Primary functions
Here are examples of the main functions currently supported in ggstatsplot
.
Note: If you are reading this on GitHub
repository, the documentation below is for the development version of the package. So you may see some features available here that are not currently present in the stable version of this package on CRAN. For documentation relevant for the CRAN
version, see: https://CRAN.Rproject.org/package=ggstatsplot/readme/README.html
ggbetweenstats
This function creates either a violin plot, a box plot, or a mix of two for betweengroup or betweencondition comparisons with results from statistical tests in the subtitle. The simplest function call looks like this
# for reproducibility
set.seed(123)
library(ggstatsplot)
# plot
ggbetweenstats(
data = iris,
x = Species,
y = Sepal.Length,
title = "Distribution of sepal length across Iris species"
)
📝 Defaults return
✅ raw data + distributions
✅ descriptive statistics
✅ inferential statistics
✅ effect size + CIs
✅ pairwise comparisons
✅ Bayesian hypothesistesting
✅ Bayesian estimation
A number of other arguments can be specified to make this plot even more informative or change some of the default options. Additionally, there is also a grouped_
variant of this function that makes it easy to repeat the same operation across a single grouping variable:
# for reproducibility
set.seed(123)
# plot
grouped_ggbetweenstats(
data = dplyr::filter(movies_long, genre %in% c("Action", "Comedy")),
x = mpaa,
y = length,
grouping.var = genre, # grouping variable
outlier.tagging = TRUE, # whether outliers need to be tagged
outlier.label = title, # variable to be used for tagging outliers
outlier.coef = 2,
ggsignif.args = list(textsize = 4, tip_length = 0.01),
p.adjust.method = "bonferroni", # method for adjusting pvalues for multiple comparisons
# adding new components to `ggstatsplot` default
ggplot.component = list(ggplot2::scale_y_continuous(sec.axis = ggplot2::dup_axis())),
caption = substitute(paste(italic("Source"), ": IMDb (Internet Movie Database)")),
palette = "default_jama",
package = "ggsci",
plotgrid.args = list(nrow = 1),
annotation.args = list(title = "Differences in movie length by mpaa ratings for different genres")
)
Note here that the function can be used to tag outliers!
Summary of tests
The central tendency measure displayed will depend on the statistics:
Type  Measure  Function used 

Parametric  mean  parameters::describe_distribution 
Nonparametric  median  parameters::describe_distribution 
Robust  trimmed mean  parameters::describe_distribution 
Bayesian  MAP estimate  parameters::describe_distribution 
MAP: maximum a posteriori probability
Following (betweensubjects) tests are carried out for each type of analyses
Type  No. of groups  Test  Function used 

Parametric  > 2  Fisher’s or Welch’s oneway ANOVA  stats::oneway.test 
Nonparametric  > 2  Kruskal–Wallis oneway ANOVA  stats::kruskal.test 
Robust  > 2  Heteroscedastic oneway ANOVA for trimmed means  WRS2::t1way 
Bayes Factor  > 2  Fisher’s ANOVA  BayesFactor::anovaBF 
Parametric  2  Student’s or Welch’s ttest  stats::t.test 
Nonparametric  2  Mann–Whitney U test  stats::wilcox.test 
Robust  2  Yuen’s test for trimmed means  WRS2::yuen 
Bayesian  2  Student’s ttest  BayesFactor::ttestBF 
Following effect sizes (and confidence intervals/CI) are available for each type of test
Type  No. of groups  Effect size  CI?  Function used 

Parametric  > 2  ,  ✅ 
effectsize::omega_squared , effectsize::eta_squared

Nonparametric  > 2  ✅  effectsize::rank_epsilon_squared 

Robust  > 2  (Explanatory measure of effect size)  ✅  WRS2::t1way 
Bayes Factor  > 2  ✅  performance::r2_bayes 

Parametric  2  Cohen’s d, Hedge’s g  ✅ 
effectsize::cohens_d , effectsize::hedges_g

Nonparametric  2  r (rankbiserial correlation)  ✅  effectsize::rank_biserial 
Robust  2  (Explanatory measure of effect size)  ✅  WRS2::yuen.effect.ci 
Bayesian  2  ✅  bayestestR::describe_posterior 
Here is a summary of multiple pairwise comparison tests supported in ggbetweenstats
Type  Equal variance?  Test  pvalue adjustment?  Function used 

