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README.Rmd
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---
output:
github_document:
pandoc_args: --webtex
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
# show me all columns
options(tibble.width = Inf)
knitr::opts_chunk$set(
collapse = TRUE,
dpi = 200,
out.width = "100%",
comment = "#>",
fig.path = "man/figures/README-"
)
```
# `ggstatsplot`: `ggplot2` Based Plots with Statistical Details
Package | Status | Usage | GitHub | References
----------------- | ----------------- | ----------------- | ----------------- | -----------------
[](https://CRAN.R-project.org/package=ggstatsplot) | [](https://travis-ci.org/IndrajeetPatil/ggstatsplot) | [](https://CRAN.R-project.org/package=ggstatsplot) | [](https://github.com/IndrajeetPatil/ggstatsplot/) | [](https://indrajeetpatil.github.io/ggstatsplot/)
[](https://cran.r-project.org/web/checks/check_results_ggstatsplot.html) | [](https://ci.appveyor.com/project/IndrajeetPatil/ggstatsplot) | [](https://CRAN.R-project.org/package=ggstatsplot) | [![Forks](https://img.shields.io/badge/forks-`r itdepends:::get_github_info("ggstatsplot")$forks[[1]]`-blue.svg)](https://github.com/IndrajeetPatil/ggstatsplot/) | [](https://www.rdocumentation.org/packages/ggstatsplot)
[](https://cran.r-project.org/) | [](https://www.tidyverse.org/lifecycle/) | [](https://CRAN.R-project.org/package=ggstatsplot) | [![Github Issues](https://img.shields.io/badge/issues-`r itdepends:::get_github_info("ggstatsplot")$open_issues[[1]]`-red.svg)](https://github.com/IndrajeetPatil/ggstatsplot/issues) | [](https://CRAN.R-project.org/package=ggstatsplot/vignettes/)
[](https://github.com/IndrajeetPatil/ggstatsplot) | [](https://coveralls.io/github/IndrajeetPatil/ggstatsplot?branch=master) | [](https://CRAN.R-project.org/package=ggstatsplot) | [](https://github.com/IndrajeetPatil/ggstatsplot) | [](https://doi.org/10.5281/zenodo.2074621)
[](https://www.gnu.org/licenses/gpl-3.0.en.html) | [](https://codecov.io/gh/IndrajeetPatil/ggstatsplot?branch=master) | [](http://hits.dwyl.io/IndrajeetPatil/ggstatsplot) | [)`-yellowgreen.svg)](https://github.com/IndrajeetPatil/ggstatsplot/commits/master) | [](https://github.com/IndrajeetPatil/ggstatsplot/commits/master)
[](https://CRAN.R-project.org/package=ggstatsplot) | [](https://github.com/IndrajeetPatil/ggstatsplot/blob/master/tests/README.md) | [](https://saythanks.io/to/IndrajeetPatil) | [](http://www.repostatus.org/#active) | [](https://github.com/IndrajeetPatil/ggstatsplot/issues)
[](https://gitter.im/ggstatsplot/community) | | | |
# 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`](https://indrajeetpatil.github.io/ggstatsplot/) is an extension
of [`ggplot2`](https://github.com/tidyverse/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 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:
Function | Plot | Description
------- | ---------- | -----------------
`ggbetweenstats` | **violin plots** | for comparisons *between* groups/conditions
`ggwithinstats` | **violin plots** | for comparisons *within* groups/conditions
`gghistostats` | **histograms** | for distribution about numeric variable
`ggdotplotstats` | **dot plots/charts** | for distribution about labeled numeric variable
`ggpiestats` | **pie charts** | for categorical data
`ggbarstats` | **bar charts** | for categorical data
`ggscatterstats` | **scatterplots** | for correlations between two variables
`ggcorrmat` | **correlation matrices** | for correlations between multiple variables
`ggcoefstats` | **dot-and-whisker plots** | for regression models
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,
and **regression** analyses.
