https://github.com/danheck/ggstatsplot
Collection of functions to enhance ggplot2 plots with results from statistical tests.
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Collection of functions to enhance ggplot2 plots with results from statistical tests.
Basic Info
- Host: GitHub
- Owner: danheck
- License: gpl-3.0
- Language: R
- Default Branch: master
- Homepage: https://indrajeetpatil.github.io/ggstatsplot/
- Size: 467 MB
Statistics
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- Watchers: 0
- Forks: 0
- Open Issues: 0
- Releases: 0
Fork of IndrajeetPatil/ggstatsplot
Created almost 7 years ago
· Last pushed almost 7 years ago
https://github.com/danheck/ggstatsplot/blob/master/
# `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) | [](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) | [](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) | [](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) | [](https://dependencyci.com/github/IndrajeetPatil/ggstatsplot) | | | |
# Overview
[`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 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.
It, therefore, produces a limited kinds of plots for the supported
analyses:
- **violin plots** (for comparisons *between* groups or conditions),
- **pie charts** and **bar charts** (for categorical data),
- **scatterplots** (for correlations between two variables),
- **correlation matrices** (for correlations between multiple
variables),
- **histograms** and **dot plots/charts** (for hypothesis about
distributions),
- **dot-and-whisker plots** (for regression models).
In addition to these basic plots, `ggstatsplot` also provides
**`grouped_`** versions for most functions that makes it easy to repeat
the same analysis for any grouping variable.
Future versions will include other types of statistical analyses and
plots as well.
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 | Yes | Yes | Yes | Yes |
| `ggwithinstats` | Within group/condition comparisons | Yes | Yes | Yes | Yes |
| `gghistostats`, `ggdotplotstats` | Distribution of a numeric variable | Yes | Yes | Yes | Yes |
| `ggcorrmat` | Correlation matrix | Yes | Yes | Yes | No |
| `ggscatterstats` | Correlation between two variables | Yes | Yes | Yes | Yes |
| `ggpiestats`, `ggbarstats` | Association between categorical variables | Yes | `NA` | `NA` | Yes |
| `ggpiestats`, `ggbarstats` | Equal proportions for categorical variable levels | Yes | `NA` | `NA` | Yes |
| `ggcoefstats` | Regression model coefficients | Yes | No | Yes | No |
# 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 Yuens 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:
| Functions | Type | Test | Effect size | 95% CI available? |
| ------------------------------- | -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- |
| `ggbetweenstats` | Parametric | Students and Welchs *t*-test | Cohens *d*, Hedges *g* |  |
| `ggbetweenstats` | Parametric | Fishers and Welchs one-way ANOVA |  |  |
| `ggbetweenstats` | Non-parametric | Mann-Whitney *U*-test | *r* |  |
| `ggbetweenstats` | Non-parametric | Kruskal-Wallis Rank Sum Test |  |  |
| `ggbetweenstats` | Robust | Yuens test for trimmed means |  |  |
| `ggbetweenstats` | Robust | Heteroscedastic one-way ANOVA for trimmed means |  |  |
| `ggwithinstats` | Parametric | Students *t*-test | Cohens *d*, Hedges *g* |  |
| `ggwithinstats` | Parametric | Fishers one-way repeated measures ANOVA |  |  |
| `ggwithinstats` | Non-parametric | Wilcoxon signed-rank test | *r* |  |
| `ggwithinstats` | Non-parametric | Friedman test |  |  |
| `ggwithinstats` | Robust | Yuens test on trimmed means for dependent samples |  |  |
| `ggwithinstats` | Robust | Heteroscedastic one-way repeated measures ANOVA for trimmed means |  |  |
| `ggpiestats` | Parametric |  | Cramrs *V* |  |
| `ggpiestats` | Parametric | McNemars test | Cohens *g* |  |
| `ggpiestats` | Parametric | One-sample proportion test | Cramrs *V* |  |
| `ggscatterstats`/`ggcorrmat` | Parametric | Pearsons *r* | *r* |  |
| `ggscatterstats`/`ggcorrmat` | Non-parametric |  |  |  |
| `ggscatterstats`/`ggcorrmat` | Robust | Percentage bend correlation | *r* |  |
| `gghistostats`/`ggdotplotstats` | Parametric | One-sample *t*-test | Cohens *d*, Hedges *g* |  |
| `gghistostats` | Non-parametric | One-sample Wilcoxon signed rank test | *r* |  |
| `gghistostats`/`ggdotplotstats` | Robust | One-sample percentile bootstrap | robust estimator |  |
| `gghistostats`/`ggdotplotstats` | Parametric | Regression models |  |  |
Work is in progress to add some of the currently missing functionality.
