A combined plot of comparison plot created for levels of a grouping variable.

grouped_ggbetweenstats(
  data,
  x,
  y,
  grouping.var,
  outlier.label = NULL,
  title.prefix = NULL,
  output = "plot",
  ...,
  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)
)

Arguments

data

A dataframe (or a tibble) from which variables specified are to be taken. A matrix or tables will not be accepted.

x

The grouping variable from the dataframe data.

y

The response (a.k.a. outcome or dependent) variable from the dataframe data.

grouping.var

A single grouping variable (can be entered either as a bare name x or as a string "x").

outlier.label

Label to put on the outliers that have been tagged. This can't be the same as x argument.

title.prefix

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.

output

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. For functions ggpiestats and ggbarstats, setting output = "proptest" will return a dataframe containing results from proportion tests.

...

Arguments passed on to ggbetweenstats

plot.type

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).

xlab

Labels for x and y axis variables. If NULL (default), variable names for x and y will be used.

ylab

Labels for x and y axis variables. If NULL (default), variable names for x and y will be used.

pairwise.comparisons

Logical that decides whether pairwise comparisons are to be displayed (default: FALSE). 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.

p.adjust.method

Adjustment method for p-values for multiple comparisons. Possible methods are: "holm" (default), "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none".

pairwise.display

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.

bf.prior

A number between 0.5 and 2 (default 0.707), the prior width to use in calculating Bayes factors.

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: TRUE).

results.subtitle

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.

subtitle

The text for the plot subtitle. Will work only if results.subtitle = FALSE.

caption

The text for the plot caption.

sample.size.label

Logical that decides whether sample size information should be displayed for each level of the grouping variable x (Default: TRUE).

notch

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). This gives a roughly 95% confidence interval for comparing medians. IQR: Inter-Quartile Range.

notchwidth

For a notched box plot, width of the notch relative to the body (default 0.5).

outlier.color

Default aesthetics for outliers (Default: "black").

outlier.tagging

Decides whether outliers should be tagged (Default: FALSE).

outlier.shape

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.

outlier.point.args

A list of additional aesthetic arguments to be passed to ggplot2::geom_point and ggrepel::geom_label_repel geoms involved outlier value plotting.

outlier.label.args

A list of additional aesthetic arguments to be passed to ggplot2::geom_point and ggrepel::geom_label_repel geoms involved outlier value plotting.

outlier.coef

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).

mean.plotting

Logical that decides whether mean is to be highlighted and its value to be displayed (Default: TRUE).

mean.ci

Logical that decides whether 95% confidence interval for mean is to be displayed (Default: FALSE).

point.args

A list of additional aesthetic arguments to be passed to the geom_point displaying the raw data.

violin.args

A list of additional aesthetic arguments to be passed to the geom_violin.

ggplot.component

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.

package

Name of package from which the palette is desired as string or symbol.

palette

Name of palette as string or symbol.

mean.point.args

A list of additional aesthetic arguments to be passed to ggplot2::geom_point and ggrepel::geom_label_repel geoms involved mean value plotting.

mean.label.args

A list of additional aesthetic arguments to be passed to ggplot2::geom_point and ggrepel::geom_label_repel geoms involved mean value plotting.

ggtheme

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.).

ggstatsplot.layer

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.

effsize.type

Type of effect size needed for parametric tests. The argument can be "biased" (equivalent to "d" for Cohen's d for t-test; "partial_eta" for partial eta-squared for anova) or "unbiased" (equivalent to "g" Hedge's g for t-test; "partial_omega" for partial omega-squared for anova)).

partial

If TRUE, return partial indices.

k

Number of digits after decimal point (should be an integer) (Default: k = 2).

var.equal

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.

conf.level

Scalar between 0 and 1. If unspecified, the defaults return 95% lower and upper confidence intervals (0.95).

type

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.

nboot

Number of bootstrap samples for computing confidence interval for the effect size (Default: 100).

tr

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.

plotgrid.args

A list of additional arguments to cowplot::plot_grid.

title.text

String or plotmath expression to be drawn as title for the combined plot.

title.args

A list of additional arguments provided to title, caption and sub, resp.

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 title, caption and sub, resp.

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 title, caption and sub, resp.

Details

For parametric tests, Welch's ANOVA/t-test are used as a default (i.e., var.equal = FALSE). References:

  • ANOVA: Delacre, Leys, Mora, & Lakens, PsyArXiv, 2018

  • t-test: Delacre, Lakens, & Leys, International Review of Social Psychology, 2017

If robust tests are selected, following tests are used is .

For more about how the effect size measures (for nonparametric tests) and their confidence intervals are computed, see ?rcompanion::wilcoxonR.

For repeated measures designs, use ggwithinstats.

References

https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggbetweenstats.html

See also

Examples

# \donttest{ # to get reproducible results from bootstrapping set.seed(123) # the most basic function call ggstatsplot::grouped_ggbetweenstats( data = dplyr::filter(ggplot2::mpg, drv != "4"), x = year, y = hwy, grouping.var = drv, conf.level = 0.99 )
# modifying individual plots using `ggplot.component` argument ggstatsplot::grouped_ggbetweenstats( data = dplyr::filter( ggstatsplot::movies_long, genre %in% c("Action", "Comedy"), mpaa %in% c("R", "PG") ), x = genre, y = rating, grouping.var = mpaa, results.subtitle = FALSE, ggplot.component = ggplot2::scale_y_continuous( breaks = seq(1, 9, 1), limits = (c(1, 9)) ), messages = FALSE )
# }