# ggwithinstats

0th

Percentile

##### Box/Violin plots for group or condition comparisons in within-subjects (or repeated measures) designs.

A combination of box and violin plots along with raw (unjittered) data points for within-subjects designs with statistical details included in the plot as a subtitle.

##### Usage
ggwithinstats(data, x, y, type = "parametric",
pairwise.comparisons = FALSE, pairwise.annotation = "asterisk",
pairwise.display = "significant", p.adjust.method = "holm",
effsize.type = "unbiased", partial = TRUE,
effsize.noncentral = TRUE, bf.prior = 0.707, bf.message = TRUE,
sphericity.correction = TRUE, results.subtitle = TRUE, xlab = NULL,
ylab = NULL, caption = NULL, title = NULL, subtitle = NULL,
sample.size.label = TRUE, k = 2, conf.level = 0.95, nboot = 100,
tr = 0.1, path.point = TRUE, path.mean = TRUE, sort = "none",
sort.fun = mean, axes.range.restrict = FALSE, mean.label.size = 3,
mean.label.fontface = "bold", mean.label.color = "black",
notch = FALSE, notchwidth = 0.5, linetype = "solid",
outlier.tagging = FALSE, outlier.shape = 19, outlier.label = NULL,
outlier.label.color = "black", outlier.color = "black",
outlier.coef = 1.5, mean.plotting = TRUE, mean.ci = FALSE,
mean.size = 5, mean.color = "darkred",
ggtheme = ggplot2::theme_bw(), ggstatsplot.layer = TRUE,
package = "RColorBrewer", palette = "Dark2", direction = 1,
ggplot.component = NULL, return = "plot", messages = TRUE)
##### 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.

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.

pairwise.comparisons

Logical that decides whether pairwise comparisons are to be displayed. Only significant comparisons will be shown by default. (default: FALSE). To change this behavior, select appropriate option with pairwise.display argument.

pairwise.annotation

Character that decides the annotations to use for pairwise comparisons. Either "p.value" or "asterisk" (default).

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.

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

effsize.type

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

partial

Logical that decides if partial eta-squared or omega-squared are returned (Default: TRUE). If FALSE, eta-squared or omega-squared will be returned. Valid only for objects of class lm, aov, anova, or aovlist.

effsize.noncentral

Logical indicating whether to use non-central t-distributions for computing the confidence interval for Cohen's d or Hedge's g (Default: TRUE).

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).

sphericity.correction

Logical that decides whether to apply correction to account for violation of sphericity in a repeated measures design ANOVA (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.

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.

caption

The text for the plot caption.

title

The text for the plot title.

subtitle

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

sample.size.label

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

k

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

conf.level

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

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.

path.point, path.mean

Logical that decides whether individual data points and means, respectively, should be connected using geom_path. Both default to TRUE. Note that path.point argument is relevant only when there are two groups (i.e., in case of a t-test). In case of large number of data points, it is advisable to set path.point = FALSE as these lines can overwhelm the plot.

sort

If "ascending" (default), x-axis variable factor levels will be sorted based on increasing values of y-axis variable. If "descending", the opposite. If "none", no sorting will happen.

sort.fun

The function used to sort (default: mean).

axes.range.restrict

Logical that decides whether to restrict the axes values ranges to min and max values of the axes variables (Default: FALSE), only relevant for functions where axes variables are of numeric type.

mean.label.size

Aesthetics for the label displaying mean. Defaults: 3, "bold","black", respectively.

mean.label.fontface

Aesthetics for the label displaying mean. Defaults: 3, "bold","black", respectively.

mean.label.color

Aesthetics for the label displaying mean. Defaults: 3, "bold","black", respectively.

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).

linetype

Character strings ("blank", "solid", "dashed", "dotted", "dotdash", "longdash", and "twodash") specifying the type of line to draw box plots (Default: "solid"). Alternatively, the numbers 0 to 6 can be used (0 for "blank", 1 for "solid", etc.).

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.label

Label to put on the outliers that have been tagged.

outlier.label.color

Color for the label to to put on the outliers that have been tagged (Default: "black").

outlier.color

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

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 is to be displayed (Default: FALSE).

mean.size

Point size for the data point corresponding to mean (Default: 5).

mean.color

Color for the data point corresponding to mean (Default: "darkred").

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).

package

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

palette

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".

direction

Either 1 or -1. If -1 the palette will be reversed.

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. 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.

return

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, which will be a NULL if you set results.subtitle = FALSE. Setting this to "caption" will return the expression containing details about Bayes Factor analysis, but valid only when type = "p" and bf.message = TRUE, otherwise this will return a NULL.

messages

Decides whether messages references, notes, and warnings are to be displayed (Default: TRUE).

##### Details

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

For independent measures designs, use ggbetweenstats.

grouped_ggbetweenstats, ggbetweenstats, grouped_ggwithinstats, pairwise_p

##### Aliases
• ggwithinstats
##### Examples
# NOT RUN {
# setup
set.seed(123)
library(ggstatsplot)

# two groups (t-test)
ggstatsplot::ggwithinstats(
data = VR_dilemma,
x = modality,
y = score,
xlab = "Presentation modality",
ylab = "Proportion of utilitarian decisions"
)

# more than two groups (anova)
library(WRS2)

ggstatsplot::ggwithinstats(
data = tibble::as_tibble(WineTasting),
x = Wine,
y = Taste,
type = "r",
pairwise.comparisons = TRUE,
outlier.tagging = TRUE,
outlier.label = Taster
)
# }

Documentation reproduced from package ggstatsplot, version 0.0.11, License: GPL-3 | file LICENSE

### Community examples

Looks like there are no examples yet.