ggstatsplot: ggplot2 Based Plots with Statistical Details

Raison d’être

“What is to be sought in designs for the display of information is the clear portrayal of complexity. Not the complication of the simple; rather … the revelation of the complex.”
- Edward R. Tufte

ggstatsplot is an extension of ggplot2 package for creating graphics with details from statistical tests included in the information-rich plots themselves. In a typical exploratory data analysis workflow, data visualization and statistical modeling are two different phases: visualization informs modeling, and modeling 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.

Summary of available plots

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

In addition to these basic plots, ggstatsplot also provides grouped_ versions (see below) that makes it easy to repeat the same analysis for any grouping variable.

Summary of types of statistical analyses

Currently, it supports only the most common types of statistical tests: parametric, nonparametric, robust, and bayesian versions of t-test/anova, correlation analyses, contingency table analysis, meta-analysis, and regression analyses.

The table below summarizes all the different types of analyses currently supported in this package-

Statistical reporting

For all statistical tests reported in the plots, the default template abides by the APA gold standard for statistical reporting. For example, here are results from Yuen’s test for trimmed means (robust t-test):

Summary of statistical tests and effect sizes

Here is a summary table of all the statistical tests currently supported across various functions: https://indrajeetpatil.github.io/statsExpressions/articles/stats_details.html

Installation

To get the latest, stable CRAN release:

install.packages("ggstatsplot")

Note: If you are on a linux machine, you will need to have OpenGL libraries installed (specifically, libx11, mesa and Mesa OpenGL Utility library - glu) for the dependency package rgl to work.

You can get the development version of the package from GitHub. 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
install.packages("remotes")

# downloading the package from GitHub (needs `remotes` package to be installed)
remotes::install_github(
  repo = "IndrajeetPatil/ggstatsplot", # package path on GitHub
  dependencies = FALSE, # assumes you have already installed needed packages
  quick = TRUE # skips docs, demos, and vignettes
)

If time is not a constraint-

remotes::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:

citation("ggstatsplot")
#> 
#>   Patil, I. (2018). ggstatsplot: 'ggplot2' Based Plots with Statistical
#>   Details. CRAN. Retrieved from
#>   https://cran.r-project.org/web/packages/ggstatsplot/index.html
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Article{,
#>     title = {{ggstatsplot}: 'ggplot2' Based Plots with Statistical Details},
#>     author = {Indrajeet Patil},
#>     year = {2018},
#>     journal = {CRAN},
#>     url = {https://CRAN.R-project.org/package=ggstatsplot},
#>     doi = {10.5281/zenodo.2074621},
#>   }

There is currently a publication in preparation corresponding to 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:

• README: https://CRAN.R-project.org/package=ggstatsplot/readme/README.html
• Presentation: https://indrajeetpatil.github.io/ggstatsplot_slides/slides/ggstatsplot_presentation.html#1
• Vignettes: https://CRAN.R-project.org/package=ggstatsplot/vignettes/additional.html

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/.

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.R-project.org/package=ggstatsplot/readme/README.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-

# loading needed libraries
library(ggstatsplot)

# for reproducibility
set.seed(123)

# plot
ggstatsplot::ggbetweenstats(
  data = iris,
  x = Species,
  y = Sepal.Length,
  title = "Distribution of sepal length across Iris species",
  messages = FALSE
)

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 t-test.

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.

# for reproducibility
set.seed(123)
library(ggplot2)

# plot
ggstatsplot::ggbetweenstats(
  data = ToothGrowth,
  x = supp,
  y = len,
  notch = TRUE, # show notched box plot
  mean.ci = TRUE, # whether to display confidence interval for means
  k = 3, # number of decimal places for statistical results
  outlier.tagging = TRUE, # whether outliers need to be tagged
  outlier.label = dose, # variable to be used for the outlier tag
  xlab = "Supplement type", # label for the x-axis variable
  ylab = "Tooth length", # label for the y-axis variable
  title = "The Effect of Vitamin C on Tooth Growth", # 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
)

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
  p.adjust.method = "bonferroni", # method for adjusting p-values for multiple comparisons
  # adding new components to `ggstatsplot` default
  ggplot.component = list(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,
  plotgrid.args = list(nrow = 2),
  title.text = "Differences in movie length by mpaa ratings for different genres"
)

