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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{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 = 300, ## 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?branch=master)
[](https://github.com/IndrajeetPatil/ggstatsplot) | [](https://CRAN.R-project.org/package=ggstatsplot) | [](https://CRAN.R-project.org/package=ggstatsplot)
[](https://github.com/IndrajeetPatil/ggstatsplot/actions) | [](https://doi.org/10.21105/joss.03167) | [](https://lifecycle.r-lib.org/articles/stages.html)
## Raison d'être <img src="man/figures/logo.png" align="right" width="360" />
> "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) | `remotes::install_github("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 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
It, therefore, produces a limited kinds of plots for the supported analyses:
Function | Plot | Description | Lifecycle
------- | ---------- | ------------------------- | ----
`ggbetweenstats` | **violin plots** | for comparisons *between* groups/conditions | [](https://lifecycle.r-lib.org/articles/stages.html)
`ggwithinstats` | **violin plots** | for comparisons *within* groups/conditions | [](https://lifecycle.r-lib.org/articles/stages.html)
`gghistostats` | **histograms** | for distribution about numeric variable | [](https://lifecycle.r-lib.org/articles/stages.html)
`ggdotplotstats` | **dot plots/charts** | for distribution about labeled numeric variable | [](https://lifecycle.r-lib.org/articles/stages.html)
`ggscatterstats` | **scatterplots** | for correlation between two variables | [](https://lifecycle.r-lib.org/articles/stages.html)
`ggcorrmat` | **correlation matrices** | for correlations between multiple variables | [](https://lifecycle.r-lib.org/articles/stages.html)
`ggpiestats` | **pie charts** | for categorical data | [](https://lifecycle.r-lib.org/articles/stages.html)
`ggbarstats` | **bar charts** | for categorical data | [](https://lifecycle.r-lib.org/articles/stages.html)
`ggcoefstats` | **dot-and-whisker plots** | for regression models and meta-analysis | [](https://lifecycle.r-lib.org/articles/stages.html)
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):
<img src="man/figures/stats_reporting_format.png" align="center" />
## 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:
<https://indrajeetpatil.github.io/statsExpressions/articles/stats_details.html>
## 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 ggbetweenstats1}
set.seed(123)
ggbetweenstats(
data = iris,
x = Species,
y = Sepal.Length,
title = "Distribution of sepal length across Iris species"
)
```
**Defaults** return<br>
✅ raw data + distributions <br>
✅ descriptive statistics <br>
✅ inferential statistics <br>
✅ effect size + CIs <br>
✅ pairwise comparisons <br>
✅ Bayesian hypothesis-testing <br>
✅ Bayesian estimation <br>
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 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,
outlier.tagging = TRUE,
outlier.label = title,
outlier.coef = 2,
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")
)
```
Note here that the function can be used to tag outliers!
##### Summary of graphics
graphical element | `geom_` used | argument for further modification
--------- | ------- | --------------------------
raw data | `ggplot2::geom_point` | `point.args`
box plot | `ggplot2::geom_boxplot` | ❌
density plot | `ggplot2::geom_violin` | `violin.args`
centrality measure point | `ggplot2::geom_point` | `centrality.point.args`
centrality measure label | `ggrepel::geom_label_repel` | `centrality.label.args`
outlier point | `ggplot2::stat_boxplot` | ❌
outlier label | `ggrepel::geom_label_repel` | `outlier.label.args`
pairwise comparisons | `ggsignif::geom_signif` | `ggsignif.args`
##### Summary of tests
**Central tendency measure**
Type | Measure | Function used
----------- | --------- | ------------------
Parametric | mean | `datawizard::describe_distribution`
Non-parametric | median | `datawizard::describe_distribution`
Robust | trimmed mean | `datawizard::describe_distribution`
Bayesian | MAP (maximum *a posteriori* probability) estimate | `datawizard::describe_distribution`
**Hypothesis testing**
Type | No. of groups | Test | Function used
----------- | --- | ------------------------- | -----
Parametric | > 2 | Fisher's or Welch's one-way ANOVA | `stats::oneway.test`
Non-parametric | > 2 | Kruskal–Wallis one-way ANOVA | `stats::kruskal.test`
Robust | > 2 | Heteroscedastic one-way ANOVA for trimmed means | `WRS2::t1way`
Bayes Factor | > 2 | Fisher's ANOVA | `BayesFactor::anovaBF`
Parametric | 2 | Student's or Welch's *t*-test | `stats::t.test`
Non-parametric | 2 | Mann–Whitney *U* test | `stats::wilcox.test`
Robust | 2 | Yuen's test for trimmed means | `WRS2::yuen`
Bayesian | 2 | Student's *t*-test | `BayesFactor::ttestBF`
