Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

rename trans to transform #1202

Merged
merged 3 commits into from
Mar 11, 2024
Merged

rename trans to transform #1202

merged 3 commits into from
Mar 11, 2024

Conversation

pawelru
Copy link
Contributor

@pawelru pawelru commented Mar 8, 2024

when working on tlg-catalog I have found a lifecycle warning coming out of tern

[141/144] graphs/efficacy/mmrmg02.qmd

processing file: mmrmg02.qmd
1/20                  
2/20 [knitr_utils]    
3/20                  
4/20 [setup]          
5/20                  
6/20 [unnamed-chunk-1]
7/20                  
8/20 [plot1]          

Quitting from lines 84-105 [plot1] (mmrmg02.qmd)
Error:
! The `trans` argument of `continuous_scale()` is deprecated as of
  ggplot2 3.5.0.
ℹ Please use the `transform` argument instead.
Backtrace:
 1. tern::g_forest(...)
 2. ggplot2::scale_x_continuous(...)
 3. ggplot2::continuous_scale(...)
 4. ggplot2:::deprecate_soft0(...)
 5. lifecycle::deprecate_soft(..., user_env = user_env)
 6. lifecycle:::deprecate_stop0(msg)

@pawelru pawelru enabled auto-merge (squash) March 8, 2024 14:35
Copy link
Contributor

github-actions bot commented Mar 8, 2024

badge

Code Coverage Summary

Filename                                   Stmts    Miss  Cover    Missing
---------------------------------------  -------  ------  -------  ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
R/abnormal_by_baseline.R                      68       2  97.06%   78-79
R/abnormal_by_marked.R                        55       5  90.91%   78-82
R/abnormal_by_worst_grade_worsen.R           116       3  97.41%   240-242
R/abnormal_by_worst_grade.R                   60       0  100.00%
R/abnormal.R                                  43       0  100.00%
R/analyze_variables.R                        190       9  95.26%   488-489, 505, 529, 685-686, 692, 710-711
R/analyze_vars_in_cols.R                     179      35  80.45%   168-169, 184, 207-212, 227, 241-242, 250-258, 264-270, 349-355
R/bland_altman.R                              92      55  40.22%   37, 78-133
R/combination_function.R                       9       0  100.00%
R/compare_variables.R                        124      17  86.29%   131-135, 247, 325-334, 389-390, 396
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                          50       1  98.00%   67
R/count_missed_doses.R                        34       0  100.00%
R/count_occurrences_by_grade.R               113       5  95.58%   101, 151-153, 156
R/count_occurrences.R                        115       1  99.13%   108
R/count_patients_events_in_cols.R             67       1  98.51%   53
R/count_patients_with_event.R                 47       0  100.00%
R/count_patients_with_flags.R                 58       4  93.10%   56-57, 62-63
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, 239, 254, 262, 268-269
R/d_pkparam.R                                406       0  100.00%
R/decorate_grob.R                            173      40  76.88%   235-266, 326-328, 339, 360-397
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%   63
R/estimate_proportion.R                      205      12  94.15%   78-85, 89, 94, 315, 482, 588
R/fit_rsp_step.R                              36       0  100.00%
R/fit_survival_step.R                         36       0  100.00%
R/formatting_functions.R                     181       2  98.90%   145, 280
R/g_forest.R                                 569     412  27.59%   183-186, 189-192, 195-201, 204-207, 210-213, 240, 252-255, 260-261, 277, 287-290, 335-338, 345, 414, 491-1011
R/g_lineplot.R                               206      34  83.50%   168, 181, 210, 236-239, 315-322, 340-341, 347-357, 449, 455, 457, 499-500, 504-505
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                    45       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           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                        67       3  95.52%   68-69, 95
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       2  88.24%   54-55
R/incidence_rate.R                            96       7  92.71%   44-51
R/individual_patient_plot.R                  133       0  100.00%
R/kaplan_meier_plot.R                        695      76  89.06%   254-257, 297-332, 341-345, 556, 700-701, 733, 743-745, 753-755, 780, 787-788, 961-964, 1187, 1381-1386, 1422, 1522-1533
R/logistic_regression.R                      102       0  100.00%
R/missing_data.R                              21       3  85.71%   32, 66, 76
R/odds_ratio.R                               109       0  100.00%
R/prop_diff_test.R                            91       0  100.00%
R/prop_diff.R                                265      16  93.96%   62-65, 97, 282-289, 432, 492, 597
R/prune_occurrences.R                         57      10  82.46%   138-142, 188-192
R/response_biomarkers_subgroups.R             68       6  91.18%   189-194
R/response_subgroups.R                       192      10  94.79%   95-100, 276, 324-326
R/riskdiff.R                                  59       7  88.14%   102-105, 114, 124-125
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                         104       2  98.08%   172, 177
R/summarize_change.R                          30       0  100.00%
R/summarize_colvars.R                         13       2  84.62%   72-73
R/summarize_coxreg.R                         178       6  96.63%   201-202, 209, 346-347, 442
R/summarize_glm_count.R                      195      27  86.15%   206, 224-256, 301-302
R/summarize_num_patients.R                    99       9  90.91%   108-110, 160-161, 252-257
R/summarize_patients_exposure_in_cols.R       96       1  98.96%   42
R/survival_biomarkers_subgroups.R             70       6  91.43%   112-117
R/survival_coxph_pairwise.R                   79      11  86.08%   45-46, 58-66
R/survival_duration_subgroups.R              191       6  96.86%   119-124
R/survival_time.R                             79       0  100.00%
R/survival_timepoint.R                       113       7  93.81%   120-126
R/utils_checkmate.R                           68       0  100.00%
R/utils_default_stats_formats_labels.R       136       4  97.06%   72, 577-580
R/utils_factor.R                             109       2  98.17%   84, 302
R/utils_ggplot.R                              72       0  100.00%
R/utils_grid.R                               111       5  95.50%   149, 258-265
R/utils_rtables.R                            100       9  91.00%   39, 46, 51, 58-62, 403-404
R/utils_split_funs.R                          52       2  96.15%   81, 93
R/utils.R                                    141      10  92.91%   92, 94, 98, 118, 121, 124, 128, 137-138, 332
TOTAL                                      10307    1036  89.95%

Diff against main

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

Results for commit: e1ba82d

Minimum allowed coverage is 80%

♻️ This comment has been updated with latest results

Copy link
Contributor

github-actions bot commented Mar 8, 2024

Unit Tests Summary

    1 files     83 suites   1m 1s ⏱️
  821 tests   794 ✅  27 💤 0 ❌
1 730 runs  1 070 ✅ 660 💤 0 ❌

Results for commit e1ba82d.

♻️ This comment has been updated with latest results.

Copy link
Contributor

@Melkiades Melkiades left a comment

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Lgtm! Thanks @pawelru :)

@pawelru pawelru merged commit 796663e into main Mar 11, 2024
24 checks passed
@pawelru pawelru deleted the ggplot2_3.5.0 branch March 11, 2024 08:42
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

Successfully merging this pull request may close these issues.

2 participants