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
# loading needed libraries
library(ggstatsplot)
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggbetweenstats(
data = iris,
x = Species,
y = Sepal.Length,
messages = FALSE
) + # further modification outside of ggstatsplot
ggplot2::coord_cartesian(ylim = c(3, 8)) +
ggplot2::scale_y_continuous(breaks = seq(3, 8, by = 1))
Note that this function returns object of class ggplot
and thus can be further modified using ggplot2
functions.
A number of other arguments can be specified to make this plot even more informative or change some of the default options. Additionally, this time we will use a grouping variable that has only two levels. The function will automatically switch from carrying out an ANOVA analysis to a ttest.
The type
(of test) argument also accepts the following abbreviations: "p"
(for parametric) or "np"
(for nonparametric) or "r"
(for robust) or "bf"
(for Bayes Factor). Additionally, the type of plot to be displayed can also be modified ("box"
, "violin"
, or "boxviolin"
).
A number of other arguments can be specified to make this plot even more informative or change some of the default options.
library(ggplot2)
# for reproducibility
set.seed(123)
# let's leave out one of the factor levels and see if instead of anova, a ttest will be run
iris2 < dplyr::filter(.data = iris, Species != "setosa")
# let's change the levels of our factors, a common routine in data analysis
# pipeline, to see if this function respects the new factor levels
iris2$Species < factor(x = iris2$Species, levels = c("virginica", "versicolor"))
# plot
ggstatsplot::ggbetweenstats(
data = iris2,
x = Species,
y = Sepal.Length,
notch = TRUE, # show notched box plot
mean.plotting = TRUE, # whether mean for each group is to be displayed
mean.ci = TRUE, # whether to display confidence interval for means
mean.label.size = 2.5, # size of the label for mean
type = "parametric", # which type of test is to be run
k = 3, # number of decimal places for statistical results
outlier.tagging = TRUE, # whether outliers need to be tagged
outlier.label = Sepal.Width, # variable to be used for the outlier tag
outlier.label.color = "darkgreen", # changing the color for the text label
xlab = "Type of Species", # label for the xaxis variable
ylab = "Attribute: Sepal Length", # label for the yaxis variable
title = "Dataset: Iris flower data set", # title text for the plot
ggtheme = ggthemes::theme_fivethirtyeight(), # choosing a different theme
ggstatsplot.layer = FALSE, # turn off ggstatsplot theme layer
package = "wesanderson", # package from which color palette is to be taken
palette = "Darjeeling1", # choosing a different color palette
messages = FALSE
)
As can be seen from the plot, the function by default returns Bayes Factor for the test (here, Student’s ttest). If the null hypothesis can’t be rejected with the null hypothesis significance testing (NHST) approach, the Bayesian approach can help index evidence in favor of the null hypothesis (i.e.,
By default, natural logarithms are shown because Bayes Factor values can sometimes be pretty large. Having values on logarithmic scale also makes it easy to compare evidence in favor alternative (
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
ggstatsplot::grouped_ggbetweenstats(
data = dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
x = mpaa,
y = length,
grouping.var = genre, # grouping variable
pairwise.comparisons = TRUE, # display significant pairwise comparisons
pairwise.annotation = "p.value", # how do you want to annotate the pairwise comparisons
p.adjust.method = "bonferroni", # method for adjusting pvalues for multiple comparisons
conf.level = 0.99, # changing confidence level to 99%
ggplot.component = list( # adding new components to `ggstatsplot` default
ggplot2::scale_y_continuous(sec.axis = ggplot2::dup_axis())
),
k = 3,
title.prefix = "Movie genre",
caption = substitute(paste(
italic("Source"),
":IMDb (Internet Movie Database)"
)),
palette = "default_jama",
package = "ggsci",
messages = FALSE,
nrow = 2,
title.text = "Differences in movie length by mpaa ratings for different genres"
)
Summary of tests
Following (betweensubjects) tests are carried out for each type of analyses
Type  No. of groups  Test 

Parametric  > 2  Fisher’s or Welch’s oneway ANOVA 
Nonparametric  > 2  Kruskal–Wallis oneway ANOVA 
Robust  > 2  Heteroscedastic oneway ANOVA for trimmed means 
Bayes Factor  > 2  Fisher’s ANOVA 
Parametric  2  Student’s or Welch’s ttest 
Nonparametric  2  Mann–Whitney U test 
Robust  2  Yuen’s test for trimmed means 
Bayes Factor  2  Student’s ttest 
The omnibus effect in oneway ANOVA design can also be followed up with more focal pairwise comparison tests. Here is a summary of multiple pairwise comparison tests supported in ggbetweenstats
Type  Equal variance?  Test  pvalue adjustment? 

