ggstatsplot v0.0.5

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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.

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ggstatsplot: ggplot2 Based Plots with Statistical Details

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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 statistical tests (parametric, nonparametric, and robust versions of t-tets/anova, correlation, and contingency tables analyses).

It, therefore, produces a limited kinds of plots for the supported analyses:

  • violin plots (for comparisons between groups or conditions),
  • pie charts (for categorical data),
  • scatterplots (for correlations between two variables),
  • correlation matrices (for correlations between multiple variables),
  • histograms (for hypothesis about distributions), and
  • dot-and-whisker plots (for regression models).

In addition to these basic plots, ggstatsplot also provides grouped_ versions of all functions that makes it easy to repeat the same anlysis for any grouping variable.

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

Installation

To get the latest, stable CRAN release (0.0.5):

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

You can get the development version of the package from GitHub (0.0.4.9000). To see what new changes (and bug fixes) have been made to the package since the last release on CRAN, you can check the detailed log of changes here: https://indrajeetpatil.github.io/ggstatsplot/news/index.html

If you are in hurry and want to reduce the time of installation, prefer-

# needed package to download from GitHub repo
utils::install.packages(pkgs = "devtools")   

# downloading the package from GitHub
devtools::install_github(
  repo = "IndrajeetPatil/ggstatsplot", # package path on GitHub
  dependencies = FALSE,                # assumes that you already have all packages installed needed for this package to work
  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
  upgrade_dependencies = TRUE          # updates any out of date dependencies
)

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")

There is currently a publication in preparation corresponding this package and the citation will be updated once it’s published.

Help

There is a dedicated website to ggstatplot, which is updated after every new commit: https://indrajeetpatil.github.io/ggstatsplot/.

In R, documentation for any function can be accessed with the standard help command-

?ggbetweenstats
?ggscatterstats
?gghistostats
?ggpiestats
?ggcorrmat
?ggcoefstats
?combine_plots
?grouped_ggbetweenstats
?grouped_ggscatterstats
?grouped_gghistostats
?grouped_ggpiestats
?grouped_ggcorrmat

Another handy tool to see arguments to any of the functions is args. For example-

args(name = ggstatsplot::ggscatterstats)
#> function (data, x, y, xlab = NULL, ylab = NULL, line.size = 1.5, 
#>     line.color = "blue", marginal = TRUE, marginal.type = "histogram", 
#>     marginal.size = 5, margins = c("both", "x", "y"), width.jitter = NULL, 
#>     height.jitter = NULL, xfill = "#009E73", yfill = "#D55E00", 
#>     xalpha = 1, yalpha = 1, xsize = 0.7, ysize = 0.7, centrality.para = NULL, 
#>     type = "pearson", results.subtitle = NULL, title = NULL, 
#>     caption = NULL, nboot = 100, beta = 0.1, k = 3, axes.range.restrict = FALSE, 
#>     ggtheme = ggplot2::theme_bw(), messages = TRUE) 
#> NULL

In case you want to look at the function body for any of the functions, just type the name of the function without the paranetheses:

ggstatsplot::theme_mprl
#> function (ggtheme = ggplot2::theme_bw()) 
#> {
#>     ggtheme + ggplot2::theme(axis.title.x = ggplot2::element_text(size = 12, 
#>         face = "bold"), strip.text.x = ggplot2::element_text(size = 12, 
#>         face = "bold"), strip.text.y = ggplot2::element_text(size = 12, 
#>         face = "bold"), strip.text = ggplot2::element_text(size = 12, 
#>         face = "bold"), axis.title.y = ggplot2::element_text(size = 12, 
#>         face = "bold"), axis.text.x = ggplot2::element_text(size = 12, 
#>         face = "bold"), axis.text.y = ggplot2::element_text(size = 12, 
#>         face = "bold"), axis.line = ggplot2::element_line(), 
#>         legend.text = ggplot2::element_text(size = 12), legend.title = ggplot2::element_text(size = 12, 
#>             face = "bold"), legend.title.align = 0.5, legend.text.align = 0.5, 
#>         legend.key.height = grid::unit(x = 1, units = "line"), 
#>         legend.key.width = grid::unit(x = 1, units = "line"), 
#>         plot.margin = grid::unit(x = c(1, 1, 1, 1), units = "lines"), 
#>         panel.border = ggplot2::element_rect(color = "black", 
#>             fill = NA, size = 1), plot.title = ggplot2::element_text(color = "black", 
#>             size = 13, face = "bold", hjust = 0.5), plot.subtitle = ggplot2::element_text(color = "black", 
#>             size = 11, face = "bold", hjust = 0.5))
#> }
#> <bytecode: 0x0000000026be4960>
#> <environment: namespace:ggstatsplot>

