Make sure extract_stats() and cousins work out of the box with grouped plots

[IndrajeetPatil]

library(ggstatsplot)

p <- grouped_ggpiestats(mtcars, x = cyl, grouping.var = am)
extract_stats(p)
#> [[1]]
#> $subtitle_data
#> # A tibble: 1 × 13
#>   statistic    df p.value method                                   effectsize 
#>       <dbl> <dbl>   <dbl> <chr>                                    <chr>      
#> 1      7.68     2  0.0214 Chi-squared test for given probabilities Pearson's C
#>   estimate conf.level conf.low conf.high conf.method conf.distribution n.obs
#>      <dbl>      <dbl>    <dbl>     <dbl> <chr>       <chr>             <int>
#> 1    0.537       0.95   0.0666     0.725 ncp         chisq                19
#>   expression
#>   <list>    
#> 1 <language>
#> 
#> $caption_data
#> # A tibble: 1 × 4
#>    bf10 prior.scale method                                      expression
#>   <dbl>       <dbl> <chr>                                       <list>    
#> 1  1.15           1 Bayesian one-way contingency table analysis <language>
#> 
#> $pairwise_comparisons_data
#> NULL
#> 
#> $descriptive_data
#> # A tibble: 3 × 4
#>   cyl   counts  perc .label
#>   <fct>  <int> <dbl> <chr> 
#> 1 8         12  63.2 63%   
#> 2 6          4  21.1 21%   
#> 3 4          3  15.8 16%   
#> 
#> $one_sample_data
#> NULL
#> 
#> $tidy_data
#> NULL
#> 
#> $glance_data
#> NULL
#> 
#> attr(,"class")
#> [1] "ggstatsplot_stats" "list"             
#> 
#> [[2]]
#> $subtitle_data
#> # A tibble: 1 × 13
#>   statistic    df p.value method                                   effectsize 
#>       <dbl> <dbl>   <dbl> <chr>                                    <chr>      
#> 1      4.77     2  0.0921 Chi-squared test for given probabilities Pearson's C
#>   estimate conf.level conf.low conf.high conf.method conf.distribution n.obs
#>      <dbl>      <dbl>    <dbl>     <dbl> <chr>       <chr>             <int>
#> 1    0.518       0.95        0     0.741 ncp         chisq                13
#>   expression
#>   <list>    
#> 1 <language>
#> 
#> $caption_data
#> # A tibble: 1 × 4
#>    bf10 prior.scale method                                      expression
#>   <dbl>       <dbl> <chr>                                       <list>    
#> 1 0.434           1 Bayesian one-way contingency table analysis <language>
#> 
#> $pairwise_comparisons_data
#> NULL
#> 
#> $descriptive_data
#> # A tibble: 3 × 4
#>   cyl   counts  perc .label
#>   <fct>  <int> <dbl> <chr> 
#> 1 8          2  15.4 15%   
#> 2 6          3  23.1 23%   
#> 3 4          8  61.5 62%   
#> 
#> $one_sample_data
#> NULL
#> 
#> $tidy_data
#> NULL
#> 
#> $glance_data
#> NULL
#> 
#> attr(,"class")
#> [1] "ggstatsplot_stats" "list"
extract_subtitle(p)
#> [[1]]
#> list(chi["gof"]^2 * "(" * 2 * ")" == "7.68", italic(p) == "0.02", 
#>     widehat(italic("C"))["Pearson"] == "0.54", CI["95%"] ~ "[" * 
#>         "0.07", "0.73" * "]", italic("n")["obs"] == "19")
#> 
#> [[2]]
#> list(chi["gof"]^2 * "(" * 2 * ")" == "4.77", italic(p) == "0.09", 
#>     widehat(italic("C"))["Pearson"] == "0.52", CI["95%"] ~ "[" * 
#>         "0.00", "0.74" * "]", italic("n")["obs"] == "13")
extract_caption(p)
#> [[1]]
#> list(log[e] * (BF["01"]) == "-0.14", italic("a")["Gunel-Dickey"] == 
#>     "1.00")
#> 
#> [[2]]
#> list(log[e] * (BF["01"]) == "0.83", italic("a")["Gunel-Dickey"] == 
#>     "1.00")

^{Created on 2024-07-27 with reprex v2.1.1}