Helper function for ggstatsplot::ggbarstats to apply this function across
multiple levels of a given factor and combining the resulting plots using
ggstatsplot::combine_plots.
grouped_ggbarstats(
data,
x,
y,
counts = NULL,
grouping.var,
output = "plot",
plotgrid.args = list(),
annotation.args = list(),
...
)A dataframe (or a tibble) from which variables specified are to be taken. Other data types (e.g., matrix,table, array, etc.) will not be accepted.
The variable to use as the rows in the contingency table. Please note that if there are empty factor levels in your variable, they will be dropped.
The variable to use as the columns in the contingency table.
Please note that if there are empty factor levels in your variable, they
will be dropped. Default is NULL. If NULL, one-sample proportion test
(a goodness of fit test) will be run for the x variable. Otherwise an
appropriate association test will be run. This argument can not be NULL
for ggbarstats function.
A string naming a variable in data containing counts, or NULL
if each row represents a single observation.
A single grouping variable (can be entered either as a
bare name x or as a string "x").
Character that describes what is to be returned: can be
"plot" (default) or "subtitle" or "caption". Setting this to
"subtitle" will return the expression containing statistical results. If
you have set results.subtitle = FALSE, then this will return a NULL.
Setting this to "caption" will return the expression containing details
about Bayes Factor analysis, but valid only when type = "parametric" and
bf.message = TRUE, otherwise this will return a NULL.
A list of additional arguments passed to
patchwork::wrap_plots, except for guides argument which is already
separately specified here.
A list of additional arguments passed to
patchwork::plot_annotation.
Arguments passed on to ggbarstats
xlabCustom text for the x axis label (Default: NULL, which
will cause the x axis label to be the x variable).
ylabCustom text for the y axis label (Default: NULL).
typeA character specifying the type of statistical approach. Four possible options:
"parametric"
"nonparametric"
"robust"
"bayes"
Corresponding abbreviations are also accepted: "p" (for parametric),
"np" (for nonparametric), "r" (for robust), or "bf" (for Bayesian).
pairedLogical indicating whether data came from a within-subjects or
repeated measures design study (Default: FALSE). If TRUE, McNemar's
test expression will be returned. If FALSE, Pearson's chi-square test will
be returned.
results.subtitleDecides whether the results of statistical tests are
to be displayed as a subtitle (Default: TRUE). If set to FALSE, only
the plot will be returned.
labelCharacter decides what information needs to be displayed
on the label in each pie slice. Possible options are "percentage"
(default), "counts", "both".
label.argsAdditional aesthetic arguments that will be passed to
geom_label.
kNumber of digits after decimal point (should be an integer)
(Default: k = 2L).
proportion.testDecides whether proportion test for x variable is to
be carried out for each level of y (Default: TRUE). In ggbarstats,
only p-values from this test will be displayed.
perc.kNumeric that decides number of decimal places for percentage
labels (Default: 0).
bf.messageLogical that decides whether to display Bayes Factor in
favor of the null hypothesis. This argument is relevant only for
parametric test (Default: TRUE).
ratioA vector of proportions: the expected proportions for the
proportion test (should sum to 1). Default is NULL, which means the null
is equal theoretical proportions across the levels of the nominal variable.
This means if there are two levels this will be ratio = c(0.5,0.5) or if
there are four levels this will be ratio = c(0.25,0.25,0.25,0.25), etc.
conf.levelScalar between 0 and 1. If unspecified, the defaults
return 95% confidence/credible intervals (0.95).
sampling.planCharacter describing the sampling plan. Possible options
are "indepMulti" (independent multinomial; default), "poisson",
"jointMulti" (joint multinomial), "hypergeom" (hypergeometric). For
more, see ?BayesFactor::contingencyTableBF().
fixed.marginFor the independent multinomial sampling plan, which
margin is fixed ("rows" or "cols"). Defaults to "rows".
prior.concentrationSpecifies the prior concentration parameter, set
to 1 by default. It indexes the expected deviation from the null
hypothesis under the alternative, and corresponds to Gunel and Dickey's
(1974) "a" parameter.
subtitleThe text for the plot subtitle. Will work only if
results.subtitle = FALSE.
captionThe text for the plot caption.
legend.titleTitle text for the legend.
ggthemeA function, ggplot2 theme name. Default value is
ggplot2::theme_bw(). Any of the ggplot2 themes, or themes from
extension packages are allowed (e.g., ggthemes::theme_fivethirtyeight(),
hrbrthemes::theme_ipsum_ps(), etc.).
ggstatsplot.layerLogical that decides whether theme_ggstatsplot
theme elements are to be displayed along with the selected ggtheme
(Default: TRUE). theme_ggstatsplot is an opinionated theme layer that
override some aspects of the selected ggtheme.
packageName of the package from which the given palette is to
be extracted. The available palettes and packages can be checked by running
View(paletteer::palettes_d_names).
paletteName of the package from which the given palette is to
be extracted. The available palettes and packages can be checked by running
View(paletteer::palettes_d_names).
ggplot.componentA ggplot component to be added to the plot prepared
by ggstatsplot. This argument is primarily helpful for grouped_
variants of all primary functions. Default is NULL. The argument should
be entered as a ggplot2 function or a list of ggplot2 functions.
# NOT RUN {
# for reproducibility
set.seed(123)
library(ggstatsplot)
# let's create a smaller dataframe
diamonds_short <- ggplot2::diamonds %>%
dplyr::filter(.data = ., cut %in% c("Very Good", "Ideal")) %>%
dplyr::filter(.data = ., clarity %in% c("SI1", "SI2", "VS1", "VS2")) %>%
dplyr::sample_frac(tbl = ., size = 0.05)
# plot
# let's skip statistical analysis
grouped_ggbarstats(
data = diamonds_short,
x = color,
y = clarity,
grouping.var = cut,
plotgrid.args = list(nrow = 2)
)
# }
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