Parametric  No  GamesHowell test  ✅  stats::pairwise.t.test 
Parametric  Yes  Student’s ttest  ✅  PMCMRplus::gamesHowellTest 
Nonparametric  No  Dunn test  ✅  PMCMRplus::kwAllPairsDunnTest 
Robust  No  Yuen’s trimmed means test  ✅  WRS2::lincon 
Bayes Factor  ❌  Student’s ttest  ❌  BayesFactor::ttestBF 
For more, see the ggbetweenstats
vignette: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggbetweenstats.html
ggwithinstats
ggbetweenstats
function has an identical twin function ggwithinstats
for repeated measures designs that behaves in the same fashion with a few minor tweaks introduced to properly visualize the repeated measures design. As can be seen from an example below, the only difference between the plot structure is that now the group means are connected by paths to highlight the fact that these data are paired with each other.
# for reproducibility and data
set.seed(123)
library(WRS2) # for data
library(afex) # to run anova
# plot
ggwithinstats(
data = WineTasting,
x = Wine,
y = Taste,
title = "Wine tasting",
caption = "Data source: `WRS2` R package",
ggtheme = ggthemes::theme_fivethirtyeight(),
ggstatsplot.layer = FALSE
)
📝 Defaults return
✅ raw data + distributions
✅ descriptive statistics
✅ inferential statistics
✅ effect size + CIs
✅ pairwise comparisons
✅ Bayesian hypothesistesting
✅ Bayesian estimation
The central tendency measure displayed will depend on the statistics:
Type  Measure  Function used 

Parametric  mean  parameters::describe_distribution 
Nonparametric  median  parameters::describe_distribution 
Robust  trimmed mean  parameters::describe_distribution 
Bayesian  MAP estimate  parameters::describe_distribution 
As with the ggbetweenstats
, this function also has a grouped_
variant that makes repeating the same analysis across a single grouping variable quicker. We will see an example with only repeated measurements
# common setup
set.seed(123)
# plot
grouped_ggwithinstats(
data = dplyr::filter(
.data = bugs_long,
region %in% c("Europe", "North America"),
condition %in% c("LDLF", "LDHF")
),
x = condition,
y = desire,
type = "np", # nonparametric statistics
xlab = "Condition",
ylab = "Desire to kill an artrhopod",
grouping.var = region,
outlier.tagging = TRUE,
outlier.label = education
)
Summary of tests
The central tendency measure displayed will depend on the statistics:
Type  Measure  Function used 

Parametric  mean  parameters::describe_distribution 
Nonparametric  median  parameters::describe_distribution 
Robust  trimmed mean  parameters::describe_distribution 
Bayesian  MAP estimate  parameters::describe_distribution 
MAP: maximum a posteriori probability
Following (withinsubjects) tests are carried out for each type of analyses
Type  No. of groups  Test  Function used 

Parametric  > 2  Oneway repeated measures ANOVA  afex::aov_ez 
Nonparametric  > 2  Friedman rank sum test  stats::friedman.test 
Robust  > 2  Heteroscedastic oneway repeated measures ANOVA for trimmed means  WRS2::rmanova 
Bayes Factor  > 2  Oneway repeated measures ANOVA  BayesFactor::anovaBF 
Parametric  2  Student’s ttest  stats::t.test 
Nonparametric  2  Wilcoxon signedrank test  stats::wilcox.test 
Robust  2  Yuen’s test on trimmed means for dependent samples  WRS2::yuend 
Bayesian  2  Student’s ttest  BayesFactor::ttestBF 
Following effect sizes (and confidence intervals/CI) are available for each type of test
Type  No. of groups  Effect size  CI?  Function used 

Parametric  > 2  ,  ✅ 
effectsize::omega_squared , effectsize::eta_squared

Nonparametric  > 2  (Kendall’s coefficient of concordance)  ✅  effectsize::kendalls_w 
Robust  > 2  ✅  AlginaKeselmanPenfield robust standardized difference average  WRS2::wmcpAKP 
Bayes Factor  > 2  ✅  performance::r2_bayes 

Parametric  2  Cohen’s d, Hedge’s g  ✅ 
effectsize::cohens_d , effectsize::hedges_g

Nonparametric  2  r (rankbiserial correlation)  ✅  effectsize::rank_biserial 
Robust  2  (AlginaKeselmanPenfield robust standardized difference)  ✅  WRS2::dep.effect 
Bayesian  2  ✅  bayestestR::describe_posterior 
Here is a summary of multiple pairwise comparison tests supported in ggwithinstats
Type  Test  pvalue adjustment?  Function used 