If you are not familiar either with what the namespace :: does or how to use pipe operator %>%, something this package and its documentation relies a lot on, you can check out these links-

Usage

ggstatsplot relies on non-standard evaluation, which means you shouldn’t enter arguments in the following manner: data = NULL, x = data$x, y = data$y. You must always specify the 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 argument messages = FALSE in the function call.

Here are examples of the main functions currently supported in ggstatsplot. Note: 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: https://cran.r-project.org/web/packages/ggstatsplot/index.html

ggbetweenstats

This function creates either a violin plot, a box plot, or a mix of two for between-group or between-condition comparisons with results from statistical tests in the subtitle. The simplest function call looks like this-

# for reproducibility
set.seed(123)

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

A number of other arguments can be specified to make this plot even more informative or change some of the default options. Additionally, this function returns a ggplot2 object and thus any of the graphics layers can be further modified:

library(ggplot2)

# for reproducibility
set.seed(123)

# let's leave out one of the factor levels and see if instead of anova, a t-test will be run
iris2 <- dplyr::filter(.data = datasets::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$Species <-
  base::factor(x = iris2$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 = 2,                                          # 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 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")
  ),
  ggtheme = ggplot2::theme_grey(),                # choosing a different theme
  palette = "Set1",                               # choosing a different color palette
  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). Additionally, the type of plot to be displayed can also be modified ("box", "violin", or "boxviolin").

For more, see the ggbetweenstats vignette: https://indrajeetpatil.github.io/ggstatsplot/articles/ggbetweenstats.html

** This function is not appropriate for within-subjects designs.**

Variant of this function ggwithinstats is currently under work. You can still use this function just to prepare the plot for exploratory data analysis, but the statistical details displayed in the subtitle will be incorrect. You can remove them by adding + ggplot2::labs(subtitle = NULL).

ggscatterstats

This function creates a scatterplot with marginal histograms/boxplots/density/violin/densigram plots from 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)

# for reproducibility
set.seed(123)

# plot
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.color = "black",                          # changing regression line color 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",                                # color fill for x-axis marginal distribution 
  yfill = "red",                                 # color fill for y-axis marginal distribution
  xalpha = 0.5,                                  # transparency for x-axis marginal distribution
  yalpha = 0.5,                                  # transparency 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
)

For more, see the ggscatterstats vignette: https://indrajeetpatil.github.io/ggstatsplot/articles/ggscatterstats.html

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, results from one-sample proportion test will be displayed as a subtitle.

# for reproducibility
set.seed(123)

# plot
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)

# for reproducibility
set.seed(123)

# plot
ggstatsplot::ggpiestats(
  data = datasets::mtcars,
  main = cyl,
  condition = am,
  title = "Dataset: Motor Trend Car Road Tests",      
  messages = FALSE
) + # further modification outside of ggstatsplot to change the default palette as an example 
  ggplot2::scale_fill_brewer(palette = "Set1")

As with the other functions, this basic plot can further be modified with additional arguments:

# for reproducibility
set.seed(123)

# plot
ggstatsplot::ggpiestats(
  data = datasets::mtcars,
  main = am,
  condition = cyl,
  title = "Dataset: Motor Trend Car Road Tests",      # title for the plot
  stat.title = "interaction: ",                       # title for the results from Pearson's chi-squared test
  legend.title = "Transmission",                      # title for the legend
  factor.levels = c("1 = manual", "0 = automatic"),   # 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
)

In case of within-subjects designs, setting paired = TRUE will produce results from McNemar test-

# 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,
  main = `1st survey`,
  condition = `2nd survey`,
  counts = Counts,
  paired = TRUE,                      # within-subjects design
  stat.title = "McNemar Test: ",
  palette = "Set1"
)