The table below summarizes all the different types of analyses currently
supported in this package-
Functions | Description | Parametric | Non-parametric | Robust | Bayes Factor
------- | ------------------ | ---- | ----- | ----| -----
`ggbetweenstats` | Between group/condition comparisons | <font color="green">Yes</font> | <font color="green">Yes</font> | <font color="green">Yes</font> | <font color="green">Yes</font>
`ggwithinstats` | Within group/condition comparisons | <font color="green">Yes</font> | <font color="green">Yes</font> | <font color="green">Yes</font> | <font color="green">Yes</font>
`gghistostats`, `ggdotplotstats` | Distribution of a numeric variable | <font color="green">Yes</font> | <font color="green">Yes</font> | <font color="green">Yes</font> | <font color="green">Yes</font>
`ggcorrmat` | Correlation matrix | <font color="green">Yes</font> | <font color="green">Yes</font> | <font color="green">Yes</font> | <font color="red">No</font>
`ggscatterstats` | Correlation between two variables | <font color="green">Yes</font> | <font color="green">Yes</font> | <font color="green">Yes</font> | <font color="green">Yes</font>
`ggpiestats`, `ggbarstats` | Association between categorical variables | <font color="green">Yes</font> | `NA` | `NA` | <font color="green">Yes</font>
`ggpiestats`, `ggbarstats` | Equal proportions for categorical variable levels | <font color="green">Yes</font> | `NA` | `NA` | <font color="green">Yes</font>
`ggcoefstats` | Regression model coefficients | <font color="green">Yes</font> | <font color="red">No</font> | <font color="green">Yes</font> | <font color="red">No</font>
# Statistical reporting
For **all** statistical tests reported in the plots, the default template abides
by the [APA](https://my.ilstu.edu/~jhkahn/apastats.html) gold standard for
statistical reporting. For example, here are results from Yuen's test for
trimmed means (robust *t*-test):
<img src="man/figures/stats_reporting_format.png" align="center" />
# Summary of statistical tests and effect sizes
Here is a summary table of all the statistical tests currently supported across
various functions:
Functions | Type | Test | Effect size | 95% CI available?
-------------- | ----------- | --------------------- | -------- | -----
`ggbetweenstats` (2 groups) | Parametric | Student's and Welch's *t*-test | Cohen's *d*, Hedge's *g* | <font color="green">$\checkmark$</font>
`ggbetweenstats` (> 2 groups) | Parametric | Fisher's and Welch's one-way ANOVA | $$\eta^2, \eta^2_p, \omega^2, \omega^2_p$$ | <font color="green">$\checkmark$</font>
`ggbetweenstats` (2 groups) | Non-parametric | Mann-Whitney *U*-test | *r* | <font color="green">$\checkmark$</font>
`ggbetweenstats` (> 2 groups) | Non-parametric | Kruskal-Wallis Rank Sum Test | $$\epsilon^2$$ | <font color="green">$\checkmark$</font>
`ggbetweenstats` (2 groups) | Robust | Yuen's test for trimmed means | $$\xi$$ | <font color="green">$\checkmark$</font>
`ggbetweenstats` (> 2 groups) | Robust | Heteroscedastic one-way ANOVA for trimmed means | $$\xi$$ | <font color="green">$\checkmark$</font>
`ggwithinstats` (2 groups) | Parametric | Student's *t*-test | Cohen's *d*, Hedge's *g* | <font color="green">$\checkmark$</font>
`ggwithinstats` (> 2 groups) | Parametric | Fisher's one-way repeated measures ANOVA | $$\eta^2_p, \omega^2$$ | <font color="green">$\checkmark$</font>
`ggwithinstats` (2 groups) | Non-parametric | Wilcoxon signed-rank test | *r* | <font color="green">$\checkmark$</font>
`ggwithinstats` (> 2 groups) | Non-parametric | Friedman rank sum test | $$W_{Kendall}$$ | <font color="green">$\checkmark$</font>
`ggwithinstats` (2 groups) | Robust | Yuen's test on trimmed means for dependent samples | $$\xi$$ | <font color="green">$\checkmark$</font>
`ggwithinstats` (> 2 groups) | Robust | Heteroscedastic one-way repeated measures ANOVA for trimmed means | <font color="red">$\times$</font> | <font color="red">$\times$</font>
