# ggstatsplot v0.0.2

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## 'ggplot2' Based Plots with Statistical Details

Extension of 'ggplot2', 'ggstatsplot' creates graphics with details from statistical tests (parametric, non-parametric, or robust) included in the plots themselves. It is targeted primarily at behavioral sciences community to provide a one-line code to generate information-rich plots for statistical analysis of continuous (violin plots, scatterplots, histograms) or categorical (pie charts) data.

# ggstatsplot: ggplot2 Based Plots with Statistical Details

## Overview

ggstatsplot is an extension of ggplot2 package for creating graphics with details from statistical tests included in the plots themselves and targeted primarily at behavioral sciences community to provide a one-line code to produce information-rich plots. Currently, it supports only the most common types of statisticla tests (parametric, nonparametric, and robust versions of t-tets/anova, correlation, and contingency tables analyses). Accordingly, it produces limited kinds of plots: violin plots (for comparisons between groups or conditions), pie charts (for categorical data), scatterplots (for correlations between variables), and histograms (for hypothesis about distributions).

Future versions will include other types of analyses and plots as well.

## Installation

To get the latest, stable CRAN release:

utils::install.packages(pkgs = "ggstatsplot")


You can get the development version from GitHub. If you are in hurry and want to reduce the time of installation, prefer-

# install.packages("devtools")                                # needed package to download from GitHub repo
devtools::install_github(repo = "IndrajeetPatil/ggstatsplot", # package path on GitHub
quick = TRUE)                        # skips docs, demos, and vignettes


If time is not a constraint-

devtools::install_github(repo = "IndrajeetPatil/ggstatsplot", # package path on GitHub
dependencies = TRUE,                 # installs packages which ggstatsplot depends on
)


If you are not using the RStudio IDE and you get an error related to “pandoc” you will either need to remove the argument build_vignettes = TRUE (to avoid building the vignettes) or install pandoc. If you have the rmarkdown R package installed then you can check if you have pandoc by running the following in R:

rmarkdown::pandoc_available()
#> [1] TRUE


## Citation

If you want to cite this package in a scientific journal or in any other context, run the following code in your R console:

utils::citation(package = "ggstatsplot")


## Help

Documentation for any function can be accessed with the standard help command-

?ggbetweenstats
?ggscatterstats
?gghistostats
?ggpiestats
?ggcorrmat
?combine_plots
?grouped_gghistostats


## Usage

ggstatsplot relies on non-standard evaluation, which means you can’t enter arguments in the following manner: x = data$x, y = data$y. This may work well for most of the functions most of the time, but is highly discouraged. You should always specify data argument for all functions.

Additionally, ggstatsplot is a very chatty package and will by default output information about references for tests, notes on assumptions about linear models, and warnings. If you don’t want your console to be cluttered with such messages, they can be turned off by setting messages = FALSE.

Here are examples of the main functions currently supported in ggstatsplot:

• ggbetweenstats

This function creates a violin plot for between-group or between-condition comparisons with results from statistical tests in the subtitle:

ggstatsplot::ggbetweenstats(data = datasets::iris,
x = Species,
y = Sepal.Length,
messages = FALSE)


Number of other arguments can be specified to make this plot even more informative and, additionally, this function returns a ggplot2 object and thus any of the graphics layers can be further modified:

library(ggplot2)

ggstatsplot::ggbetweenstats(
data = datasets::iris,
x = Species,
y = Sepal.Length,
mean.plotting = TRUE,                           # whether mean for each group id to be displayed
type = "robust",                                # which type of test is to be run
outlier.tagging = TRUE,                         # whether outliers need to be tagged
outlier.label = Sepal.Width,                    # variable to be used for the outlier tag
xlab = "Type of Species",                       # label for the x-axis variable
ylab = "Attribute: Sepal Length",               # label for the y-axis variable
title = "Dataset: Iris flower data set",        # title text for the plot
caption = expression(                           # caption text for the plot
paste(italic("Note"), ": this is a demo")
),
messages = FALSE
) +                                               # further modification outside of ggstatsplot
ggplot2::coord_cartesian(ylim = c(3, 8)) +
ggplot2::scale_y_continuous(breaks = seq(3, 8, by = 1))


The type (of test) argument also accepts the following abbreviations: “p” (for parametric), “np” (for nonparametric), “r” (for robust).

