Skip to contents

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.

Usage

ggbetweenstats(
  data,
  x,
  y,
  type = "parametric",
  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,
  k = 2L,
  var.equal = FALSE,
  conf.level = 0.95,
  nboot = 100L,
  tr = 0.2,
  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,
    min.segment.length = 0),
  point.args = list(position = ggplot2::position_jitterdodge(dodge.width = 0.6), alpha =
    0.4, size = 3, stroke = 0, na.rm = TRUE),
  boxplot.args = list(width = 0.3, alpha = 0.2, 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(),
  package = "RColorBrewer",
  palette = "Dark2",
  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.

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.

p.adjust.method

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 "eta" (partial eta-squared) or "omega" (partial omega-squared).

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.

k

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

var.equal

a logical variable indicating whether to treat the two variances as being equal. If TRUE then the pooled variance is used to estimate the variance otherwise the Welch (or Satterthwaite) approximation to the degrees of freedom is used.

conf.level

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

nboot

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

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.

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:

  • "parameteric" (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.

point.args

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

boxplot.args

A list of additional aesthetic arguments passed on to ggplot2::geom_boxplot().

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 ggstatsplot::theme_ggstatsplot(). Any of the {ggplot2} themes (e.g., 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.

package, palette

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).

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.

Summary of graphics

graphical elementgeom usedargument for further modification
raw dataggplot2::geom_point()point.args
box plotggplot2::geom_boxplot()boxplot.args
density plotggplot2::geom_violin()violin.args
centrality measure pointggplot2::geom_point()centrality.point.args
centrality measure labelggrepel::geom_label_repel()centrality.label.args
pairwise comparisonsggsignif::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

TypeMeasureFunction used
Parametricmeandatawizard::describe_distribution()
Non-parametricmediandatawizard::describe_distribution()
Robusttrimmed meandatawizard::describe_distribution()
BayesianMAPdatawizard::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

TypeNo. of groupsTestFunction used
Parametric2Student's or Welch's t-teststats::t.test()
Non-parametric2Mann-Whitney U teststats::wilcox.test()
Robust2Yuen's test for trimmed meansWRS2::yuen()
Bayesian2Student's t-testBayesFactor::ttestBF()

Effect size estimation

TypeNo. of groupsEffect sizeCI available?Function used
Parametric2Cohen's d, Hedge's gYeseffectsize::cohens_d(), effectsize::hedges_g()
Non-parametric2r (rank-biserial correlation)Yeseffectsize::rank_biserial()
Robust2Algina-Keselman-Penfield robust standardized differenceYesWRS2::akp.effect()
Bayesian2differenceYesbayestestR::describe_posterior()

within-subjects

Hypothesis testing

TypeNo. of groupsTestFunction used
Parametric2Student's t-teststats::t.test()
Non-parametric2Wilcoxon signed-rank teststats::wilcox.test()
Robust2Yuen's test on trimmed means for dependent samplesWRS2::yuend()
Bayesian2Student's t-testBayesFactor::ttestBF()

Effect size estimation

TypeNo. of groupsEffect sizeCI available?Function used
Parametric2Cohen's d, Hedge's gYeseffectsize::cohens_d(), effectsize::hedges_g()
Non-parametric2r (rank-biserial correlation)Yeseffectsize::rank_biserial()
Robust2Algina-Keselman-Penfield robust standardized differenceYesWRS2::wmcpAKP()
Bayesian2differenceYesbayestestR::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

TypeNo. of groupsTestFunction used
Parametric> 2Fisher's or Welch's one-way ANOVAstats::oneway.test()
Non-parametric> 2Kruskal-Wallis one-way ANOVAstats::kruskal.test()
Robust> 2Heteroscedastic one-way ANOVA for trimmed meansWRS2::t1way()
Bayes Factor> 2Fisher's ANOVABayesFactor::anovaBF()

Effect size estimation

TypeNo. of groupsEffect sizeCI available?Function used
Parametric> 2partial eta-squared, partial omega-squaredYeseffectsize::omega_squared(), effectsize::eta_squared()
Non-parametric> 2rank epsilon squaredYeseffectsize::rank_epsilon_squared()
Robust> 2Explanatory measure of effect sizeYesWRS2::t1way()
Bayes Factor> 2Bayesian R-squaredYesperformance::r2_bayes()

within-subjects

Hypothesis testing

TypeNo. of groupsTestFunction used
Parametric> 2One-way repeated measures ANOVAafex::aov_ez()
Non-parametric> 2Friedman rank sum teststats::friedman.test()
Robust> 2Heteroscedastic one-way repeated measures ANOVA for trimmed meansWRS2::rmanova()
Bayes Factor> 2One-way repeated measures ANOVABayesFactor::anovaBF()