`ggpiestats` and `ggbarstats` (unpaired) | Parametric | $$\text{Pearson's}~ \chi^2 ~\text{test}$$ | Cramér's *V* | <font color="green">$\checkmark$</font>
`ggpiestats` and `ggbarstats` (paired) | Parametric | McNemar's test | Cohen's *g* | <font color="green">$\checkmark$</font>
`ggpiestats` | Parametric | One-sample proportion test | Cramér's *V* | <font color="green">$\checkmark$</font>
`ggscatterstats` and `ggcorrmat` | Parametric | Pearson's *r* | *r* | <font color="green">$\checkmark$</font>
`ggscatterstats` and `ggcorrmat` | Non-parametric | $$\text{Spearman's}~ \rho$$ | $$\rho$$ | <font color="green">$\checkmark$</font>
`ggscatterstats`and `ggcorrmat` | Robust | Percentage bend correlation | *r* | <font color="green">$\checkmark$</font>
`gghistostats` and `ggdotplotstats` | Parametric | One-sample *t*-test | Cohen's *d*, Hedge's *g* | <font color="green">$\checkmark$</font>
`gghistostats` | Non-parametric | One-sample Wilcoxon signed rank test | *r* | <font color="green">$\checkmark$</font>
`gghistostats` and `ggdotplotstats` | Robust | One-sample percentile bootstrap | robust estimator | <font color="green">$\checkmark$</font>
`ggcoefstats` | Parametric | Regression models | $$\beta$$ | <font color="green">$\checkmark$</font>
# Installation
To get the latest, stable `CRAN` release (`0.1.2`):
```{r installationCRAN, eval = FALSE}
utils::install.packages(pkgs = "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
(`0.1.2.9000`). 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-
```{r installation1, eval = FALSE}
# needed package to download from GitHub repo
utils::install.packages(pkgs = "remotes")
# downloading the package from GitHub
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-
```{r installation2, eval = FALSE}
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](https://www.rstudio.com/) 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](http://pandoc.org/). If you have the `rmarkdown` R package installed
then you can check if you have pandoc by running the following in R:
```{r pandoc}
rmarkdown::pandoc_available()
```
# 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:
```{r citation, eval = FALSE}
citation("ggstatsplot")
```
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/>.
## Help
In `R`, documentation for any function can be accessed with the standard `help`
command (e.g., `?ggbetweenstats`).
Another handy tool to see arguments to any of the functions is `args`. For
example-
```{r args, message = FALSE, warning = FALSE}
args(name = ggstatsplot::specify_decimal_p)
```
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:
```{r funbody, message = FALSE, warning = FALSE}
# function to convert class of any object to `ggplot` class
ggstatsplot::ggplot_converter
```
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-
- <http://r-pkgs.had.co.nz/namespace.html>
- <http://r4ds.had.co.nz/pipes.html>
# 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-
```{r ggbetweenstats1, message = FALSE, warning = FALSE}
# loading needed libraries
library(ggstatsplot)
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggbetweenstats(
data = iris,
x = Species,
y = Sepal.Length,
messages = FALSE
) + # further modification outside of ggstatsplot
ggplot2::coord_cartesian(ylim = c(3, 8)) +
ggplot2::scale_y_continuous(breaks = seq(3, 8, by = 1))
```
Note that this function returns object of class `ggplot` and thus can be further
modified using `ggplot2` functions.
A number of other arguments can be specified to make this plot even more
informative or change some of the default options. Additionally, this time we
will use a grouping variable that has only two levels. The function will
automatically switch from carrying out an ANOVA analysis to a *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.