For example,

library(ggplot2)
library(cowplot)

# parametric t-tet
p1 <- ggstatsplot::ggbetweenstats(
data = datasets::mtcars,
x = am,
y = mpg,
type = "p",
messages = FALSE
)
#> Warning:  aesthetic x was not a factor; converting it to factor

# Mann-Whitney U-test
p2 <- ggstatsplot::ggbetweenstats(
data = mtcars,
x = am,
y = mpg,
type = "np",
messages = FALSE
)
#> Warning:  aesthetic x was not a factor; converting it to factor

# robust t-test
p3 <- ggstatsplot::ggbetweenstats(
data = mtcars,
x = am,
y = mpg,
type = "r",
messages = FALSE
)
#> Warning:  aesthetic x was not a factor; converting it to factor

# combining the individual plots into a single plot
cowplot::plot_grid(p1, p2, p3,
nrow = 3,
ncol = 1,
labels = c("(a)", "(b)", "(c)")
)


Variant of this function ggwithinstats is currently under work. You can still use this function just to prepare the plot, but the statistical details will be incorrect. You can remove them by adding labs(subtitle = NULL) to your code which will remove the subtitle containing stats.

• ggscatterstats

This function creates a scatterplot with marginal histograms/boxplots/density/violin plots from ggExtra::ggMarginal() and results from statistical tests in the subtitle:

ggstatsplot::ggscatterstats(data = datasets::iris,
x = Sepal.Length,
y = Petal.Length,
title = "Dataset: Iris flower data set",
messages = FALSE)


Number of other arguments can be specified to modify this basic plot-

library(datasets)