**Effect size estimation**
Type | No. of groups | Effect size | CI? | Function used
----------- | --- | ------------------------- | --- | -----
Parametric | > 2 | $\eta_{p}^2$, $\omega_{p}^2$ | ✅ | `effectsize::omega_squared`, `effectsize::eta_squared`
Non-parametric | > 2 | $\epsilon_{ordinal}^2$ | ✅ | `effectsize::rank_epsilon_squared`
Robust | > 2 | $\xi$ (Explanatory measure of effect size) | ✅ | `WRS2::t1way`
Bayes Factor | > 2 | $R_{posterior}^2$ | ✅ | `performance::r2_bayes`
Parametric | 2 | Cohen's *d*, Hedge's *g* | ✅ | `effectsize::cohens_d`, `effectsize::hedges_g`
Non-parametric | 2 | *r* (rank-biserial correlation) | ✅ | `effectsize::rank_biserial`
Robust | 2 | $\xi$ (Explanatory measure of effect size) | ✅ | `WRS2::yuen.effect.ci`
Bayesian | 2 | $\delta_{posterior}$ | ✅ | `bayestestR::describe_posterior`
**Pairwise comparison tests**
Type | Equal variance? | Test | *p*-value adjustment? | Function used
----------- | --- | ------------------------- | --- | -----
Parametric | No | Games-Howell test | ✅ | `PMCMRplus::gamesHowellTest`
Parametric | Yes | Student's *t*-test | ✅ | `stats::pairwise.t.test`
Non-parametric | No | Dunn test | ✅ | `PMCMRplus::kwAllPairsDunnTest`
Robust | No | Yuen's trimmed means test | ✅ | `WRS2::lincon`
Bayesian | `NA` | Student's *t*-test | `NA` | `BayesFactor::ttestBF`
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, 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",
ggtheme = hrbrthemes::theme_ipsum_es()
)
```
**Defaults** return<br>
✅ raw data + distributions <br>
✅ descriptive statistics <br>
✅ inferential statistics <br>
✅ effect size + CIs <br>
✅ pairwise comparisons <br>
✅ Bayesian hypothesis-testing <br>
✅ Bayesian estimation <br>
The central tendency measure displayed will depend on the statistics:
Type | Measure | Function used
----------- | --------- | ------------------
Parametric | mean | `datawizard::describe_distribution`
Non-parametric | median | `datawizard::describe_distribution`
Robust | trimmed mean | `datawizard::describe_distribution`
Bayesian | MAP estimate | `datawizard::describe_distribution`
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, 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,
outlier.tagging = TRUE,
outlier.label = education
)
```
##### Summary of graphics
graphical element | `geom_` used | argument for further modification
--------- | ------- | --------------------------
raw data | `ggplot2::geom_point` | `point.args`
point path | `ggplot2::geom_path` | `point.path.args`
box plot | `ggplot2::geom_boxplot` | `boxplot.args`
density plot | `ggplot2::geom_violin` | `violin.args`
centrality measure point | `ggplot2::geom_point` | `centrality.point.args`
centrality measure point path | `ggplot2::geom_path` | `centrality.path.args`
centrality measure label | `ggrepel::geom_label_repel` | `centrality.label.args`
outlier point | `ggplot2::stat_boxplot` | ❌
outlier label | `ggrepel::geom_label_repel` | `outlier.label.args`
pairwise comparisons | `ggsignif::geom_signif` | `ggsignif.args`
##### Summary of tests
**Central tendency measure**
Type | Measure | Function used
----------- | --------- | ------------------
Parametric | mean | `datawizard::describe_distribution`
Non-parametric | median | `datawizard::describe_distribution`
Robust | trimmed mean | `datawizard::describe_distribution`
Bayesian | MAP (maximum *a posteriori* probability) estimate | `datawizard::describe_distribution`
**Hypothesis testing**
Type | No. of groups | Test | Function used
----------- | --- | ------------------------- | -----
Parametric | > 2 | One-way repeated measures ANOVA | `afex::aov_ez`
Non-parametric | > 2 | Friedman rank sum test | `stats::friedman.test`
Robust | > 2 | Heteroscedastic one-way repeated measures ANOVA for trimmed means | `WRS2::rmanova`
Bayes Factor | > 2 | One-way repeated measures ANOVA | `BayesFactor::anovaBF`
Parametric | 2 | Student's *t*-test | `stats::t.test`
Non-parametric | 2 | Wilcoxon signed-rank test | `stats::wilcox.test`
Robust | 2 | Yuen's test on trimmed means for dependent samples | `WRS2::yuend`
Bayesian | 2 | Student's *t*-test | `BayesFactor::ttestBF`
**Effect size estimation**
Type | No. of groups | Effect size | CI? | Function used
----------- | --- | ------------------------- | --- | -----
Parametric | > 2 | $\eta_{p}^2$, $\omega_{p}^2$ | ✅ | `effectsize::omega_squared`, `effectsize::eta_squared`
Non-parametric | > 2 | $W_{Kendall}$ (Kendall's coefficient of concordance) | ✅ | `effectsize::kendalls_w`
Robust | > 2 | $\delta_{R-avg}^{AKP}$ (Algina-Keselman-Penfield robust standardized difference average) | ✅ | `WRS2::wmcpAKP`
Bayes Factor | > 2 | $R_{Bayesian}^2$ | ✅ | `performance::r2_bayes`
Parametric | 2 | Cohen's *d*, Hedge's *g* | ✅ | `effectsize::cohens_d`, `effectsize::hedges_g`