```{r ggbetweenstats2, message = FALSE, warning = FALSE}
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 = iris, Species != "setosa")
# let's change the levels of our factors, a common routine in data analysis
# pipeline, to see if this function respects the new factor levels
iris2$Species <- factor(x = iris2$Species, levels = c("virginica", "versicolor"))
# plot
ggstatsplot::ggbetweenstats(
data = iris2,
x = Species,
y = Sepal.Length,
notch = TRUE, # show notched box plot
mean.plotting = TRUE, # whether mean for each group is to be displayed
mean.ci = TRUE, # whether to display confidence interval for means
mean.label.size = 2.5, # size of the label for mean
type = "parametric", # which type of test is to be run
k = 3, # number of decimal places for statistical results
outlier.tagging = TRUE, # whether outliers need to be tagged
outlier.label = Sepal.Width, # variable to be used for the outlier tag
outlier.label.color = "darkgreen", # changing the color for the text label
xlab = "Type of Species", # label for the 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
)
```
As can be seen from the plot, the function by default returns Bayes Factor for
the test (here, Student's *t*-test). If the null hypothesis can't be rejected
with the null hypothesis significance testing (NHST) approach, the Bayesian
approach can help index evidence in favor of the null hypothesis (i.e.,
$BF_{01}$).
By default, natural logarithms are shown because Bayes Factor values can
sometimes be pretty large. Having values on logarithmic scale also makes it easy
to compare evidence in favor alternative ($BF_{10}$) versus null ($BF_{01}$)
hypotheses (since $log_{e}(BF_{01}) = - log_{e}(BF_{01})$).
Additionally, there is also a `grouped_` variant of this function that makes it
easy to repeat the same operation across a **single** grouping variable:
```{r ggbetweenstats3, message = FALSE, warning = FALSE, fig.height = 14, fig.width = 12}
# for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_ggbetweenstats(
data = dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
x = mpaa,
y = length,
grouping.var = genre, # grouping variable
pairwise.comparisons = TRUE, # display significant pairwise comparisons
pairwise.annotation = "p.value", # how do you want to annotate the pairwise comparisons
p.adjust.method = "bonferroni", # method for adjusting p-values for multiple comparisons
conf.level = 0.99, # changing confidence level to 99%
ggplot.component = list( # adding new components to `ggstatsplot` default
ggplot2::scale_y_continuous(sec.axis = ggplot2::dup_axis())
),
k = 3,
title.prefix = "Movie genre",
caption = substitute(paste(
italic("Source"),
":IMDb (Internet Movie Database)"
)),
palette = "default_jama",
package = "ggsci",
messages = FALSE,
nrow = 2,
title.text = "Differences in movie length by mpaa ratings for different genres"
)
```
### Summary of tests
Following (between-subjects) tests are carried out for each type of analyses-
Type | No. of groups | Test
----------- | --- | -------------------------
Parametric | > 2 | Fisher's or Welch's one-way ANOVA
Non-parametric | > 2 | Kruskal–Wallis one-way ANOVA
Robust | > 2 | Heteroscedastic one-way ANOVA for trimmed means
Bayes Factor | > 2 | Fisher's ANOVA
Parametric | 2 | Student's or Welch's *t*-test
Non-parametric | 2 | Mann–Whitney *U* test
Robust | 2 | Yuen's test for trimmed means
Bayes Factor | 2 | Student's *t*-test
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*-
Type | Equal variance? | Test | *p*-value adjustment?
----------- | --- | ------------------------- | ---
Parametric | No | Games-Howell test | <font color="green">Yes</font>
Parametric | Yes | Student's *t*-test | <font color="green">Yes</font>
Non-parametric | No | Dwass-Steel-Crichtlow-Fligner test | <font color="green">Yes</font>
Robust | No | Yuen's trimmed means test | <font color="green">Yes</font>
Bayes Factor | No | <font color="red">No</font> | <font color="red">No</font>
Bayes Factor | Yes | <font color="red">No</font> | <font color="red">No</font>
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.
