# 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. In a typical exploratory data analysis workflow, data visualization and statistical modelling are two different phases: visualization informs modelling, and modelling in its turn can suggest a different visualization method, and so on and so forth. The central idea of ggstatsplot is simple: combine these two phases into one in the form of graphics with statistical details, which makes data exploration simpler and faster.

Currently, it supports only the most common types of statistical tests (parametric, nonparametric, and robust versions of t-test, anova, and correlation analyses, contingency table analysis, and regression 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 for most functions that makes it easy to repeat the same analysis 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 (0.0.6):

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

You can get the development version of the package from GitHub (0.0.6.9000). 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")

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
)

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.

# Documentation and Examples

To see the detailed documentation for each function in the stable CRAN version of the package, see:

To see the documentation relevant for the development version of the package, see the dedicated website for ggstatplot, which is updated after every new commit: https://indrajeetpatil.github.io/ggstatsplot/.

## Help

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

# primary functions
?ggbetweenstats
?ggscatterstats
?gghistostats
?ggpiestats
?ggcorrmat
?ggcoefstats

# grouped variants of primary functions
?grouped_ggbetweenstats
?grouped_ggscatterstats
?grouped_gghistostats
?grouped_ggpiestats
?grouped_ggcorrmat

# helper functions
?combine_plots
?theme_ggstatsplot

# helper functions for making text with results from statistical tests
?subtitle_contigency_tab
?subtitle_ggbetween_anova_parametric
?subtitle_ggbetween_kw_nonparametric
?subtitle_ggbetween_mann_nonparametric
?subtitle_ggbetween_rob_anova
?subtitle_ggbetween_t_bayes
?subtitle_ggbetween_t_parametric
?subtitle_ggbetween_t_rob
?subtitle_ggscatterstats
?subtitle_onesample
?subtitle_onesample_proptest

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

args(name = ggstatsplot::ggscatterstats)
#> function (data, x, y, type = "pearson", bf.prior = 0.707, bf.message = FALSE,
#>     label.var = NULL, label.expression = NULL, xlab = NULL, ylab = NULL,
#>     method = "lm", method.args = list(), formula = y ~ x, point.color = "black",
#>     point.size = 3, point.alpha = 0.4, point.width.jitter = NULL,
#>     point.height.jitter = NULL, line.size = 1.5, line.color = "blue",
#>     marginal = TRUE, marginal.type = "histogram", marginal.size = 5,
#>     margins = c("both", "x", "y"), package = "wesanderson", palette = "Royal1",
#>     direction = 1, xfill = "#009E73", yfill = "#D55E00", xalpha = 1,
#>     yalpha = 1, xsize = 0.7, ysize = 0.7, centrality.para = NULL,
#>     results.subtitle = TRUE, title = NULL, subtitle = NULL, caption = NULL,
#>     nboot = 100, beta = 0.1, k = 3, axes.range.restrict = FALSE,
#>     ggtheme = ggplot2::theme_bw(), ggstatsplot.layer = TRUE,
#>     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 parentheses:

ggstatsplot::theme_ggstatsplot
#> function(ggtheme = ggplot2::theme_bw(),
#>                               ggstatsplot.layer = TRUE) {
#>   if (isTRUE(ggstatsplot.layer)) {
#>     ggtheme +
#>       ggplot2::theme(
#>         axis.title.x = ggplot2::element_text(size = 10, face = "bold"),
#>         strip.text.x = ggplot2::element_text(size = 10, face = "bold"),
#>         strip.text.y = ggplot2::element_text(size = 10, face = "bold"),
#>         strip.text = ggplot2::element_text(size = 10, face = "bold"),
#>         axis.title.y = ggplot2::element_text(size = 10, face = "bold"),
#>         axis.text.x = ggplot2::element_text(size = 10, face = "bold"),
#>         axis.text.y = ggplot2::element_text(size = 10, face = "bold"),
#>         axis.line = ggplot2::element_line(),
#>         legend.text = ggplot2::element_text(size = 10),
#>         legend.title = ggplot2::element_text(size = 10, 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 = 10,
#>           face = "plain",
#>           hjust = 0.5
#>         )
#>       )
#>   } else {
#>     ggtheme
#>   }
#> }
#> <bytecode: 0x000000002dc59d28>
#> <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-

