Running ‘spelling.R’ [0s/1s]
  Running ‘testthat.R’ [222s/294s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(ggstatsplot)
  > 
  > test_check("ggstatsplot")
  Note: Shapiro-Wilk Normality Test for cty : p-value = < 0.001
  Note: Bartlett's test for homogeneity of variances for factor drv : p-value = 0.0045
  Note: No model diagnostics information available for the object of class tbl_df .
  Note: No model diagnostics information available for the object of class tbl_df .
  Note: No model diagnostics information available for the object of class tbl_df .
  Note: No p-values and/or statistic available for regression coefficients from tbl_df object, so skipping labels.
  Note: No model diagnostics information available for the object of class tbl_df .
  Note: No p-values and/or statistic available for regression coefficients from tbl_df object, so skipping labels.
  Note: No model diagnostics information available for the object of class tbl_df .
  Note: The object of class kmeans aren't currently supported.
  Note: In the correlation matrix,
  the upper triangle: p-values adjusted for multiple comparisons
  the lower triangle: unadjusted p-values.
  Note: Shapiro-Wilk Normality Test for Speed : p-value = 0.3410
  Note: Shapiro-Wilk Normality Test for height : p-value = < 0.001
  Note: Results from faceted one-sample proportion tests:
  # A tibble: 2 x 7
    condition Male   Female `Chi-squared`    df `p-value` significance
    <fct>     <chr>  <chr>          <dbl> <dbl>     <dbl> <chr>       
  1 No        91.54% 8.46%       1029.        1     0     ***         
  2 Yes       51.62% 48.38%         0.744     1     0.388 ns          
  Note: 95% CI for effect size estimate was computed with 25 bootstrap samples.
  Error in chisq.test(counts, p = expProps) : 
    'x' must at least have 2 elements
  Warning: Proportion test will not be run because it requires x to have at least 
  2 levels with non-zero frequencies.
  Warning: This plot can't be further modified with `ggplot2` functions.
  In case you want a `ggplot` object, set `marginal = FALSE`.
  Note: Shapiro-Wilk Normality Test for rating : p-value = 0.003
  Note: Bartlett's test for homogeneity of variances for factor genre : p-value = 0.120
  Note: Shapiro-Wilk Normality Test for rating : p-value = 0.001
  Note: Bartlett's test for homogeneity of variances for factor genre : p-value = 0.104
  Warning: Individual plots in the combined `grouped_` plots
  can't be further modified with `ggplot2` functions.
  Warning: Individual plots in the combined `grouped_` plots
  can't be further modified with `ggplot2` functions.
  Note: Shapiro-Wilk Normality Test for cty : p-value = 0.762
  Note: Shapiro-Wilk Normality Test for cty : p-value = 0.238
  Note: Shapiro-Wilk Normality Test for cty : p-value = 0.034
  Warning: Individual plots in the combined `grouped_` plots
  can't be further modified with `ggplot2` functions.
  Note: Shapiro-Wilk Normality Test for brainwt : p-value = 0.002
  Note: Shapiro-Wilk Normality Test for brainwt : p-value = < 0.001
  Note: Shapiro-Wilk Normality Test for brainwt : p-value = 0.020
  Note: Shapiro-Wilk Normality Test for brainwt : p-value = < 0.001
  Warning: Individual plots in the combined `grouped_` plots
  can't be further modified with `ggplot2` functions.
  Note: Results from faceted one-sample proportion tests:
  # A tibble: 2 x 8
    condition `6`     `4`    `8`    `Chi-squared`    df `p-value` significance
    <fct>     <chr>   <chr>  <chr>          <dbl> <dbl>     <dbl> <chr>       
  1 suv       34.21%  10.53% 55.26%          11.4     2     0.003 **          
  2 midsize   100.00% <NA>   <NA>             2       2     0.368 ns          
  Note: 95% CI for effect size estimate was computed with 25 bootstrap samples.
  Note: Results from faceted one-sample proportion tests:
  # A tibble: 1 x 7
    condition `4`    `6`    `Chi-squared`    df `p-value` significance
    <fct>     <chr>  <chr>          <dbl> <dbl>     <dbl> <chr>       
  1 midsize   38.89% 61.11%         0.889     1     0.346 ns          
  Error: Row variable 'condition' contains less than 2 levels.
  Chi-squared test can't be run; no subtitle displayed.
  Warning: Individual plots in the combined `grouped_` plots
  can't be further modified with `ggplot2` functions.
  Error: Row variable 'condition' contains less than 2 levels.
  Chi-squared test can't be run; no subtitle displayed.
  Error: Identical variable ( Species ) was used for both grouping and labeling, which is not allowed.
  Warning: Individual plots in the combined `grouped_` plots
  can't be further modified with `ggplot2` functions.
  Warning: No Bayes Factor pairwise comparisons currently available.
  Warning: No Bayes Factor pairwise comparisons currently available.
  Error in chisq.test(counts, p = expProps) : 
    'x' must at least have 2 elements
  Warning: Proportion test will not be run because it requires x to have at least 
  2 levels with non-zero frequencies.
  Error in chisq.test(counts, p = expProps) : 
    'x' must at least have 2 elements
  ── 1. Failure: parametric t-test works (between-subjects without NAs) (@test_sub
  `using_function1` not equal to `results1`.
  target, current do not match when deparsed
  
  ══ testthat results  ═══════════════════════════════════════════════════════════
  OK: 653 SKIPPED: 0 FAILED: 1
  1. Failure: parametric t-test works (between-subjects without NAs) (@test_subtitle_t_parametric.R#63) 
  
  Error: testthat unit tests failed
  Execution halted