
R/summarize_glm_count.R                      166      29  82.53%   160, 164-215, 263-264
R/summarize_num_patients.R                   101       9  91.09%   107-109, 158-159, 242-247
R/summarize_patients_exposure_in_cols.R       98       1  98.98%   58
R/survival_biomarkers_subgroups.R             60       0  100.00%
R/survival_coxph_pairwise.R                   75       9  88.00%   61-69
R/survival_duration_subgroups.R              174       0  100.00%
R/survival_time.R                             79       0  100.00%
R/survival_timepoint.R                       120       7  94.17%   128-134
R/utils_checkmate.R                           68       0  100.00%
R/utils_default_stats_formats_labels.R       136       4  97.06%   72, 577-580
R/utils_factor.R                             109       2  98.17%   84, 302
R/utils_grid.R                               111       5  95.50%   149, 258-264
R/utils_rtables.R                             90       7  92.22%   24, 31-35, 376-377
R/utils_split_funs.R                          52       2  96.15%   81, 93
R/utils.R                                    137      10  92.70%   92, 94, 98, 118, 121, 124, 128, 137-138, 311
TOTAL                                       9815     523  94.67%

Diff against main

Filename                       Stmts    Miss  Cover
---------------------------  -------  ------  --------
R/abnormal_by_worst_grade.R      +20       0  +100.00%
TOTAL                            +20       0  +0.01%

Results for commit: e3548eb

Minimum allowed coverage is 80%

♻️ This comment has been updated with latest results

[Melkiades]

Lgtm!!! Nice, now it is much slimmer, also in the examples :) Thanks Emily!