Parametric  Student’s ttest  ✅  stats::pairwise.t.test 
Nonparametric  DurbinConover test  ✅  PMCMRplus::durbinAllPairsTest 
Robust  Yuen’s trimmed means test  ✅  WRS2::rmmcp 
Bayesian  Student’s ttest  ❌  BayesFactor::ttestBF 
For more, see the ggwithinstats
vignette: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggwithinstats.html
gghistostats
To visualize the distribution of a single variable and check if its mean is significantly different from a specified value with a onesample test, gghistostats
can be used.
# for reproducibility
set.seed(123)
# plot
gghistostats(
data = ggplot2::msleep, # dataframe from which variable is to be taken
x = awake, # numeric variable whose distribution is of interest
title = "Amount of time spent awake", # title for the plot
caption = substitute(paste(italic("Source: "), "Mammalian sleep data set")),
test.value = 12, # default value is 0
binwidth = 1, # binwidth value (experiment)
ggtheme = hrbrthemes::theme_ipsum_tw(), # choosing a different theme
ggstatsplot.layer = FALSE # turn off ggstatsplot theme layer
)
📝 Defaults return
✅ counts + proportion for bins
✅ descriptive statistics
✅ inferential statistics
✅ effect size + CIs
✅ Bayesian hypothesistesting
✅ Bayesian estimation
There is also a grouped_
variant of this function that makes it easy to repeat the same operation across a single grouping variable:
# for reproducibility
set.seed(123)
# plot
grouped_gghistostats(
data = dplyr::filter(movies_long, genre %in% c("Action", "Comedy")),
x = budget,
test.value = 50,
type = "nonparametric",
xlab = "Movies budget (in million US$)",
grouping.var = genre, # grouping variable
normal.curve = TRUE, # superimpose a normal distribution curve
normal.curve.args = list(color = "red", size = 1),
ggtheme = ggthemes::theme_tufte(),
# modify the defaults from `ggstatsplot` for each plot
ggplot.component = ggplot2::labs(caption = "Source: IMDB.com"),
plotgrid.args = list(nrow = 1),
annotation.args = list(title = "Movies budgets for different genres")
)
Summary of tests
The central tendency measure displayed will depend on the statistics:
Type  Measure  Function used 

Parametric  mean  parameters::describe_distribution 
Nonparametric  median  parameters::describe_distribution 
Robust  trimmed mean  parameters::describe_distribution 
Bayesian  MAP estimate  parameters::describe_distribution 
Following tests are carried out for each type of analyses
Type  Test  Function used 

Parametric  Onesample Student’s ttest  stats::t.test 
Nonparametric  Onesample Wilcoxon test  stats::wilcox.test 
Robust  Bootstrapt method for onesample test 
trimcibt (custom) 
Bayesian  Onesample Student’s ttest  BayesFactor::ttestBF 
Following effect sizes (and confidence intervals/CI) are available for each type of test
Type  Effect size  CI?  Function used 