Parametric  No  GamesHowell test  Yes 
Parametric  Yes  Student’s ttest  Yes 
Nonparametric  No  DwassSteelCrichtlowFligner test  Yes 
Robust  No  Yuen’s trimmed means test  Yes 
Bayes Factor  No  No  No 
Bayes Factor  Yes  No  No 
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)
# plot
ggstatsplot::ggwithinstats(
data = WRS2::WineTasting,
x = Wine,
y = Taste,
sort = "descending", # ordering groups along the xaxis based on
sort.fun = median, # values of `y` variable
pairwise.comparisons = TRUE,
pairwise.display = "s",
pairwise.annotation = "p",
title = "Wine tasting",
caption = "Data from: `WRS2` R package",
ggtheme = ggthemes::theme_fivethirtyeight(),
ggstatsplot.layer = FALSE,
messages = FALSE
)
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)
# getting data in tidy format
data_bugs < ggstatsplot::bugs_long %>%
dplyr::filter(.data = ., region %in% c("Europe", "North America"))
# plot
ggstatsplot::grouped_ggwithinstats(
data = dplyr::filter(data_bugs, condition %in% c("LDLF", "LDHF")),
x = condition,
y = desire,
xlab = "Condition",
ylab = "Desire to kill an artrhopod",
grouping.var = region,
outlier.tagging = TRUE,
outlier.label = education,
ggtheme = hrbrthemes::theme_ipsum_tw(),
ggstatsplot.layer = FALSE,
messages = FALSE
)
Summary of tests
Following (withinsubjects) tests are carried out for each type of analyses
Type  No. of groups  Test 

Parametric  > 2  Oneway repeated measures ANOVA 
Nonparametric  > 2  Friedman test 
Robust  > 2  Heteroscedastic oneway repeated measures ANOVA for trimmed means 
Bayes Factor  > 2  Oneway repeated measures ANOVA 
Parametric  2  Student’s ttest 
Nonparametric  2  Wilcoxon signedrank test 
Robust  2  Yuen’s test on trimmed means for dependent samples 
Bayes Factor  2  Student’s ttest 
The omnibus effect in oneway ANOVA design can also be followed up with more focal pairwise comparison tests. Here is a summary of multiple pairwise comparison tests supported in ggwithinstats
Type  Test  pvalue adjustment? 

Parametric  Student’s ttest  Yes 
Nonparametric  DurbinConover test  Yes 
Robust  Yuen’s trimmed means test  Yes 
Bayes Factor  No  No 
For more, see the ggwithinstats
vignette: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggwithinstats.html
ggscatterstats
This function creates a scatterplot with marginal distributions overlaid on the axes (from ggExtra::ggMarginal
) and results from statistical tests in the subtitle:
ggstatsplot::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",
messages = FALSE
)
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
ggstatsplot::ggscatterstats(
data = dplyr::filter(.data = ggstatsplot::movies_long, genre == "Action"),
x = budget,
y = rating,
type = "robust", # type of test that needs to be run
conf.level = 0.99, # confidence level
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
line.color = "yellow", # changing regression line color line
title = "Movie budget and IMDB rating (action)", # title text for the plot
caption = expression( # caption text for the plot
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 = "density", # type of marginal distribution to be displayed
xfill = "#0072B2", # color fill for xaxis marginal distribution
yfill = "#009E73", # color fill for yaxis marginal distribution
xalpha = 0.6, # transparency for xaxis marginal distribution
yalpha = 0.6, # transparency for yaxis marginal distribution
centrality.para = "median", # central tendency lines to be displayed
messages = FALSE # turn off messages and notes
)
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 for this particular function):
# for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_ggscatterstats(
data = dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
x = rating,
y = length,
label.var = title,
label.expression = length > 200,
conf.level = 0.99,
k = 3, # no. of decimal places in the results
xfill = "#E69F00",
yfill = "#8b3058",
xlab = "IMDB rating",
grouping.var = genre, # grouping variable
title.prefix = "Movie genre",
ggtheme = ggplot2::theme_grey(),
ggplot.component = list(
ggplot2::scale_x_continuous(breaks = seq(2, 9, 1), limits = (c(2, 9)))
),
messages = FALSE,
nrow = 2,
title.text = "Relationship between movie length by IMDB ratings for different genres"
)
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? 

