Helper function for ggstatsplot::ggpiestats
to apply this
function across multiple levels of a given factor and combining the
resulting plots using ggstatsplot::combine_plots
.
grouped_ggpiestats( data, x = NULL, y = NULL, counts = NULL, grouping.var, title.prefix = NULL, output = "plot", main, condition = NULL, ..., plotgrid.args = list(), title.text = NULL, title.args = list(size = 16, fontface = "bold"), caption.text = NULL, caption.args = list(size = 10), sub.text = NULL, sub.args = list(size = 12) )
data  A dataframe (or a tibble) from which variables specified are to be taken. A matrix or tables will not be accepted. 

x  The variable to use as the rows in the contingency table. 
y  The variable to use as the columns in the contingency
table. Default is 
counts  A string naming a variable in data containing counts, or 
grouping.var  A single grouping variable (can be entered either as a
bare name 
title.prefix  Character string specifying the prefix text for the fixed
plot title (name of each factor level) (Default: 
output  Character that describes what is to be returned: can be

main  The variable to use as the rows in the contingency table. 
condition  The variable to use as the columns in the contingency
table. Default is 
...  Arguments passed on to

plotgrid.args  A list of additional arguments to 
title.text  String or plotmath expression to be drawn as title for the combined plot. 
title.args  A list of additional arguments
provided to 
caption.text  String or plotmath expression to be drawn as the caption for the combined plot. 
caption.args  A list of additional arguments
provided to 
sub.text  The label with which the combined plot should be annotated. Can be a plotmath expression. 
sub.args  A list of additional arguments
provided to 
Unlike a number of statistical softwares, ggstatsplot
doesn't
provide the option for Yates' correction for the Pearson's chisquared
statistic. This is due to compelling amount of MonteCarlo simulation
research which suggests that the Yates' correction is overly conservative,
even in small sample sizes. As such it is recommended that it should not
ever be applied in practice (Camilli & Hopkins, 1978, 1979; Feinberg, 1980;
Larntz, 1978; Thompson, 1988).
For more about how the effect size measures and their confidence intervals
are computed, see ?rcompanion::cohenG
, ?rcompanion::cramerV
, and
?rcompanion::cramerVFit
.
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggpiestats.html
# \donttest{ # grouped onesample proportion tests ggstatsplot::grouped_ggpiestats( data = mtcars, grouping.var = am, x = cyl )#> Warning: Chisquared approximation may be incorrect# the following will take slightly more amount of time # for reproducibility set.seed(123) # let's create a smaller dataframe diamonds_short < ggplot2::diamonds %>% dplyr::filter(.data = ., cut %in% c("Fair", "Very Good", "Ideal")) %>% dplyr::sample_frac(tbl = ., size = 0.10) # plot ggstatsplot::grouped_ggpiestats( data = diamonds_short, x = color, y = clarity, grouping.var = cut, nboot = 20, sampling.plan = "poisson", title.prefix = "Quality", slice.label = "both", messages = FALSE, perc.k = 1, plotgrid.args = list(nrow = 3) )#> Warning: Chisquared approximation may be incorrect#> Warning: Chisquared approximation may be incorrect#> Warning: Chisquared approximation may be incorrect#> Warning: Chisquared approximation may be incorrect#> Warning: Chisquared approximation may be incorrect#> Warning: Chisquared approximation may be incorrect#> Warning: Chisquared approximation may be incorrect#> Warning: Chisquared approximation may be incorrect#> Warning: Chisquared approximation may be incorrect#> Warning: Chisquared approximation may be incorrect#> Warning: Chisquared approximation may be incorrect# }