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https://github.com/IndrajeetPatil/ggstatsplot
Enhancing {ggplot2} plots with statistical analysis 📊📣
https://github.com/IndrajeetPatil/ggstatsplot
bayes-factors datascience dataviz effect-size ggplot-extension hypothesis-testing non-parametric-statistics r regression-models statistical-analysis
Last synced: 6 days ago
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Enhancing {ggplot2} plots with statistical analysis 📊📣
- Host: GitHub
- URL: https://github.com/IndrajeetPatil/ggstatsplot
- Owner: IndrajeetPatil
- License: gpl-3.0
- Created: 2018-01-08T19:16:42.000Z (almost 7 years ago)
- Default Branch: main
- Last Pushed: 2024-10-31T08:00:19.000Z (12 days ago)
- Last Synced: 2024-10-31T09:16:49.241Z (12 days ago)
- Topics: bayes-factors, datascience, dataviz, effect-size, ggplot-extension, hypothesis-testing, non-parametric-statistics, r, regression-models, statistical-analysis
- Language: R
- Homepage: https://indrajeetpatil.github.io/ggstatsplot/
- Size: 2.14 GB
- Stars: 2,028
- Watchers: 44
- Forks: 186
- Open Issues: 62
-
Metadata Files:
- Readme: README.Rmd
- Changelog: NEWS.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Code of conduct: .github/CODE_OF_CONDUCT.md
- Citation: CITATION.cff
- Codeowners: .github/CODEOWNERS.txt
- Support: .github/SUPPORT.md
- Codemeta: codemeta.json
Awesome Lists containing this project
README
---
output: github_document
---
```{r}
#| echo = FALSE,
#| warning = FALSE,
#| message = FALSE
## show me all columns
options(
tibble.width = Inf,
pillar.bold = TRUE,
pillar.neg = TRUE,
pillar.subtle_num = TRUE,
pillar.min_chars = Inf
)
knitr::opts_chunk$set(
collapse = TRUE,
dpi = 150, ## change to 300 once on CRAN
warning = FALSE,
message = FALSE,
out.width = "100%",
comment = "#>",
fig.path = "man/figures/README-"
)
library(ggstatsplot)
```
## `{ggstatsplot}`: `{ggplot2}` Based Plots with Statistical Details
Status | Usage | Miscellaneous
----------------- | ----------------- | -----------------
[](https://github.com/IndrajeetPatil/ggstatsplot) | [](https://CRAN.R-project.org/package=ggstatsplot) | [](https://app.codecov.io/gh/IndrajeetPatil/ggstatsplot)
[](https://lifecycle.r-lib.org/articles/stages.html) | [](https://CRAN.R-project.org/package=ggstatsplot) | [](https://doi.org/10.21105/joss.03167)
## 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 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.
## Installation
Type | Source | Command
---|---|---
Release | [](https://cran.r-project.org/package=ggstatsplot) | `install.packages("ggstatsplot")`
Development | [](https://www.repostatus.org/##active) | `pak::pak("IndrajeetPatil/ggstatsplot")`
## 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}
#| label = "citation",
#| comment = ""
citation("ggstatsplot")
```
## Acknowledgments
I would like to thank all the contributors to `{ggstatsplot}` who pointed out
bugs or requested features I hadn't considered. I would especially like to thank
other package developers (especially Daniel Lüdecke, Dominique Makowski, Mattan
S. Ben-Shachar, Brenton Wiernik, Patrick Mair, Salvatore Mangiafico, etc.) who
have patiently and diligently answered my relentless questions and supported
feature requests in their projects. I also want to thank Chuck Powell for his
initial contributions to the package.
The hexsticker was generously designed by Sarah Otterstetter (Max Planck
Institute for Human Development, Berlin). This package has also benefited from
the larger `#rstats` community on Twitter, LinkedIn, and `StackOverflow`.