# Installation
To get the latest, stable `CRAN` release (`0.0.12`):
``` r
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
(`0.0.12.9000`). 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:
If you are in hurry and want to reduce the time of installation, prefer-
``` r
# 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
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
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:
``` r
citation("ggstatsplot")
```
There is currently a publication in preparation corresponding to this
package and the citation will be updated once its published.
# Documentation and Examples
To see the detailed documentation for each function in the stable
**CRAN** version of the package, see:
- README:
- Presentation:
- Vignettes:
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:
#>
```
If you are not familiar either with what the namespace `::` does or how
to use pipe operator `%>%`, something this package and its documentation
relies a lot on, you can check out these links-
-
-
# Usage and syntax
`ggstatsplot` relies on non-standard evaluation (NSE), i.e., rather than
looking at the values of arguments (`x`, `y`), it instead looks at their
expressions. This means that you **shouldnt** enter arguments with the
`$` operator and set `data = NULL` (e.g., `data = NULL, x = data$x, y =
data$y`). You **must** always specify the `data` argument for all
functions. On the plus side, you can enter arguments either as a string
(`x = "x", y = "y"`) or as a bare expression (`x = x, y = y`) and it
wouldnt matter. To read more about NSE, see-
`ggstatsplot` is a very chatty package and will by default print helpful
notes on assumptions about statistical tests, warnings, etc. If you
dont want your console to be cluttered with such messages, they can
be turned off by setting argument `messages = FALSE` in the function
call.
Most functions share a `type` (of test) argument that is helpful to
specify the type of statistical analysis:
- `"p"` (for **parametric**)
- `"np"` (for **non-parametric**)
- `"r"` (for **robust**)
- `"bf"` (for **Bayes Factor**)
All relevant functions in `ggstatsplot` have a `return` argument which
can be used to not only return plots (which is the default), but also to
return a `subtitle` or `caption`, which are objects of type `call` and
can be used to display statistical details in conjunction with a custom
plot and at a custom location in the plot.
Additionally, all functions share the `ggtheme` and `palette` arguments
that can be used to specify your favorite `ggplot` theme and color
palette.
# 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:
## `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
# 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 a `ggplot2` object and thus any of the
graphics layers can be further modified.
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
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 <-
base::factor(
x = iris2$Species,
levels = c("virginica", "versicolor")
)
# plot
ggstatsplot::ggbetweenstats(
data = iris2,
x = Species,
y = Sepal.Length,
notch = TRUE, # show notched box plot
mean.plotting = TRUE, # whether mean for each group is to be displayed
mean.ci = TRUE, # whether to display confidence interval for means
mean.label.size = 2.5, # size of the label for mean
type = "p", # which type of test is to be run
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, Students *t*-test). If the null hypothesis
cant 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.,
).
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
() versus null
() hypotheses (since ).