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 = "p", # which type of test is to be run bf.message = TRUE, # add a message with bayes factor in favor of the null 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 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 )  In case of a parametric t-test, setting bf.message = TRUE will also attach results from Bayesian Student’s t-test. That way, if the null hypothesis can’t be rejected with the NHST approach, the Bayesian approach can help index evidence in favor of the null hypothesis (i.e., BF01). By default, Bayes Factor quantifies the support for the alternative hypothesis (H1) over the null hypothesis (H0) (i.e., BF10 is displayed). Natural logarithms are shown because BF values can be pretty large. This also makes it easy to compare evidence in favor alternative (BF10) versus null (BF01) hypotheses (since log(BF10) = - log(BF01)). 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 = ggstatsplot::movies_long, x = mpaa, y = length, grouping.var = genre, # grouping variable k = 2, title.prefix = "Movie genre", palette = "default_jama", package = "ggsci", messages = FALSE, nrow = 2, ncol = 2, title.text = "Differences in movie length by mpaa ratings for different genres" ) 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) to your function call. As a temporary solution, you can use the helper function from ggstatsplot to display results from within-subjects version of the test in question. Here is an example- # for reproducibility set.seed(123) # creating a smaller dataframe intent_short <- ggstatsplot::intent_morality %>% dplyr::filter(.data = ., condition %in% c("accidental", "attempted")) # getting text results using a helper function results_subtitle <- ggstatsplot::subtitle_ggbetween_t_parametric( data = intent_short, x = condition, y = rating, paired = TRUE ) # displaying the subtitle on the plot ggstatsplot::ggbetweenstats( data = intent_short, x = condition, y = rating, messages = FALSE ) + ggplot2::labs(subtitle = results_subtitle) ## ggscatterstats This function creates a scatterplot with marginal histograms/boxplots/density/violin/densigram plots 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 ) Number of other arguments can be specified to modify this basic plot- library(datasets) # 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 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 > 150", # 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 x-axis marginal distribution
yfill = "#009E73",                              # color fill for y-axis marginal distribution
xalpha = 0.6,                                   # transparency for x-axis marginal distribution
yalpha = 0.6,                                   # transparency for y-axis marginal distribution
centrality.para = "median",                     # which type of central tendency lines are to be displayed
point.width.jitter = 0.2,                       # amount of horizontal jitter for data points
point.height.jitter = 0.4,                      # amount of vertical jitter for data points
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:

# for reproducibility
set.seed(123)

# plot
ggstatsplot::grouped_ggscatterstats(
data = ggstatsplot::movies_long,
x = rating,
y = length,
bf.message = TRUE,               # display bayes factor message
xfill = "#E69F00",
yfill = "#8b3058",
xlab = "IMDB rating",
grouping.var = genre,            # grouping variable
title.prefix = "Movie genre",
ggtheme = ggplot2::theme_grey(),
messages = FALSE,
nrow = 2,
ncol = 2,
title.text = "Relationship between movie length by IMDB ratings for different genres"
)

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

## ggpiestats

This function creates a pie chart for categorical or nominal variables with results from contingency table analysis (Pearson’s chi-squared test for between-subjects design and McNemar’s test for within-subjects design) 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 = ggplot2::msleep,
main = vore,
title = "Composition of vore types among mammals",
messages = FALSE
)

This function can also be used to study an interaction between two categorical variables. Additionally, this basic plot can further be modified with additional arguments and the function returns a ggplot2 object that can further be modified with ggplot2 syntax:

# 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
legend.title = "Transmission",                      # title for the legend
factor.levels = c("1 = manual", "0 = automatic"),   # renaming the factor level names (main)
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 = 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: ",
package = "wesanderson",
palette = "Royal1"
)
#> Note: Results from faceted one-sample proportion tests:
#> # A tibble: 2 x 7
#>   condition  Approve Disapprove Chi-squared    df p-value significance
#>   <fct>      <chr>   <chr>              <dbl> <dbl>     <dbl> <chr>
#> 1 Approve    90.23%  9.77%               570.     1         0 ***
#> 2 Disapprove 20.83%  79.17%              245      1         0 ***

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(
data = ggstatsplot::movies_long,
main = mpaa,
grouping.var = genre,            # grouping variable
title.prefix = "Movie genre",    # prefix for the facetted title
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,
ncol = 2,
title.text = "Composition of MPAA ratings for different genres"
)

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.