Parametric  Pearson’s correlation coefficient  Yes 
Nonparametric  Spearman’s rank correlation coefficient  Yes 
Robust  Percentage bend correlation coefficient  Yes 
Bayes Factor  Pearson’s correlation coefficient  No 
For more, see the ggscatterstats
vignette: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggscatterstats.html
ggpiestats
This function creates a pie chart for categorical or nominal variables with results from contingency table analysis (Pearson’s
Here is an example of a case where the theoretical question is about proportions for different levels of a single nominal variable:
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggpiestats(
data = ggplot2::msleep,
x = vore,
title = "Composition of vore types among mammals",
messages = FALSE
)
This function can also be used to study an interaction between two categorical variables:
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggpiestats(
data = mtcars,
x = am,
y = cyl,
conf.level = 0.99, # confidence interval for effect size measure
title = "Dataset: Motor Trend Car Road Tests", # title for the plot
stat.title = "interaction: ", # title for the results
legend.title = "Transmission", # title for the legend
factor.levels = c("1 = manual", "0 = automatic"), # renaming the factor level names (`x`)
facet.wrap.name = "No. of cylinders", # name for the facetting variable
slice.label = "counts", # show counts data instead of percentages
package = "ggsci", # package from which color palette is to be taken
palette = "default_jama", # choosing a different color palette
caption = substitute( # text for the caption
paste(italic("Source"), ": 1974 Motor Trend US magazine")
),
messages = FALSE # turn off messages and notes
)
In case of repeated measures designs, setting paired = TRUE
will produce results from McNemar’s
# for reproducibility
set.seed(123)
# data
survey.data < data.frame(
`1st survey` = c("Approve", "Approve", "Disapprove", "Disapprove"),
`2nd survey` = c("Approve", "Disapprove", "Approve", "Disapprove"),
`Counts` = c(794, 150, 86, 570),
check.names = FALSE
)
# plot
ggstatsplot::ggpiestats(
data = survey.data,
x = `1st survey`,
y = `2nd survey`,
counts = Counts,
paired = TRUE, # withinsubjects design
conf.level = 0.99, # confidence interval for effect size measure
package = "wesanderson",
palette = "Royal1"
)
#> Note: 99% CI for effect size estimate was computed with 100 bootstrap samples.
#> # A tibble: 2 x 11
#> `2nd survey` counts perc N Approve Disapprove statistic
#> <fct> <int> <dbl> <chr> <chr> <chr> <dbl>
#> 1 Disapprove 720 45 (n = 720) 20.83% 79.17% 245
#> 2 Approve 880 55. (n = 880) 90.23% 9.77% 570.
#> p.value parameter method significance
#> <dbl> <dbl> <chr> <chr>
#> 1 3.20e 55 1 Chisquared test for given probabilities ***
#> 2 6.80e126 1 Chisquared test for given probabilities ***
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
ggstatsplot::grouped_ggpiestats(
dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
x = mpaa,
grouping.var = genre, # grouping variable
title.prefix = "Movie genre", # prefix for the facetted title
label.text.size = 3, # text size for slice labels
slice.label = "both", # show both counts and percentage data
perc.k = 1, # no. of decimal places for percentages
palette = "brightPastel",
package = "quickpalette",
messages = FALSE,
nrow = 2,
title.text = "Composition of MPAA ratings for different genres"
)
Summary of tests
Following tests are carried out for each type of analyses
Type of data  Design  Test 

Unpaired 

Pearson’s 
Paired 

McNemar’s 
Frequency 

Goodness of fit ( 
Following effect sizes (and confidence intervals/CI) are available for each type of test
Type  Effect size  CI? 

