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, main, condition = NULL, counts = NULL,
grouping.var, title.prefix = NULL, ratio = NULL, paired = FALSE,
results.subtitle = TRUE, factor.levels = NULL, stat.title = NULL,
sample.size.label = TRUE, label.separator = "\n",
label.text.size = 4, label.fill.color = "white",
label.fill.alpha = 1, bf.message = TRUE,
sampling.plan = "indepMulti", fixed.margin = "rows",
prior.concentration = 1, subtitle = NULL, caption = NULL,
conf.level = 0.95, bf.prior = 0.707, nboot = 100,
simulate.p.value = FALSE, B = 2000, bias.correct = FALSE,
legend.title = NULL, facet.wrap.name = NULL, k = 2, perc.k = 0,
slice.label = "percentage", facet.proptest = TRUE,
ggtheme = ggplot2::theme_bw(), ggstatsplot.layer = TRUE,
package = "RColorBrewer", palette = "Dark2", direction = 1,
ggplot.component = NULL, return = "plot", messages = TRUE,
x = NULL, y = NULL, ...)A dataframe (or a tibble) from which variables specified are to be taken. A matrix or tables will not be accepted.
The variable to use as the rows in the contingency table.
The variable to use as the columns in the contingency
table. 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.
A string naming a variable in data containing counts, or NULL
if each row represents a single observation (Default).
A single grouping variable (can be entered either as a
bare name x or as a string "x").
Character string specifying the prefix text for the fixed
plot title (name of each factor level) (Default: NULL). If NULL, the
variable name entered for grouping.var will be used.
A 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.
Logical indicating whether data came from a within-subjects or
repeated measures design study (Default: FALSE). If TRUE, McNemar's
test subtitle will be returned. If FALSE, Pearson's chi-square test will
be returned.
Decides 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.
A character vector with labels for factor levels of
main variable.
Title for the effect being investigated with the chi-square
test. The default is NULL, i.e. no title will be added to describe the
effect being shown. An example of a stat.title argument will be something
like "main x condition" or "interaction".
Logical that decides whether sample size information
should be displayed for each level of the grouping variable y
(Default: TRUE).
If "both" counts and proportion information is to be
displayed in a label, this argument decides whether these two pieces of
information are going to be on the same line (" ") or on separate lines
("\n").
Numeric that decides text size for slice/bar labels
(Default: 4).
Character that specifies fill color for slice/bar
labels (Default: white).
Numeric that specifies fill color transparency or
"alpha" for slice/bar labels (Default: 1 range 0 to 1).
Logical that decides whether to display a caption with
results from Bayes Factor test in favor of the null hypothesis (default:
FALSE).
Character describing the sampling plan. Possible options
are "indepMulti" (independent multinomial; default), "poisson",
"jointMulti" (joint multinomial), "hypergeom" (hypergeometric). For
more, see ?BayesFactor::contingencyTableBF().
For the independent multinomial sampling plan, which
margin is fixed ("rows" or "cols"). Defaults to "rows".
Specifies 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.
The text for the plot subtitle. Will work only if
results.subtitle = FALSE.
The text for the plot caption.
Scalar between 0 and 1. If unspecified, the defaults return
95% lower and upper confidence intervals (0.95).
A numeric value between 0.5 and 2 (default 0.707), the
prior width to use in calculating Bayes Factors.
Number of bootstrap samples for computing confidence interval
for the effect size (Default: 100).
a logical indicating whether to compute p-values by Monte Carlo simulation.
an integer specifying the number of replicates used in the Monte Carlo test.
If TRUE, a bias correction will be applied to Cramer's
V.
Title text for the legend.
The text for the facet_wrap variable label.
Number of digits after decimal point (should be an integer)
(Default: k = 2).
Numeric that decides number of decimal places for percentage
labels (Default: 0).
Character decides what information needs to be displayed
on the label in each pie slice. Possible options are "percentage"
(default), "counts", "both".
Decides whether proportion test for main variable is
to be carried out for each level of condition (Default: TRUE).
A 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.).
Logical 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.
Name of package from which the palette is desired as string or symbol.
If a character string (e.g., "Set1"), will use that named
palette. If a number, will index into the list of palettes of appropriate
type. Default palette is "Dark2".
Either 1 or -1. If -1 the palette will be reversed.
A ggplot component to be added to the plot prepared
by ggstatsplot. This argument is primarily helpful for grouped_ variant
of the current function. Default is NULL. The argument should be entered
as a function. If the given function has an argument axes.range.restrict
and if it has been set to TRUE, the added ggplot component might not
work as expected.
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.
Decides whether messages references, notes, and warnings are
to be displayed (Default: TRUE).
The variable to use as the rows in the contingency table.
The variable to use as the columns in the contingency
table. 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.
Arguments passed on to combine_plots
String or plotmath expression to be drawn as title for the combined plot.
Text color for title.
Point size of title text.
Vertical justification for title. Default = 0.5
(centered on y). 0 = baseline at y, 1 = ascender at y.
Horizontal justification for title. Default = 0.5
(centered on x). 0 = flush-left at x, 1 = flush-right.
The font face ("plain", "bold" (default),
"italic", "bold.italic") for title.
String or plotmath expression to be drawn as the caption for the combined plot.
Text color for caption.
Point size of title text.
Vertical justification for caption. Default = 0.5
(centered on y). 0 = baseline at y, 1 = ascender at y.
Horizontal justification for caption. Default = 0.5
(centered on x). 0 = flush-left at x, 1 = flush-right.
The font face ("plain" (default), "bold",
"italic", "bold.italic") for caption.
The label with which the combined plot should be annotated. Can be a plotmath expression.
Text color for annotation label (Default: "black").
Point size of annotation text (Default: 12).
The x position of annotation label (Default: 0.5).
The y position of annotation label (Default: 0.5).
Horizontal justification for annotation label (Default:
0.5).
Vertical justification for annotation label (Default:
0.5).
Vertical padding. The total vertical space added to the
label, given in grid units. By default, this is added equally above and
below the label. However, by changing the y and vjust parameters, this can
be changed (Default: grid::unit(1, "lines")).
The font face ("plain" (default), "bold", "italic",
"bold.italic") for the annotation label.
Angle at which annotation label is to be drawn (Default:
0).
Line height of annotation label.
Numerical vector of relative columns heights while combining (title, plot, caption).
Numerical vector of relative columns heights while combining (title, plot).
Numerical vector of relative columns heights while combining (plot, caption).
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.
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggpiestats.html
# NOT RUN {
# }
# NOT RUN {
# grouped one-sample proportion tests
ggstatsplot::grouped_ggpiestats(
data = mtcars,
grouping.var = am,
x = cyl
)
# without condition and with count data
library(jmv)
ggstatsplot::grouped_ggpiestats(
data = as.data.frame(HairEyeColor),
x = Hair,
counts = Freq,
grouping.var = Sex
)
# 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,
nrow = 3
)
# }
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