R Under development (unstable) (2026-01-07 r89288 ucrt) -- "Unsuffered Consequences"
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> # This file is part of the standard setup for testthat.
> # It is recommended that you do not modify it.
> #
> # Where should you do additional test configuration?
> # Learn more about the roles of various files in:
> # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
> # * https://testthat.r-lib.org/articles/special-files.html
> 
> library(testthat)
> library(clinpubr)
> 
> test_check("clinpubr")
Assuming '1' is [Event] and '0' is [non-Event]
Assuming '1' is [Event] and '0' is [non-Event]
Assuming '1' is [Event] and '0' is [non-Event]
=== Data Overview Summary ===
Dataset: 32 rows, 11 columns

Variable Types:
  numeric   : 11 variables

Found 7 potential quality issues:
  outliers                 : 5 cases
  low_cardinality          : 2 cases

Recommendations:
  - Review outliers in these numeric variables: mpg, hp, wt, qsec, carb
  - Consider converting these low-cardinality variables to factor: cyl, gear
=== Data Overview Summary ===
Dataset: 32 rows, 11 columns

Variable Types:
  numeric   : 11 variables

Found 7 potential quality issues:
  outliers                 : 5 cases
  low_cardinality          : 2 cases

Recommendations:
  - Review outliers in these numeric variables: mpg, hp, wt, qsec, carb
  - Consider converting these low-cardinality variables to factor: cyl, gear
=== Data Overview Summary ===
Dataset: 32 rows, 11 columns

Variable Types:
  numeric   : 11 variables

Found 4 potential quality issues:
  outliers                 : 2 cases
  low_cardinality          : 2 cases

Recommendations:
  - Review outliers in these numeric variables: vs, am
  - Consider converting these low-cardinality variables to factor: cyl, gear
=== Data Overview Summary ===
Dataset: 32 rows, 11 columns

Variable Types:
  numeric   : 11 variables

Found 3 potential quality issues:
  outliers                 : 1 cases
  low_cardinality          : 2 cases

Recommendations:
  - Review outliers in these numeric variables: carb
  - Consider converting these low-cardinality variables to factor: cyl, gear
=== Data Overview Summary ===
Dataset: 32 rows, 11 columns

Variable Types:
  numeric   : 11 variables

Found 7 potential quality issues:
  outliers                 : 5 cases
  low_cardinality          : 2 cases

Recommendations:
  - Review outliers in these numeric variables: mpg, hp, wt, qsec, carb
  - Consider converting these low-cardinality variables to factor: cyl, gear
=== Data Overview Summary ===
Dataset: 32 rows, 11 columns

Variable Types:
  numeric   : 11 variables

Found 2 potential quality issues:
  low_cardinality          : 2 cases

Recommendations:
  - Consider converting these low-cardinality variables to factor: cyl, gear
=== Data Overview Summary ===
Dataset: 32 rows, 11 columns

Variable Types:
  numeric   : 11 variables

Found 3 potential quality issues:
  outliers                 : 1 cases
  low_cardinality          : 2 cases

Recommendations:
  - Review outliers in these numeric variables: carb
  - Consider converting these low-cardinality variables to factor: cyl, gear
=== Data Overview Summary ===
Dataset: 32 rows, 11 columns

Variable Types:
  numeric   : 11 variables

Found 8 potential quality issues:
  outliers                 : 6 cases
  low_cardinality          : 2 cases

Recommendations:
  - Review outliers in these numeric variables: mpg, hp, drat, wt, qsec, carb
  - Consider converting these low-cardinality variables to factor: cyl, gear
=== Data Overview Summary ===
Dataset: 12 rows, 7 columns

Variable Types:
  numeric   : 2 variables
  character : 4 variables
  logical   : 1 variables

Found 7 potential quality issues:
  numeric_as_character     : 2 cases
  outliers                 : 1 cases
  missing_values           : 2 cases
  near_zero_variance       : 1 cases
  duplicate_rows           : 1 cases

Recommendations:
  - Consider converting these character variables to numeric: age, score
  - Review outliers in these numeric variables: income
  - Variables with < 50 % missing values: gender, active - consider imputation
  - Consider removing constant or near-zero variance variables: constant
  - Found 2 duplicate rows - consider removing them
=== Data Overview Summary ===
Dataset: 0 rows, 0 columns

Variable Types:

No major quality issues detected.
=== Data Overview Summary ===
Dataset: 10 rows, 1 columns

Variable Types:
  numeric   : 1 variables

No major quality issues detected.
=== Data Overview Summary ===
Dataset: 10 rows, 2 columns

Variable Types:
  logical   : 2 variables

Found 15 potential quality issues:
  missing_values           : 2 cases
  duplicate_rows           : 1 cases
  empty_columns            : 2 cases
  empty_rows               : 10 cases

Recommendations:
  - Variables with > 50 % missing values: x, y - consider dropping or imputation strategy
  - Found 9 duplicate rows - consider removing them
  - Found completely empty columns: x, y - consider removing them
  - Found 10 completely empty rows at positions: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10  - consider removing them
=== Data Overview Summary ===
Dataset: 10 rows, 7 columns

Variable Types:
  numeric   : 2 variables
  character : 2 variables
  logical   : 1 variables
  date      : 1 variables
  other     : 1 variables

Found 2 potential quality issues:
  numeric_as_character     : 1 cases
  missing_values           : 1 cases