Pearson’s chisquared test  Cramér’s V  Yes 
McNemar’s test  Cohen’s g  Yes 
Goodness of fit  Cramér’s V  Yes 
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
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggbarstats(
data = ggstatsplot::movies_long,
x = mpaa,
y = genre,
sampling.plan = "jointMulti",
title = "MPAA Ratings by Genre",
xlab = "movie genre",
perc.k = 1,
x.axis.orientation = "slant",
ggtheme = hrbrthemes::theme_modern_rc(),
ggstatsplot.layer = FALSE,
ggplot.component = ggplot2::theme(axis.text.x = ggplot2::element_text(face = "italic")),
palette = "Set2",
messages = FALSE
)
Note that pvalues for results from onesample proportion tests are displayed for each bar in the form of asterisks with the following convention:

: 
: 
: 
:
And, needless to say, there is also a grouped_
variant of this function
# setup
set.seed(123)
# smaller dataset
df < dplyr::filter(
.data = forcats::gss_cat,
race %in% c("Black", "White"),
relig %in% c("Protestant", "Catholic", "None"),
!partyid %in% c("No answer", "Don't know", "Other party")
)
# plot
ggstatsplot::grouped_ggbarstats(
data = df,
x = relig,
y = partyid,
grouping.var = race,
title.prefix = "Race",
xlab = "Party affiliation",
ggtheme = ggthemes::theme_tufte(base_size = 12),
ggstatsplot.layer = FALSE,
messages = FALSE,
title.text = "Race, religion, and political affiliation",
nrow = 2
)
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.
ggstatsplot::gghistostats(
data = ToothGrowth, # dataframe from which variable is to be taken
x = len, # numeric variable whose distribution is of interest
xlab = "Tooth length", # `x`axis label
title = "Distribution of Tooth Length", # title for the plot
fill.gradient = TRUE, # use color gradient
test.value = 10, # the comparison value for onesample test
test.value.line = TRUE, # display a vertical line at test value
type = "bayes", # bayes factor for one sample ttest
bf.prior = 0.8, # prior width for calculating the bayes factor
messages = FALSE # turn off the messages
)
The aesthetic defaults can be easily modified
# for reproducibility
set.seed(123)
# plot
ggstatsplot::gghistostats(
data = iris, # dataframe from which variable is to be taken
x = Sepal.Length, # numeric variable whose distribution is of interest
title = "Distribution of Iris sepal length", # title for the plot
caption = substitute(paste(italic("Source:"), "Ronald Fisher's Iris data set")),
type = "parametric", # one sample ttest
conf.level = 0.99, # changing confidence level for effect size
bar.measure = "mix", # what does the bar length denote
test.value = 5, # default value is 0
test.value.line = TRUE, # display a vertical line at test value
test.value.color = "#0072B2", # color for the line for test value
centrality.para = "mean", # which measure of central tendency is to be plotted
centrality.color = "darkred", # decides color for central tendency line
binwidth = 0.10, # binwidth value (experiment)
bf.prior = 0.8, # prior width for computing bayes factor
messages = FALSE, # turn off the messages
ggtheme = hrbrthemes::theme_ipsum_tw(), # choosing a different theme
ggstatsplot.layer = FALSE # turn off ggstatsplot theme layer
)
As can be seen from the plot, bayes factor can be attached (bf.message = TRUE
) to assess evidence in favor of the null hypothesis.
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
ggstatsplot::grouped_gghistostats(
data = dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
x = budget,
xlab = "Movies budget (in million US$)",
type = "robust", # use robust location measure
grouping.var = genre, # grouping variable
normal.curve = TRUE, # superimpose a normal distribution curve
normal.curve.color = "red",
title.prefix = "Movie genre",
ggtheme = ggthemes::theme_tufte(),
ggplot.component = list( # modify the defaults from `ggstatsplot` for each plot
ggplot2::scale_x_continuous(breaks = seq(0, 200, 50), limits = (c(0, 200)))
),
messages = FALSE,
nrow = 2,
title.text = "Movies budgets for different genres"
)
Summary of tests
Following tests are carried out for each type of analyses
Type  Test 

Parametric  Onesample Student’s ttest 
Nonparametric  Onesample Wilcoxon test 
Robust  Onesample percentile bootstrap 
Bayes Factor  Onesample Student’s ttest 
Following effect sizes (and confidence intervals/CI) are available for each type of test
Type  Effect size  CI? 

