Fix tibble snapshots

[edelarua]

Converted most snapshot tibbles to data.frames. Replaced 2 unnecessary tibble snapshots with different tests.

Closes #866

[github-actions]

Code Coverage Summary

Filename                                   Stmts    Miss  Cover    Missing
---------------------------------------  -------  ------  -------  ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
R/abnormal_by_baseline.R                      63       0  100.00%
R/abnormal_by_marked.R                        52       5  90.38%   126-130
R/abnormal_by_worst_grade_worsen.R           113       3  97.35%   230-232
R/abnormal_by_worst_grade.R                   37       0  100.00%
R/abnormal.R                                  40       0  100.00%
R/analyze_vars_in_cols.R                      37       1  97.30%   114
R/combination_function.R                       9       0  100.00%
R/compare_variables.R                        138       3  97.83%   132, 242, 259
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                          47       1  97.87%   61
R/count_missed_doses.R                        31       0  100.00%
R/count_occurrences_by_grade.R                84       6  92.86%   152-154, 157, 172-173
R/count_occurrences.R                         61       1  98.36%   90
R/count_patients_events_in_cols.R             67       1  98.51%   73
R/count_patients_with_event.R                 72       0  100.00%
R/count_values.R                              24       0  100.00%
R/cox_regression_inter.R                     142       6  95.77%   91, 171-175
R/cox_regression.R                           208      11  94.71%   367-375, 394-396
R/coxph.R                                    168       7  95.83%   224-228, 272, 287, 295, 301-302
R/d_pkparam.R                                406       0  100.00%
R/decorate_grob.R                            167       6  96.41%   269-275, 382, 414, 424, 431
R/desctools_binom_diff.R                     663      66  90.05%   66, 101-102, 142-143, 146, 225, 251-260, 299, 301, 321, 325, 329, 333, 390, 393, 396, 399, 460, 468, 480-481, 487-490, 498, 501, 510, 513, 561-562, 564-565, 567-568, 570-571, 641, 653-666, 671, 718, 731, 735
R/df_explicit_na.R                            30       0  100.00%
R/estimate_multinomial_rsp.R                  47       1  97.87%   56
R/estimate_proportion.R                      198      11  94.44%   71-78, 82, 87, 451, 552
R/fit_rsp_step.R                              36       0  100.00%
R/fit_survival_step.R                         36       0  100.00%
R/formats.R                                  115       3  97.39%   101, 138, 148
R/g_forest.R                                 437      23  94.74%   195, 246-247, 314, 331-332, 337-338, 351, 367, 414, 445, 521, 530, 611-615, 625, 693, 696, 820
R/g_lineplot.R                               199      29  85.43%   162, 175, 203, 229-232, 309-316, 334-335, 341-351, 449, 457
R/g_step.R                                    68       1  98.53%   107
R/g_waterfall.R                               47       0  100.00%
R/h_adsl_adlb_merge_using_worst_flag.R        74       0  100.00%
R/h_biomarkers_subgroups.R                    38       0  100.00%
R/h_cox_regression.R                         110       0  100.00%
R/h_logistic_regression.R                    468       3  99.36%   197-198, 265
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           74       0  100.00%
R/h_response_subgroups.R                     171      12  92.98%   242-255
R/h_stack_by_baskets.R                        65       1  98.46%   93
R/h_step.R                                   180       0  100.00%
R/h_survival_biomarkers_subgroups.R           78       0  100.00%
R/h_survival_duration_subgroups.R            200      12  94.00%   249-261
R/incidence_rate.R                            93       7  92.47%   69-76
R/individual_patient_plot.R                  133       0  100.00%
R/kaplan_meier_plot.R                        567      61  89.24%   258-293, 302-306, 501, 668-670, 678-680, 706, 713-714, 885, 1066, 1304-1315
R/logistic_regression.R                      101       0  100.00%
R/missing_data.R                              21       3  85.71%   30, 61, 71
R/odds_ratio.R                               106       0  100.00%
R/prop_diff_test.R                            88       0  100.00%
R/prop_diff.R                                255      12  95.29%   95, 250-257, 396, 458, 567
R/prune_occurrences.R                         57      10  82.46%   130-134, 174-178
R/response_biomarkers_subgroups.R             59       0  100.00%
R/response_subgroups.R                       165       4  97.58%   262, 304-306
R/rtables_access.R                            38       4  89.47%   142-145
R/score_occurrences.R                         20       1  95.00%   114
R/split_cols_by_groups.R                      49       0  100.00%
R/stat.R                                      47       3  93.62%   76-77, 132
R/summarize_ancova.R                          95       1  98.95%   192
R/summarize_change.R                          27       0  100.00%
R/summarize_colvars.R                          6       0  100.00%
R/summarize_glm_count.R                      164       4  97.56%   180, 185, 247, 316
R/summarize_num_patients.R                    68       5  92.65%   97-99, 193-194
R/summarize_patients_exposure_in_cols.R       47       0  100.00%
R/summarize_variables.R                      212       2  99.06%   266, 476
R/survival_biomarkers_subgroups.R             59       0  100.00%
R/survival_coxph_pairwise.R                   73       9  87.67%   66-74
R/survival_duration_subgroups.R              172       0  100.00%
R/survival_time.R                             47       0  100.00%
R/survival_timepoint.R                       114       7  93.86%   149-155
R/utils_checkmate.R                           68       0  100.00%
R/utils_factor.R                              87       1  98.85%   93
R/utils_grid.R                               111       5  95.50%   148, 257-263
R/utils_rtables.R                             86       7  91.86%   25, 32-36, 346-347
R/utils.R                                    137      10  92.70%   100, 102, 106, 126, 129, 132, 136, 145-146, 332
TOTAL                                       8796     370  95.79%

Diff against main

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

Results for commit: 7a2eaf4

Minimum allowed coverage is 80%

♻️ This comment has been updated with latest results

[github-actions]

Unit Tests Summary

       1 files    76 suites   2m 6s ⏱️
   718 tests 718 ✔️     0 💤 0 ❌
1 525 runs  962 ✔️ 563 💤 0 ❌

Results for commit 4dbea3e.

[Melkiades]

nice!

[Melkiades]

well solved

[Melkiades]

Lgtm! Thanks Emily for this. I was just wondering how you selected tibbles to make them data.frame and do you think is it worth it to go the extra mile and add tests for col types? (imo not but maybe..)

[edelarua]

I made all of the tibbles into data.frames except 2 which I chose to replace with other tests because they were huge tables and the function being tested was supposed to convert all values in the affected columns to the same value so it was much easier to just check that.
I don't think that we need to test col types since only a few columns are affected by each function and we should (theoretically) be able to tell the output type from the function code itself, but if you think it's something we should add it doesn't hurt either way.