```{r ggwithinstats1, message = FALSE, warning = FALSE, fig.width = 8, fig.height = 6}
# for reproducibility and data
set.seed(123)
library(WRS2)
# plot
ggstatsplot::ggwithinstats(
data = WRS2::WineTasting,
x = Wine,
y = Taste,
sort = "descending", # ordering groups along the x-axis based on
sort.fun = median, # values of `y` variable
pairwise.comparisons = TRUE,
pairwise.display = "s",
pairwise.annotation = "p",
title = "Wine tasting",
caption = "Data from: `WRS2` R package",
ggtheme = ggthemes::theme_fivethirtyeight(),
ggstatsplot.layer = FALSE,
messages = FALSE
)
```
As with the `ggbetweenstats`, this function also has a `grouped_` variant that
makes repeating the same analysis across a single grouping variable quicker. We
will see an example with only repeated measurements-
```{r ggwithinstats2, message = FALSE, warning = FALSE, fig.height = 6, fig.width = 12}
# common setup
set.seed(123)
# getting data in tidy format
data_bugs <- ggstatsplot::bugs_long %>%
dplyr::filter(.data = ., region %in% c("Europe", "North America"))
# plot
ggstatsplot::grouped_ggwithinstats(
data = dplyr::filter(data_bugs, condition %in% c("LDLF", "LDHF")),
x = condition,
y = desire,
xlab = "Condition",
ylab = "Desire to kill an artrhopod",
grouping.var = region,
outlier.tagging = TRUE,
outlier.label = education,
ggtheme = hrbrthemes::theme_ipsum_tw(),
ggstatsplot.layer = FALSE,
messages = FALSE
)
```
### Summary of tests
Following (within-subjects) tests are carried out for each type of analyses-
Type | No. of groups | Test
----------- | --- | -------------------------
Parametric | > 2 | One-way repeated measures ANOVA
Non-parametric | > 2 | Friedman test
Robust | > 2 | Heteroscedastic one-way repeated measures ANOVA for trimmed means
Bayes Factor | > 2 | One-way repeated measures ANOVA
Parametric | 2 | Student's *t*-test
Non-parametric | 2 | Wilcoxon signed-rank test
Robust | 2 | Yuen's test on trimmed means for dependent samples
Bayes Factor | 2 | Student's *t*-test
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*-
Type | Test | *p*-value adjustment?
----------- | ---------------------------- | ---
Parametric | Student's *t*-test | <font color="green">Yes</font>
Non-parametric | Durbin-Conover test | <font color="green">Yes</font>
Robust | Yuen's trimmed means test | <font color="green">Yes</font>
Bayes Factor | <font color="red">No</font> | <font color="red">No</font>
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:
```{r ggscatterstats1, message = FALSE, warning = FALSE}
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-
```{r ggscatterstats2, message = FALSE, warning = FALSE}
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggscatterstats(
data = dplyr::filter(.data = ggstatsplot::movies_long, genre == "Action"),
x = budget,
y = rating,
type = "robust", # type of test that needs to be run
conf.level = 0.99, # confidence level
xlab = "Movie budget (in million/ US$)", # label for x axis
ylab = "IMDB rating", # label for y axis
label.var = "title", # variable for labeling data points
label.expression = "rating < 5 & budget > 100", # expression that decides which points to label
line.color = "yellow", # changing regression line color line
title = "Movie budget and IMDB rating (action)", # title text for the plot
caption = expression( # caption text for the plot
paste(italic("Note"), ": IMDB stands for Internet Movie DataBase")
),
ggtheme = hrbrthemes::theme_ipsum_ps(), # choosing a different theme
ggstatsplot.layer = FALSE, # turn off ggstatsplot theme layer
marginal.type = "density", # type of marginal distribution to be displayed
xfill = "#0072B2", # color fill for 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", # 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):
```{r ggscatterstats3, message = FALSE, warning = FALSE, fig.height = 12, fig.width = 14}
# for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_ggscatterstats(
data = dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
x = rating,
y = length,
label.var = title,
label.expression = length > 200,
conf.level = 0.99,
k = 3, # no. of decimal places in the results
xfill = "#E69F00",
yfill = "#8b3058",
xlab = "IMDB rating",
grouping.var = genre, # grouping variable
title.prefix = "Movie genre",
ggtheme = ggplot2::theme_grey(),
ggplot.component = list(
ggplot2::scale_x_continuous(breaks = seq(2, 9, 1), limits = (c(2, 9)))
),
messages = FALSE,
nrow = 2,
title.text = "Relationship between movie length by IMDB ratings for different genres"
)
```
### Summary of tests
Following tests are carried out for each type of analyses. Additionally, the
correlation coefficients (and their confidence intervals) are used as effect
sizes-
Type | Test | CI?