Effect size estimation

TypeNo. of groupsEffect sizeCI available?Function used
Parametric> 2partial eta-squared, partial omega-squaredYeseffectsize::omega_squared(), effectsize::eta_squared()
Non-parametric> 2Kendall's coefficient of concordanceYeseffectsize::kendalls_w()
Robust> 2Algina-Keselman-Penfield robust standardized difference averageYesWRS2::wmcpAKP()
Bayes Factor> 2Bayesian R-squaredYesperformance::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

TypeEqual variance?Testp-value adjustment?Function used
ParametricNoGames-Howell testYesPMCMRplus::gamesHowellTest()
ParametricYesStudent's t-testYesstats::pairwise.t.test()
Non-parametricNoDunn testYesPMCMRplus::kwAllPairsDunnTest()
RobustNoYuen's trimmed means testYesWRS2::lincon()
BayesianNAStudent's t-testNABayesFactor::ttestBF()

Effect size estimation

Not supported.

within-subjects

Hypothesis testing

TypeTestp-value adjustment?Function used
ParametricStudent's t-testYesstats::pairwise.t.test()
Non-parametricDurbin-Conover testYesPMCMRplus::durbinAllPairsTest()
RobustYuen's trimmed means testYesWRS2::rmmcp()
BayesianStudent's t-testNABayesFactor::ttestBF()

Effect size estimation

Not supported.

Examples

# for reproducibility
set.seed(123)
library(PMCMRplus) # for pairwise comparisons

# create a plot
p <- ggbetweenstats(mtcars, am, mpg)

# looking at the plot
p


# extracting details from statistical tests
extract_stats(p)
#> $subtitle_data
#> # A tibble: 1 × 18
#>   parameter1 parameter2 mean.parameter1 mean.parameter2 statistic df.error
#>   <chr>      <chr>                <dbl>           <dbl>     <dbl>    <dbl>
#> 1 mpg        am                    17.1            24.4     -3.77     18.3
#>   p.value method                  alternative effectsize estimate conf.level
#>     <dbl> <chr>                   <chr>       <chr>         <dbl>      <dbl>
#> 1 0.00137 Welch Two Sample t-test two.sided   Hedges' g     -1.35       0.95
#>   conf.low conf.high conf.method conf.distribution n.obs expression
#>      <dbl>     <dbl> <chr>       <chr>             <int> <list>    
#> 1    -2.17    -0.512 ncp         t                    32 <language>
#> 
#> $caption_data
#> # A tibble: 1 × 16
#>   term       effectsize      estimate conf.level conf.low conf.high    pd
#>   <chr>      <chr>              <dbl>      <dbl>    <dbl>     <dbl> <dbl>
#> 1 Difference Bayesian t-test    -6.44       0.95    -10.1     -2.74 0.999
#>   prior.distribution prior.location prior.scale  bf10 method         
#>   <chr>                       <dbl>       <dbl> <dbl> <chr>          
#> 1 cauchy                          0       0.707  86.6 Bayesian t-test
#>   conf.method log_e_bf10 n.obs expression
#>   <chr>            <dbl> <int> <list>    
#> 1 ETI               4.46    32 <language>
#> 
#> $pairwise_comparisons_data
#> NULL
#> 
#> $descriptive_data
#> NULL
#> 
#> $one_sample_data
#> NULL
#> 
#> $tidy_data
#> NULL
#> 
#> $glance_data
#> NULL
#> 

# modifying defaults
ggbetweenstats(
  morley,
  x    = Expt,
  y    = Speed,
  type = "robust",
  xlab = "The experiment number",
  ylab = "Speed-of-light measurement"
)


# you can remove a specific geom to reduce complexity of the plot
ggbetweenstats(
  mtcars,
  am,
  wt,
  # to remove violin plot
  violin.args = list(width = 0, linewidth = 0),
  # to remove boxplot
  boxplot.args = list(width = 0),
  # to remove points
  point.args = list(alpha = 0)
)