Box/Violin plots for repeated measures comparisons

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",
  subject.id = NULL,
  pairwise.display = "significant",
  pairwise.alpha = 0.05,
  p.adjust.method = "holm",
  bf.prior = 0.707,
  bf.message = TRUE,
  results.subtitle = TRUE,
  xlab = NULL,
  ylab = NULL,
  caption = NULL,
  title = NULL,
  subtitle = NULL,
  digits = 2L,
  conf.level = 0.95,
  tr = 0.2,
  alternative = "two.sided",
  centrality.plotting = TRUE,
  centrality.type = type,
  centrality.point.args = list(size = 5, color = "darkred"),
  centrality.label.args = list(size = 3, nudge_x = 0.4, segment.linetype = 4),
  centrality.path = TRUE,
  centrality.path.args = list(linewidth = 1, color = "red", alpha = 0.5),
  point.args = list(size = 3, alpha = 0.5, na.rm = TRUE),
  point.path = TRUE,
  point.path.args = list(alpha = 0.5, linetype = "dashed"),
  boxplot.args = list(width = 0.2, alpha = 0.5, na.rm = TRUE),
  violin.args = list(width = 0.5, alpha = 0.2, na.rm = TRUE),
  ggsignif.args = list(textsize = 3, tip_length = 0.01, na.rm = TRUE),
  ggtheme = ggstatsplot::theme_ggstatsplot(),
  palette = "ggthemes::gdoc",
  ggplot.component = NULL,
  ...
)

Arguments

data

A data frame (or a tibble) from which variables specified are to be taken. Other data types (e.g., matrix,table, array, etc.) will not be accepted. Additionally, grouped data frames from {dplyr} should be ungrouped before they are entered as data.

x

The grouping (or independent) variable from data. In case of a repeated measures or within-subjects design, if subject.id argument is not available or not explicitly specified, the function assumes that the data has already been sorted by such an id by the user and creates an internal identifier. So if your data is not sorted, the results can be inaccurate when there are more than two levels in x and there are NAs present. The data is expected to be sorted by user in subject-1, subject-2, ..., pattern.

y

The response (or outcome or dependent) variable from data.

type

A character specifying the type of statistical approach:

• "parametric"

• "nonparametric"

• "robust"

• "bayes"

You can specify just the initial letter.

subject.id

Across repeated measures conditions, each row in the dataset must correspond to a unique unit (e.g., subject or participant). If your data frame is already in such a format, you can ignore the subject.id argument (the function will use row number to pair observations). But if you are not sure, it is always better to specify this argument. Note that if there are any missing values (i.e., NA) in the dependent variable and the subject.id is not specified, they will be dropped using a list-wise approach. If you specify subject.id, partially observed subjects will still be shown in the plot, but inferential statistics will be computed using only complete repeated-measures pairs.

pairwise.display

Decides which pairwise comparisons to display. Available options are:

• "significant" (abbreviation accepted: "s")

• "non-significant" (abbreviation accepted: "ns")

• "all"

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. If set to "none", no pairwise comparisons will be displayed.

pairwise.alpha

Numeric alpha threshold used to decide which pairwise comparisons are displayed when pairwise.display = "significant" or pairwise.display = "non-significant" (Default: 0.05).

p.adjust.method

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

bf.prior

A number between 0.5 and 2 (default 0.707), the prior width to use in calculating Bayes factors and posterior estimates. In addition to numeric arguments, several named values are also recognized: "medium", "wide", and "ultrawide", corresponding to r scale values of 1/2, sqrt(2)/2, and 1, respectively. In case of an ANOVA, this value corresponds to scale for fixed effects.

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.

xlab

Label for x axis variable. If NULL (default), variable name for x will be used.

ylab

Labels for y axis variable. If NULL (default), variable name for y will be used.

caption

The text for the plot caption. This argument is relevant only if bf.message = FALSE.

title

The text for the plot title.

subtitle

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

digits

Number of digits for rounding or significant figures. May also be "signif" to return significant figures or "scientific" to return scientific notation. Control the number of digits by adding the value as suffix, e.g. digits = "scientific4" to have scientific notation with 4 decimal places, or digits = "signif5" for 5 significant figures (see also signif()).

conf.level

Scalar between 0 and 1 (default: 95% confidence/credible intervals, 0.95). If NULL, no confidence intervals will be computed.

tr

Trim level for the mean when carrying out robust tests. In case of an error, try reducing the value of tr, which is by default set to 0.2. Lowering the value might help.

alternative

a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". You can specify just the initial letter.

centrality.plotting

Logical that decides whether centrality tendency measure is to be displayed as a point with a label (Default: TRUE). Function decides which central tendency measure to show depending on the type argument.