Thanks are also due to my postdoc advisers (Mina Cikara and Fiery Cushman at
Harvard University; Iyad Rahwan at Max Planck Institute for Human Development)
who patiently supported me spending hundreds (?) of hours working on this
package rather than what I was paid to do. 😁
## Documentation and Examples
To see the detailed documentation for each function in the stable **CRAN**
version of the package, see:
- [Publication](https://joss.theoj.org/papers/10.21105/joss.03167)
- [Vignettes](https://indrajeetpatil.github.io/ggstatsplot/articles/)
- [Presentation](https://indrajeetpatil.github.io/ggstatsplot_slides/slides/ggstatsplot_presentation.html#1)
## Summary of available plots
| 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 |
| `ggscatterstats()` | **scatterplots** | for correlation between two variables |
| `ggcorrmat()` | **correlation matrices** | for correlations between multiple variables |
| `ggpiestats()` | **pie charts** | for categorical data |
| `ggbarstats()` | **bar charts** | for categorical data |
| `ggcoefstats()` | **dot-and-whisker plots** | for regression models and meta-analysis |
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
The table below summarizes all the different types of analyses currently
supported in this package-
| Functions | Description | Parametric | Non-parametric | Robust | Bayesian |
| :----------------------------------- | :------------------------------------------------ | :--------- | :------------- | :----- | :------- |
| `ggbetweenstats()` | Between group/condition comparisons | ✅ | ✅ | ✅ | ✅ |
| `ggwithinstats()` | Within group/condition comparisons | ✅ | ✅ | ✅ | ✅ |
| `gghistostats()`, `ggdotplotstats()` | Distribution of a numeric variable | ✅ | ✅ | ✅ | ✅ |
| `ggcorrmat` | Correlation matrix | ✅ | ✅ | ✅ | ✅ |
| `ggscatterstats()` | Correlation between two variables | ✅ | ✅ | ✅ | ✅ |
| `ggpiestats()`, `ggbarstats()` | Association between categorical variables | ✅ | ✅ | ❌ | ✅ |
| `ggpiestats()`, `ggbarstats()` | Equal proportions for categorical variable levels | ✅ | ✅ | ❌ | ✅ |
| `ggcoefstats()` | Regression model coefficients | ✅ | ✅ | ✅ | ✅ |
| `ggcoefstats()` | Random-effects meta-analysis | ✅ | ❌ | ✅ | ✅ |
Summary of Bayesian analysis
| Analysis | Hypothesis testing | Estimation |
| :------------------------------ | :----------------- | :--------- |
| (one/two-sample) *t*-test | ✅ | ✅ |
| one-way ANOVA | ✅ | ✅ |
| correlation | ✅ | ✅ |
| (one/two-way) contingency table | ✅ | ✅ |
| random-effects meta-analysis | ✅ | ✅ |
## Statistical reporting
For **all** statistical tests reported in the plots, the default template abides
by the 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
Statistical analysis is carried out by `{statsExpressions}` package, and thus
a summary table of all the statistical tests currently supported across
various functions can be found in article for that package:
## Primary functions
### `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}
#| label = "ggbetweenstats1"
set.seed(123)
ggbetweenstats(
data = iris,
x = Species,
y = Sepal.Length,
title = "Distribution of sepal length across Iris species"
)
```
**Defaults** return
✅ raw data + distributions
✅ descriptive statistics
✅ inferential statistics
✅ effect size + CIs
✅ pairwise comparisons
✅ Bayesian hypothesis-testing
✅ Bayesian estimation
A number of other arguments can be specified to make this plot even more
informative or change some of the default options. 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}
#| label = "ggbetweenstats2",
#| fig.height = 8,
#| fig.width = 12
set.seed(123)
grouped_ggbetweenstats(
data = dplyr::filter(movies_long, genre %in% c("Action", "Comedy")),
x = mpaa,
y = length,
grouping.var = genre,
ggsignif.args = list(textsize = 4, tip_length = 0.01),
p.adjust.method = "bonferroni",
palette = "default_jama",
package = "ggsci",
plotgrid.args = list(nrow = 1),
annotation.args = list(title = "Differences in movie length by mpaa ratings for different genres")
)
```
Details about underlying functions used to create graphics and statistical tests carried out can be found in the function documentation:
For more, also read the following vignette:
### `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}
#| label = "ggwithinstats1",
#| fig.width = 8,
#| fig.height = 6
set.seed(123)
library(WRS2) ## for data
library(afex) ## to run ANOVA
ggwithinstats(
data = WineTasting,
x = Wine,
y = Taste,
title = "Wine tasting"
)
```
**Defaults** return
✅ raw data + distributions
✅ descriptive statistics
✅ inferential statistics
✅ effect size + CIs
✅ pairwise comparisons
✅ Bayesian hypothesis-testing
✅ Bayesian estimation
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}
#| label = "ggwithinstats2",
#| fig.height = 6,
#| fig.width = 14
set.seed(123)
grouped_ggwithinstats(
data = dplyr::filter(bugs_long, region %in% c("Europe", "North America"), condition %in% c("LDLF", "LDHF")),
x = condition,
y = desire,
type = "np",
xlab = "Condition",
ylab = "Desire to kill an artrhopod",
grouping.var = region
)
```
Details about underlying functions used to create graphics and statistical tests carried out can be found in the function documentation:
For more, also read the following vignette:
### `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}
#| label = "gghistostats1",
#| fig.width = 8
set.seed(123)
gghistostats(
data = ggplot2::msleep,
x = awake,
title = "Amount of time spent awake",
test.value = 12,
binwidth = 1
)
```
**Defaults** return
✅ counts + proportion for bins
✅ descriptive statistics
✅ inferential statistics
✅ effect size + CIs
✅ Bayesian hypothesis-testing
✅ Bayesian estimation
There is also a `grouped_` variant of this function that makes it
easy to repeat the same operation across a **single** grouping variable:
```{r}
#| label = "gghistostats2",
#| fig.height = 6,
#| fig.width = 12
set.seed(123)
grouped_gghistostats(
data = dplyr::filter(movies_long, genre %in% c("Action", "Comedy")),
x = budget,
test.value = 50,
type = "nonparametric",
xlab = "Movies budget (in million US$)",
grouping.var = genre,
ggtheme = ggthemes::theme_tufte(),
## modify the defaults from `{ggstatsplot}` for each plot
plotgrid.args = list(nrow = 1),
annotation.args = list(title = "Movies budgets for different genres")
)
```
Details about underlying functions used to create graphics and statistical tests carried out can be found in the function documentation:
For more, also read the following vignette:
### `ggdotplotstats()`
This function is similar to `gghistostats()`, but is intended to be used when the
numeric variable also has a label.
```{r}
#| label = "ggdotplotstats1",
#| fig.height = 10,
#| fig.width = 8
set.seed(123)
ggdotplotstats(
data = dplyr::filter(gapminder::gapminder, continent == "Asia"),
y = country,
x = lifeExp,
test.value = 55,
type = "robust",
title = "Distribution of life expectancy in Asian continent",
xlab = "Life expectancy"
)
```
**Defaults** return
✅ descriptives (mean + sample size)
✅ inferential statistics
✅ effect size + CIs
✅ Bayesian hypothesis-testing
✅ Bayesian estimation
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}
#| label = "ggdotplotstats2",
#| fig.height = 6,
#| fig.width = 12
set.seed(123)
grouped_ggdotplotstats(
data = dplyr::filter(ggplot2::mpg, cyl %in% c("4", "6")),
x = cty,
y = manufacturer,
type = "bayes",
xlab = "city miles per gallon",
ylab = "car manufacturer",
grouping.var = cyl,
test.value = 15.5,
point.args = list(color = "red", size = 5, shape = 13),
annotation.args = list(title = "Fuel economy data")
)
```
Details about underlying functions used to create graphics and statistical tests carried out can be found in the function documentation:
For more, also read the following vignette:
### `ggscatterstats()`
This function creates a scatterplot with marginal distributions overlaid on the
axes and results from statistical tests in the subtitle:
```{r}
#| label = "ggscatterstats1",
#| fig.height = 6
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"
)
```
**Defaults** return
✅ raw data + distributions
✅ marginal distributions
✅ inferential statistics
✅ effect size + CIs
✅ Bayesian hypothesis-testing
✅ Bayesian estimation
There is also a `grouped_` variant of this function that makes it
easy to repeat the same operation across a **single** grouping variable.
