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Fix s_summary return for empty logical vectors #1079

Merged
merged 3 commits into from
Oct 10, 2023
Merged

Fix s_summary return for empty logical vectors #1079

merged 3 commits into from
Oct 10, 2023

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edelarua
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@edelarua edelarua commented Oct 5, 2023 •
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Pull Request

Fixes #649

Outputting NE instead of NA will be enabled by #1071.

@edelarua edelarua added the sme label Oct 5, 2023
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github-actions bot commented Oct 5, 2023 •
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Code Coverage Summary

Filename                                   Stmts    Miss  Cover    Missing
---------------------------------------  -------  ------  -------  -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
R/abnormal_by_baseline.R                      67       2  97.01%   76-77
R/abnormal_by_marked.R                        54       5  90.74%   115-119
R/abnormal_by_worst_grade_worsen.R           115       3  97.39%   233-235
R/abnormal_by_worst_grade.R                   39       0  100.00%
R/abnormal.R                                  42       0  100.00%
R/analyze_variables.R                        199       9  95.48%   476-477, 493, 696-697, 702-703, 721-722
R/analyze_vars_in_cols.R                     170      31  81.76%   167-168, 205-210, 225, 239-240, 248-253, 259-265, 341-347
R/combination_function.R                       9       0  100.00%
R/compare_variables.R                        124      17  86.29%   127-131, 243, 321-330, 384-385, 391
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%   63
R/count_missed_doses.R                        33       0  100.00%
R/count_occurrences_by_grade.R               105       4  96.19%   156-158, 161
R/count_occurrences.R                         74       1  98.65%   92
R/count_patients_events_in_cols.R             68       1  98.53%   62
R/count_patients_with_event.R                 46       0  100.00%
R/count_patients_with_flags.R                 57       4  92.98%   71-72, 77-78
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                            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                  49       1  97.96%   60
R/estimate_proportion.R                      201      12  94.03%   75-82, 86, 91, 298, 465, 570
R/fit_rsp_step.R                              36       0  100.00%
R/fit_survival_step.R                         36       0  100.00%
R/formatting_functions.R                     174       3  98.28%   145, 155, 280
R/g_forest.R                                 438      23  94.75%   199, 251-252, 319, 336-337, 342-343, 356, 372, 419, 450, 526, 535, 616-620, 630, 705, 708, 832
R/g_lineplot.R                               199      30  84.92%   160, 173, 201, 227-230, 307-314, 332-333, 339-349, 441, 447, 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                    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%   53-60
R/individual_patient_plot.R                  133       0  100.00%
R/kaplan_meier_plot.R                        683      65  90.48%   230-233, 273-308, 317-321, 530, 717-719, 727-729, 761-762, 935-938, 1161, 1478-1489
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%   72-75, 107, 271-278, 417, 477, 582
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                                  48       7  85.42%   85-88, 95, 105-106
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%   180
R/summarize_change.R                          29       0  100.00%
R/summarize_colvars.R                         12       2  83.33%   72-73
R/summarize_coxreg.R                         173       6  96.53%   196-197, 204, 340-341, 434
R/summarize_glm_count.R                      166      29  82.53%   158, 162-213, 261-262
R/summarize_num_patients.R                    99       9  90.91%   103-105, 154-155, 238-243
R/summarize_patients_exposure_in_cols.R       98       1  98.98%   56
R/survival_biomarkers_subgroups.R             60       0  100.00%
R/survival_coxph_pairwise.R                   75       9  88.00%   59-67
R/survival_duration_subgroups.R              174       0  100.00%
R/survival_time.R                             49       0  100.00%
R/survival_timepoint.R                       120       7  94.17%   126-132
R/utils_checkmate.R                           68       0  100.00%
R/utils_default_stats_formats_labels.R        95       3  96.84%   408-411
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.R                                    137      10  92.70%   92, 94, 98, 118, 121, 124, 128, 137-138, 311
TOTAL                                       9646     515  94.66%

Diff against main

Filename      Stmts    Miss  Cover
----------  -------  ------  --------
TOTAL             0       0  +100.00%

Results for commit: 918d6fd

Minimum allowed coverage is 80%

♻️ This comment has been updated with latest results

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github-actions bot commented Oct 5, 2023 •
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Unit Tests Summary

       1 files       80 suites   1m 55s ⏱️
   780 tests    771 ✔️     9 💤 0 ❌
1 657 runs  1 040 ✔️ 617 💤 0 ❌

Results for commit 918d6fd.

♻️ This comment has been updated with latest results.

@edelarua edelarua enabled auto-merge (squash) October 5, 2023 21:57
Melkiades
Melkiades reviewed Oct 10, 2023
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R/analyze_variables.R
@@ -410,7 +414,7 @@ s_summary.logical <- function(x,
N_col = .N_col
)
y$count <- count
y$count_fraction <- c(count, ifelse(dn > 0, count / dn, NA))
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it should be inf btw ahah

No I was wondering if we want 0 as std here. I think it is better to have NA and then use na_str to set it to NE, right? or I am missing something?

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Hi @Melkiades, as per Tim's comment on the issue (#649 (comment)) for counts and percentages (3.1.1) "0" should be returned when denominator is zero. This change aligns the logical output with the factor/character output as expected.

@edelarua edelarua disabled auto-merge October 10, 2023 12:19
Melkiades
Melkiades approved these changes Oct 10, 2023
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Lgtm! Thanks, Emily for rectifying this!

@edelarua edelarua merged commit 0be3f41 into main Oct 10, 2023
@edelarua edelarua deleted the 649_empty_logical_stats@main branch October 10, 2023 14:15
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@Melkiades Melkiades Melkiades approved these changes

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[Feature Request]: a_summary.logical formatting not dealing as a_summary.factor when denom = 0
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@edelarua @Melkiades