A combination of box and violin plots along with jittered data points for between-subjects designs with statistical details included in the plot as a subtitle.
ggbetweenstats(
data,
x,
y,
plot.type = "boxviolin",
type = "parametric",
pairwise.comparisons = TRUE,
pairwise.display = "significant",
p.adjust.method = "holm",
effsize.type = "unbiased",
bf.prior = 0.707,
bf.message = TRUE,
results.subtitle = TRUE,
xlab = NULL,
ylab = NULL,
caption = NULL,
title = NULL,
subtitle = NULL,
sample.size.label = TRUE,
k = 2L,
var.equal = FALSE,
conf.level = 0.95,
nboot = 100L,
tr = 0.1,
mean.plotting = TRUE,
mean.ci = FALSE,
mean.point.args = list(size = 5, color = "darkred"),
mean.label.args = list(size = 3),
notch = FALSE,
notchwidth = 0.5,
outlier.tagging = FALSE,
outlier.label = NULL,
outlier.coef = 1.5,
outlier.shape = 19,
outlier.color = "black",
outlier.label.args = list(size = 3),
point.args = list(position = ggplot2::position_jitterdodge(dodge.width = 0.6), alpha
= 0.4, size = 3, stroke = 0),
violin.args = list(width = 0.5, alpha = 0.2),
ggsignif.args = list(textsize = 3, tip_length = 0.01),
ggtheme = ggplot2::theme_bw(),
ggstatsplot.layer = TRUE,
package = "RColorBrewer",
palette = "Dark2",
ggplot.component = NULL,
output = "plot",
...
)A dataframe (or a tibble) from which variables specified are to be taken. A matrix or tables will not be accepted.
The grouping variable from the dataframe data.
The response (a.k.a. outcome or dependent) variable from the
dataframe data.
Character describing the type of plot. Currently supported
plots are "box" (for pure boxplots), "violin" (for pure violin plots),
and "boxviolin" (for a combination of box and violin plots; default).
Type of statistic expected ("parametric" or "nonparametric"
or "robust" or "bayes").Corresponding abbreviations are also accepted:
"p" (for parametric), "np" (nonparametric), "r" (robust), or
"bf"resp.
Logical that decides whether pairwise comparisons
are to be displayed (default: TRUE). Please note that only
significant comparisons will be shown by default. To change this
behavior, select appropriate option with pairwise.display argument. The
pairwise comparison dataframes are prepared using the
pairwiseComparisons::pairwise_comparisons function. For more details
about pairwise comparisons, see the documentation for that function.
Decides which pairwise comparisons to display.
Available options are "significant" (abbreviation accepted: "s") or
"non-significant" (abbreviation accepted: "ns") or
"everything"/"all". The default is "significant". You can use this
argument to make sure that your plot is not uber-cluttered when you have
multiple groups being compared and scores of pairwise comparisons being
displayed.
Adjustment method for p-values for multiple
comparisons. Possible methods are: "holm" (default), "hochberg",
"hommel", "bonferroni", "BH", "BY", "fdr", "none".
Type of effect size needed for parametric tests. The
argument can be "eta" (partial eta-squared) or "omega" (partial
omega-squared).
A number between 0.5 and 2 (default 0.707), the prior
width to use in calculating Bayes factors.
Logical that decides whether to display Bayes Factor in
favor of the null hypothesis. This argument is relevant only for
parametric test (Default: TRUE).
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.
Labels for x and y axis variables. If NULL (default),
variable names for x and y will be used.
The text for the plot caption.
The text for the plot title.
The text for the plot subtitle. Will work only if
results.subtitle = FALSE.
Logical that decides whether sample size information
should be displayed for each level of the grouping variable x (Default:
TRUE).
Number of digits after decimal point (should be an integer)
(Default: k = 2L).
a logical variable indicating whether to treat the
variances in the samples as equal. If TRUE, then a simple F
test for the equality of means in a one-way analysis of variance is
performed. If FALSE, an approximate method of Welch (1951)
is used, which generalizes the commonly known 2-sample Welch test to
the case of arbitrarily many samples.
Scalar between 0 and 1. If unspecified, the defaults return
95% confidence/credible intervals (0.95).
Number of bootstrap samples for computing confidence interval
for the effect size (Default: 100).
Trim level for the mean when carrying out robust tests. If you
get error stating "Standard error cannot be computed because of Winsorized
variance of 0 (e.g., due to ties). Try to decrease the trimming level.",
try to play around with the value of tr, which is by default set to
0.1. Lowering the value might help.
Logical that decides whether mean is to be highlighted
and its value to be displayed (Default: TRUE).
Logical that decides whether 95% confidence interval for
mean is to be displayed (Default: FALSE).
A list of additional aesthetic
arguments to be passed to ggplot2::geom_point and
ggrepel::geom_label_repel geoms involved mean value plotting.
A logical. If FALSE (default), a standard box plot will be
displayed. If TRUE, a notched box plot will be used. Notches are used to
compare groups; if the notches of two boxes do not overlap, this suggests
that the medians are significantly different. In a notched box plot, the
notches extend 1.58 * IQR / sqrt(n), where IQR: Inter-Quartile Range.
This gives a roughly 95% confidence interval for comparing medians.
For a notched box plot, width of the notch relative to the
body (default 0.5).
Decides whether outliers should be tagged (Default:
FALSE).
Label to put on the outliers that have been tagged. This
can't be the same as x argument.
Coefficient for outlier detection using Tukey's method.
With Tukey's method, outliers are below (1st Quartile) or above (3rd
Quartile) outlier.coef times the Inter-Quartile Range (IQR) (Default:
1.5).
Hiding the outliers can be achieved by setting
outlier.shape = NA. Importantly, this does not remove the outliers,
it only hides them, so the range calculated for the y-axis will be
the same with outliers shown and outliers hidden.
Default aesthetics for outliers (Default: "black").
A list of additional aesthetic arguments to be
passed to ggrepel::geom_label_repel for outlier label plotting.
A list of additional aesthetic arguments to be passed to
the geom_point displaying the raw data.
A list of additional aesthetic arguments to be passed to
the geom_violin.
A list of additional aesthetic
arguments to be passed to ggsignif::geom_signif.
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 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).
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.
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.
Currently ignored.
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggbetweenstats.html
grouped_ggbetweenstats, ggwithinstats,
grouped_ggwithinstats
# NOT RUN {
# to get reproducible results from bootstrapping
set.seed(123)
library(ggstatsplot)
# simple function call with the defaults
ggstatsplot::ggbetweenstats(
data = mtcars,
x = am,
y = mpg,
title = "Fuel efficiency by type of car transmission",
caption = "Transmission (0 = automatic, 1 = manual)"
)
# more detailed function call
ggstatsplot::ggbetweenstats(
data = datasets::morley,
x = Expt,
y = Speed,
type = "nonparametric",
plot.type = "box",
xlab = "The experiment number",
ylab = "Speed-of-light measurement",
pairwise.comparisons = TRUE,
p.adjust.method = "fdr",
outlier.tagging = TRUE,
outlier.label = Run,
ggtheme = ggplot2::theme_grey(),
ggstatsplot.layer = FALSE
)
# }
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