Parametric  Cohen’s d, Hedge’s g  ✅ 
effectsize::cohens_d , effectsize::hedges_g

Nonparametric  r (rankbiserial correlation)  ✅  effectsize::rank_biserial 
Robust  trimmed mean  ✅ 
trimcibt (custom) 
Bayes Factor  ✅  bayestestR::describe_posterior 
For more, including information about the variant of this function grouped_gghistostats
, see the gghistostats
vignette: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/gghistostats.html
ggdotplotstats
This function is similar to gghistostats
, but is intended to be used when the numeric variable also has a label.
# for reproducibility
set.seed(123)
# plot
ggdotplotstats(
data = dplyr::filter(.data = gapminder::gapminder, continent == "Asia"),
y = country,
x = lifeExp,
test.value = 55,
type = "robust",
title = "Distribution of life expectancy in Asian continent",
xlab = "Life expectancy",
caption = substitute(
paste(
italic("Source"),
": Gapminder dataset from https://www.gapminder.org/"
)
)
)
📝 Defaults return
✅ descriptives (mean + sample size)
✅ inferential statistics
✅ effect size + CIs
✅ Bayesian hypothesistesting
✅ Bayesian estimation
As with the rest of the functions in this package, there is also a grouped_
variant of this function to facilitate looping the same operation for all levels of a single grouping variable.
# for reproducibility
set.seed(123)
# plot
grouped_ggdotplotstats(
data = dplyr::filter(.data = ggplot2::mpg, cyl %in% c("4", "6")),
x = cty,
y = manufacturer,
type = "bayes", # Bayesian test
xlab = "city miles per gallon",
ylab = "car manufacturer",
grouping.var = cyl, # grouping variable
test.value = 15.5,
point.args = list(color = "red", size = 5, shape = 13),
annotation.args = list(title = "Fuel economy data")
)
ggscatterstats
This function creates a scatterplot with marginal distributions overlaid on the axes (from ggExtra::ggMarginal
) and results from statistical tests in the subtitle:
ggscatterstats(
data = ggplot2::msleep,
x = sleep_rem,
y = awake,
xlab = "REM sleep (in hours)",
ylab = "Amount of time spent awake (in hours)",
title = "Understanding mammalian sleep"
)
📝 Defaults return
✅ raw data + distributions
✅ marginal distributions
✅ inferential statistics
✅ effect size + CIs
✅ Bayesian hypothesistesting
✅ Bayesian estimation
The available marginal distributions are
 histograms
 boxplots
 density
 violin
 densigram (density + histogram)
Number of other arguments can be specified to modify this basic plot
# for reproducibility
set.seed(123)
# plot
ggscatterstats(
data = dplyr::filter(movies_long, genre == "Action"),
x = budget,
y = rating,
type = "robust", # type of test that needs to be run
xlab = "Movie budget (in million/ US$)", # label for x axis
ylab = "IMDB rating", # label for y axis
label.var = title, # variable for labeling data points
label.expression = rating < 5 & budget > 100, # expression that decides which points to label
title = "Movie budget and IMDB rating (action)", # title text for the plot
caption = expression(paste(italic("Note"), ": IMDB stands for Internet Movie DataBase")),
ggtheme = hrbrthemes::theme_ipsum_ps(), # choosing a different theme
ggstatsplot.layer = FALSE, # turn off `ggstatsplot` theme layer
marginal.type = "boxplot", # type of marginal distribution to be displayed
xfill = "pink", # color fill for xaxis marginal distribution
yfill = "#009E73" # color fill for yaxis marginal distribution
)
Additionally, there is also a grouped_
variant of this function that makes it easy to repeat the same operation across a single grouping variable. Also, note that, as opposed to the other functions, this function does not return a ggplot
object and any modification you want to make can be made in advance using ggplot.component
argument (available for all functions, but especially useful here):
# for reproducibility
set.seed(123)
# plot
grouped_ggscatterstats(
data = dplyr::filter(movies_long, genre %in% c("Action", "Comedy")),
x = rating,
y = length,
grouping.var = genre, # grouping variable
label.var = title,
label.expression = length > 200,
xlab = "IMDB rating",
ggtheme = ggplot2::theme_grey(),
ggplot.component = list(
ggplot2::scale_x_continuous(breaks = seq(2, 9, 1), limits = (c(2, 9)))
),
plotgrid.args = list(nrow = 1),
annotation.args = list(title = "Relationship between movie length and IMDB ratings")
)
Summary of tests
Following tests are carried out for each type of analyses. Additionally, the correlation coefficients (and their confidence intervals) are used as effect sizes
Type  Test  CI?  Function used 