----------- | ------------------------- | ---
Parametric | Pearson's correlation coefficient | <font color="green">Yes<font>
Non-parametric | Spearman's rank correlation coefficient | <font color="green">Yes<font>
Robust | Percentage bend correlation coefficient | <font color="green">Yes<font>
Bayes Factor | Pearson's correlation coefficient | <font color="red">No<font>
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 $\chi^2$ test for
between-subjects design and McNemar's $\chi^2$ 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 $\chi^2$ goodness of
fit test) will be displayed as a subtitle.
Here is an example of a case where the theoretical question is about proportions
for different levels of a single nominal variable:
```{r ggpiestats1, message = FALSE, warning = FALSE}
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggpiestats(
data = ggplot2::msleep,
x = vore,
title = "Composition of vore types among mammals",
messages = FALSE
)
```
This function can also be used to study an interaction between two categorical
variables:
```{r ggpiestats2, message = FALSE, warning = FALSE}
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggpiestats(
data = mtcars,
x = am,
y = cyl,
conf.level = 0.99, # confidence interval for effect size measure
title = "Dataset: Motor Trend Car Road Tests", # title for the plot
stat.title = "interaction: ", # title for the results
legend.title = "Transmission", # title for the legend
factor.levels = c("1 = manual", "0 = automatic"), # renaming the factor level names (`x`)
facet.wrap.name = "No. of cylinders", # name for the facetting variable
slice.label = "counts", # show counts data instead of percentages
package = "ggsci", # package from which color palette is to be taken
palette = "default_jama", # choosing a different color palette
caption = substitute( # text for the caption
paste(italic("Source"), ": 1974 Motor Trend US magazine")
),
messages = FALSE # turn off messages and notes
)
```
In case of repeated measures designs, setting `paired = TRUE` will produce
results from McNemar's $\chi^2$ test-
```{r ggpiestats3, message = FALSE, warning = FALSE}
# for reproducibility
set.seed(123)
# data
survey.data <- data.frame(
`1st survey` = c("Approve", "Approve", "Disapprove", "Disapprove"),
`2nd survey` = c("Approve", "Disapprove", "Approve", "Disapprove"),
`Counts` = c(794, 150, 86, 570),
check.names = FALSE
)
# plot
ggstatsplot::ggpiestats(
data = survey.data,
x = `1st survey`,
y = `2nd survey`,
counts = Counts,
paired = TRUE, # within-subjects design
conf.level = 0.99, # confidence interval for effect size measure
package = "wesanderson",
palette = "Royal1"
)
```
Additionally, there is also a `grouped_` variant of this function that makes it
easy to repeat the same operation across a **single** grouping variable:
```{r ggpiestats4, message = FALSE, warning = FALSE, fig.height = 10, fig.width = 10}
# for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_ggpiestats(
dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
x = mpaa,
grouping.var = genre, # grouping variable
title.prefix = "Movie genre", # prefix for the facetted title
label.text.size = 3, # text size for slice labels
slice.label = "both", # show both counts and percentage data
perc.k = 1, # no. of decimal places for percentages
palette = "brightPastel",
package = "quickpalette",
messages = FALSE,
nrow = 2,
title.text = "Composition of MPAA ratings for different genres"
)
```
### Summary of tests
Following tests are carried out for each type of analyses-
Type of data | Design | Test
----------- | ------------ | -------------------------
Unpaired | $n \times p$ contingency table | Pearson's $\chi^{2}$ test
Paired | $n \times p$ contingency table | McNemar's $\chi^{2}$ test
Frequency | $n \times 1$ contingency table | Goodness of fit ($\chi^{2}$)
Following effect sizes (and confidence intervals/CI) are available for each type
of test-
Type | Effect size | CI?