• mean for parametric statistics

• median for non-parametric statistics

• trimmed mean for robust statistics

• MAP estimator for Bayesian statistics

If you want default centrality parameter, you can specify this using centrality.type argument.

centrality.type

Decides which centrality parameter is to be displayed. The default is to choose the same as type argument. You can specify this to be:

• "parametric" (for mean)

• "nonparametric" (for median)

• robust (for trimmed mean)

• bayes (for MAP estimator)

Just as type argument, abbreviations are also accepted.

centrality.point.args, centrality.label.args

A list of additional aesthetic arguments to be passed to ggplot2::geom_point() and ggrepel::geom_label_repel() geoms, which are involved in mean plotting.

centrality.path.args, point.path.args

A list of additional aesthetic arguments passed on to ggplot2::geom_path() connecting raw data points and mean points.

point.args

A list of additional aesthetic arguments to be passed to the ggplot2::geom_point().

point.path, centrality.path

Logical that decides whether individual data points and means, respectively, should be connected using ggplot2::geom_path(). Both default to TRUE. Note that point.path 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 point.path = FALSE as these lines can overwhelm the plot.

boxplot.args

A list of additional aesthetic arguments passed on to ggplot2::geom_boxplot(). By default, the whiskers extend to 1.5 times the interquartile range (IQR) from the box (Tukey-style). To customize whisker length, you can use the coef parameter, e.g., boxplot.args = list(coef = 3) for whiskers extending to 3 * IQR, or boxplot.args = list(coef = 0) to show only the range of the data.

violin.args

A list of additional aesthetic arguments to be passed to the ggplot2::geom_violin().

ggsignif.args

A list of additional aesthetic arguments to be passed to ggsignif::geom_signif().

ggtheme

A {ggplot2} theme. Default value is theme_ggstatsplot(). Any of the {ggplot2} themes (e.g., ggplot2::theme_bw()), or themes from extension packages are allowed (e.g., ggthemes::theme_fivethirtyeight(), hrbrthemes::theme_ipsum_ps(), etc.). But note that sometimes these themes will remove some of the details that {ggstatsplot} plots typically contains. For example, if relevant, ggbetweenstats() shows details about multiple comparison test as a label on the secondary Y-axis. Some themes (e.g. ggthemes::theme_fivethirtyeight()) will remove the secondary Y-axis and thus the details as well.

palette

Name of the palette in "package::palette" format to be used for coloring. Passed to paletteer::scale_color_paletteer_d(). Run View(paletteer::palettes_d_names) to see all available options.

ggplot.component

A 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.

...

Currently ignored.

Details

For details, see: https://www.indrapatil.com/ggstatsplot/articles/web_only/ggwithinstats.html

Summary of graphics

graphical element	geom used	argument for further modification
raw data	ggplot2::geom_point()	point.args
point path	ggplot2::geom_path()	point.path.args
box plot	ggplot2::geom_boxplot()	boxplot.args
density plot	ggplot2::geom_violin()	violin.args
centrality measure point	ggplot2::geom_point()	centrality.point.args
centrality measure point path	ggplot2::geom_path()	centrality.path.args
centrality measure label	ggrepel::geom_label_repel()	centrality.label.args
pairwise comparisons	ggsignif::geom_signif()	ggsignif.args

Centrality measures

The table below provides summary about:

• statistical test carried out for inferential statistics

• type of effect size estimate and a measure of uncertainty for this estimate

• functions used internally to compute these details

Type	Measure	Function used
Parametric	mean	datawizard::describe_distribution()
Non-parametric	median	datawizard::describe_distribution()
Robust	trimmed mean	datawizard::describe_distribution()
Bayesian	MAP	datawizard::describe_distribution()

