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401 add rate01 tlg@main #739

Merged
merged 12 commits into from
Nov 11, 2022
Merged

401 add rate01 tlg@main #739

merged 12 commits into from
Nov 11, 2022

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ayogasekaram
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added unit tests for summarize_glm_count

adding rate01 to tlg MR found here: https://code.roche.com/nest/docs/tlg-catalog/devel/-/merge_requests/162

closes #538

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github-actions bot commented Nov 9, 2022

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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%   205-207
R/abnormal_by_worst_grade.R                   37       0  100.00%
R/abnormal.R                                  40       0  100.00%
R/combination_function.R                       9       0  100.00%
R/compare_variables.R                        138       3  97.83%   135, 247, 266
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%   52
R/count_missed_doses.R                        31       0  100.00%
R/count_occurrences_by_grade.R                84       3  96.43%   148, 163-164
R/count_occurrences.R                         61       1  98.36%   89
R/count_patients_events_in_cols.R             67       0  100.00%
R/count_patients_with_event.R                 72       0  100.00%
R/count_values.R                              24       0  100.00%
R/cox_regression_inter.R                     142       0  100.00%
R/cox_regression.R                           318       0  100.00%
R/coxph.R                                    168       7  95.83%   232-236, 281, 297, 306, 312-313
R/d_pkparam.R                                405       0  100.00%
R/decorate_grob.R                            167       6  96.41%   270-276, 383, 415, 425, 432
R/desctools_binom_diff.R                     668      68  89.82%   65, 100-101, 141-142, 145, 224, 250-261, 300, 302, 322, 326, 330, 334, 386, 389, 392, 395, 456, 464, 476-477, 483-486, 494, 497, 506, 509, 557-558, 560-561, 563-564, 566-567, 640, 652-665, 670, 717, 730, 734
R/df_explicit_na.R                            30       0  100.00%
R/estimate_multinomial_rsp.R                  47       1  97.87%   53
R/estimate_proportion.R                      198      10  94.95%   421-428, 432, 437, 545
R/fit_rsp_step.R                              36       0  100.00%
R/fit_survival_step.R                         36       0  100.00%
R/footnotes.R                                  5       0  100.00%
R/formats.R                                   88       1  98.86%   66
R/g_forest.R                                 441      42  90.48%   172, 200, 229, 252-253, 257-258, 326, 339, 343-344, 349-350, 363, 379, 426, 457, 533, 542, 614-634, 637, 648, 705, 708, 834
R/g_lineplot.R                               192      29  84.90%   177, 190, 218, 244-247, 320-327, 345-346, 352-362, 458, 466
R/g_step.R                                    68       1  98.53%   106
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_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%   248-261
R/h_stack_by_baskets.R                        65       2  96.92%   96, 143
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%   254-266
R/incidence_rate.R                            93       7  92.47%   69-76
R/individual_patient_plot.R                  133       0  100.00%
R/kaplan_meier_plot.R                        532      29  94.55%   256-260, 456, 627-629, 637-639, 665, 672-673, 845, 1030, 1272-1283
R/logistic_regression.R                      569       3  99.47%   279-280, 347
R/missing_data.R                              20       3  85.00%   29, 61, 71
R/odds_ratio.R                               106       0  100.00%
R/prop_diff_test.R                            87       0  100.00%
R/prop_diff.R                                255      11  95.69%   56-63, 190, 364, 508
R/prune_occurrences.R                         57       0  100.00%
R/response_biomarkers_subgroups.R             59       0  100.00%
R/response_subgroups.R                       164       0  100.00%
R/rtables_access.R                            21       0  100.00%
R/score_occurrences.R                         20       1  95.00%   112
R/split_cols_by_groups.R                      49       0  100.00%
R/stat.R                                      47       0  100.00%
R/summarize_ancova.R                          94       1  98.94%   197
R/summarize_change.R                          27       0  100.00%
R/summarize_colvars.R                          6       0  100.00%
R/summarize_glm_count.R                      162      34  79.01%   171, 234, 303, 326-359
R/summarize_num_patients.R                    47       3  93.62%   93-95
R/summarize_patients_exposure_in_cols.R       46       0  100.00%
R/summarize_variables_in_cols.R               64       6  90.62%   35, 64, 91, 93, 120, 122
R/summarize_variables.R                      212       1  99.53%   477
R/survival_biomarkers_subgroups.R             59       0  100.00%
R/survival_coxph_pairwise.R                   73       9  87.67%   69-77
R/survival_duration_subgroups.R              171       0  100.00%
R/survival_time.R                             47       0  100.00%
R/survival_timepoint.R                       114       7  93.86%   147-153
R/utils_checkmate.R                           68       0  100.00%
R/utils_factor.R                              95       1  98.95%   93
R/utils_grid.R                               111       5  95.50%   150, 260-266
R/utils_rtables.R                             74       2  97.30%   317-318
R/utils.R                                    137      10  92.70%   100, 102, 106, 126, 129, 132, 136, 145-146, 334
R/wrap_text.R                                 65       5  92.31%   37, 58, 78, 85, 107
TOTAL                                       8782     346  96.06%