```{r}
#| label = "ggscatterstats2",
#| fig.height = 8,
#| fig.width = 14
set.seed(123)
grouped_ggscatterstats(
data = dplyr::filter(movies_long, genre %in% c("Action", "Comedy")),
x = rating,
y = length,
grouping.var = genre,
label.var = title,
label.expression = length > 200,
xlab = "IMDB rating",
ggtheme = ggplot2::theme_grey(),
ggplot.component = list(ggplot2::scale_x_continuous(breaks = seq(2, 9, 1), limits = (c(2, 9)))),
plotgrid.args = list(nrow = 1),
annotation.args = list(title = "Relationship between movie length and IMDB ratings")
)
```
Details about underlying functions used to create graphics and statistical tests carried out can be found in the function documentation:
For more, also read the following vignette:
### `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}
#| label = "ggcorrmat1"
set.seed(123)
## as a default this function outputs a correlation matrix plot
ggcorrmat(
data = ggplot2::msleep,
colors = c("#B2182B", "white", "#4D4D4D"),
title = "Correlalogram for mammals sleep dataset",
subtitle = "sleep units: hours; weight units: kilograms"
)
```
**Defaults** return
✅ effect size + significance
✅ careful handling of `NA`s
If there are `NA`s present in the selected variables, the legend will display
minimum, median, and maximum number of pairs used for correlation tests.
There is also a `grouped_` variant of this function that makes it
easy to repeat the same operation across a **single** grouping variable:
```{r}
#| label = "ggcorrmat2",
#| fig.height = 6,
#| fig.width = 10
set.seed(123)
grouped_ggcorrmat(
data = dplyr::filter(movies_long, genre %in% c("Action", "Comedy")),
type = "robust",
colors = c("#cbac43", "white", "#550000"),
grouping.var = genre,
matrix.type = "lower"
)
```
Details about underlying functions used to create graphics and statistical tests carried out can be found in the function documentation:
For more, also read the following vignette:
### `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:
```{r}
#| label = "ggpiestats1",
#| fig.height = 4,
#| fig.width = 8
set.seed(123)
ggpiestats(
data = mtcars,
x = am,
y = cyl,
package = "wesanderson",
palette = "Royal1",
title = "Dataset: Motor Trend Car Road Tests",
legend.title = "Transmission"
)
```
**Defaults** return
✅ descriptives (frequency + %s)
✅ inferential statistics
✅ effect size + CIs
✅ Goodness-of-fit tests
✅ Bayesian hypothesis-testing
✅ Bayesian estimation
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:
```{r}
#| label = "ggpiestats2",
#| fig.height = 6,
#| fig.width = 10
set.seed(123)
grouped_ggpiestats(
data = mtcars,
x = cyl,
grouping.var = am,
label.repel = TRUE,
package = "ggsci",
palette = "default_ucscgb"
)
```
Details about underlying functions used to create graphics and statistical tests carried out can be found in the function documentation:
For more, also read the following vignette:
### `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.
N.B. The *p*-values from one-sample proportion test are displayed on top of each
bar.
```{r}
#| label = "ggbarstats1",
#| fig.height = 8,
#| fig.width = 10
set.seed(123)
library(ggplot2)
ggbarstats(
data = movies_long,
x = mpaa,
y = genre,
title = "MPAA Ratings by Genre",
xlab = "movie genre",
legend.title = "MPAA rating",
ggplot.component = list(ggplot2::scale_x_discrete(guide = ggplot2::guide_axis(n.dodge = 2))),
palette = "Set2"
)
```
**Defaults** return
✅ descriptives (frequency + %s)
✅ inferential statistics
✅ effect size + CIs
✅ Goodness-of-fit tests
✅ Bayesian hypothesis-testing
✅ Bayesian estimation
And, needless to say, there is also a `grouped_` variant of this function-
```{r}
#| label = "ggbarstats2",
#| fig.height = 6,
#| fig.width = 12
## setup
set.seed(123)
grouped_ggbarstats(
data = mtcars,
x = am,
y = cyl,
grouping.var = vs,
package = "wesanderson",
palette = "Darjeeling2" # ,
# ggtheme = ggthemes::theme_tufte(base_size = 12)
)
```
Details about underlying functions used to create graphics and statistical tests carried out can be found in the function documentation:
For more, also read the following vignette:
### `ggcoefstats()`
The function `ggcoefstats()` generates **dot-and-whisker plots** for
regression models saved in a tidy data frame. The tidy data frames are prepared
using `parameters::model_parameters()`. Additionally, if available, the model
summary indices are also extracted from `performance::model_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`-, $\chi^{2}$-, 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 label attached to dot will provide more details from the statistical
test carried out and it will typically contain estimate, statistic, and
*p*-value.e
- The caption will 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) with `{ggplot2}` functions.