For more, including information about the variant of this function grouped_ggpiestats, see the ggpiestats vignette: https://indrajeetpatil.github.io/ggstatsplot/articles/ggpiestats.html

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)

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.color = "darkred",                  # decides color of vertical line representing central tendency
  binwidth = 0.10,                               # binwidth value (experiment until you find the best one)
  messages = FALSE                               # turn off the messages
)

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,
  title = "Distribution of variable x",
  x = stats::rnorm(n = 1000, mean = 0, sd = 1),
  test.value = 1,
  test.value.line = TRUE,
  test.value.color = "black",
  centrality.para = "mean",
  type = "bf",
  bf.prior = 0.8,
  messages = FALSE,
  caption = expression(                              
    paste(italic("Note"), ": black line - test value; blue line - observed mean", sep = "")
  )
)

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).

For more, including information about the variant of this function grouped_gghistostats, see the gghistostats vignette: https://indrajeetpatil.github.io/ggstatsplot/articles/gghistostats.html

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"
    )
  )
)

Multiple arguments can be modified to change the appearance of the correlation matrix.

Alternatively, you can use it just to get the correlation matrices and their corresponding p-values (in a tibble format). This is especially useful for robust correlation coefficient, which is not currently supported in ggcorrmat plot.

# getting the correlation coefficient matrix
ggstatsplot::ggcorrmat(
  data = datasets::iris,
  cor.vars = Sepal.Length:Petal.Width,
  corr.method = "robust",
  output = "correlations",             # specifying the needed output
  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 p-value matrix
ggstatsplot::ggcorrmat(
  data = datasets::iris,
  cor.vars = Sepal.Length:Petal.Width,
  corr.method = "robust",
  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      0.0818       0             0         
#> 2 Sepal.Width        0.0818 0            0.0000000529  0.00000252
#> 3 Petal.Length       0      0.0000000529 0             0         
#> 4 Petal.Width        0      0.00000252   0             0

For examples and more information, see the ggcorrmat vignette: https://indrajeetpatil.github.io/ggstatsplot/articles/ggcorrmat.html

ggcoefstats

ggcoefstats creates a lot with the regression coefficients’ point estimates as dots with confidence interval whiskers. This is a wrapper function around GGally::ggcoef.

ggstatsplot::ggcoefstats(x = stats::lm(formula = mpg ~ am * cyl,
                                       data = datasets::mtcars))

The basic can be further modified to one’s liking with additional arguments:

ggstatsplot::ggcoefstats(
  x = stats::lm(formula = mpg ~ am * cyl,
                data = datasets::mtcars),
  point.color = "red",
  vline.color = "#CC79A7",
  vline.linetype = "dotdash",
  stats.label.size = 3.5,
  stats.label.color = c("#0072B2", "#D55E00", "darkgreen"),
  title = "Car performance predicted by transmission and cylinder count",
  subtitle = "Source: 1974 Motor Trend US magazine"
) +                                    
  # 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)

All the regression model classes that are supported in the broom package with tidy and glance methods (https://broom.tidyverse.org/articles/available-methods.html) are also supported by ggcoefstats. Let’s see few examples:

library(dplyr)
library(lme4)

# for reproducibility
set.seed(200)

# creating dataframe needed for one of the analyses below
d <- as.data.frame(Titanic)

# combining plots together
ggstatsplot::combine_plots(
  # generalized linear model
  ggstatsplot::ggcoefstats(
    x = stats::glm(
      formula = Survived ~ Sex + Age,
      data = d,
      weights = d$Freq,
      family = "binomial"
    ),
    exponentiate = TRUE,
    exclude.intercept = FALSE,
    title = "generalized linear model"
  ),
  # nonlinear least squares
  ggstatsplot::ggcoefstats(
    x = stats::nls(
      formula = mpg ~ k / wt + b,
      data = datasets::mtcars,
      start = list(k = 1, b = 0)
    ),
    point.color = "darkgreen",
    title = "non-linear least squares"
  ),
  # linear mmodel
  ggstatsplot::ggcoefstats(
    x = lme4::lmer(
      formula = Reaction ~ Days + (Days | Subject),
      data = lme4::sleepstudy
    ),
    point.color = "red",
    exclude.intercept = TRUE,
    title = "linear mixed-effects model"
  ),
  # generalized linear mixed-effects model
  ggstatsplot::ggcoefstats(
    x = lme4::glmer(
      formula = cbind(incidence, size - incidence) ~ period + (1 | herd),
      data = lme4::cbpp,
      family = binomial
    ),
    exclude.intercept = FALSE,
    title = "generalized linear mixed-effects model"
  ),
  labels = c("(a)", "(b)", "(c)", "(d)"),
  nrow = 2,
  ncol = 2
)