--------------- | ------------------------- | ---
Pearson's chi-squared test | Cramér's *V* | <font color="green">Yes<font>
McNemar's test | Cohen's *g* | <font color="green">Yes<font>
Goodness of fit | Cramér's *V* | <font color="green">Yes<font>
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-
```{r ggbarstats1, message = FALSE, warning = FALSE, fig.height = 8, fig.width = 10}
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggbarstats(
data = ggstatsplot::movies_long,
x = mpaa,
y = genre,
sampling.plan = "jointMulti",
title = "MPAA Ratings by Genre",
xlab = "movie genre",
perc.k = 1,
x.axis.orientation = "slant",
ggtheme = hrbrthemes::theme_modern_rc(),
ggstatsplot.layer = FALSE,
ggplot.component = ggplot2::theme(axis.text.x = ggplot2::element_text(face = "italic")),
palette = "Set2",
messages = FALSE
)
```
Note that *p*-values for results from one-sample proportion tests are displayed
for each bar in the form of asterisks with the following convention: <br>
- $***$: $p < 0.001$
- $**$: $p < 0.01$
- $*$: $p < 0.05$
- $ns$: $p > 0.05$
And, needless to say, there is also a `grouped_` variant of this function-
```{r ggbarstats2, message = FALSE, warning = FALSE, fig.height = 12, fig.width = 10}
# setup
set.seed(123)
# smaller dataset
df <- dplyr::filter(
.data = forcats::gss_cat,
race %in% c("Black", "White"),
relig %in% c("Protestant", "Catholic", "None"),
!partyid %in% c("No answer", "Don't know", "Other party")
)
# plot
ggstatsplot::grouped_ggbarstats(
data = df,
x = relig,
y = partyid,
grouping.var = race,
title.prefix = "Race",
xlab = "Party affiliation",
ggtheme = ggthemes::theme_tufte(base_size = 12),
ggstatsplot.layer = FALSE,
messages = FALSE,
title.text = "Race, religion, and political affiliation",
nrow = 2
)
```
### 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.
```{r gghistostats1, message = FALSE, warning = FALSE}
ggstatsplot::gghistostats(
data = ToothGrowth, # dataframe from which variable is to be taken
x = len, # numeric variable whose distribution is of interest
xlab = "Tooth length", # `x`-axis label
title = "Distribution of Tooth Length", # title for the plot
fill.gradient = TRUE, # use color gradient
test.value = 10, # the comparison value for one-sample test
test.value.line = TRUE, # display a vertical line at test value
type = "bayes", # 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-
```{r gghistostats2, message = FALSE, warning = FALSE}
# for reproducibility
set.seed(123)
# plot
ggstatsplot::gghistostats(
data = iris, # dataframe from which variable is to be taken
x = Sepal.Length, # numeric variable whose distribution is of interest
title = "Distribution of Iris sepal length", # title for the plot
caption = substitute(paste(italic("Source:"), "Ronald Fisher's Iris data set")),
type = "parametric", # one sample t-test
conf.level = 0.99, # changing confidence level for effect size
bar.measure = "mix", # what does the bar length denote
test.value = 5, # default value is 0
test.value.line = TRUE, # display a vertical line at test value
test.value.color = "#0072B2", # color for the line for test value
centrality.para = "mean", # which measure of central tendency is to be plotted
centrality.color = "darkred", # decides color for central tendency line
binwidth = 0.10, # binwidth value (experiment)
bf.prior = 0.8, # prior width for computing bayes factor
messages = FALSE, # turn off the messages
ggtheme = hrbrthemes::theme_ipsum_tw(), # choosing a different theme
ggstatsplot.layer = FALSE # turn off ggstatsplot theme layer
)
```
As can be seen from the plot, bayes factor can be attached (`bf.message = TRUE`)
to assess evidence in favor of the null hypothesis.