Summary of tests

Following (between-subjects) tests are carried out for each type of analyses-

The omnibus effect in one-way 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-

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 = WineTasting,
  x = Wine,
  y = Taste,
  pairwise.comparisons = TRUE, # show pairwise comparison test results
  title = "Wine tasting",
  caption = "Data source: `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)

# plot
ggstatsplot::grouped_ggwithinstats(
  data = dplyr::filter(
    .data = ggstatsplot::bugs_long,
    region %in% c("Europe", "North America"),
    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 (within-subjects) tests are carried out for each type of analyses-

The omnibus effect in one-way 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-

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
  xlab = "Movie budget (in million/ US$)", # label for x axis
  ylab = "IMDB rating", # label for y axis
  label.var = "title", # variable for labeling data points
  label.expression = "rating < 5 & budget > 100", # expression that decides which points to label
  title = "Movie budget and IMDB rating (action)", # title text for the plot
  caption = expression(paste(italic("Note"), ": IMDB stands for Internet Movie DataBase")),
  ggtheme = hrbrthemes::theme_ipsum_ps(), # choosing a different theme
  ggstatsplot.layer = FALSE, # turn off `ggstatsplot` theme layer
  marginal.type = "density", # type of marginal distribution to be displayed
  xfill = "pink", # color fill for x-axis marginal distribution
  yfill = "#009E73", # color fill for y-axis marginal distribution
  centrality.parameter = "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,
  grouping.var = genre, # grouping variable
  label.var = title,
  label.expression = length > 200,
  xfill = "#E69F00",
  yfill = "#8b3058",
  xlab = "IMDB rating",
  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,
  plotgrid.args = list(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-

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 chi-squared test for between-subjects design and McNemar’s chi-squared 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 (i.e., a chi-squared goodness of fit test) will be displayed as a subtitle.

To study an interaction between two categorical variables:

# for reproducibility
set.seed(123)

# plot
ggstatsplot::ggpiestats(
  data = mtcars,
  x = am,
  y = cyl,
  title = "Dataset: Motor Trend Car Road Tests", # title for the plot
  legend.title = "Transmission", # title for the legend
  caption = substitute(paste(italic("Source"), ": 1974 Motor Trend US magazine")),
  messages = FALSE
)

In case of repeated measures designs, setting paired = TRUE will produce results from McNemar’s chi-squared test-

# for reproducibility
set.seed(123)

# plot
ggstatsplot::ggpiestats(
  data = data.frame(
    "before" = c("Approve", "Approve", "Disapprove", "Disapprove"),
    "after" = c("Approve", "Disapprove", "Approve", "Disapprove"),
    counts = c(794, 150, 86, 570),
    check.names = FALSE
  ),
  x = before,
  y = after,
  counts = counts,
  title = "Survey results before and after the intervention",
  label = "both",
  paired = TRUE, # within-subjects design
  package = "wesanderson",
  palette = "Royal1"
)
#> Note: 95% CI for effect size estimate was computed with 100 bootstrap samples.
#> # A tibble: 2 x 11
#>   after      counts  perc N         Approve Disapprove statistic   p.value
#>   <fct>       <int> <dbl> <chr>     <chr>   <chr>          <dbl>     <dbl>
#> 1 Disapprove    720   45  (n = 720) 20.83%  79.17%          245  3.20e- 55
#> 2 Approve       880   55. (n = 880) 90.23%  9.77%           570. 6.80e-126
#>   parameter method                                   significance
#>       <dbl> <chr>                                    <chr>       
#> 1         1 Chi-squared test for given probabilities ***         
#> 2         1 Chi-squared 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. Following example is a case where the theoretical question is about proportions for different levels of a single nominal variable:

# for reproducibility
set.seed(123)

# plot
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
  messages = FALSE,
  package = "ggsci", # package from which color palette is to be taken
  palette = "default_jama", # choosing a different color palette
  title.text = "Composition of MPAA ratings for different genres"
)

Summary of tests

Following tests are carried out for each type of analyses-

Following effect sizes (and confidence intervals/CI) are available for each type of test-

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)
library(ggplot2)

# plot
ggstatsplot::ggbarstats(
  data = ggstatsplot::movies_long,
  x = mpaa,
  y = genre,
  sampling.plan = "jointMulti",
  title = "MPAA Ratings by Genre",
  xlab = "movie genre",
  legend.title = "MPAA rating",
  ggtheme = hrbrthemes::theme_ipsum_pub(),
  ggplot.component = list(scale_x_discrete(guide = guide_axis(n.dodge = 2))),
  palette = "Set2",
  messages = FALSE
)

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",
  plotgrid.args = list(nrow = 2)
)

Summary of tests

This is identical to the ggpiestats function summary of tests.

gghistostats

To visualize the distribution of a single variable and check if its mean is significantly different from a specified value with a one-sample test, gghistostats can be used.