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
# 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 | Students or Welchs one-way ANOVA |
| Non-parametric | \> 2 | KruskalWallis one-way ANOVA |
| Robust | \> 2 | Heteroscedastic one-way ANOVA for trimmed means |
| Bayes Factor | \> 2 | Students ANOVA |
| Parametric | 2 | Students or Welchs *t*-test |
| Non-parametric | 2 | MannWhitney *U* test |
| Robust | 2 | Yuens test for trimmed means |
| Bayes Factor | 2 | Students *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 | Yes |
| Parametric | Yes | Students *t*-test | Yes |
| Non-parametric | No | Dwass-Steel-Crichtlow-Fligner test | Yes |
| Robust | No | Yuens trimmed means test | Yes |
| Bayes Factor | No | No | No |
| Bayes Factor | Yes | No | No |
For more, see the `ggbetweenstats` 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
# 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
# common setup
set.seed(123)
library(jmv)
data("bugs", package = "jmv")
# getting data in tidy format
data_bugs <- bugs %>%
tibble::as_tibble(x = .) %>%
tidyr::gather(data = ., key, value, LDLF:HDHF) %>%
dplyr::filter(.data = ., Region %in% c("Europe", "North America"))
# plot
ggstatsplot::grouped_ggwithinstats(
data = dplyr::filter(data_bugs, key %in% c("LDLF", "LDHF")),
x = key,
y = value,
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 | Students *t*-test |
| Non-parametric | 2 | Wilcoxon signed-rank test |
| Robust | 2 | Yuens test on trimmed means for dependent samples |
| Bayes Factor | 2 | Students *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 | Students *t*-test | Yes |
| Non-parametric | Durbin-Conover test | Yes |
| Robust | Yuens trimmed means test | Yes |
| Bayes Factor | No | No |
For more, see the `ggwithinstats` vignette:
## `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
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
# 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
# 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,
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"
)
```
**Using `ggscatterstats()` in R Notebooks or R Markdown**
If you include a `ggscatterstats()` plot inside an `R Notebook` or `R
Markdown` code chunk, you will notice that running the chunk doesnt
return any output nor does it give any error. In order to get a
`ggscatterstats()` to show up in these contexts, you need to save the
`ggscatterstats` plot as a variable in one code chunk, and explicitly
print it using the `grid` package in another chunk, like this:
``` r
# include the following code in your code chunk inside R Notebook or Markdown
grid::grid.newpage()
grid::grid.draw(
ggstatsplot::ggscatterstats(
data = ggstatsplot::movies_wide,
x = budget,
y = rating,
marginal = TRUE,
messages = FALSE
)
)
```
Another option - or rather a compromise - is not to include marginal
distribution at all by setting `marginal = FALSE`.
### 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 | Pearsons correlation coefficient | Yes |
| Non-parametric | Spearmans rank correlation coefficient | Yes |
| Robust | Percentage bend correlation coefficient | Yes |
| Bayes Factor | Pearsons correlation coefficient | No |
For more, see the `ggscatterstats` vignette:
## `ggpiestats`
This function creates a pie chart for categorical or nominal variables
with results from contingency table analysis (Pearsons
test for between-subjects design and McNemars
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
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
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggpiestats(
data = ggplot2::msleep,
main = vore,
title = "Composition of vore types among mammals",
messages = FALSE
)
```
This function can also be used to study an interaction between two
categorical variables:
``` r
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggpiestats(
data = mtcars,
main = am,
condition = 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 (`main`)
facet.wrap.name = "No. of cylinders", # name for the facetting variable
slice.label = "counts", # show counts data instead of percentages
package = "ggsci", # package from which color palette is to be taken
palette = "default_jama", # choosing a different color palette
caption = 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 McNemars
test-
``` r
# for reproducibility
set.seed(123)
# data
survey.data <- data.frame(
`1st survey` = c("Approve", "Approve", "Disapprove", "Disapprove"),
`2nd survey` = c("Approve", "Disapprove", "Approve", "Disapprove"),
`Counts` = c(794, 150, 86, 570),
check.names = FALSE
)
# plot
ggstatsplot::ggpiestats(
data = survey.data,
main = `1st survey`,
condition = `2nd survey`,
counts = Counts,
paired = TRUE, # within-subjects design
conf.level = 0.99, # confidence interval for effect size measure
stat.title = "McNemar's Test: ",
package = "wesanderson",
palette = "Royal1"
)
#> Note: 99% CI for effect size estimate was computed with 100 bootstrap samples.
#> Note: Results from one-sample proportion tests for each level of the variable
#> 2nd survey testing for equal proportions of the variable 1st survey.