```{r}
#| label = "ggcoefstats1",
#| fig.height = 5,
#| fig.width = 6
set.seed(123)
## model
mod <- stats::lm(formula = mpg ~ am * cyl, data = mtcars)
ggcoefstats(mod)
```
**Defaults** return
✅ inferential statistics
✅ estimate + CIs
✅ model summary (AIC and BIC)
Details about underlying functions used to create graphics and statistical tests carried out can be found in the function documentation:
For more, also read the following vignette:
### Extracting expressions and data frames with statistical details
`{ggstatsplot}` also offers a convenience function to extract data frames with
statistical details that are used to create expressions displayed in
`{ggstatsplot}` plots.
```{r}
#| label = "extract_stats"
set.seed(123)
p <- ggbetweenstats(mtcars, cyl, mpg)
# extracting expression present in the subtitle
extract_subtitle(p)
# extracting expression present in the caption
extract_caption(p)
# a list of tibbles containing statistical analysis summaries
extract_stats(p)
```
Note that all of this analysis is carried out by `{statsExpressions}`
package:
### Using `{ggstatsplot}` statistical details with custom plots
Sometimes you may not like the default plots produced by `{ggstatsplot}`. In such
cases, you can use other **custom** plots (from `{ggplot2}` or other plotting
packages) and still use `{ggstatsplot}` functions to display results from relevant
statistical test.
For example, in the following chunk, we will create our own plot using
`{ggplot2}` package, and use `{ggstatsplot}` function for extracting expression:
```{r}
#| label = "customplot",
#| fig.height = 5,
#| fig.width = 6
## loading the needed libraries
set.seed(123)
library(ggplot2)
## using `{ggstatsplot}` to get expression with statistical results
stats_results <- ggbetweenstats(morley, Expt, Speed) %>% extract_subtitle()
## creating a custom plot of our choosing
ggplot(morley, aes(x = as.factor(Expt), y = Speed)) +
geom_boxplot() +
labs(
title = "Michelson-Morley experiments",
subtitle = stats_results,
x = "Speed of light",
y = "Experiment number"
)
```
## Summary of benefits of using `{ggstatsplot}`
- No need to use scores of packages for statistical analysis
(e.g., one to get stats, one to get effect sizes, another to get Bayes
Factors, and yet another to get pairwise comparisons, etc.).
- Minimal amount of code needed for all functions (typically only `data`, `x`,
and `y`), which minimizes chances of error and makes for tidy scripts.
- Conveniently toggle between statistical approaches.
- Truly makes your figures worth a thousand words.
- No need to copy-paste results to the text editor (MS-Word, e.g.).
- Disembodied figures stand on their own and are easy to evaluate for the reader.
- More breathing room for theoretical discussion and other text.
- No need to worry about updating figures and statistical details separately.
## Misconceptions about `{ggstatsplot}`
This package is...
❌ an alternative to learning `{ggplot2}`
✅ (The better you know `{ggplot2}`, the more you can modify the defaults to your
liking.)
❌ meant to be used in talks/presentations
✅ (Default plots can be too complicated for effectively communicating results in
time-constrained presentation settings, e.g. conference talks.)
❌ the only game in town
✅ (GUI software alternatives: [JASP](https://jasp-stats.org/) and [jamovi](https://www.jamovi.org/)).
## Extensions
In case you use the GUI software [`jamovi`](https://www.jamovi.org/), you can
install a module called [`jjstatsplot`](https://github.com/sbalci/jjstatsplot),
which is a wrapper around `{ggstatsplot}`.
## 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.
Here are some simple ways in which you can contribute (in the increasing order
of commitment):
- Read and correct any inconsistencies in the
[documentation](https://indrajeetpatil.github.io/ggstatsplot/)
- Raise issues about bugs or wanted features
- Review code
- Add new functionality (in the form of new plotting functions or helpers for
preparing subtitles)
Please note that this project is released with a
[Contributor Code of Conduct](https://indrajeetpatil.github.io/ggstatsplot/CODE_OF_CONDUCT.html). By participating in this project you agree to abide by its terms.