# 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")),
  bar.measure = "both",
  test.value = 5, # default value is 0
  test.value.line = TRUE, # display a vertical line at test value
  centrality.parameter = "mean", # which measure of central tendency is to be plotted
  centrality.line.args = list(color = "darkred"), # aesthetics for central tendency line
  binwidth = 0.10, # binwidth value (experiment)
  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.args = list(color = "red", size = 1),
  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,
  plotgrid.args = list(nrow = 2),
  title.text = "Movies budgets for different genres"
)

Summary of tests

Following tests are carried out for each type of analyses-

Following effect sizes (and confidence intervals/CI) are available for each type of test-

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,
  centrality.parameter = "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)

# plot
ggstatsplot::grouped_ggdotplotstats(
  data = dplyr::filter(.data = ggplot2::mpg, cyl %in% c("4", "6")),
  x = cty,
  y = manufacturer,
  xlab = "city miles per gallon",
  ylab = "car manufacturer",
  type = "nonparametric", # non-parametric test
  grouping.var = cyl, # grouping variable
  test.value = 15.5,
  test.value.line = TRUE,
  title.prefix = "cylinder count",
  point.args = list(color = "red", size = 5, shape = 13),
  messages = FALSE,
  title.text = "Fuel economy data"
)

Summary of tests

This is identical to summary of tests for gghistostats.

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. For example, multiple aesthetics-related arguments can be modified to change the appearance of the correlation matrix.

# for reproducibility
set.seed(123)

# as a default this function outputs a correlation matrix plot
ggstatsplot::ggcorrmat(
  data = ggplot2::msleep,
  type = "robust", # correlation method
  p.adjust.method = "holm", # p-value 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",
  caption = "Source: `ggplot2` R package"
)

Two things to note:

• If there are NAs present in the selected variables, the legend will display minimum, median, and maximum number of pairs used for correlation tests.

• If cor.vars are not specified, all numeric variables will be used.

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_ggcorrmat(
  data = dplyr::filter(
    .data = ggstatsplot::movies_long,
    genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
  ),
  cor.vars = length:votes,
  colors = c("#cbac43", "white", "#550000"),
  grouping.var = genre, # grouping variable
  k = 3L, # number of digits after decimal point
  title.prefix = "Movie genre",
  messages = FALSE,
  plotgrid.args = list(nrow = 2)
)

You can also get a dataframe containing all relevant details from the statistical tests:

# setup
set.seed(123)

# dataframe in long format
ggcorrmat(
  data = ggplot2::msleep,
  type = "bayes",
  output = "dataframe"
)
#> # A tibble: 15 x 12
#>    parameter1  parameter2     rho ci_low ci_high    pd rope_percentage
#>    <chr>       <chr>        <dbl>  <dbl>   <dbl> <dbl>           <dbl>
#>  1 sleep_total sleep_rem    0.735  0.617  0.810  1              0     
#>  2 sleep_total sleep_cycle -0.436 -0.645 -0.194  0.998          0.0225
#>  3 sleep_total awake       -1.00  -1.00  -1.00   1              0     
#>  4 sleep_total brainwt     -0.344 -0.525 -0.157  0.997          0.0222
#>  5 sleep_total bodywt      -0.295 -0.456 -0.142  0.997          0.0318
#>  6 sleep_rem   sleep_cycle -0.308 -0.539 -0.0463 0.969          0.0985
#>  7 sleep_rem   awake       -0.733 -0.827 -0.640  1              0     
#>  8 sleep_rem   brainwt     -0.206 -0.413  0.0106 0.924          0.208 
#>  9 sleep_rem   bodywt      -0.313 -0.492 -0.132  0.994          0.0368
#> 10 sleep_cycle awake        0.440  0.213  0.659  0.992          0.0205
#> 11 sleep_cycle brainwt      0.823  0.716  0.910  1              0     
#> 12 sleep_cycle bodywt       0.379  0.133  0.607  0.988          0.0385
#> 13 awake       brainwt      0.341  0.160  0.520  0.996          0.03  
#> 14 awake       bodywt       0.302  0.144  0.463  0.998          0.0295
#> 15 brainwt     bodywt       0.925  0.892  0.955  1              0     
#>    prior_distribution prior_location prior_scale        bf  nobs
#>    <chr>                       <dbl>       <dbl>     <dbl> <int>
#>  1 cauchy                          0       0.707  3.00e+ 9    61
#>  2 cauchy                          0       0.707  8.85e+ 0    32
#>  3 cauchy                          0       0.707 NA           83
#>  4 cauchy                          0       0.707  7.29e+ 0    56
#>  5 cauchy                          0       0.707  9.28e+ 0    83
#>  6 cauchy                          0       0.707  1.42e+ 0    32
#>  7 cauchy                          0       0.707  3.01e+ 9    61
#>  8 cauchy                          0       0.707  6.54e- 1    48
#>  9 cauchy                          0       0.707  4.80e+ 0    61
#> 10 cauchy                          0       0.707  8.85e+ 0    32
#> 11 cauchy                          0       0.707  3.80e+ 6    30
#> 12 cauchy                          0       0.707  3.76e+ 0    32
#> 13 cauchy                          0       0.707  7.29e+ 0    56
#> 14 cauchy                          0       0.707  9.27e+ 0    83
#> 15 cauchy                          0       0.707  1.58e+22    56

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-

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

ggcoefstats

The function ggstatsplot::ggcoefstats generates dot-and-whisker plots for regression models saved in a tidy data frame. The tidy dataframes are prepared using the following packages: broom, broom.mixed, and parameters. Additionally, if available, the model summary indices are also extracted from the following packages: broom, broom.mixed, and performance.

Although the statistical models displayed in the plot may differ based on the class of models being investigated, there are few aspects of the plot that will be invariant across models:

• The dot-whisker 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 the F statistic) or regression coefficients (for tests with t and z statistic), etc. The function will, by default, display a helpful x-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.

• The output of this function will be a ggplot2 object and, thus, it can be further modified (e.g., change themes, etc.) with ggplot2 functions.

# for reproducibility
set.seed(123)

# model
mod <- stats::lm(formula = mpg ~ am * cyl, data = mtcars)

# plot
ggstatsplot::ggcoefstats(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)

# plot
ggstatsplot::ggcoefstats(
  x = MASS::rlm(formula = mpg ~ am * cyl, data = mtcars),
  point.args = list(color = "red", size = 3, shape = 15),
  vline.args = list(size = 1, color = "#CC79A7", linetype = "dotdash"),
  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
) + # 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 underlying packages are also supported by ggcoefstats. For example-

aareg, anova, aov, aovlist, Arima, bayesx, bayesGARCH, BBmm, BBreg, bcplm, bglmerMod, bife, bigglm, biglm, blavaan, bmlm, blmerMod, bracl, brglm2, brmsfit, brmultinom, btergm, cch, cgam, cgamm, cglm, clm, clm2, clmm, clmm2, coeftest, complmrob, confusionMatrix, coxme, coxph, cpglm, cpglmm, crch, DirichReg, drc, emmGrid, epi.2by2, ergm, feis, felm, fitdistr, flexsurvreg, glmc, glmerMod, glmmTMB, gls, gam, Gam, gamlss, garch, glm, glmmadmb, glmmPQL, glmRob, glmrob, glmx, gmm, hurdle, ivreg, iv_robust, lavaan, lm, lm.beta, lmerMod, lmerModLmerTest, lmodel2, lmRob, lmrob, LORgee, lrm, mcmc, mcmc.list, MCMCglmm, mclogit, mmclogit, mediate, mixor, mjoint, mle2, mlm, multinom, negbin, nlmerMod, nlrq, nlreg, nls, orcutt, plm, polr, ridgelm, rjags, rlm, rlmerMod, robmixglm, rq, rqss, slm, speedglm, speedlm, stanfit, stanreg, survreg, svyglm, svyolr, svyglm, tobit, truncreg, vgam, wbgee, wblm, zcpglm, zeroinfl, etc.

Although not shown here, this function can also be used to carry out both frequentist and Bayesian random-effects meta-analysis.

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