Fix bug in decorate_grob

[edelarua]

Closes #940

[edelarua]

New output image generate from code originally posted in issue (see output before bug fix):

[github-actions]

Unit Tests Summary

       1 files    78 suites   1m 11s ⏱️
   735 tests 735 ✔️     0 💤 0 ❌
1 560 runs  979 ✔️ 581 💤 0 ❌

Results for commit 0800fcd.

♻️ This comment has been updated with latest results.

[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%   124-128
R/abnormal_by_worst_grade_worsen.R           113       3  97.35%   233-235
R/abnormal_by_worst_grade.R                   37       0  100.00%
R/abnormal.R                                  40       0  100.00%
R/analyze_vars_in_cols.R                     113      23  79.65%   164, 188-193, 206, 219-225, 268-274, 305
R/combination_function.R                       9       0  100.00%
R/compare_variables.R                        139       5  96.40%   126-127, 137, 241, 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%   63
R/count_missed_doses.R                        31       0  100.00%
R/count_occurrences_by_grade.R                84       6  92.86%   156-158, 161, 176-177
R/count_occurrences.R                         61       1  98.36%   92
R/count_patients_events_in_cols.R             67       1  98.51%   74
R/count_patients_with_event.R                 33       0  100.00%
R/count_patients_with_flags.R                 39       0  100.00%
R/count_values.R                              24       0  100.00%
R/cox_regression_inter.R                     142       0  100.00%
R/cox_regression.R                           161       0  100.00%
R/coxph.R                                    169       9  94.67%   19-20, 227-231, 275, 290, 298, 304-305
R/d_pkparam.R                                406       0  100.00%
R/decorate_grob.R                            169      40  76.33%   232-263, 361-363, 370, 391-428
R/desctools_binom_diff.R                     663      66  90.05%   68, 103-104, 144-145, 148, 227, 253-262, 301, 303, 323, 327, 331, 335, 391, 394, 397, 400, 461, 469, 481-482, 488-491, 499, 502, 511, 514, 562-563, 565-566, 568-569, 571-572, 642, 654-667, 672, 719, 732, 736
R/df_explicit_na.R                            30       0  100.00%
R/estimate_multinomial_rsp.R                  47       1  97.87%   60
R/estimate_proportion.R                      198      11  94.44%   75-82, 86, 91, 460, 565
R/fit_rsp_step.R                              36       0  100.00%
R/fit_survival_step.R                         36       0  100.00%
R/formatting_functions.R                     115       3  97.39%   107, 145, 155
R/g_forest.R                                 437      23  94.74%   197, 248-249, 316, 333-334, 339-340, 353, 369, 416, 447, 523, 532, 613-617, 627, 697, 700, 824
R/g_lineplot.R                               199      29  85.43%   160, 173, 201, 227-230, 307-314, 332-333, 339-349, 441, 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        74       0  100.00%
R/h_biomarkers_subgroups.R                    40       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                  54       0  100.00%
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                        65       1  98.46%   91
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/incidence_rate.R                            93       7  92.47%   68-75
R/individual_patient_plot.R                  133       0  100.00%
R/kaplan_meier_plot.R                        575      61  89.39%   248-283, 292-296, 494, 666-668, 676-678, 703, 710-711, 881, 1071, 1318-1329
R/logistic_regression.R                      101       0  100.00%
R/missing_data.R                              21       3  85.71%   32, 66, 76
R/odds_ratio.R                               106       0  100.00%
R/prop_diff_test.R                            88       0  100.00%
R/prop_diff.R                                260      16  93.85%   72-75, 107, 267-274, 413, 473, 578
R/prune_occurrences.R                         57      10  82.46%   140-144, 190-194
R/response_biomarkers_subgroups.R             60       0  100.00%
R/response_subgroups.R                       165       4  97.58%   279, 321-323
R/rtables_access.R                            38       4  89.47%   161-164
R/score_occurrences.R                         20       1  95.00%   124
R/split_cols_by_groups.R                      49       0  100.00%
R/stat.R                                      47       3  93.62%   73-74, 129
R/summarize_ancova.R                          95       1  98.95%   189
R/summarize_change.R                          27       0  100.00%
R/summarize_colvars.R                          6       0  100.00%
R/summarize_coxreg.R                         140       0  100.00%
R/summarize_glm_count.R                      164       4  97.56%   188, 193, 254, 322
R/summarize_num_patients.R                    74       9  87.84%   103-105, 150-151, 217-222
R/summarize_patients_exposure_in_cols.R       97       1  98.97%   67
R/summarize_variables.R                      220       2  99.09%   266, 492
R/survival_biomarkers_subgroups.R             60       0  100.00%
R/survival_coxph_pairwise.R                   73       9  87.67%   64-72
R/survival_duration_subgroups.R              172       0  100.00%
R/survival_time.R                             47       0  100.00%
R/survival_timepoint.R                       116       7  93.97%   150-156
R/utils_checkmate.R                           68       0  100.00%
R/utils_factor.R                              87       1  98.85%   91
R/utils_grid.R                               111       5  95.50%   152, 262-268
R/utils_rtables.R                             86       7  91.86%   24, 31-35, 346-347
R/utils.R                                    137      10  92.70%   105, 107, 111, 131, 134, 137, 141, 150-151, 334
TOTAL                                       9054     422  95.34%

Diff against main

Filename             Stmts    Miss  Cover
-----------------  -------  ------  -------
R/decorate_grob.R       +2      +2  -0.91%
TOTAL                   +2      +2  -0.02%

Results for commit: 2a60cd1

Minimum allowed coverage is 80%

♻️ This comment has been updated with latest results

[Melkiades]

Do you think we can test the output in any way?

[edelarua]

Do you think we can test the output in any way?

Other than testing the helper function split_string which is already done (we could add more tests of that function if needed), I don't know how we would be able to test the output since it is always an image.

[Melkiades]

I tested both issues and seem to be resolved. Great job!

btw I was wondering if there is a way to make these legends scale: