R Under development (unstable) (2025-12-13 r89166 ucrt) -- "Unsuffered Consequences"
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Platform: x86_64-w64-mingw32/x64

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> pkg_name <- "tern"
> library(testthat)
> test_check(pkg_name, reporter = ParallelProgressReporter$new())
Loading required package: tern
Loading required package: rtables
Loading required package: formatters

Attaching package: 'formatters'

The following object is masked from 'package:base':

    %||%

Loading required package: magrittr

Attaching package: 'magrittr'

The following objects are masked from 'package:testthat':

    equals, is_less_than, not


Attaching package: 'rtables'

The following object is masked from 'package:utils':

    str

Registered S3 method overwritten by 'tern':
  method   from 
  tidy.glm broom

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

✔ | F W  S  OK | Context

⠋ [ FAIL 0 | WARN 0 | SKIP 0 | PASS 0 ] Starting up...                          
✔ |      7   7 | abnormal                                                       

✔ |      5  10 | abnormal_by_baseline                                           

✔ |      3   4 | abnormal_by_marked [3.4s]                                      

✔ |      2   3 | abnormal_by_worst_grade                                        

✔ |      8  12 | abnormal_lab_worsen_by_baseline                                

✔ |     31  83 | analyze_variables [6.0s]                                       

✔ |      7  19 | analyze_vars_in_cols [7.5s]                                    

✔ |      2   7 | bland-altman                                                   

✔ |         16 | combination_function                                           

✔ |      9  20 | compare_variables                                              

✔ |      1   3 | control_logistic                                               

✔ |      1   4 | control_step                                                   

✔ |      3  11 | control_survival                                               

✔ |      8   8 | count_cumulative                                               

✔ |      4   4 | count_missed_doses                                             
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd A.1.1.1.1  -> { dcd A.1.1.1.1, dcd A.1.1.1.1[2], dcd A.1.1.1.1[3] }]
  To control table names use split_rows_by*(, parent_name =.) or  analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd A.1.1.1.2  -> { dcd A.1.1.1.2, dcd A.1.1.1.2[2], dcd A.1.1.1.2[3] }]
  To control table names use split_rows_by*(, parent_name =.) or  analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd B.1.1.1.1  -> { dcd B.1.1.1.1, dcd B.1.1.1.1[2], dcd B.1.1.1.1[3] }]
  To control table names use split_rows_by*(, parent_name =.) or  analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd B.2.1.2.1  -> { dcd B.2.1.2.1, dcd B.2.1.2.1[2], dcd B.2.1.2.1[3] }]
  To control table names use split_rows_by*(, parent_name =.) or  analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd B.2.2.3.1  -> { dcd B.2.2.3.1, dcd B.2.2.3.1[2], dcd B.2.2.3.1[3] }]
  To control table names use split_rows_by*(, parent_name =.) or  analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd C.1.1.1.3  -> { dcd C.1.1.1.3, dcd C.1.1.1.3[2], dcd C.1.1.1.3[3] }]
  To control table names use split_rows_by*(, parent_name =.) or  analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd C.2.1.2.1  -> { dcd C.2.1.2.1, dcd C.2.1.2.1[2], dcd C.2.1.2.1[3] }]
  To control table names use split_rows_by*(, parent_name =.) or  analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd D.1.1.1.1  -> { dcd D.1.1.1.1, dcd D.1.1.1.1[2], dcd D.1.1.1.1[3] }]
  To control table names use split_rows_by*(, parent_name =.) or  analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd D.1.1.4.2  -> { dcd D.1.1.4.2, dcd D.1.1.4.2[2], dcd D.1.1.4.2[3] }]
  To control table names use split_rows_by*(, parent_name =.) or  analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[dcd D.2.1.5.3  -> { dcd D.2.1.5.3, dcd D.2.1.5.3[2], dcd D.2.1.5.3[3] }]
  To control table names use split_rows_by*(, parent_name =.) or  analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[LOW  -> { LOW, LOW[2], LOW[3] }]
  To control table names use split_rows_by*(, parent_name =.) or  analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[MEDIUM  -> { MEDIUM, MEDIUM[2], MEDIUM[3] }]
  To control table names use split_rows_by*(, parent_name =.) or  analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[HIGH  -> { HIGH, HIGH[2], HIGH[3] }]
  To control table names use split_rows_by*(, parent_name =.) or  analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[LOW  -> { LOW, LOW[2], LOW[3] }]
  To control table names use split_rows_by*(, parent_name =.) or  analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[MEDIUM  -> { MEDIUM, MEDIUM[2], MEDIUM[3] }]
  To control table names use split_rows_by*(, parent_name =.) or  analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[HIGH  -> { HIGH, HIGH[2], HIGH[3] }]
  To control table names use split_rows_by*(, parent_name =.) or  analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE

✔ |      8  20 | count_occurrences [3.3s]                                       

✔ |     17  27 | count_occurrences_by_grade [2.7s]                              

✔ |      4   4 | count_patients_events_in_cols                                  

✔ |      8   9 | count_patients_with_event                                      

✔ |     11  18 | count_patients_with_flags [1.1s]                               

✔ |     11  17 | count_values                                                   
skipping term strata(ecog.ps)

✔ |      5  15 | coxph                                                          

✔ |     24  50 | coxreg [1.2s]                                                  

✔ |      2  13 | decorate_grob [1.5s]                                           

✔ |      5   5 | desctools_binom_diff                                           

✔ |      5  13 | df_explicit_na                                                 

✔ |      3   7 | estimate_multinomial_rsp                                       

✔ |     18  24 | estimate_proportion [1.1s]                                     

✔ |      3  16 | fit_rsp_step                                                   

✔ |      3  12 | fit_survival_step                                              

✔ |     21  35 | formatting_functions                                           

✔ |      3  18 | g_forest [5.6s]                                                

✔ |      9  18 | g_km [7.8s]                                                    

✔ |      8  15 | g_lineplot [3.0s]                                              

✔ |      4  16 | g_step                                                         

✔ |      2   2 | g_waterfall                                                    

✔ |      5   1 | h_adsl_adlb_merge_using_worst_flag                             

✔ |      2   5 | h_biomarkers_subgroups                                         

✔ |      1   2 | h_format_row                                                   

✔ |      5   5 | h_incidence_rate                                               

✔ |     13  26 | h_km                                                           

✔ |     16  62 | h_logistic_regression                                          

✔ |      7   7 | h_map_for_count_abnormal                                       

✔ |      2   2 | h_pkparam_sort                                                 

✔ |      4   4 | h_response_biomarkers_subgroups                                

✔ |     14  15 | h_response_subgroups                                           

✔ |      3   8 | h_stack_by_baskets                                             

✔ |      8  62 | h_step                                                         

✔ |      3   6 | h_survival_biomarkers_subgroups                                

✔ |     16  20 | h_survival_duration_subgroups                                  

✔ |      2   0 | imputation_rule                                                

✔ |      8  12 | incidence_rate                                                 

✔ |          7 | individual_patient_plot                                        

✔ |      6  22 | logistic_regression [2.8s]                                     

✔ |      7  23 | make_afun                                                      

✔ |      8  12 | odds_ratio                                                     

✔ |     15  36 | prop_diff                                                      

✔ |      7  33 | prune_occurrences [2.8s]                                       
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[root  -> { root, root[2] }]
  To control table names use split_rows_by*(, parent_name =.) or  analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[biomarker_label  -> { biomarker_label, biomarker_label[2] }]
  To control table names use split_rows_by*(, parent_name =.) or  analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[root  -> { root, root[2] }]
  To control table names use split_rows_by*(, parent_name =.) or  analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE

✔ |      6  13 | response_biomarkers_subgroups [2.0s]                           

✔ |     14  19 | response_subgroups [8.8s]                                      

✔ |      5  15 | rtables_access [3.4s]                                          

✔ |      5   8 | score_occurrences [1.3s]                                       

✔ |     15  19 | split_cols_by_groups                                           

✔ |      9  43 | stat                                                           

✔ |      5  13 | summarize_ancova [1.9s]                                        

✔ |      5   7 | summarize_change                                               

✔ |      3   3 | summarize_colvars [1.2s]                                       

✔ |     13  23 | summarize_coxreg [7.7s]                                        

✔ |     17  27 | summarize_glm_count [2.2s]                                     

✔ |     17  27 | summarize_num_patients [2.7s]                                  

✔ |      9   9 | summarize_patients_exposure_in_cols [1.0s]                     
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[root  -> { root, root[2] }]
  To control table names use split_rows_by*(, parent_name =.) or  analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[biomarker_label  -> { biomarker_label, biomarker_label[2] }]
  To control table names use split_rows_by*(, parent_name =.) or  analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[root  -> { root, root[2] }]
  To control table names use split_rows_by*(, parent_name =.) or  analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[root  -> { root, root[2] }]
  To control table names use split_rows_by*(, parent_name =.) or  analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE

✔ |      7  11 | survival_biomarkers_subgroups [3.5s]                           

✔ |      9  11 | survival_coxph_pairwise                                        

✔ |     14  21 | survival_duration_subgroups [9.1s]                             

✔ |      7   9 | survival_time                                                  

✔ |     10  11 | survival_timepoint [1.0s]                                      

✔ |     16  30 | test_proportion_diff [1.2s]                                    

✔ |     59  84 | utils [1.4s]                                                   

✔ |         42 | utils_checkmate                                                

✔ |      6  47 | utils_default_stats_formats_labels                             

✔ |     13  32 | utils_factor                                                   

✔ |      3   3 | utils_ggplot                                                   

✔ |          8 | utils_grid                                                     

✔ |     19  34 | utils_rtables [5.0s]                                           

✔ |      6  13 | utils_split_fun [1.9s]                                         

                                                                                
══ Results ═════════════════════════════════════════════════════════════════════
Duration: 128.6 s

── Skipped tests (709) ─────────────────────────────────────────────────────────
• On CRAN (680): 'test-abnormal.R:1:1', 'test-abnormal.R:27:1',
  'test-abnormal.R:54:1', 'test-abnormal.R:83:1', 'test-abnormal.R:106:1',
  'test-abnormal.R:137:1', 'test-abnormal.R:163:1',
  'test-abnormal_by_baseline.R:1:1', 'test-abnormal_by_baseline.R:28:1',
  'test-abnormal_by_baseline.R:59:1', 'test-abnormal_by_baseline.R:83:1',
  'test-abnormal_by_baseline.R:97:1', 'test-abnormal_by_marked.R:28:1',
  'test-abnormal_by_marked.R:83:1', 'test-abnormal_by_marked.R:192:1',
  'test-abnormal_by_worst_grade.R:19:1', 'test-abnormal_by_worst_grade.R:46:1',
  'test-abnormal_lab_worsen_by_baseline.R:12:1',
  'test-abnormal_lab_worsen_by_baseline.R:67:1',
  'test-abnormal_lab_worsen_by_baseline.R:85:1',
  'test-abnormal_lab_worsen_by_baseline.R:105:1',
  'test-abnormal_lab_worsen_by_baseline.R:125:1',
  'test-abnormal_lab_worsen_by_baseline.R:145:1',
  'test-abnormal_lab_worsen_by_baseline.R:165:1',
  'test-abnormal_lab_worsen_by_baseline.R:187:1',
  'test-analyze_variables.R:1:1', 'test-analyze_variables.R:10:1',
  'test-analyze_variables.R:26:1', 'test-analyze_variables.R:34:1',
  'test-analyze_variables.R:42:1', 'test-analyze_variables.R:57:1',
  'test-analyze_variables.R:65:1', 'test-analyze_variables.R:75:1',
  'test-analyze_variables.R:84:1', 'test-analyze_variables.R:107:1',
  'test-analyze_variables.R:121:1', 'test-analyze_variables.R:130:1',
  'test-analyze_variables.R:137:1', 'test-analyze_variables.R:146:1',
  'test-analyze_variables.R:156:1', 'test-analyze_variables.R:221:1',
  'test-analyze_variables.R:244:1', 'test-analyze_variables.R:282:1',
  'test-analyze_variables.R:305:1', 'test-analyze_variables.R:316:1',
  'test-analyze_variables.R:331:1', 'test-analyze_variables.R:342:1',
  'test-analyze_variables.R:353:1', 'test-analyze_variables.R:373:1',
  'test-analyze_variables.R:399:1', 'test-analyze_variables.R:445:1',
  'test-analyze_variables.R:458:1', 'test-analyze_variables.R:478:1',
  'test-analyze_variables.R:495:1', 'test-analyze_variables.R:513:1',
  'test-analyze_variables.R:528:1', 'test-analyze_vars_in_cols.R:3:1',
  'test-analyze_vars_in_cols.R:42:1', 'test-analyze_vars_in_cols.R:93:1',
  'test-analyze_vars_in_cols.R:148:1', 'test-analyze_vars_in_cols.R:216:1',
  'test-analyze_vars_in_cols.R:279:1', 'test-analyze_vars_in_cols.R:384:1',
  'test-bland-altman.R:64:1', 'test-compare_variables.R:1:1',
  'test-compare_variables.R:12:1', 'test-compare_variables.R:23:1',
  'test-compare_variables.R:35:1', 'test-compare_variables.R:60:1',
  'test-compare_variables.R:72:1', 'test-compare_variables.R:93:1',
  'test-compare_variables.R:103:1', 'test-compare_variables.R:118:1',
  'test-control_logistic.R:1:1', 'test-control_step.R:1:1',
  'test-control_survival.R:1:1', 'test-control_survival.R:15:1',
  'test-control_survival.R:30:1', 'test-count_cumulative.R:1:1',
  'test-count_cumulative.R:15:1', 'test-count_cumulative.R:33:1',
  'test-count_cumulative.R:47:1', 'test-count_cumulative.R:66:1',
  'test-count_cumulative.R:85:1', 'test-count_cumulative.R:107:1',
  'test-count_cumulative.R:132:1', 'test-count_missed_doses.R:1:1',
  'test-count_missed_doses.R:8:1', 'test-count_missed_doses.R:20:1',
  'test-count_missed_doses.R:40:1', 'test-count_occurrences.R:1:1',
  'test-count_occurrences.R:57:1', 'test-count_occurrences.R:93:1',
  'test-count_occurrences.R:113:1', 'test-count_occurrences.R:139:1',
  'test-count_occurrences.R:165:1', 'test-count_occurrences.R:206:1',
  'test-count_occurrences.R:234:1', 'test-count_occurrences_by_grade.R:15:1',
  'test-count_occurrences_by_grade.R:29:1',
  'test-count_occurrences_by_grade.R:43:1',
  'test-count_occurrences_by_grade.R:60:1',
  'test-count_occurrences_by_grade.R:70:1',
  'test-count_occurrences_by_grade.R:121:1',
  'test-count_occurrences_by_grade.R:138:1',
  'test-count_occurrences_by_grade.R:150:1',
  'test-count_occurrences_by_grade.R:166:1',
  'test-count_occurrences_by_grade.R:190:1',
  'test-count_occurrences_by_grade.R:206:1',
  'test-count_occurrences_by_grade.R:246:1',
  'test-count_occurrences_by_grade.R:286:1',
  'test-count_occurrences_by_grade.R:313:1',
  'test-count_occurrences_by_grade.R:412:1',