Additionally, there is also a `grouped_` variant of this function that makes it
easy to repeat the same operation across a **single** grouping variable:
```{r gghistostats4, message = FALSE, warning = FALSE, fig.height = 10, fig.width = 10}
# for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_gghistostats(
data = dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
x = budget,
xlab = "Movies budget (in million US$)",
type = "robust", # use robust location measure
grouping.var = genre, # grouping variable
normal.curve = TRUE, # superimpose a normal distribution curve
normal.curve.color = "red",
title.prefix = "Movie genre",
ggtheme = ggthemes::theme_tufte(),
ggplot.component = list( # modify the defaults from `ggstatsplot` for each plot
ggplot2::scale_x_continuous(breaks = seq(0, 200, 50), limits = (c(0, 200)))
),
messages = FALSE,
nrow = 2,
title.text = "Movies budgets for different genres"
)
```
### Summary of tests
Following tests are carried out for each type of analyses-
Type | Test
----------- | -------------------------
Parametric | One-sample Student's *t*-test
Non-parametric | One-sample Wilcoxon test
Robust | One-sample percentile bootstrap
Bayes Factor | One-sample Student's *t*-test
Following effect sizes (and confidence intervals/CI) are available for each type
of test-
Type | Effect size | CI?
----------- | ------------------------- | ---
Parametric | Cohen's *d*, Hedge's *g* (central-and noncentral-*t* distribution based) | <font color="green">Yes<font>
Non-parametric | *r* (computed as $Z/\sqrt{N_{obs}}$) | <font color="green">Yes<font>
Robust | $M_{robust}$ (Robust location measure) | <font color="green">Yes<font>
Bayes Factor | <font color="red">No<font> | <font color="red">No<font>
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.
```{r ggdotplotstats1, message = FALSE, warning = FALSE, fig.height = 10, fig.width = 8}
# for reproducibility
set.seed(123)
# plot
ggdotplotstats(
data = dplyr::filter(.data = gapminder::gapminder, continent == "Asia"),
y = country,
x = lifeExp,
test.value = 55,
test.value.line = TRUE,
test.line.labeller = TRUE,
test.value.color = "red",
centrality.para = "median",
centrality.k = 0,
title = "Distribution of life expectancy in Asian continent",
xlab = "Life expectancy",
messages = FALSE,
caption = substitute(
paste(
italic("Source"),
": Gapminder dataset from https://www.gapminder.org/"
)
)
)
```
As with the rest of the functions in this package, there is also a `grouped_`
variant of this function to facilitate looping the same operation for all levels
of a single grouping variable.
```{r ggdotplotstats2, message = FALSE, warning = FALSE, fig.height = 6, fig.width = 10}
# for reproducibility
set.seed(123)
# removing factor level with very few no. of observations
df <- dplyr::filter(.data = ggplot2::mpg, cyl %in% c("4", "6"))
# plot
ggstatsplot::grouped_ggdotplotstats(
data = df,
x = cty,
y = manufacturer,
xlab = "city miles per gallon",
ylab = "car manufacturer",
type = "nonparametric", # non-parametric test
grouping.var = cyl, # grouping variable
test.value = 15.5,
title.prefix = "cylinder count",
point.color = "red",
point.size = 5,
point.shape = 13,
test.value.line = TRUE,
ggtheme = ggthemes::theme_par(),
messages = FALSE,
title.text = "Fuel economy data"
)
```
### 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.
```{r ggcorrmat1, message = FALSE, warning = FALSE}
# for reproducibility
set.seed(123)
# as a default this function outputs a correlalogram plot
ggstatsplot::ggcorrmat(
data = ggplot2::msleep,
corr.method = "robust", # correlation method