Bar charts for categorical data with statistical details included in the plot as a subtitle.
ggbarstats( data, main, condition, counts = NULL, ratio = NULL, paired = FALSE, results.subtitle = TRUE, sample.size.label = TRUE, label = "percentage", perc.k = 0, label.args = list(alpha = 1, fill = "white"), bf.message = TRUE, sampling.plan = "indepMulti", fixed.margin = "rows", prior.concentration = 1, title = NULL, subtitle = NULL, caption = NULL, conf.level = 0.95, nboot = 100, legend.title = NULL, xlab = NULL, ylab = NULL, k = 2, proportion.test = TRUE, ggtheme = ggplot2::theme_bw(), ggstatsplot.layer = TRUE, package = "RColorBrewer", palette = "Dark2", ggplot.component = NULL, output = "plot", messages = TRUE, x = NULL, y = NULL, ... )
data | A dataframe (or a tibble) from which variables specified are to be taken. A matrix or tables will not be accepted. |
---|---|
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 |
counts | A string naming a variable in data containing counts, or |
ratio | A vector of proportions: the expected proportions for the
proportion test (should sum to 1). Default is |
paired | Logical indicating whether data came from a within-subjects or
repeated measures design study (Default: |
results.subtitle | Decides whether the results of statistical tests are
to be displayed as a subtitle (Default: |
sample.size.label | Logical that decides whether sample size information
should be displayed for each level of the grouping variable |
label | Character decides what information needs to be
displayed on the label in each pie slice. Possible options are
|
perc.k | Numeric that decides number of decimal places for percentage
labels (Default: |
label.args | Additional aesthetic arguments that will be passed to
|
bf.message | Logical that decides whether to display Bayes Factor in
favor of the null hypothesis. This argument is relevant only for
parametric test (Default: |
sampling.plan | Character describing the sampling plan. Possible options
are |
fixed.margin | For the independent multinomial sampling plan, which
margin is fixed ( |
prior.concentration | Specifies the prior concentration parameter, set
to |
title | The text for the plot title. |
subtitle | The text for the plot subtitle. Will work only if
|
caption | The text for the plot caption. |
conf.level | Scalar between 0 and 1. If unspecified, the defaults return
|
nboot | Number of bootstrap samples for computing confidence interval
for the effect size (Default: |
legend.title | Title text for the legend. |
xlab | Custom text for the |
ylab | Custom text for the |
k | Number of digits after decimal point (should be an integer)
(Default: |
proportion.test | Decides whether proportion test for |
ggtheme | A function, |
ggstatsplot.layer | Logical that decides whether |
package | Name of package from which the palette is desired as string or symbol. |
palette | Name of palette as string or symbol. |
ggplot.component | A |
output | Character that describes what is to be returned: can be
|
messages | Decides whether messages references, notes, and warnings are
to be displayed (Default: |
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 |
... | Currently ignored. |
Unlike a number of statistical softwares, ggstatsplot
doesn't
provide the option for Yates' correction for the Pearson's chi-squared
statistic. This is due to compelling amount of Monte-Carlo 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
.
# for reproducibility set.seed(123) # association test (or contingency table analysis) ggstatsplot::ggbarstats( data = mtcars, x = vs, y = cyl, nboot = 10, legend.title = "Engine" )#> Warning: Chi-squared approximation may be incorrect#> Note: 95% CI for effect size estimate was computed with 10 bootstrap samples. #>#>#> Warning: Chi-squared approximation may be incorrect#> # A tibble: 3 x 11 #> cyl counts perc N `0` `1` statistic p.value parameter #> <fct> <int> <dbl> <chr> <chr> <chr> <dbl> <dbl> <dbl> #> 1 8 14 43.8 (n = 14) 100.00% NA 14 0.000183 1 #> 2 6 7 21.9 (n = 7) 42.86% 57.14% 0.143 0.705 1 #> 3 4 11 34.4 (n = 11) 9.09% 90.91% 7.36 0.00666 1 #> method significance #> <chr> <chr> #> 1 Chi-squared test for given probabilities *** #> 2 Chi-squared test for given probabilities ns #> 3 Chi-squared test for given probabilities **