ggstatsplot::ggscatterstats(
data = subset(datasets::iris, iris$Species == "setosa"), x = Sepal.Length, y = Petal.Length, type = "robust", # type of test that needs to be run xlab = "Attribute: Sepal Length", # label for x axis ylab = "Attribute: Petal Length", # label for y axis line.colour = "black", # changing regression line colour line title = "Dataset: Iris flower data set", # title text for the plot caption = expression( # caption text for the plot paste(italic("Note"), ": this is a demo") ), marginal.type = "density", # type of marginal distribution to be displayed xfill = "blue", # colour fill for x-axis marginal distribution yfill = "red", # colour fill for y-axis marginal distribution centrality.para = "median", # which type of central tendency lines are to be displayed width.jitter = 0.2, # amount of horizontal jitter for data points height.jitter = 0.4, # amount of vertical jitter for data points messages = FALSE # turn off messages and notes )  The type (of test) argument also accepts the following abbreviations: “p” (for parametric/pearson’s), “np” (for nonparametric/spearman), “r” (for robust). Important: In contrast to all other functions in this package, the ggscatterstats function returns object that is not further modifiable with ggplot2. This can be avoided by not plotting the marginal distributions (marginal = FALSE). Currently trying to find a workaround this problem. • ggpiestats This function creates a pie chart for categorical variables with results from contingency table analysis included in the subtitle of the plot. If only one categorical variable is entered, proportion test will be carried out. ggstatsplot::ggpiestats(data = datasets::iris, main = Species, messages = FALSE)  This function can also be used to study an interaction between two categorical variables. Additionally, as with the other functions in ggstatsplot, this function returns a ggplot2 object and can further be modified with ggplot2 syntax (e.g., we can change the color palette after ggstatsplot has produced the plot)- library(ggplot2) ggstatsplot::ggpiestats(data = datasets::mtcars, main = cyl, condition = am, messages = FALSE) + ggplot2::scale_fill_brewer(palette = "Dark2") # further modification outside of ggstatsplot  As with the other functions, this basic plot can further be modified with additional arguments: library(ggplot2) ggstatsplot::ggpiestats( data = datasets::mtcars, main = am, condition = cyl, title = "Dataset: Motor Trend Car Road Tests", # title for the plot stat.title = "interaction effect", # title for the results from Pearson's chi-squared test legend.title = "Transmission", # title for the legend factor.levels = c("0 = automatic", "1 = manual"), # renaming the factor level names for main variable facet.wrap.name = "No. of cylinders", # name for the facetting variable facet.proptest = FALSE, # turning of facetted proportion test results caption = expression( # text for the caption paste(italic("Note"), ": this is a demo") ), messages = FALSE # turn off messages and notes )  • gghistostats In case you would like to see the distribution of one variable and check if it is significantly different from a specified value with a one sample test, this function will let you do that. library(datasets) library(viridis) ggstatsplot::gghistostats( data = datasets::iris, x = Sepal.Length, title = "Distribution of Iris sepal length", type = "parametric", # one sample t-test test.value = 3, # default value is 0 centrality.para = "mean", # which measure of central tendency is to be plotted centrality.colour = "red", # decides colour of vertical line representing central tendency binwidth.adjust = TRUE, # whether binwidth needs to be adjusted binwidth = 0.10, # binwidth value (needs to be toyed around with until you find the best one) messages = FALSE # turn off the messages ) + viridis::scale_fill_viridis() # further modification outside of ggstatsplot  The type (of test) argument also accepts the following abbreviations: “p” (for parametric) or “np” (for nonparametric) or “bf” (for Bayes Factor). ggstatsplot::gghistostats( data = NULL, x = stats::rnorm(n = 1000, mean = 0, sd = 1), centrality.para = "mean", type = "bf", bf.prior = 0.8, messages = FALSE )  As seen here, by default, Bayes Factor quantifies the support for the alternative hypothesis (H1) over the null hypothesis (H0) (i.e., BF10 is displayed). In case you run parametric t-test and the effect is not significant, caption will be displayed containing information about evidence in favor of the null hypothesis (H0). This is not recommended, but if you want to turn off this behavior, you can use the argument bf.message = FALSE. ggstatsplot::gghistostats( data = datasets::ToothGrowth, x = len, centrality.para = "mean", test.value = 20, test.value.line = TRUE, xlab = "Tooth length", messages = FALSE )  • ggcorrmat ggcorrmat makes correlalograms with minimal amount of code. Just sticking to the defaults itself produces publication-ready correlation matrices. (Wrapper around ggcorrplot) # as a default this function outputs a correlalogram plot ggstatsplot::ggcorrmat( data = datasets::iris, corr.method = "spearman", # correlation method sig.level = 0.005, # threshold of significance cor.vars = Sepal.Length:Petal.Width, # a range of variables can be selected cor.vars.names = c("Sepal Length", "Sepal Width", "Petal Length", "Petal Width"), title = "Correlalogram for length measures for Iris species", subtitle = "Iris dataset by Anderson", caption = expression( paste( italic("Note"), ": X denotes correlation non-significant at ", italic("p "), "< 0.005; adjusted alpha" ) ) )  Alternatively, you can use it just to get the correlation matrices and their corresponding p-values (in a tibble format). # getting correlations ggstatsplot::ggcorrmat( data = datasets::iris, cor.vars = Sepal.Length:Petal.Width, output = "correlations" ) #> # A tibble: 4 x 5 #> variable Sepal.Length Sepal.Width Petal.Length Petal.Width #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 Sepal.Length 1 -0.12 0.87 0.82 #> 2 Sepal.Width -0.12 1 -0.43 -0.37 #> 3 Petal.Length 0.87 -0.43 1 0.96 #> 4 Petal.Width 0.82 -0.37 0.96 1 # getting p-values ggstatsplot::ggcorrmat( data = datasets::iris, cor.vars = Sepal.Length:Petal.Width, output = "p-values" ) #> # A tibble: 4 x 5 #> variable Sepal.Length Sepal.Width Petal.Length Petal.Width #> <chr> <dbl> <dbl> <dbl> <dbl> #> 1 Sepal.Length 0. 0.152 1.04e-47 2.33e-37 #> 2 Sepal.Width 1.52e- 1 0 4.51e- 8 4.07e- 6 #> 3 Petal.Length 1.04e-47 0.0000000451 0. 4.68e-86 #> 4 Petal.Width 2.33e-37 0.00000407 4.68e-86 0.  • combine_plots ggstatsplot also contains a helper function combine_plots to combine multiple plots. This is a wrapper around cowplot::plot_grid and lets you combine multiple plots and add combination of title, caption, and annotation texts with suitable default parameters. The full power of ggstatsplot can be leveraged with a functional programming package like purrr that replaces many for loops with code that is both more succinct and easier to read and, therefore, purrr should be preferrred. An example is provided below.  library(glue) library(tidyverse) ### creating a list column with ggstatsplot plots plots <- datasets::iris %>% dplyr::mutate(.data = ., Species2 = Species) %>% # just creates a copy of this variable dplyr::group_by(.data = ., Species) %>% tidyr::nest(data = .) %>% # creates a nested dataframe with list column called data dplyr::mutate( # creating a new list column of ggstatsplot outputs .data = ., plot = data %>% purrr::map( .x = ., .f = ~ ggstatsplot::ggscatterstats( data = ., x = Sepal.Length, y = Sepal.Width, messages = FALSE, # turns off all the warnings, notes, and reference messages marginal.type = "boxplot", title = glue::glue("Species: {.$Species2} (n = {length(.$Sepal.Length)})") ) ) ) ### display the new object (notice that the class of the plot list column is S3: gg) plots #> # A tibble: 3 x 3 #> Species data plot #> <fct> <list> <list> #> 1 setosa <tibble [50 x 5]> <S3: ggExtraPlot> #> 2 versicolor <tibble [50 x 5]> <S3: ggExtraPlot> #> 3 virginica <tibble [50 x 5]> <S3: ggExtraPlot> ### creating a grid with cowplot ggstatsplot::combine_plots( plotlist = plots$plot,                           # list column containing all ggstatsplot objects
labels = c("(a)", "(b)", "(c)"),
nrow = 3,
ncol = 1,
title.text = "Relationship between sepal length and width for all Iris species",
title.size = 14,
title.colour = "blue",
caption.text = expression(
paste(
italic("Note"),
": Iris flower dataset was collected by Edgar Anderson."
),
caption.size = 10
)
)