Two-sample tests

The table below provides summary about:

• statistical test carried out for inferential statistics

• type of effect size estimate and a measure of uncertainty for this estimate

• functions used internally to compute these details

between-subjects

Hypothesis testing

Type	No. of groups	Test	Function used
Parametric	2	Student's or Welch's t-test	stats::t.test()
Non-parametric	2	Mann-Whitney U test	stats::wilcox.test()
Robust	2	Yuen's test for trimmed means	WRS2::yuen()
Bayesian	2	Student's t-test	BayesFactor::ttestBF()

Effect size estimation

Type	No. of groups	Effect size	CI available?	Function used
Parametric	2	Cohen's d, Hedge's g	Yes	effectsize::cohens_d(), effectsize::hedges_g()
Non-parametric	2	r (rank-biserial correlation)	Yes	effectsize::rank_biserial()
Robust	2	Algina-Keselman-Penfield robust standardized difference	Yes	WRS2::akp.effect()
Bayesian	2	difference	Yes	bayestestR::describe_posterior()

within-subjects

Data requirement: Paired tests assume exactly one observation per subject per condition. If your data has multiple trials per cell, aggregate first (e.g., take the mean).

Hypothesis testing

Type	No. of groups	Test	Function used
Parametric	2	Student's t-test	stats::t.test()
Non-parametric	2	Wilcoxon signed-rank test	stats::wilcox.test()
Robust	2	Yuen's test on trimmed means for dependent samples	WRS2::yuend()
Bayesian	2	Student's t-test	BayesFactor::ttestBF()

Effect size estimation

Type	No. of groups	Effect size	CI available?	Function used
Parametric	2	Cohen's d, Hedge's g	Yes	effectsize::cohens_d(), effectsize::hedges_g()
Non-parametric	2	r (rank-biserial correlation)	Yes	effectsize::rank_biserial()
Robust	2	Algina-Keselman-Penfield robust standardized difference	Yes	WRS2::wmcpAKP()
Bayesian	2	difference	Yes	bayestestR::describe_posterior()

One-way ANOVA

The table below provides summary about:

• statistical test carried out for inferential statistics

• type of effect size estimate and a measure of uncertainty for this estimate

• functions used internally to compute these details

between-subjects

Hypothesis testing

Type	No. of groups	Test	Function used
Parametric	> 2	Fisher's or Welch's one-way ANOVA	stats::oneway.test()
Non-parametric	> 2	Kruskal-Wallis one-way ANOVA	stats::kruskal.test()
Robust	> 2	Heteroscedastic one-way ANOVA for trimmed means	WRS2::t1way()
Bayesian	> 2	Fisher's ANOVA	BayesFactor::anovaBF()

Effect size estimation

Type	No. of groups	Effect size	CI available?	Function used
Parametric	> 2	partial eta-squared, partial omega-squared	Yes	effectsize::omega_squared(), effectsize::eta_squared()
Non-parametric	> 2	rank epsilon squared	Yes	effectsize::rank_epsilon_squared()
Robust	> 2	Explanatory measure of effect size	Yes	WRS2::t1way()
Bayesian	> 2	Bayesian R-squared	Yes	performance::r2_bayes()

within-subjects

Data requirement: Repeated measures tests assume a complete design with exactly one observation per subject per condition. If your data has multiple trials per cell, aggregate first (e.g., take the mean). Verify with table(data$subject, data$condition) — every cell should equal 1.

Hypothesis testing

Type	No. of groups	Test	Function used
Parametric	> 2	One-way repeated measures ANOVA	afex::aov_ez()
Non-parametric	> 2	Friedman rank sum test	stats::friedman.test()
Robust	> 2	Heteroscedastic one-way repeated measures ANOVA for trimmed means	WRS2::rmanova()
Bayesian	> 2	One-way repeated measures ANOVA	BayesFactor::anovaBF()

Effect size estimation

Type	No. of groups	Effect size	CI available?	Function used
Parametric	> 2	partial eta-squared, partial omega-squared	Yes	effectsize::omega_squared(), effectsize::eta_squared()
Non-parametric	> 2	Kendall's coefficient of concordance	Yes	effectsize::kendalls_w()
Robust	> 2	Algina-Keselman-Penfield robust standardized difference average	Yes	WRS2::wmcpAKP()
Bayesian	> 2	Bayesian R-squared	Yes	performance::r2_bayes()