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).

ggstatsplot::gghistostats(
data = datasets::ToothGrowth,             # dataframe from which variable is to be taken
x = len,                                  # numeric variable whose distribution is of interest
title = "Distribution of Sepal.Length",   # title for the plot
test.value = 10,                          # the comparison value for t-test
test.value.line = TRUE,                   # display a vertical line at test value
type = "bf",                              # bayes factor for one sample t-test
bf.prior = 0.8,                           # prior width for calculating the bayes factor
messages = FALSE                          # turn off the messages
)

The aesthetic defaults can be easily modified-

Note: To use bar.measure = "mix" option, you will need to get the development version of ggplot2 from GitHub.

# getting development version of ggplot2
# devtools::install_github(repo = "tidyverse/ggplot2", dependencies = FALSE)

# plot
ggstatsplot::gghistostats(
data = datasets::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
type = "parametric",                           # one sample t-test
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.message = TRUE,                             # display bayes factor for null over alternative
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 (using 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 = ggstatsplot::movies_long,
x = budget,
xlab = "Movies budget (in million US\$)",
grouping.var = genre,            # grouping variable
title.prefix = "Movie genre",
ggtheme = ggthemes::theme_tufte(),
messages = FALSE,
nrow = 2,
ncol = 2,
title.text = "Movies budgets for different genres"
)

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 a correlalogram (a matrix of correlation coefficients) with minimal amount of code. Just sticking to the defaults itself produces publication-ready correlation matrices. But, for the sake of exploring the available options, let’s change some of the defaults.

# 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
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: In the correlation matrix, the upper triangle is based on p-values adjusted for multiple comparisons,
#> while the lower triangle is based on unadjusted p-values.

Note that if there are NAs present in the selected dataframe, the legend will display minimum, median, and maximum number of pairs used for correlation matrices.

Multiple aesthetics-related 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).

# show four digits in a tibble
options(pillar.sigfig = 4)

# 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 ("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 p-value matrix
ggstatsplot::ggcorrmat(
data = ggplot2::msleep,
cor.vars = sleep_total:bodywt,
corr.method = "robust",
output = "p.values",                  # only "p" or "p-values" will also work
)
#> Note: In the correlation matrix, the upper triangle denotes p-values adjusted for multiple comparisons,
#> while the lower triangle denotes unadjusted p-values.
#> # A tibble: 6 x 7
#>   variable sleep_total sleep_rem sleep_cycle     awake   brainwt    bodywt
#>   <chr>          <dbl>     <dbl>       <dbl>     <dbl>     <dbl>     <dbl>
#> 1 sleep_t~   0.        5.291e-12   9.138e- 3 0.        3.170e- 5 2.568e- 6
#> 2 sleep_r~   4.070e-13 0.          1.978e- 2 5.291e-12 9.698e- 3 3.762e- 3
#> 3 sleep_c~   2.285e- 3 1.978e- 2   0.        9.138e- 3 1.637e- 9 1.696e- 5
#> 4 awake      0.        4.070e-13   2.285e- 3 0.        3.170e- 5 2.568e- 6
#> 5 brainwt    4.528e- 6 4.849e- 3   1.488e-10 4.528e- 6 0.        4.509e-17
#> 6 bodywt     2.568e- 7 7.524e- 4   2.120e- 6 2.568e- 7 3.221e-18 0.

# getting the confidence intervals for correlations
ggstatsplot::ggcorrmat(
data = ggplot2::msleep,
cor.vars = sleep_total:bodywt,
corr.method = "kendall",
output = "ci",
)
#> # A tibble: 15 x 7
#>    <chr>         <dbl>      <dbl>      <dbl>     <dbl>     <dbl>     <dbl>
#>  1 slp_t-slp_r  0.5922  0.4000     0.7345    4.981e- 7   0.3027    0.7817
#>  2 slp_t-slp_c -0.3481 -0.6214     0.0006818 5.090e- 2  -0.6789    0.1002
#>  3 slp_t-awake -1      -1         -1         0.         -1        -1
#>  4 slp_t-brnwt -0.4293 -0.6220    -0.1875    9.621e- 4  -0.6858   -0.07796
#>  5 slp_t-bdywt -0.3851 -0.5547    -0.1847    3.247e- 4  -0.6050   -0.1106
#>  6 slp_r-slp_c -0.2066 -0.5180     0.1531    2.566e- 1  -0.5180    0.1531
#>  7 slp_r-awake -0.5922 -0.7345    -0.4000    4.981e- 7  -0.7832   -0.2990
#>  8 slp_r-brnwt -0.2636 -0.5096     0.02217   7.022e- 2  -0.5400    0.06404
#>  9 slp_r-bdywt -0.3163 -0.5262    -0.07004   1.302e- 2  -0.5662   -0.01317
#> 10 slp_c-awake  0.3481 -0.0006818  0.6214    5.090e- 2  -0.1145    0.6867
#> 11 slp_c-brnwt  0.7125  0.4739     0.8536    1.001e- 5   0.3239    0.8954
#> 12 slp_c-bdywt  0.6545  0.3962     0.8168    4.834e- 5   0.2459    0.8656
#> 13 awake-brnwt  0.4293  0.1875     0.6220    9.621e- 4   0.08322   0.6829
#> 14 awake-bdywt  0.3851  0.1847     0.5547    3.247e- 4   0.1049    0.6087
#> 15 brnwt-bdywt  0.8378  0.7373     0.9020    8.181e-16   0.6716    0.9238