Parametric  Pearson’s correlation coefficient  ✅  correlation::correlation 
Nonparametric  Spearman’s rank correlation coefficient  ✅  correlation::correlation 
Robust  Winsorized Pearson correlation coefficient  ✅  correlation::correlation 
Bayesian  Pearson’s correlation coefficient  ✅  correlation::correlation 
For more, see the ggscatterstats
vignette: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggscatterstats.html
ggcorrmat
ggcorrmat
makes a correlalogram (a matrix of correlation coefficients) with minimal amount of code. Just sticking to the defaults itself produces publicationready correlation matrices. But, for the sake of exploring the available options, let’s change some of the defaults. For example, multiple aestheticsrelated arguments can be modified to change the appearance of the correlation matrix.
# for reproducibility
set.seed(123)
# as a default this function outputs a correlation matrix plot
ggcorrmat(
data = ggplot2::msleep,
colors = c("#B2182B", "white", "#4D4D4D"),
title = "Correlalogram for mammals sleep dataset",
subtitle = "sleep units: hours; weight units: kilograms"
)
📝 Defaults return
✅ effect size + significance
✅ careful handling of NA
s
If there are NA
s present in the selected variables, the legend will display minimum, median, and maximum number of pairs used for correlation tests.
There is also a grouped_
variant of this function that makes it easy to repeat the same operation across a single grouping variable:
# for reproducibility
set.seed(123)
# plot
grouped_ggcorrmat(
data = dplyr::filter(movies_long, genre %in% c("Action", "Comedy")),
type = "robust", # correlation method
colors = c("#cbac43", "white", "#550000"),
grouping.var = genre, # grouping variable
matrix.type = "lower" # type of matrix
)
You can also get a dataframe containing all relevant details from the statistical tests:
# setup
set.seed(123)
# dataframe in long format
ggcorrmat(
data = ggplot2::msleep,
type = "bayes",
output = "dataframe"
)
#> # A tibble: 15 x 14
#> parameter1 parameter2 estimate conf.level conf.low conf.high pd
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 sleep_total sleep_rem 0.731 0.95 0.617 0.810 1
#> 2 sleep_total sleep_cycle 0.432 0.95 0.678 0.223 0.995
#> 3 sleep_total awake 1.00 0.95 1.00 1.00 1
#> 4 sleep_total brainwt 0.339 0.95 0.523 0.156 0.996
#> 5 sleep_total bodywt 0.300 0.95 0.458 0.142 0.997
#> 6 sleep_rem sleep_cycle 0.306 0.95 0.535 0.0555 0.965
#> 7 sleep_rem awake 0.734 0.95 0.824 0.638 1
#> 8 sleep_rem brainwt 0.202 0.95 0.410 0.0130 0.927
#> 9 sleep_rem bodywt 0.315 0.95 0.481 0.120 0.994
#> 10 sleep_cycle awake 0.441 0.95 0.226 0.662 0.995
#> 11 sleep_cycle brainwt 0.823 0.95 0.720 0.911 1
#> 12 sleep_cycle bodywt 0.386 0.95 0.145 0.610 0.992
#> 13 awake brainwt 0.341 0.95 0.154 0.524 0.992
#> 14 awake bodywt 0.299 0.95 0.139 0.454 0.998
#> 15 brainwt bodywt 0.926 0.95 0.896 0.957 1
#> rope.percentage prior.distribution prior.location prior.scale bayes.factor
#> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 0 beta 1.41 1.41 3.00e+ 9
#> 2 0.0173 beta 1.41 1.41 8.85e+ 0
#> 3 0 beta 1.41 1.41 NA
#> 4 0.028 beta 1.41 1.41 7.29e+ 0
#> 5 0.0292 beta 1.41 1.41 9.28e+ 0
#> 6 0.091 beta 1.41 1.41 1.42e+ 0
#> 7 0 beta 1.41 1.41 3.01e+ 9
#> 8 0.212 beta 1.41 1.41 6.54e 1
#> 9 0.0362 beta 1.41 1.41 4.80e+ 0
#> 10 0.0158 beta 1.41 1.41 8.85e+ 0
#> 11 0 beta 1.41 1.41 3.80e+ 6
#> 12 0.0392 beta 1.41 1.41 3.76e+ 0
#> 13 0.0253 beta 1.41 1.41 7.29e+ 0
#> 14 0.0265 beta 1.41 1.41 9.27e+ 0
#> 15 0 beta 1.41 1.41 1.58e+22
#> method n.obs
#> <chr> <int>
#> 1 Bayesian Pearson correlation 61
#> 2 Bayesian Pearson correlation 32
#> 3 Bayesian Pearson correlation 83
#> 4 Bayesian Pearson correlation 56
#> 5 Bayesian Pearson correlation 83
#> 6 Bayesian Pearson correlation 32
#> 7 Bayesian Pearson correlation 61
#> 8 Bayesian Pearson correlation 48
#> 9 Bayesian Pearson correlation 61
#> 10 Bayesian Pearson correlation 32
#> 11 Bayesian Pearson correlation 30
#> 12 Bayesian Pearson correlation 32
#> 13 Bayesian Pearson correlation 56
#> 14 Bayesian Pearson correlation 83
#> 15 Bayesian Pearson correlation 56
Additionally, partial correlation are also supported:
# setup
set.seed(123)
# dataframe in long format
ggcorrmat(
data = ggplot2::msleep,
type = "bayes",
partial = TRUE,
output = "dataframe"
)
#> # A tibble: 15 x 14
#> parameter1 parameter2 estimate conf.level conf.low conf.high pd
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 sleep_total sleep_rem 0.279 0.95 0.0202 0.550 0.940
#> 2 sleep_total sleep_cycle 0.0181 0.95 0.306 0.254 0.543
#> 3 sleep_total awake 1 0.95 1 1 1
#> 4 sleep_total brainwt 0.0818 0.95 0.352 0.192 0.678
#> 5 sleep_total bodywt 0.163 0.95 0.425 0.121 0.818
#> 6 sleep_rem sleep_cycle 0.0666 0.95 0.335 0.222 0.643
#> 7 sleep_rem awake 0.0505 0.95 0.212 0.328 0.611
#> 8 sleep_rem brainwt 0.0811 0.95 0.235 0.326 0.668
#> 9 sleep_rem bodywt 0.0190 0.95 0.296 0.265 0.544
#> 10 sleep_cycle awake 0.00603 0.95 0.278 0.279 0.516
#> 11 sleep_cycle brainwt 0.764 0.95 0.637 0.871 1
#> 12 sleep_cycle bodywt 0.0865 0.95 0.351 0.187 0.691
#> 13 awake brainwt 0.0854 0.95 0.349 0.205 0.690
#> 14 awake bodywt 0.407 0.95 0.630 0.146 0.991
#> 15 brainwt bodywt 0.229 0.95 0.0341 0.484 0.904
#> rope.percentage prior.distribution prior.location prior.scale bayes.factor
#> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 0.133 beta 1.41 1.41 1.04
#> 2 0.418 beta 1.41 1.41 0.277
#> 3 0 beta 1.41 1.41 NA
#> 4 0.390 beta 1.41 1.41 0.311
#> 5 0.294 beta 1.41 1.41 0.417
#> 6 0.404 beta 1.41 1.41 0.297
#> 7 0.411 beta 1.41 1.41 0.287
#> 8 0.380 beta 1.41 1.41 0.303
#> 9 0.424 beta 1.41 1.41 0.280
#> 10 0.422 beta 1.41 1.41 0.276
#> 11 0 beta 1.41 1.41 131029.
#> 12 0.393 beta 1.41 1.41 0.309
#> 13 0.390 beta 1.41 1.41 0.310
#> 14 0.033 beta 1.41 1.41 4.82
#> 15 0.206 beta 1.41 1.41 0.637
#> method n.obs
#> <chr> <int>
#> 1 Bayesian Pearson correlation 30
#> 2 Bayesian Pearson correlation 30
#> 3 Bayesian Pearson correlation 30
#> 4 Bayesian Pearson correlation 30
#> 5 Bayesian Pearson correlation 30
#> 6 Bayesian Pearson correlation 30
#> 7 Bayesian Pearson correlation 30
#> 8 Bayesian Pearson correlation 30
#> 9 Bayesian Pearson correlation 30
#> 10 Bayesian Pearson correlation 30
#> 11 Bayesian Pearson correlation 30
#> 12 Bayesian Pearson correlation 30
#> 13 Bayesian Pearson correlation 30
#> 14 Bayesian Pearson correlation 30
#> 15 Bayesian Pearson correlation 30
Summary of tests
Type  Test  CI?  partial?  Function used 