Parametric  Cohen’s d, Hedge’s g (centraland noncentralt distribution based)  Yes 
Nonparametric 
r (computed as 
Yes 
Robust 

Yes 
Bayes Factor  No  No 
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,
test.value.line = TRUE,
test.line.labeller = TRUE,
test.value.color = "red",
centrality.para = "median",
centrality.k = 0,
title = "Distribution of life expectancy in Asian continent",
xlab = "Life expectancy",
messages = FALSE,
caption = substitute(
paste(
italic("Source"),
": Gapminder dataset from https://www.gapminder.org/"
)
)
)
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)
# removing factor level with very few no. of observations
df < dplyr::filter(.data = ggplot2::mpg, cyl %in% c("4", "6"))
# plot
ggstatsplot::grouped_ggdotplotstats(
data = df,
x = cty,
y = manufacturer,
xlab = "city miles per gallon",
ylab = "car manufacturer",
type = "nonparametric", # nonparametric test
grouping.var = cyl, # grouping variable
test.value = 15.5,
title.prefix = "cylinder count",
point.color = "red",
point.size = 5,
point.shape = 13,
test.value.line = TRUE,
ggtheme = ggthemes::theme_par(),
messages = FALSE,
title.text = "Fuel economy data"
)
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 correlalogram plot
ggstatsplot::ggcorrmat(
data = ggplot2::msleep,
corr.method = "robust", # correlation method
sig.level = 0.001, # threshold of significance
p.adjust.method = "holm", # pvalue adjustment method for multiple comparisons
cor.vars = c(sleep_rem, awake:bodywt), # a range of variables can be selected
cor.vars.names = c(
"REM sleep", # variable names
"time awake",
"brain weight",
"body weight"
),
matrix.type = "upper", # type of visualization matrix
colors = c("#B2182B", "white", "#4D4D4D"),
title = "Correlalogram for mammals sleep dataset",
subtitle = "sleep units: hours; weight units: kilograms"
)
Note that 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.
Alternatively, you can use it just to get the correlation matrices and their corresponding pvalues (in a tibble
format).
# for reproducibility
set.seed(123)
# show four digits in a tibble
options(pillar.sigfig = 4)
# getting the correlation coefficient matrix
ggstatsplot::ggcorrmat(
data = iris, # all numeric variables from data will be used
corr.method = "robust",
output = "correlations", # specifying the needed output ("r" or "corr" will also work)
digits = 3 # number of digits to be dispayed for correlation coefficient
)
#> # A tibble: 4 x 5
#> variable Sepal.Length Sepal.Width Petal.Length Petal.Width
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Sepal.Length 1 0.143 0.878 0.837
#> 2 Sepal.Width 0.143 1 0.426 0.373
#> 3 Petal.Length 0.878 0.426 1 0.966
#> 4 Petal.Width 0.837 0.373 0.966 1
# getting the pvalue matrix
ggstatsplot::ggcorrmat(
data = ggplot2::msleep,
cor.vars = sleep_total:bodywt,
corr.method = "robust",
output = "p.values", # only "p" or "pvalues" will also work
p.adjust.method = "holm"
)
#> # A tibble: 6 x 7
#> variable sleep_total sleep_rem sleep_cycle awake brainwt