Diff against main

Filename                   Stmts    Miss  Cover
-----------------------  -------  ------  -------
R/summarize_glm_count.R     +162     +34  +79.01%
TOTAL                       +162     +34  -0.32%

Results for commit: 181aa89

Minimum allowed coverage is 80%

♻️ This comment has been updated with latest results

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github-actions bot commented Nov 9, 2022

Unit Tests Summary

       1 files     126 suites   2m 48s ⏱️
   865 tests    865 ✔️ 0 💤 0
1 304 runs  1 304 ✔️ 0 💤 0

Results for commit 90d2d29.

♻️ This comment has been updated with latest results.

@shajoezhu shajoezhu added the sme label Nov 9, 2022
Comment on lines 30 to 34
#' library(scda)
#' library(dplyr)
#' anl <- synthetic_cdisc_dataset("latest", "adtte") %>%
#' filter(PARAMCD == "TNE")
#' anl$AVAL_f <- as.factor(anl$AVAL)
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I don't think this part is needed, as the below example is not ran

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I think it is needed for the last example which should be on the same doc file. Still, I would not repeat this there

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the last example has its own already

.null_ref_cells = FALSE
)

#' @describeIn summarize_glm_count Layout creating function which can be be used for creating
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I think we can move from line 411 to the end of function summarize_glm_count to the top of this script, so the documentation is clearer. As all the other parts are internal, and examples won't need to be run.

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It makes sense but we can always put comment titles to describe the "internal" steps. Not sure. Maybe you are right and having only "runnable" docs is better

Comment on lines 2 to 6
library(scda)
library(dplyr)
anl <- synthetic_cdisc_dataset("latest", "adtte") %>%
filter(PARAMCD == "TNE")
anl$AVAL_f <- as.factor(anl$AVAL)
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I think this part of data loading can be external to the testing functions. so the testing data can be reused, and will be more efficient.

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actually the libraries should be in setup_libraries and the dataset should be taken from the ones in setup_data

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Excellent! That is the proper way to do it now! Thanks @Melkiades

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Thanks @ayogasekaram ! it looks really good. I made some samll change requests. thanks!

@shajoezhu shajoezhu self-assigned this Nov 10, 2022
inst/WORDLIST Outdated Show resolved Hide resolved
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Great job Abi!! :) I did not review it in detail, as Joe is on it, but maybe we could aim at a bit higher coverage (>90%)

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changes lgtm! Thanks @ayogasekaram

@shajoezhu shajoezhu merged commit 98212d3 into main Nov 11, 2022
@shajoezhu shajoezhu deleted the 401_add_rate01_tlg@main branch November 11, 2022 07:26
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Create summarize_glm_count.R from RATET01 design doc
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