# 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

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
# let's use only 50% of the data to speed up the process
ggstatsplot::grouped_ggcorrmat(
data = dplyr::sample_frac(ggstatsplot::movies_long, size = 0.5),
corr.method = "np",
colors = c("#cbac43", "white", "#550000"),
grouping.var = genre,                      # grouping variable
title.prefix = "Movie genre",
messages = FALSE,
nrow = 2,
ncol = 2
)

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.

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 (also, let’s use a robust linear model instead of a simple linear model now):

ggstatsplot::ggcoefstats(
x = MASS::rlm(formula = mpg ~ am * cyl,
data = datasets::mtcars),
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 = ggthemes::theme_stata(),
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)

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. Additionally, we can make a number of aesthetic modifications by changing the defaults for theme and palette.

Let’s see a couple more examples:

library(dplyr)
library(lme4)
library(quantreg)

# for reproducibility
set.seed(200)

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

# combining plots together
ggstatsplot::combine_plots(
# quantile regression
ggstatsplot::ggcoefstats(
x = quantreg::rq(
formula = stack.loss ~ stack.x,
data = stackloss,
method = "br"
),
se.type = "iid",
title = "quantile regression"
),
# linear model
ggstatsplot::ggcoefstats(
x = lme4::lmer(
formula = scale(Reaction) ~ scale(Days) + (Days | Subject),
data = lme4::sleepstudy
),
point.color = "red",
stats.label.color = "black",
ggtheme = hrbrthemes::theme_ipsum_ps(),
ggstatsplot.layer = FALSE,
exclude.intercept = FALSE,
title = "linear mixed-effects model"
),
labels = c("(a)", "(b)"),
nrow = 2,
ncol = 1
)

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

## theme_ggstatsplot

All plots from ggstatsplot have a default theme: theme_ggstatsplot. You can change this theme by using the argument ggtheme for all functions.

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 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 = datasets::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 = datasets::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 chosen ggtheme"
)

For more on how to modify it, see the associated vignette- https://indrajeetpatil.github.io/ggstatsplot/articles/theme_ggstatsplot.html

## Using ggstatsplot helpers to display text results

Sometimes you may not like the default plot produced by ggstatsplot. In such cases, you can use other custom plots (from ggplot2 or other plotting packages) and still use ggstatsplot (subtitle) helper functions to display results from relevant statistical test. For example, in the following chunk, we will use pirateplot from yarrr package and use ggstatsplot helper function to display the results.


# for reproducibility
set.seed(123)

library(yarrr)
library(ggstatsplot)

# using ggstatsplot to prepare text with statistical results
stats_results <-
ggstatsplot::subtitle_ggbetween_anova_parametric(
data = ChickWeight,
x = Time,
y = weight,
messages = FALSE
)

# using yarrr to create plot
yarrr::pirateplot(
formula = weight ~ Time,
data = ChickWeight,
theme = 1,
main = stats_results
)

# Code coverage

As the code stands right now, here is the code coverage for all primary functions involved: https://codecov.io/gh/IndrajeetPatil/ggstatsplot/tree/master/R

# Contributing

I’m happy to receive bug reports, suggestions, questions, and (most of all) contributions to fix problems and add features. I personally prefer using the Github issues system over trying to reach out to me in other ways (personal e-mail, Twitter, etc.). Pull requests for contributions are encouraged.

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.