Parametric  Pearson’s correlation coefficient  ✅  ✅  correlation::correlation 
Nonparametric  Spearman’s rank correlation coefficient  ✅  ✅  correlation::correlation 
Robust  Winsorized Pearson correlation coefficient  ✅  ✅  correlation::correlation 
Bayesian  Pearson’s correlation coefficient  ✅  ✅  correlation::correlation 
For examples and more information, see the ggcorrmat
vignette: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggcorrmat.html
ggpiestats
This function creates a pie chart for categorical or nominal variables with results from contingency table analysis (Pearson’s chisquared test for betweensubjects design and McNemar’s chisquared test for withinsubjects design) included in the subtitle of the plot. If only one categorical variable is entered, results from onesample proportion test (i.e., a chisquared goodness of fit test) will be displayed as a subtitle.
To study an interaction between two categorical variables:
# for reproducibility
set.seed(123)
# plot
ggpiestats(
data = mtcars,
x = am,
y = cyl,
package = "wesanderson",
palette = "Royal1",
title = "Dataset: Motor Trend Car Road Tests", # title for the plot
legend.title = "Transmission", # title for the legend
caption = substitute(paste(italic("Source"), ": 1974 Motor Trend US magazine"))
)
📝 Defaults return
✅ descriptives (frequency + %s)
✅ inferential statistics
✅ effect size + CIs
✅ Goodnessoffit tests
✅ Bayesian hypothesistesting
✅ Bayesian estimation
There is also a grouped_
variant of this function that makes it easy to repeat the same operation across a single grouping variable. Following example is a case where the theoretical question is about proportions for different levels of a single nominal variable:
# for reproducibility
set.seed(123)
# plot
grouped_ggpiestats(
data = mtcars,
x = cyl,
grouping.var = am, # grouping variable
label.repel = TRUE, # repel labels (helpful for overlapping labels)
package = "ggsci", # package from which color palette is to be taken
palette = "default_ucscgb" # choosing a different color palette
)
Summary of tests
Following tests are carried out for each type of analyses
Type of data  Design  Test  Function used 

