iaugusty commented 6 days ago

Pull Request

This is a draft proposal on how to use customized formats with varying decimal precision. Any feedback is more than welcome.

For now, only analyze_vars is using this approach, whereas I'd like to at least handle ancova analysis results in a similar way.

github-actions[bot] commented 6 days ago

Unit Tests Summary

1 files 84 suites 1m 39s ⏱️ 870 tests 867 ✅ 3 💤 0 ❌ 2 322 runs 2 294 ✅ 28 💤 0 ❌

Results for commit 648d5b68.

:recycle: This comment has been updated with latest results.

github-actions[bot] commented 6 days ago

Unit Test Performance Difference

Test Suite	$Status$	Time on `main`	$±Time$	$±Tests$	$±Skipped$	$±Failures$	$±Errors$
analyze_vars_in_cols	💔	$2.40$	$+3.48$	$+17$	$-7$	$0$	$0$
count_occurrences	💔	$0.74$	$+1.66$	$+10$	$-8$	$0$	$0$
count_occurrences_by_grade	💔	$1.76$	$+1.11$	$+16$	$-17$	$0$	$0$
summarize_coxreg	💔	$3.81$	$+1.55$	$+13$	$-13$	$0$	$0$
summarize_num_patients	💔	$1.11$	$+1.28$	$+18$	$-16$	$0$	$0$

Additional test case details

| Test Suite | $Status$ | Time on `main` | $±Time$ | Test Case | |:-----|:----:|:----:|:----:|:-----| | analyze_vars_in_cols | 💔 | $0.48$ | $+1.57$ | summarize_works_with_nested_analyze |

Results for commit 9ebc3bc35323f27c6d9846af1ee088a9c77805a0

♻️ This comment has been updated with latest results.

github-actions[bot] commented 6 days ago

Code Coverage Summary

Filename                                      Stmts    Miss  Cover    Missing
------------------------------------------  -------  ------  -------  ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
R/abnormal_by_baseline.R                         65       0  100.00%
R/abnormal_by_marked.R                           55       5  90.91%   93-97
R/abnormal_by_worst_grade_worsen.R              116       3  97.41%   263-265
R/abnormal_by_worst_grade.R                      60       0  100.00%
R/abnormal.R                                     43       0  100.00%
R/analyze_variables.R                           189      11  94.18%   544, 561-576, 765
R/analyze_vars_in_cols.R                        176      13  92.61%   178, 221, 235-236, 244-252
R/bland_altman.R                                 92       1  98.91%   46
R/combination_function.R                          9       0  100.00%
R/compare_variables.R                            84       2  97.62%   257, 316
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                             59       1  98.31%   74
R/count_missed_doses.R                           36       0  100.00%
R/count_occurrences_by_grade.R                  157       2  98.73%   177, 271
R/count_occurrences.R                           116       1  99.14%   120
R/count_patients_events_in_cols.R                67       1  98.51%   60
R/count_patients_with_event.R                    62       1  98.39%   123
R/count_patients_with_flags.R                    95       1  98.95%   134
R/count_values.R                                 27       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, 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                     50       1  98.00%   65
R/estimate_proportion.R                         205      11  94.63%   83-90, 94, 99, 320, 486
R/fit_rsp_step.R                                 36       0  100.00%
R/fit_survival_step.R                            36       0  100.00%
R/formatting_functions.R                        227      46  79.74%   141, 269-325, 360
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, 632, 707, 716, 722, 741, 796-816, 819, 830, 849, 904, 907, 1042-1047
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                                  260      22  91.54%   204, 378-385, 424-434, 543, 551
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                       46       0  100.00%
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              90      12  86.67%   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              88       6  93.18%   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                               86       7  91.86%   67-72, 152
R/logistic_regression.R                         102       0  100.00%
R/missing_data.R                                 21       3  85.71%   32, 66, 76
R/odds_ratio.R                                  117       0  100.00%
R/prop_diff_test.R                               91       0  100.00%
R/prop_diff.R                                   265      15  94.34%   70-73, 105, 290-297, 440, 605
R/prune_occurrences.R                            57       0  100.00%
R/response_biomarkers_subgroups.R                69       6  91.30%   196-201
R/response_subgroups.R                          213       8  96.24%   100-105, 260-261
R/riskdiff.R                                     65       5  92.31%   102-105, 114
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                            106       2  98.11%   183, 188
R/summarize_change.R                             74       1  98.65%   184
R/summarize_colvars.R                            10       0  100.00%
R/summarize_coxreg.R                            172       0  100.00%
R/summarize_glm_count.R                         209       3  98.56%   193-194, 490
R/summarize_num_patients.R                       93       4  95.70%   117-119, 266
R/summarize_patients_exposure_in_cols.R          96       1  98.96%   56
R/survival_biomarkers_subgroups.R                78       6  92.31%   117-122
R/survival_coxph_pairwise.R                      84      12  85.71%   51-52, 64-73
R/survival_duration_subgroups.R                 211       6  97.16%   124-129
R/survival_time.R                               111       0  100.00%
R/survival_timepoint.R                          124      10  91.94%   131-140
R/utils_checkmate.R                              68       0  100.00%
R/utils_default_stats_formats_labels.R          156       0  100.00%
R/utils_factor.R                                109       2  98.17%   84, 302
R/utils_ggplot.R                                110       0  100.00%
R/utils_grid.R                                  126       5  96.03%   164, 279-286
R/utils_rtables.R                               124       9  92.74%   39, 46, 403-404, 526-530
R/utils_split_funs.R                             52       2  96.15%   82, 94
R/utils.R                                       141       7  95.04%   118, 121, 124, 128, 137-138, 332
R/xutils_custom_stats_formats_varying_dp.R      102     102  0.00%    140-395
TOTAL                                         10964     628  94.27%

Diff against main

Filename                                      Stmts    Miss  Cover
------------------------------------------  -------  ------  --------
R/analyze_variables.R                           +14      +9  -4.68%
R/formatting_functions.R                        +44     +44  -19.17%
R/utils_default_stats_formats_labels.R           -1       0  +100.00%
R/xutils_custom_stats_formats_varying_dp.R     +102    +102  +100.00%
TOTAL                                          +159    +155  -1.35%

Results for commit: 648d5b688c77807d7ae853a973a80a4586797b9d

Minimum allowed coverage is 80%

:recycle: This comment has been updated with latest results

Melkiades commented 6 days ago

@iaugusty which issue is this PR addressing?

iaugusty commented 4 days ago

@iaugusty which issue is this PR addressing?

issue #1324:

alternative approach to control decimal precision, different as currently available auto approach
option to include formatting function (where we can control the rounding method - SAS vs R rounding)
option to have approach for defining other format defaults for the various stats than the tern defaults, eg mean (sd), we want to have xx.x (xx.xx) rather than xx.xx (xx.xx)

insightsengineering / tern

1324 feature request varying decimal precision in a summary #1356

Pull Request

Unit Tests Summary

Unit Test Performance Difference

Code Coverage Summary

Diff against main