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 sleep_total 0. 5.291e12 9.138e 3 0. 3.170e 5
#> 2 sleep_rem 4.070e13 0. 1.978e 2 5.291e12 9.698e 3
#> 3 sleep_cycle 2.285e 3 1.978e 2 0. 9.138e 3 1.637e 9
#> 4 awake 0. 4.070e13 2.285e 3 0. 3.170e 5
#> 5 brainwt 4.528e 6 4.849e 3 1.488e10 4.528e 6 0.
#> 6 bodywt 2.568e 7 7.524e 4 2.120e 6 2.568e 7 3.221e18
#> bodywt
#> <dbl>
#> 1 2.568e 6
#> 2 3.762e 3
#> 3 1.696e 5
#> 4 2.568e 6
#> 5 4.509e17
#> 6 0.
# getting the confidence intervals for correlations
ggstatsplot::ggcorrmat(
data = ggplot2::msleep,
cor.vars = sleep_total:bodywt,
corr.method = "spearman",
output = "ci",
p.adjust.method = "holm"
)
#> Note: In the correlation matrix,
#> the upper triangle: pvalues adjusted for multiple comparisons
#> the lower triangle: unadjusted pvalues.
#> # A tibble: 15 x 7
#> pair r lower upper p lower.adj
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 sleep_totalsleep_rem 0.7641 0.6344 0.8520 7.806e13 0.5632
#> 2 sleep_totalsleep_cycle 0.4888 0.7155 0.1689 4.530e 3 0.7609
#> 3 sleep_totalawake 1 1 1 0. 1
#> 4 sleep_totalbrainwt 0.5935 0.7408 0.3918 1.426e 6 0.7852
#> 5 sleep_totalbodywt 0.5346 0.6727 0.3605 1.931e 7 0.7121
#> 6 sleep_remsleep_cycle 0.3344 0.6118 0.01617 6.139e 2 0.6118
#> 7 sleep_remawake 0.7641 0.8520 0.6344 7.806e13 0.8807
#> 8 sleep_rembrainwt 0.4139 0.6246 0.1471 3.451e 3 0.6495
#> 9 sleep_rembodywt 0.4517 0.6317 0.2255 2.580e 4 0.6647
#> 10 sleep_cycleawake 0.4888 0.1689 0.7155 4.530e 3 0.05610
#> 11 sleep_cyclebrainwt 0.8727 0.7474 0.9380 3.250e10 0.6573
#> 12 sleep_cyclebodywt 0.8464 0.7061 0.9228 1.040e 9 0.6115
#> 13 awakebrainwt 0.5935 0.3918 0.7408 1.426e 6 0.2934
#> 14 awakebodywt 0.5346 0.3605 0.6727 1.931e 7 0.2875
#> 15 brainwtbodywt 0.9572 0.9277 0.9748 9.694e31 0.9071
#> upper.adj
#> <dbl>
#> 1 0.8798
#> 2 0.07055
#> 3 1
#> 4 0.2982
#> 5 0.2928
#> 6 0.01617
#> 7 0.5605
#> 8 0.1058
#> 9 0.1708
#> 10 0.7669
#> 11 0.9563
#> 12 0.9442
#> 13 0.7872
#> 14 0.7150
#> 15 0.9805
# getting the sample sizes for all pairs
ggstatsplot::ggcorrmat(
data = ggplot2::msleep,
cor.vars = sleep_total:bodywt,
corr.method = "robust",
output = "n" # note that n is different due to NAs
)
#> # A tibble: 6 x 7
#> variable sleep_total sleep_rem sleep_cycle awake brainwt bodywt
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 sleep_total 83 61 32 83 56 83
#> 2 sleep_rem 61 61 32 61 48 61
#> 3 sleep_cycle 32 32 32 32 30 32
#> 4 awake 83 61 32 83 56 83
#> 5 brainwt 56 48 30 56 56 56
#> 6 bodywt 83 61 32 83 56 83
Note that if cor.vars
are not specified, all numeric variables will be used.
There is 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
# let's use only 50% of the data to speed up the process
ggstatsplot::grouped_ggcorrmat(
data = dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
cor.vars = length:votes,
corr.method = "np",
colors = c("#cbac43", "white", "#550000"),
grouping.var = genre, # grouping variable
digits = 3, # number of digits after decimal point
title.prefix = "Movie genre",
messages = FALSE,
nrow = 2
)
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? 