Pairwise comparison tests

The table below provides summary about:

• statistical test carried out for inferential statistics

• type of effect size estimate and a measure of uncertainty for this estimate

• functions used internally to compute these details

between-subjects

Hypothesis testing

Type	Equal variance?	Test	p-value adjustment?	Function used
Parametric	No	Games-Howell test	Yes	PMCMRplus::gamesHowellTest()
Parametric	Yes	Student's t-test	Yes	stats::pairwise.t.test()
Non-parametric	No	Dunn test	Yes	PMCMRplus::kwAllPairsDunnTest()
Robust	No	Yuen's trimmed means test	Yes	WRS2::lincon()
Bayesian	NA	Student's t-test	NA	BayesFactor::ttestBF()

Effect size estimation

Not supported.

within-subjects

Data requirement: Paired pairwise tests assume exactly one observation per subject per condition. If your data has multiple trials per cell, aggregate first (e.g., take the mean).

Hypothesis testing

Type	Test	p-value adjustment?	Function used
Parametric	Student's t-test	Yes	stats::pairwise.t.test()
Non-parametric	Durbin-Conover test	Yes	PMCMRplus::durbinAllPairsTest()
Robust	Yuen's trimmed means test	Yes	WRS2::rmmcp()
Bayesian	Student's t-test	NA	BayesFactor::ttestBF()

Effect size estimation

Not supported.

See also

grouped_ggbetweenstats, ggbetweenstats, grouped_ggwithinstats

Examples

# for reproducibility
set.seed(123)
library(dplyr, warn.conflicts = FALSE)

# create a plot
p <- ggwithinstats(
  data       = filter(bugs_long, condition %in% c("HDHF", "HDLF")),
  x          = condition,
  y          = desire,
  type       = "np",
  subject.id = subject
)


# looking at the plot
p


# if the data are already arranged in repeated-measures order, `subject.id`
# can be omitted
ggwithinstats(
  data             = filter(bugs_long, condition %in% c("HDHF", "HDLF")),
  x                = condition,
  y                = desire,
  pairwise.display = "none",
  results.subtitle = FALSE
)


# extracting details from statistical tests
extract_stats(p)
#> $subtitle_data
#> # A tibble: 1 × 14
#>   parameter1 parameter2 statistic  p.value method                    alternative
#>   <chr>      <chr>          <dbl>    <dbl> <chr>                     <chr>      
#> 1 desire     condition       1796 0.000430 Wilcoxon signed rank test two.sided  
#>   effectsize        estimate conf.level conf.low conf.high conf.method n.obs
#>   <chr>                <dbl>      <dbl>    <dbl>     <dbl> <chr>       <int>
#> 1 r (rank biserial)    0.487       0.95    0.285     0.648 normal         90
#>   expression
#>   <list>    
#> 1 <language>
#> 
#> $caption_data
#> NULL
#> 
#> $pairwise_comparisons_data
#> NULL
#> 
#> $descriptive_data
#> NULL
#> 
#> $one_sample_data
#> NULL
#> 
#> $tidy_data
#> NULL
#> 
#> $glance_data
#> NULL
#> 
#> attr(,"class")
#> [1] "ggstatsplot_stats" "list"             

# use a stricter alpha threshold for significant pairwise comparisons
ggwithinstats(
  data = bugs_long,
  x = condition,
  y = desire,
  subject.id = subject,
  pairwise.alpha = 0.001
)


# modifying defaults
ggwithinstats(
  data       = bugs_long,
  x          = condition,
  y          = desire,
  type       = "robust",
  subject.id = subject
)


# you can remove a specific geom to reduce complexity of the plot
ggwithinstats(
  data = bugs_long,
  x = condition,
  y = desire,
  subject.id = subject,
  # to remove violin plot
  violin.args = list(width = 0, linewidth = 0, colour = NA),
  # to remove boxplot
  boxplot.args = list(width = 0),
  # to remove points
  point.args = list(alpha = 0)
)