Here is another example with ggbetweenstats-

library(tidyverse)
library(glue)

### creating a list column with ggstatsplot plots
plots <- datasets::mtcars %>%
dplyr::mutate(.data = ., cyl2 = cyl) %>%        # just creates a copy of this variable
dplyr::group_by(.data = ., cyl) %>%             #
tidyr::nest(data = .) %>%                       # creates a nested dataframe with list column called data
dplyr::mutate(                                  # creating a new list column of ggstatsplot outputs
.data = .,
plot = data %>%
purrr::map(
.x = .,
.f = ~ ggstatsplot::ggbetweenstats(
data = .,
x = am,
y = mpg,
messages = FALSE,                       # turns off all the warnings, notes, and reference messages
xlab = "Transmission",
ylab = "Miles/(US) gallon",
title = glue::glue(
"Number of cylinders: {.$cyl2}" # this is where the duplicated cyl2 column is useful ) ) ) ) #> Warning: aesthetic x was not a factor; converting it to factorWarning: aesthetic x was not a factor; converting it to factorWarning: aesthetic x was not a factor; converting it to factor ### display the new object (notice that the class of the plot list column is S3: gg) plots #> # A tibble: 3 x 3 #> cyl data plot #> <dbl> <list> <list> #> 1 6 <tibble [7 x 11]> <S3: gg> #> 2 4 <tibble [11 x 11]> <S3: gg> #> 3 8 <tibble [14 x 11]> <S3: gg> ### creating a grid with cowplot ggstatsplot::combine_plots(plotlist = plots$plot,       # list column containing all ggstatsplot objects
nrow = 3,
ncol = 1,
labels = c("(a)","(b)","(c)"),
title.text = "MPG and car transmission relationship (for each cylinder count)",
title.size = 13,
title.colour = "blue",
caption.text = expression(
paste(
italic("Transmission"),
": 0 = automatic, 1 = manual"
),
caption.size = 10
))


• theme_mprl

ggstatsplot uses a default theme theme_mprl() that can be used with any ggplot2 objects.

library(ggplot2)

# Basic scatter plot
ggplot2::ggplot(data = datasets::mtcars,
mapping = ggplot2::aes(x = wt, y = mpg)) +
ggplot2::geom_point()



# Basic scatter plot with theme_mprl() added
ggplot2::ggplot(data = datasets::mtcars,
mapping = ggplot2::aes(x = wt, y = mpg)) +
ggplot2::geom_point() +
ggstatsplot::theme_mprl()


Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

## Functions in ggstatsplot

 Name Description ggstatsplot ggstatsplot grouped_gghistostats Grouped histograms for distribution of a numeric variable ggcorrmat Visualization of a correlalogram (or correlation matrix) using 'ggplot2'/'ggcorrplot' gghistostats Histogram for distribution of a numeric variable signif_column Creating a new character type column with significance labels %>% Pipe operator combine_plots Combining multiple plots using cowplot::plot_grid() with a combination of title, caption, and annotation label grouped_proptest Function to run proportion test on grouped data. legend_title_margin Custom function to set upper and lower margins to legend title in ggplot2 ggbetweenstats violin plots for group or condition comparisons ggpiestats Pie charts with statistical tests ggscatterstats Scatterplot with marginal distributions specify_decimal_p Custom function for getting specified number of decimal places in results for p-value theme_mprl Default theme used in all ggstatsplot package plots. No Results!