Parametric  Pearson’s correlation coefficient  Yes 
Nonparametric  Spearman’s rank correlation coefficient  Yes 
Robust  Percentage bend correlation coefficient  No 
Bayes Factor  Pearson’s correlation coefficient  No 
For examples and more information, see the ggcorrmat
vignette: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggcorrmat.html
ggcoefstats
ggcoefstats
creates a dotandwhisker plot for regression models. 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 caption will always 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. Additionally, the higher the loglikelihood value the “better” is the model fit.
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
ggstatsplot::ggcoefstats(x = mod)
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
ggstatsplot::ggcoefstats(
x = mod,
point.color = "red",
point.shape = 15,
vline.color = "#CC79A7",
vline.linetype = "dotdash",
stats.label.size = 3.5,
stats.label.color = c("#0072B2", "#D55E00", "darkgreen"),
title = "Car performance predicted by transmission & cylinder count",
subtitle = "Source: 1974 Motor Trend US magazine",
ggtheme = hrbrthemes::theme_ipsum_ps(),
ggstatsplot.layer = FALSE
) + # further modification with the ggplot2 commands
# 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)
Most of the regression models that are supported in the broom
and broom.mixed
packages with tidy
and glance
methods are also supported by ggcoefstats
. For example
aareg
, anova
, aov
, aovlist
, Arima
, bglmerMod
, bigglm
, biglm
, blmerMod
, brmsfit
, btergm
, cch
, clm
, clmm
, confusionMatrix
, coxph
, drc
, emmGrid
, epi.2by2
, ergm
, felm
, fitdistr
, glmerMod
, glmmTMB
, gls
, gam
, Gam
, gamlss
, garch
, glm
, glmmadmb
, glmmPQL
, glmmTMB
, glmRob
, glmrob
, gmm
, ivreg
, lm
, lm.beta
, lmerMod
, lmodel2
, lmRob
, lmrob
, mcmc
, MCMCglmm
, mclogit
, mmclogit
, mediate
, mjoint
, mle2
, mlm
, multinom
, negbin
, nlmerMod
, nlrq
, nls
, orcutt
, plm
, polr
, ridgelm
, rjags
, rlm
, rlmerMod
, rq
, speedglm
, speedlm
, stanreg
, survreg
, svyglm
, svyolr
, svyglm
, tobit
, wblm
, etc.
Although not shown here, this function can also be used to carry out both frequentist and Bayesian randomeffects metaanalysis.
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 😻. (Another old school option to do this effectively is using the plyr
package.)
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 cowplot::plot_grid
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
theme_ggstatsplot
All plots from ggstatsplot
have a default theme: theme_ggstatsplot
. You can change this theme by using the ggtheme
argument. It is important to note that irrespective of which ggplot
theme you choose, ggstatsplot
in the backdrop adds a new layer with its idiosyncratic theme settings, chosen to make the graphs more readable or aesthetically pleasing.
Let’s see an example with gghistostats
and see how a certain theme from hrbrthemes
package looks like with and without the ggstatsplot
layer.
# to use hrbrthemes themes, first make sure you have all the necessary fonts
library(hrbrthemes)
# extrafont::ttf_import()
# extrafont::font_import()
# try this yourself
ggstatsplot::combine_plots(
# with the ggstatsplot layer
ggstatsplot::gghistostats(
data = iris,
x = Sepal.Width,
messages = FALSE,
title = "Distribution of Sepal Width",
test.value = 5,
ggtheme = hrbrthemes::theme_ipsum(),
ggstatsplot.layer = TRUE
),
# without the ggstatsplot layer
ggstatsplot::gghistostats(
data = iris,
x = Sepal.Width,
messages = FALSE,
title = "Distribution of Sepal Width",
test.value = 5,
ggtheme = hrbrthemes::theme_ipsum_ps(),
ggstatsplot.layer = FALSE
),
nrow = 1,
labels = c("(a)", "(b)"),
title.text = "Behavior of ggstatsplot theme layer with and without chosen ggtheme"
)
For more on how to modify it, see the associated vignette https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/theme_ggstatsplot.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.
set.seed(123)
# loading the needed libraries
library(ggridges)
library(ggplot2)
library(ggstatsplot)
# using `ggstatsplot` to get call with statistical results
stats_results <
ggstatsplot::ggbetweenstats(
data = morley,
x = Expt,
y = Speed,
return = "subtitle",
messages = FALSE
)
# 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")