#> # A tibble: 2 x 8
#> condition N Approve Disapprove `Chi-squared` df `p-value`
#>
#> 1 Approve (n =~ 90.23% 9.77% 570. 1 0
#> 2 Disappro~ (n =~ 20.83% 79.17% 245 1 0
#> # ... with 1 more variable: significance
```
Note that when a two-way table is present (i.e., when both `main` and
`condition` arguments are specified), *p*-values for results from
one-sample proportion tests are displayed in each facet in the form of
asterisks with the following convention:
- : 
- : 
- : 
- : 
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
# for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_ggpiestats(
dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
main = 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 |  contingency table | Pearsons  test |
| Paired |  contingency table | McNemars  test |
| Frequency |  contingency table | Goodness of fit () |
Following effect sizes (and confidence intervals/CI) are available for
each type of test-
| Type | Effect size | CI? |
| -------------------------- | ------------ | ----------------------------- |
| Pearsons chi-squared test | Cramers *V* | Yes |
| McNemars test | *g* | Yes |
| Goodness of fit | *V* | Yes |
For more, see the `ggpiestats` 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-
``` r
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggbarstats(
data = ggstatsplot::movies_long,
main = mpaa,
condition = 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
)
```
And, needless to say, there is also a `grouped_` variant of this
function-
``` r
# setup
library(ggstatsplot)
set.seed(123)
# let's create a smaller dataframe
diamonds_short <- ggplot2::diamonds %>%
dplyr::filter(.data = ., cut %in% c("Very Good", "Ideal")) %>%
dplyr::filter(.data = ., clarity %in% c("SI1", "SI2", "VS1", "VS2", "VVS1")) %>%
dplyr::sample_frac(tbl = ., size = 0.05)
# plot
ggstatsplot::grouped_ggbarstats(
data = diamonds_short,
main = color,
condition = clarity,
grouping.var = cut,
sampling.plan = "poisson",
title.prefix = "Quality",
data.label = "both",
label.text.size = 3,
perc.k = 1,
package = "palettetown",
palette = "charizard",
ggtheme = ggthemes::theme_tufte(base_size = 12),
ggstatsplot.layer = FALSE,
messages = FALSE,
title.text = "Diamond quality and color combination",
nrow = 2
)
```
### Summary of tests
This is identical to the `ggpiestats` function summary of tests.
## `gghistostats`
In case you would like to see the distribution of a single variable and
check if it is significantly different from a specified value with a one
sample test, this function will let you do that.
``` r
ggstatsplot::gghistostats(
data = ToothGrowth, # dataframe from which variable is to be taken
x = len, # numeric variable whose distribution is of interest
title = "Distribution of Sepal.Length", # title for the plot
fill.gradient = TRUE, # use color gradient
test.value = 10, # the comparison value for t-test
test.value.line = TRUE, # display a vertical line at test value
type = "bf", # bayes factor for one sample t-test
bf.prior = 0.8, # prior width for calculating the bayes factor
messages = FALSE # turn off the messages
)
```
The aesthetic defaults can be easily modified-
``` r
# 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
# 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 Students *t*-test |
| Non-parametric | One-sample Wilcoxon test |
| Robust | One-sample percentile bootstrap |
| Bayes Factor | One-sample Students *t*-test |
For more, including information about the variant of this function
`grouped_gghistostats`, see the `gghistostats` vignette:
## `ggdotplotstats`
This function is similar to `gghistostats`, but is intended to be used
when the numeric variable also has a label.
``` r
# 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
# 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 = "np", # 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, lets change some of the defaults. For
example, multiple aesthetics-related arguments can be modified to change
the appearance of the correlation matrix.
``` r
# for reproducibility
set.seed(123)
# as a default this function outputs a correlalogram plot
ggstatsplot::ggcorrmat(
data = ggplot2::msleep,
corr.method = "robust", # correlation method
sig.level = 0.001, # threshold of significance
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"
)
```
Note that 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.
Alternatively, you can use it just to get the correlation matrices and
their corresponding *p*-values (in a `tibble` format).
``` r
# for reproducibility
set.seed(123)
# show four digits in a tibble
options(pillar.sigfig = 4)
# getting the correlation coefficient matrix
ggstatsplot::ggcorrmat(
data = iris, # all numeric variables from data will be used
corr.method = "robust",
output = "correlations", # specifying the needed output ("r" or "corr" will also work)
digits = 3 # number of digits to be dispayed for correlation coefficient
)
#> # A tibble: 4 x 5
#> variable Sepal.Length Sepal.Width Petal.Length Petal.Width
#>
#> 1 Sepal.Length 1 -0.143 0.878 0.837
#> 2 Sepal.Width -0.143 1 -0.426 -0.373
#> 3 Petal.Length 0.878 -0.426 1 0.966
#> 4 Petal.Width 0.837 -0.373 0.966 1
# getting the p-value matrix
ggstatsplot::ggcorrmat(
data = ggplot2::msleep,
cor.vars = sleep_total:bodywt,
corr.method = "robust",
output = "p.values", # only "p" or "p-values" will also work
p.adjust.method = "holm"
)
#> # A tibble: 6 x 7
#> variable sleep_total sleep_rem sleep_cycle awake brainwt bodywt
#>
#> 1 sleep_to~ 0. 5.291e-12 9.138e- 3 0. 3.170e- 5 2.568e- 6
#> 2 sleep_rem 4.070e-13 0. 1.978e- 2 5.291e-12 9.698e- 3 3.762e- 3
#> 3 sleep_cy~ 2.285e- 3 1.978e- 2 0. 9.138e- 3 1.637e- 9 1.696e- 5
#> 4 awake 0. 4.070e-13 2.285e- 3 0. 3.170e- 5 2.568e- 6
#> 5 brainwt 4.528e- 6 4.849e- 3 1.488e-10 4.528e- 6 0. 4.509e-17
#> 6 bodywt 2.568e- 7 7.524e- 4 2.120e- 6 2.568e- 7 3.221e-18 0.
# getting the confidence intervals for correlations
ggstatsplot::ggcorrmat(
data = ggplot2::msleep,
cor.vars = sleep_total:bodywt,
corr.method = "kendall",
output = "ci",
p.adjust.method = "holm"
)
#> Note: In the correlation matrix,
#> the upper triangle: p-values adjusted for multiple comparisons
#> the lower triangle: unadjusted p-values.
#> # A tibble: 15 x 7
#> pair r lower upper p lower.adj upper.adj
#>
#> 1 sleep_total-s~ 0.5922 4.000e-1 7.345e-1 4.981e- 7 0.3027 0.7817
#> 2 sleep_total-s~ -0.3481 -6.214e-1 6.818e-4 5.090e- 2 -0.6789 0.1002
#> 3 sleep_total-a~ -1 -1.000e+0 -1.000e+0 0. -1 -1
#> 4 sleep_total-b~ -0.4293 -6.220e-1 -1.875e-1 9.621e- 4 -0.6858 -0.07796
#> 5 sleep_total-b~ -0.3851 -5.547e-1 -1.847e-1 3.247e- 4 -0.6050 -0.1106
#> 6 sleep_rem-sle~ -0.2066 -5.180e-1 1.531e-1 2.566e- 1 -0.5180 0.1531
#> 7 sleep_rem-awa~ -0.5922 -7.345e-1 -4.000e-1 4.981e- 7 -0.7832 -0.2990
#> 8 sleep_rem-bra~ -0.2636 -5.096e-1 2.217e-2 7.022e- 2 -0.5400 0.06404
#> 9 sleep_rem-bod~ -0.3163 -5.262e-1 -7.004e-2 1.302e- 2 -0.5662 -0.01317
#> 10 sleep_cycle-a~ 0.3481 -6.818e-4 6.214e-1 5.090e- 2 -0.1145 0.6867
#> 11 sleep_cycle-b~ 0.7125 4.739e-1 8.536e-1 1.001e- 5 0.3239 0.8954
#> 12 sleep_cycle-b~ 0.6545 3.962e-1 8.168e-1 4.834e- 5 0.2459 0.8656
#> 13 awake-brainwt 0.4293 1.875e-1 6.220e-1 9.621e- 4 0.08322 0.6829
#> 14 awake-bodywt 0.3851 1.847e-1 5.547e-1 3.247e- 4 0.1049 0.6087
#> 15 brainwt-bodywt 0.8378 7.373e-1 9.020e-1 8.181e-16 0.6716 0.9238
# getting the sample sizes for all pairs
ggstatsplot::ggcorrmat(
data = ggplot2::msleep,
cor.vars = sleep_total:bodywt,
corr.method = "robust",
output = "n" # note that n is different due to NAs
)
#> # A tibble: 6 x 7
#> variable sleep_total sleep_rem sleep_cycle awake brainwt bodywt
#>
#> 1 sleep_total 83 61 32 83 56 83
#> 2 sleep_rem 61 61 32 61 48 61
#> 3 sleep_cycle 32 32 32 32 30 32
#> 4 awake 83 61 32 83 56 83
#> 5 brainwt 56 48 30 56 56 56
#> 6 bodywt 83 61 32 83 56 83
```
Note that if `cor.vars` are not specified, all numeric variables will be
used.
There is a `grouped_` variant of this function that makes it easy to
repeat the same operation across a **single** grouping variable:
``` r
# for reproducibility
set.seed(123)
# plot
# let's use only 50% of the data to speed up the process
ggstatsplot::grouped_ggcorrmat(
data = dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
cor.vars = length:votes,
corr.method = "np",
colors = c("#cbac43", "white", "#550000"),
grouping.var = genre, # grouping variable
digits = 3, # number of digits after decimal point
title.prefix = "Movie genre",
messages = FALSE,
nrow = 2
)
```
### 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 | Pearsons correlation coefficient | Yes |
| Non-parametric | Spearmans rank correlation coefficient | Yes |
| Robust | Percentage bend correlation coefficient | No |
| Bayes Factor | Pearsons correlation coefficient | No |
For examples and more information, see the `ggcorrmat` vignette:
## `ggcoefstats`
`ggcoefstats` creates a dot-and-whisker plot point estimates for
regression coefficients as dots with confidence intervals as whiskers.
``` r
# for reproducibility
set.seed(123)
# model
mod <- stats::lm(
formula = mpg ~ am * cyl,
data = mtcars
)
# plot
ggstatsplot::ggcoefstats(x = mod)
```
This default plot can be further modified to ones liking with
additional arguments (also, lets use a robust linear model instead of a
simple linear model now):
``` r
# for reproducibility
set.seed(123)
# model
mod <- MASS::rlm(
formula = mpg ~ am * cyl,
data = mtcars
)
# plot
ggstatsplot::ggcoefstats(
x = mod,
point.color = "red",
point.shape = 15,
vline.color = "#CC79A7",
vline.linetype = "dotdash",
stats.label.size = 3.5,
stats.label.color = c("#0072B2", "#D55E00", "darkgreen"),
title = "Car performance predicted by transmission & cylinder count",
subtitle = "Source: 1974 Motor Trend US magazine",
ggtheme = hrbrthemes::theme_ipsum_ps(),
ggstatsplot.layer = FALSE
) + # further modification with the ggplot2 commands
# note the order in which the labels are entered
ggplot2::scale_y_discrete(labels = c("transmission", "cylinders", "interaction")) +
ggplot2::labs(
x = "regression coefficient",
y = NULL
)
```
Most of the regression models that are supported in the `broom` and
`broom.mixed` packages with `tidy` and `glance` methods are also
supported by `ggcoefstats`. For example-
`aareg`, `anova`, `aov`, `aovlist`, `Arima`, `bigglm`, `biglm`,
`brmsfit`, `btergm`, `cch`, `clm`, `clmm`, `confusionMatrix`, `coxph`,
`drc`, `emmGrid`, `epi.2by2`, `ergm`, `felm`, `fitdistr`, `glmerMod`,
`glmmTMB`, `gls`, `gam`, `Gam`, `gamlss`, `garch`, `glm`, `glmmadmb`,
`glmmPQL`, `glmmTMB`, `glmRob`, `glmrob`, `gmm`, `ivreg`, `lm`,
`lm.beta`, `lmerMod`, `lmodel2`, `lmRob`, `lmrob`, `mcmc`, `MCMCglmm`,
`mediate`, `mjoint`, `mle2`, `mlm`, `multinom`, `negbin`, `nlmerMod`,
`nlrq`, `nls`, `orcutt`, `plm`, `polr`, `ridgelm`, `rjags`, `rlm`,
`rlmerMod`, `rq`, `speedglm`, `speedlm`, `stanreg`, `survreg`, `svyglm`,
`svyolr`, `svyglm`, 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-
## `combine_plots`
The full power of `ggstatsplot` can be leveraged with a functional
programming package like [`purrr`](http://purrr.tidyverse.org/) that
replaces `for` loops with code that is both more succinct and easier to
read and, therefore, `purrr` should be preferrred . (Another old school
option to do this effectively is using the `plyr` package.)