Unpaired  contingency table  Pearson’s test  stats::chisq.test 
Paired  contingency table  McNemar’s test  stats::mcnemar.test 
Frequency  contingency table  Goodness of fit ( test)  stats::chisq.test 
Following effect sizes (and confidence intervals/CI) are available for each type of test
Test  Effect size  CI?  Function used 

Pearson’s test  Cramer’s  ✅  effectsize::cramers_v 
McNemar’s test  Cohen’s  ✅  effectsize::cohens_g 
Goodness of fit  Cramer’s  ✅  effectsize::cramers_v 
For more, see the ggpiestats
vignette: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggpiestats.html
ggbarstats
In case you are not a fan of pie charts (for very good reasons), you can alternatively use ggbarstats
function which has a similar syntax.
N.B. The pvalues from onesample proportion test are displayed on top of each bar.
# for reproducibility
set.seed(123)
library(ggplot2)
# plot
ggbarstats(
data = movies_long,
x = mpaa,
y = genre,
title = "MPAA Ratings by Genre",
xlab = "movie genre",
legend.title = "MPAA rating",
ggtheme = hrbrthemes::theme_ipsum_pub(),
ggplot.component = list(ggplot2::scale_x_discrete(guide = ggplot2::guide_axis(n.dodge = 2))),
palette = "Set2"
)
📝 Defaults return
✅ descriptives (frequency + %s)
✅ inferential statistics
✅ effect size + CIs
✅ Goodnessoffit tests
✅ Bayesian hypothesistesting
✅ Bayesian estimation
And, needless to say, there is also a grouped_
variant of this function
# setup
set.seed(123)
# plot
grouped_ggbarstats(
data = mtcars,
x = am,
y = cyl,
grouping.var = vs,
package = "wesanderson",
palette = "Darjeeling2",
ggtheme = ggthemes::theme_tufte(base_size = 12),
ggstatsplot.layer = FALSE
)
ggcoefstats
The function ggcoefstats
generates dotandwhisker plots for regression models saved in a tidy data frame. The tidy dataframes are prepared using parameters::model_parameters
. Additionally, if available, the model summary indices are also extracted from performance::model_performance
.
Although the statistical models displayed in the plot may differ based on the class of models being investigated, there are few aspects of the plot that will be invariant across models:
The dotwhisker plot contains a dot representing the estimate and their confidence intervals (
95%
is the default). The estimate can either be effect sizes (for tests that depend on theF
statistic) or regression coefficients (for tests witht
, , andz
statistic), etc. The function will, by default, display a helpfulx
axis label that should clear up what estimates are being displayed. The confidence intervals can sometimes be asymmetric if bootstrapping was used.The label attached to dot will provide more details from the statistical test carried out and it will typically contain estimate, statistic, and pvalue.
The caption will contain diagnostic information, if available, about models that can be useful for model selection: The smaller the Akaike’s Information Criterion (AIC) and the Bayesian Information Criterion (BIC) values, the “better” the model is.
The output of this function will be a
ggplot2
object and, thus, it can be further modified (e.g., change themes, etc.) withggplot2
functions.
# for reproducibility
set.seed(123)
# model
mod < stats::lm(formula = mpg ~ am * cyl, data = mtcars)
# plot
ggcoefstats(mod)
📝 Defaults return
✅ inferential statistics
✅ estimate + CIs
✅ model summary (AIC + BIC)
This default plot can be further modified to one’s liking with additional arguments (also, let’s use a different model now):
# for reproducibility
set.seed(123)
# model
mod < MASS::rlm(formula = mpg ~ am * cyl, data = mtcars)
# plot
ggcoefstats(
x = mod,
point.args = list(color = "red", size = 3, shape = 15),
vline.args = list(size = 1, color = "#CC79A7", linetype = "dotdash"),
title = "Car performance predicted by transmission & cylinder count",
subtitle = "Source: 1974 Motor Trend US magazine",
exclude.intercept = TRUE,
ggtheme = hrbrthemes::theme_ipsum_ps(),
ggstatsplot.layer = FALSE
) + # note the order in which the labels are entered
ggplot2::scale_y_discrete(labels = c("transmission", "cylinders", "interaction")) +
ggplot2::labs(x = "regression coefficient", y = NULL)
Supported models
Most of the regression models that are supported in the underlying packages are also supported by ggcoefstats
. For example
aareg
, afex_aov
, anova
, anova.mlm
, anova
, aov
, aovlist
, Arima
, bam
, bayesx
, bayesGARCH
, bayesQR
, BBmm
, BBreg
, bcplm
, betamfx
, betaor
, BFBayesFactor
, BGGM
, bglmerMod
, bife
, bigglm
, biglm
, blavaan
, bmlm
, blmerMod
, blrm
, bracl
, brglm
, brglm2
, brmsfit
, brmultinom
, btergm
, cch
, censReg
, cgam
, cgamm
, cglm
, clm
, clm2
, clmm
, clmm2
, coeftest
, complmrob
, confusionMatrix
, coxme
, coxph
, coxr
, coxph.penal
, cpglm
, cpglmm
, crch
, crq
, crr
, DirichReg
, drc
, eglm
, elm
, emmGrid
, epi.2by2
, ergm
, feis
, felm
, fitdistr
, fixest
, flexsurvreg
, gam
, Gam
, gamlss
, garch
, geeglm
, glmc
, glmerMod
, glmmTMB
, gls
, glht
, glm
, glmm
, glmmadmb
, glmmPQL
, glmRob
, glmrob
, glmx
, gmm
, HLfit
, hurdle
, ivFixed
, ivprobit
, ivreg
, iv_robust
, lavaan
, lm
, lm.beta
, lmerMod
, lmerModLmerTest
, lmodel2
, lmRob
, lmrob
, lm_robust
, logitmfx
, logitor
, logitsf
, LORgee
, lqm
, lqmm
, lrm
, manova
, maov
, margins
, mcmc
, mcmc.list
, MCMCglmm
, mclogit
, mice
, mmclogit
, mediate
, metafor
, merMod
, merModList
, metaplus
, mhurdle
, mixor
, mjoint
, mle2
, mlm
, multinom
, mvord
, negbin
, negbinmfx
, negbinirr
, nlmerMod
, nlrq
, nlreg
, nls
, orcutt
, orm
, plm
, poissonmfx
, poissonirr
, polr
, probitmfx
, ridgelm
, riskRegression
, rjags
, rlm
, rlmerMod
, robmixglm
, rq
, rqs
, rqss
, rrvglm
, scam
, semLm
, semLme
, slm
, speedglm
, speedlm
, stanfit
, stanreg
, summary.lm
, survreg
, svyglm
, svy_vglm
, svyolr
, tobit
, truncreg
, varest
, vgam
, vglm
, wbgee
, wblm
, zeroinfl
, etc.
Although not shown here, this function can also be used to carry out parametric, robust, and Bayesian randomeffects metaanalysis.
Summary of metaanalysis tests
Type  Test  Effect size  95% CI available?  Function used 