This is by no means an exhaustive list of models supported by ggcoefstats. For more, see the associated vignette- https://indrajeetpatil.github.io/ggstatsplot/articles/ggcoefstats.html

combine_plots

ggstatsplot also contains a helper function combine_plots to combine multiple plots. This is a wrapper around 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.

For more, see the associated vignette- https://indrajeetpatil.github.io/ggstatsplot/articles/combine_plots.html

theme_ggstatsplot

All plots from ggstatsplot have a default theme: theme_ggstatsplot, which is also called as theme_mprl (both of which are identical functions with different names). For more on how to modify it, see the associated vignette- https://indrajeetpatil.github.io/ggstatsplot/articles/theme_ggstatsplot.html

Functions in ggstatsplot

Name Description
grouped_ggcorrmat Visualization of a correlalogram (or correlation matrix) using 'ggplot2'/'ggcorrplot' for all levels of a grouping variable
grouped_gghistostats Grouped histograms for distribution of a numeric variable
specify_decimal_p Custom function for getting specified number of decimal places in results for p-value
grouped_ggpiestats Grouped pie charts with statistical tests
grouped_ggscatterstats Scatterplot with marginal distributions for all levels of a grouping variable
t1way_ci A heteroscedastic one-way ANOVA for trimmed means with confidence interval for effect size.
theme_corrmat Default theme used for correlation matrix
ggstatsplot-package ggstatsplot
normality_message Display normality test result as a message.
grouped_ggbetweenstats Violin plots for group or condition comparisons in between-subjects designs repeated across all levels of a grouping variable.
grouped_proptest Function to run proportion test on grouped data.
intent_morality Moral judgments about third-party moral behavior.
theme_mprl Default theme used in all ggstatsplot package plots
%>% Pipe operator
legend_title_margin Custom function to set upper and lower margins to legend title in ggplot2
theme_ggstatsplot Default theme used in all ggstatsplot package plots
theme_pie Default theme used for pie chart
cor_tets_ci A correlation test with confidence interval for effect size.
lm_effsize_ci Confidence intervals for partial eta-squared and omega-squared for linear models.
ggpiestats Pie charts with statistical tests
robcor_ci Robust correlation coefficient and its confidence interval
signif_column Creating a new character type column with significance labels
ggscatterstats Scatterplot with marginal distributions
movies_long Movie information and user ratings from IMDB.com (long format).
tibble Anticipate use of tibbles
tz_labeller Create text labels
untable Untable a dataset
movies_wide Movie information and user ratings from IMDB.com (wide format).
combine_plots Combining multiple plots using cowplot::plot_grid() with a combination of title, caption, and annotation label
bartlett_message Display homogeneity of variance test as a message
Titanic_full Titanic dataset.
check_outlier Finding the outliers in the dataframe using Tukey's interquartile range rule
ggcoefstats Model coefficients for fitted models with the model summary as a caption.
ggbetweenstats Violin plots for group or condition comparisons in between-subjects designs.
chisq_v_ci Chi-squared test of association with confidence interval for effect size (Cramer's V).
gghistostats Histogram for distribution of a numeric variable
ggcorrmat Visualization of a correlalogram (or correlation matrix) using 'ggplot2'/'ggcorrplot'
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Vignettes of ggstatsplot

Name
combine_plots.Rmd
ggbetweenstats.Rmd
ggcoefstats.Rmd
ggcorrmat.Rmd
gghistostats.Rmd
ggpiestats.Rmd
ggscatterstats.Rmd
theme_ggstatsplot.Rmd
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