  'test-count_occurrences_by_grade.R:446:1',
  'test-count_occurrences_by_grade.R:461:1',
  'test-count_patients_events_in_cols.R:11:1',
  'test-count_patients_events_in_cols.R:26:1',
  'test-count_patients_events_in_cols.R:42:1',
  'test-count_patients_events_in_cols.R:58:1',
  'test-count_patients_with_event.R:1:1',
  'test-count_patients_with_event.R:19:1',
  'test-count_patients_with_event.R:38:1',
  'test-count_patients_with_event.R:59:1',
  'test-count_patients_with_event.R:83:1',
  'test-count_patients_with_event.R:115:1',
  'test-count_patients_with_event.R:153:1',
  'test-count_patients_with_event.R:175:1',
  'test-count_patients_with_flags.R:1:1',
  'test-count_patients_with_flags.R:19:1',
  'test-count_patients_with_flags.R:38:1',
  'test-count_patients_with_flags.R:103:1',
  'test-count_patients_with_flags.R:128:1',
  'test-count_patients_with_flags.R:156:1',
  'test-count_patients_with_flags.R:198:1',
  'test-count_patients_with_flags.R:246:1',
  'test-count_patients_with_flags.R:285:1',
  'test-count_patients_with_flags.R:362:1',
  'test-count_patients_with_flags.R:404:1', 'test-count_values.R:1:1',
  'test-count_values.R:17:1', 'test-count_values.R:33:1',
  'test-count_values.R:42:1', 'test-count_values.R:50:1',
  'test-count_values.R:58:1', 'test-count_values.R:67:1',
  'test-count_values.R:84:1', 'test-count_values.R:94:1',
  'test-count_values.R:104:1', 'test-count_values.R:121:1', 'test-coxph.R:1:1',
  'test-coxph.R:13:1', 'test-coxph.R:20:1', 'test-coxph.R:47:1',
  'test-coxph.R:89:1', 'test-coxreg.R:36:1', 'test-coxreg.R:47:1',
  'test-coxreg.R:59:1', 'test-coxreg.R:70:1', 'test-coxreg.R:83:1',
  'test-coxreg.R:115:1', 'test-coxreg.R:129:1', 'test-coxreg.R:139:1',
  'test-coxreg.R:152:1', 'test-coxreg.R:161:1', 'test-coxreg.R:173:1',
  'test-coxreg.R:185:1', 'test-coxreg.R:196:1', 'test-coxreg.R:207:1',
  'test-coxreg.R:221:1', 'test-coxreg.R:230:1', 'test-coxreg.R:288:1',
  'test-coxreg.R:303:1', 'test-coxreg.R:314:1', 'test-coxreg.R:349:1',
  'test-coxreg.R:363:1', 'test-coxreg.R:377:1', 'test-coxreg.R:517:1',
  'test-coxreg.R:570:1', 'test-decorate_grob.R:49:1',
  'test-desctools_binom_diff.R:12:1', 'test-desctools_binom_diff.R:21:1',
  'test-desctools_binom_diff.R:36:1', 'test-desctools_binom_diff.R:45:1',
  'test-desctools_binom_diff.R:54:1', 'test-df_explicit_na.R:17:1',
  'test-df_explicit_na.R:28:1', 'test-df_explicit_na.R:39:1',
  'test-df_explicit_na.R:47:1', 'test-df_explicit_na.R:60:1',
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  'test-utils_rtables.R:224:1', 'test-utils_rtables.R:231:1',
  'test-utils_rtables.R:238:1', 'test-utils_rtables.R:252:1',
  'test-utils_split_fun.R:9:1', 'test-utils_split_fun.R:30:1',
  'test-utils_split_fun.R:42:1', 'test-utils_split_fun.R:59:1',
  'test-utils_split_fun.R:77:1', 'test-utils_split_fun.R:105:1'
• no_plot_snapshots is TRUE (29): 'test-bland-altman.R:103:3',
  'test-decorate_grob.R:124:3', 'test-g_forest.R:23:3', 'test-g_forest.R:58:3',
  'test-g_forest.R:98:3', 'test-g_km.R:17:3', 'test-g_km.R:30:3',
  'test-g_km.R:42:3', 'test-g_km.R:64:3', 'test-g_km.R:79:3',
  'test-g_km.R:116:3', 'test-g_km.R:130:3', 'test-g_km.R:149:3',
  'test-g_km.R:164:3', 'test-g_lineplot.R:9:3', 'test-g_lineplot.R:32:3',
  'test-g_lineplot.R:52:3', 'test-g_lineplot.R:70:3',
  'test-g_lineplot.R:101:3', 'test-g_lineplot.R:114:3',
  'test-g_lineplot.R:249:3', 'test-g_lineplot.R:273:3', 'test-g_step.R:18:3',
  'test-g_step.R:28:3', 'test-g_waterfall.R:7:3', 'test-g_waterfall.R:29:3',
  'test-utils_ggplot.R:19:3', 'test-utils_ggplot.R:53:3',
  'test-utils_ggplot.R:59:3'

[ FAIL 0 | WARN 0 | SKIP 709 | PASS 1517 ]
Deleting unused snapshots: 'g_forest/g_forest_custom_2.svg',
'g_forest/g_forest_custom_3.svg', 'g_forest/g_forest_or.svg',
'g_forest/g_forest_plot_only.svg', 'g_km/g_km_crop_ylim_failure.svg',
'g_km/g_km_custom_ylim.svg', 'g_km/g_km_table_only.svg',
'g_lineplot/g_lineplot_table_only.svg', 'g_lineplot/g_lineplot_xlim_ylim.svg',
'g_lineplot/g_lineplot_xticks.svg', 'utils_ggplot/df2gg_cw.svg',
'utils_ggplot/df2gg_fs.svg', 'utils_ggplot/rtable2gg_cw.svg',
'utils_ggplot/rtable2gg_fs.svg', and 'utils_ggplot/rtable2gg_lblpad.svg'
> 
> proc.time()
   user  system elapsed 
 135.07    5.46  140.54