In such cases, `ggstatsplot` contains a helper function `combine_plots`
to combine multiple plots, which can be useful for combining a list of
plots produced with `purrr`. This is a wrapper around
`cowplot::plot_grid` and lets you combine multiple plots and add a
combination of title, caption, and annotation texts with suitable
defaults.
For examples (both with `plyr` and `purrr`), see the associated
vignette-
## `theme_ggstatsplot`
All plots from `ggstatsplot` have a default theme: `theme_ggstatsplot`.
You can change this theme by using the `ggtheme` argument. It is
important to note that irrespective of which `ggplot` theme you choose,
`ggstatsplot` in the backdrop adds a new layer with its idiosyncratic
theme settings, chosen to make the graphs more readable or aesthetically
pleasing.
Lets see an example with `gghistostats` and see how a certain theme
from `hrbrthemes` package looks like with and without the `ggstatsplot`
layer.
``` r
# to use hrbrthemes themes, first make sure you have all the necessary fonts
library(hrbrthemes)
# extrafont::ttf_import()
# extrafont::font_import()
# try this yourself
ggstatsplot::combine_plots(
# with the ggstatsplot layer
ggstatsplot::gghistostats(
data = iris,
x = Sepal.Width,
messages = FALSE,
title = "Distribution of Sepal Width",
test.value = 5,
ggtheme = hrbrthemes::theme_ipsum(),
ggstatsplot.layer = TRUE
),
# without the ggstatsplot layer
ggstatsplot::gghistostats(
data = iris,
x = Sepal.Width,
messages = FALSE,
title = "Distribution of Sepal Width",
test.value = 5,
ggtheme = hrbrthemes::theme_ipsum_ps(),
ggstatsplot.layer = FALSE
),
nrow = 1,
labels = c("(a)", "(b)"),
title.text = "Behavior of ggstatsplot theme layer with and without chosen ggtheme"
)
```
For more on how to modify it, see the associated vignette-
## Using `ggstatsplot` statistical details with custom plots
Sometimes you may not like the defaults in a plot 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 plot (*pirateplot*)
using `yarrr` package and use `ggstatsplot` function for extracting
results.
``` r
# for reproducibility
set.seed(123)
# loading the needed libraries
library(yarrr)
library(ggstatsplot)
# using `ggstatsplot` to get call with statistical results
stats_results <-
ggstatsplot::ggbetweenstats(
data = ChickWeight,
x = Time,
y = weight,
return = "subtitle",
messages = FALSE
)
# using `yarrr` to create plot
yarrr::pirateplot(
formula = weight ~ Time,
data = ChickWeight,
theme = 1,
main = stats_results
)
```
# Code coverage
As the code stands right now, here is the code coverage for all primary
functions involved:
# Contributing
Im 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://github.com/IndrajeetPatil/ggstatsplot/blob/master/CONDUCT.md).
By participating in this project you agree to abide by its terms.
# Session Information
For details about the session information in which this `README` file
was rendered, see-
Owner
- Name: Daniel Heck
- Login: danheck
- Kind: user
- Location: Germany
- Company: Philipps-Universität Marburg
- Website: https://www.dwheck.de
- Twitter: Daniel_W_Heck
- Repositories: 5
- Profile: https://github.com/danheck