Recommendations:
  - Consider converting these character variables to numeric: factor_col
  - Variables with > 50 % missing values: logical_col - consider dropping or imputation strategy
=== Data Overview Summary ===
Dataset: 10 rows, 4 columns

Variable Types:
  numeric   : 2 variables
  character : 2 variables

Found 3 potential quality issues:
  outliers                 : 1 cases
  missing_values           : 1 cases
  near_zero_variance       : 1 cases

Recommendations:
  - Review outliers in these numeric variables: income
  - Variables with < 50 % missing values: gender - consider imputation
  - Consider removing constant or near-zero variance variables: constant
=== Data Overview Summary ===
Dataset: 11 rows, 3 columns

Variable Types:
  numeric   : 3 variables

Found 2 potential quality issues:
  outliers                 : 1 cases
  negative_in_positive     : 1 cases

Recommendations:
  - Review outliers in these numeric variables: age
  - Numeric variables with mostly positive values but containing negatives: age
=== Data Overview Summary ===
Dataset: 10 rows, 3 columns

Variable Types:
  numeric   : 2 variables
  logical   : 1 variables

Found 2 potential quality issues:
  missing_values           : 1 cases
  empty_columns            : 1 cases

Recommendations:
  - Variables with > 50 % missing values: empty_col - consider dropping or imputation strategy
  - Found completely empty columns: empty_col - consider removing them
=== Data Overview Summary ===
Dataset: 12 rows, 2 columns

Variable Types:
  numeric   : 2 variables

Found 5 potential quality issues:
  missing_values           : 2 cases
  duplicate_rows           : 1 cases
  empty_rows               : 2 cases

Recommendations:
  - Variables with < 50 % missing values: id, value - consider imputation
  - Found 1 duplicate rows - consider removing them
  - Found 2 completely empty rows at positions: 11, 12  - consider removing them
=== Data Overview Summary ===
Dataset: 10 rows, 3 columns

Variable Types:
  numeric   : 1 variables
  date      : 2 variables

Found 2 potential quality issues:
  missing_values           : 1 cases
  suspicious_dates         : 1 cases

Recommendations:
  - Variables with < 50 % missing values: suspicious_date - consider imputation
  - Review suspicious dates (year < 1910 or > current year) in: suspicious_date
=== Data Overview Summary ===
Dataset: 10 rows, 4 columns

Variable Types:
  numeric   : 3 variables
  character : 1 variables

Found 2 potential quality issues:
  outliers                 : 1 cases
  low_cardinality          : 1 cases

Recommendations:
  - Review outliers in these numeric variables: score
  - Consider converting these low-cardinality variables to factor: education
=== Data Overview Summary ===
Dataset: 12 rows, 2 columns

Variable Types:
  numeric   : 1 variables
  character : 1 variables

Found 1 potential quality issues:
  case_issues              : 1 cases

Recommendations:
  - These character variables have case inconsistency issues: city - consider standardizing to lowercase or uppercase

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

Loading required package: Hmisc

Attaching package: 'Hmisc'

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

    src, summarize

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

    describe

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

    format.pval, units

Taking all variables as interaction predictors
Taking all variables as group variables
Taking all variables as interaction predictors
Taking all variables as group variables
$lin

$rcs

$lin

$rcs

Ignoring unknown labels:
* colour : "age"
Ignoring unknown labels:
* colour : "age"
Ignoring unknown labels:
* colour : "age"
Ignoring unknown labels:
* colour : "age"
Taking all variables as predictors
cannot process:2020-13-01
cannot process:not_a_date
cannot process:123456789

Attaching package: 'rlang'

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

    local_options, with_options

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

    qq_show

[ FAIL 0 | WARN 18 | SKIP 37 | PASS 492 ]

══ Skipped tests (37) ══════════════════════════════════════════════════════════
• On CRAN (37): 'test-baseline_table.R:4:1', 'test-baseline_table.R:19:1',
  'test-baseline_table.R:32:1', 'test-baseline_table.R:54:1',
  'test-classif_model_compare.R:30:1', 'test-cut_by.R:1:1',
  'test-importance_plot.R:4:1', 'test-interactions.R:9:1',
  'test-interactions.R:28:1', 'test-interactions.R:61:1', 'test-misc.R:122:1',
  'test-multichoice.R:111:1', 'test-multichoice.R:117:1',
  'test-multichoice.R:123:1', 'test-multichoice.R:129:1',
  'test-multichoice.R:135:1', 'test-predictor_effect_plot.R:123:1',
  'test-predictor_effect_plot.R:143:1', 'test-rcs_plot.R:12:1',
  'test-rcs_plot.R:21:1', 'test-rcs_plot.R:36:1', 'test-rcs_plot.R:45:1',
  'test-rcs_plot.R:52:1', 'test-rcs_plot.R:64:1', 'test-rcs_plot.R:73:1',
  'test-rcs_plot.R:82:1', 'test-rcs_plot.R:91:1', 'test-regressions.R:15:1',
  'test-regressions.R:39:1', 'test-regressions.R:60:1',
  'test-regressions.R:120:1', 'test-regressions.R:139:1',
  'test-regressions.R:148:1', 'test-regressions.R:186:1',
  'test-time_roc.R:38:1', 'test-time_roc.R:60:1', 'test-time_roc.R:76:1'

[ FAIL 0 | WARN 18 | SKIP 37 | PASS 492 ]
> 
> proc.time()
   user  system elapsed 
  59.92    3.93   64.01