Feature/df explicit na factor level method by shajoezhu · Pull Request #1493 · insightsengineering/tern

shajoezhu · 2026-07-12T11:30:47Z

to trigger cicd checks

…ttern to df_explicit_na - factor_level_method: controls level ordering (sort_auto/sort_radix/data) sort_auto preserves original behaviour; sort_radix uses byte-order (ASCII) sort which is not locale-sensitive; data uses first-appearance order - factor_as_factor: when TRUE, re-encodes existing factor columns via factor_level_method (default FALSE preserves original behaviour) - factor_level_last_pattern: regex to move matching levels to end before na_level; NULL by default (no effect); only applies during re-encoding Closes #1322 Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

- Move factor_as_factor/factor_level_method/factor_level_last_pattern after na_level to preserve positional call compatibility - Update @param order in roxygen and man/df_explicit_na.Rd accordingly - Replace match.arg() with checkmate::assert_choice() so error message matches test expectation and style is consistent with rest of file - Drop expect_false(identical(...)) test line that fails under LC_COLLATE=C (testthat edition 3 forces C locale in CI)

github-actions · 2026-07-12T11:45:38Z

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                        320      11  96.56%   593-596, 818-823, 831
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                          16       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                                    165       7  95.76%   190-194, 236, 251, 259, 265-266
R/d_pkparam.R                                406       0  100.00%
R/decorate_grob.R                            116       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                            45       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                     190       2  98.95%   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                                     507      39  92.31%   137, 189-194, 287, 378, 380-381, 392-394, 413, 420-421, 423-425, 433-435, 460, 465-468, 651-654, 1108-1117
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                                   178       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                              26       5  80.77%   39, 62-63, 96, 106
R/odds_ratio.R                               157       4  97.45%   270-273
R/prop_diff_test.R                           191       2  98.95%   267, 269
R/prop_diff.R                                521      21  95.97%   95-99, 136, 332, 334, 420-427, 576, 901, 1073, 1077, 1080
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                         174       2  98.85%   355-356
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                  154       9  94.16%   55-56, 124, 138, 145, 149, 288, 290-291
R/survival_duration_subgroups.R              250      15  94.00%   124-129, 268-273, 286, 288-289
R/survival_time.R                            128       1  99.22%   261
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       201       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                                    141       7  95.04%   131, 134, 137, 141, 150-151, 345
TOTAL                                      12342     594  95.19%

Diff against main

Filename              Stmts    Miss  Cover
------------------  -------  ------  --------
R/df_explicit_na.R      +15       0  +100.00%
TOTAL                   +15       0  +0.01%

Results for commit: b60d27f

Minimum allowed coverage is 80%

♻️ This comment has been updated with latest results

github-actions · 2026-07-12T11:46:32Z

Unit Tests Summary

1 files 85 suites 1m 40s ⏱️
931 tests 922 ✅ 9 💤 0 ❌
2 313 runs 1 598 ✅ 715 💤 0 ❌

Results for commit b60d27f.

♻️ This comment has been updated with latest results.

github-actions · 2026-07-12T11:46:37Z

Unit Test Performance Difference

Additional test case details

Test Suite	$Status$	$±Time$	Test Case
df_explicit_na	👶	$+0.03$	Check_new_parameter_errors
df_explicit_na	👶	$+0.01$	factor_as_factor_FALSE_preserves_existing_factor_levels_original_behavior_
df_explicit_na	👶	$+0.01$	factor_as_factor_re_encodes_existing_factor_levels_using_factor_level_method
df_explicit_na	👶	$+0.01$	factor_level_last_pattern_NULL_does_not_change_level_order
df_explicit_na	👶	$+0.01$	factor_level_last_pattern_combined_with_na_level_na_level_stays_last
df_explicit_na	👶	$+0.01$	factor_level_last_pattern_moves_matching_levels_to_end
df_explicit_na	👶	$+0.01$	factor_level_method_data_preserves_first_appearance_order
df_explicit_na	👶	$+0.01$	factor_level_method_sort_auto_gives_same_result_as_default
df_explicit_na	👶	$+0.01$	factor_level_method_sort_radix_uses_byte_order_sort_uppercase_before_lowercase_

Results for commit 4c3ebec

♻️ This comment has been updated with latest results.

kaipingyang and others added 7 commits July 2, 2026 09:27

docs: regenerate man/df_explicit_na.Rd via devtools::document()

641d567

Merge branch 'main' into feature/df-explicit-na-factor-level-method

195264b

Merge branch 'main' into feature/df-explicit-na-factor-level-method

f3bebcd

fix: add ADaM and behaviour to WORDLIST for spell check

1a569c6

[skip style] [skip vbump] Restyle files

eb410f4

shajoezhu mentioned this pull request Jul 12, 2026

trigger test insightsengineering/scda.test#242

Closed

[skip roxygen] [skip vbump] Roxygen Man Pages Auto Update

0184463

Copilot started work on behalf of shajoezhu July 12, 2026 21:07 View session

fix: allow contributor surname in spellcheck wordlist

b60d27f

Copilot finished work on behalf of shajoezhu July 12, 2026 21:08

shajoezhu closed this Jul 13, 2026

github-actions Bot locked and limited conversation to collaborators Jul 13, 2026

shajoezhu deleted the feature/df-explicit-na-factor-level-method branch July 13, 2026 01:00

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Feature/df explicit na factor level method#1493

Feature/df explicit na factor level method#1493
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