Parametric  Metaanalysis via randomeffects models  ✅  metafor::metafor 

Robust  Metaanalysis via robust randomeffects models  ✅  metaplus::metaplus 

Bayes  Metaanalysis via Bayesian randomeffects models  ✅  metaBMA::meta_random 
For a more exhaustive account of this function, see the associated vignette https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggcoefstats.html
combine_plots
The full power of ggstatsplot
can be leveraged with a functional programming package like purrr
that replaces for
loops with code that is both more succinct and easier to read and, therefore, purrr
should be preferrred 😻.
In such cases, ggstatsplot
contains a helper function combine_plots
to combine multiple plots, which can be useful for combining a list of plots produced with purrr
. This is a wrapper around patchwork::wrap_plots
and lets you combine multiple plots and add a combination of title, caption, and annotation texts with suitable defaults.
For examples (both with plyr
and purrr
), see the associated vignette https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/combine_plots.html
Using ggstatsplot
statistical details with custom plots
Sometimes you may not like the default plots produced by ggstatsplot
. In such cases, you can use other custom plots (from ggplot2
or other plotting packages) and still use ggstatsplot
functions to display results from relevant statistical test.
For example, in the following chunk, we will create plot (ridgeplot) using ggridges
package and use ggstatsplot
function for extracting results.
# loading the needed libraries
set.seed(123)
library(ggridges)
library(ggplot2)
library(ggstatsplot)
# using `ggstatsplot` to get call with statistical results
stats_results <
ggbetweenstats(
data = morley,
x = Expt,
y = Speed,
output = "subtitle"
)
# using `ggridges` to create plot
ggplot(morley, aes(x = Speed, y = as.factor(Expt), fill = as.factor(Expt))) +
geom_density_ridges(
jittered_points = TRUE,
quantile_lines = TRUE,
scale = 0.9,
alpha = 0.7,
vline_size = 1,
vline_color = "red",
point_size = 0.4,
point_alpha = 1,
position = position_raincloud(adjust_vlines = TRUE)
) + # adding annotations
labs(
title = "MichelsonMorley experiments",
subtitle = stats_results,
x = "Speed of light",
y = "Experiment number"
) + # remove the legend
theme(legend.position = "none")