Non-parametric | 2 | *r* (rank-biserial correlation) | ✅ | `effectsize::rank_biserial`
Robust | 2 | $\delta_{R}^{AKP}$ (Algina-Keselman-Penfield robust standardized difference) | ✅ | `WRS2::wmcpAKP`
Bayesian | 2 | $\delta_{posterior}$ | ✅ | `bayestestR::describe_posterior`
**Pairwise comparison tests**
Type | Test | *p*-value adjustment? | Function used
----------- | ---------------------------- | --- | -----
Parametric | Student's *t*-test | ✅ | `stats::pairwise.t.test`
Non-parametric | Durbin-Conover test | ✅ | `PMCMRplus::durbinAllPairsTest`
Robust | Yuen's trimmed means test | ✅ | `WRS2::rmmcp`
Bayesian | Student's *t*-test | ❌ | `BayesFactor::ttestBF`
For more, see the `ggwithinstats` vignette:
<https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggwithinstats.html>
### `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, fig.width=8}
set.seed(123)
gghistostats(
data = ggplot2::msleep,
x = awake,
title = "Amount of time spent awake",
test.value = 12,
binwidth = 1,
ggtheme = hrbrthemes::theme_ipsum_tw()
)
```
**Defaults** return<br>
✅ counts + proportion for bins<br>
✅ descriptive statistics <br>
✅ inferential statistics <br>
✅ effect size + CIs <br>
✅ Bayesian hypothesis-testing <br>
✅ Bayesian estimation <br>
There is also a `grouped_` variant of this function that makes it
easy to repeat the same operation across a **single** grouping variable:
```{r 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,
normal.curve = TRUE,
normal.curve.args = list(color = "red", size = 1),
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")
)
```
##### Summary of graphics
graphical element | `geom_` used | argument for further modification
--------- | ------- | --------------------------
histogram bin | `ggplot2::stat_bin` | `bin.args`
centrality measure line | `ggplot2::geom_vline` | `centrality.line.args`
normality curve | `ggplot2::stat_function` | `normal.curve.args`
##### Summary of tests
**Central tendency measure**
Type | Measure | Function used
----------- | --------- | ------------------
Parametric | mean | `datawizard::describe_distribution`
Non-parametric | median | `datawizard::describe_distribution`
Robust | trimmed mean | `datawizard::describe_distribution`
Bayesian | MAP (maximum *a posteriori* probability) estimate | `datawizard::describe_distribution`
**Hypothesis testing**
Type | Test | Function used
------------------ | ------------------------- | -----
Parametric | One-sample Student's *t*-test | `stats::t.test`
Non-parametric | One-sample Wilcoxon test | `stats::wilcox.test`
Robust | Bootstrap-*t* method for one-sample test | `WRS2::trimcibt`
Bayesian | One-sample Student's *t*-test | `BayesFactor::ttestBF`
**Effect size estimation**
Type | Effect size | CI? | Function used
------------ | ----------------------- | --- | -----
Parametric | Cohen's *d*, Hedge's *g* | ✅ | `effectsize::cohens_d`, `effectsize::hedges_g`
Non-parametric | *r* (rank-biserial correlation) | ✅ | `effectsize::rank_biserial`
Robust | trimmed mean | ✅ | `WRS2::trimcibt`
Bayes Factor | $\delta_{posterior}$ | ✅ | `bayestestR::describe_posterior`
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, 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<br>
✅ descriptives (mean + sample size) <br>
✅ inferential statistics <br>
✅ effect size + CIs <br>
✅ Bayesian hypothesis-testing <br>
✅ Bayesian estimation <br>
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, 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")
)
```
##### Summary of graphics
graphical element | `geom_` used | argument for further modification
--------- | ------- | --------------------------
raw data | `ggplot2::geom_point` | `point.args`
centrality measure line | `ggplot2::geom_vline` | `centrality.line.args`
##### Summary of tests
**Central tendency measure**
Type | Measure | Function used
----------- | --------- | ------------------
Parametric | mean | `datawizard::describe_distribution`
Non-parametric | median | `datawizard::describe_distribution`
Robust | trimmed mean | `datawizard::describe_distribution`
Bayesian | MAP (maximum *a posteriori* probability) estimate | `datawizard::describe_distribution`
**Hypothesis testing**
Type | Test | Function used
------------------ | ------------------------- | -----
Parametric | One-sample Student's *t*-test | `stats::t.test`
Non-parametric | One-sample Wilcoxon test | `stats::wilcox.test`
Robust | Bootstrap-*t* method for one-sample test | `WRS2::trimcibt`
Bayesian | One-sample Student's *t*-test | `BayesFactor::ttestBF`
**Effect size estimation**
Type | Effect size | CI? | Function used
------------ | ----------------------- | --- | -----
Parametric | Cohen's *d*, Hedge's *g* | ✅ | `effectsize::cohens_d`, `effectsize::hedges_g`
Non-parametric | *r* (rank-biserial correlation) | ✅ | `effectsize::rank_biserial`
Robust | trimmed mean | ✅ | `WRS2::trimcibt`
Bayes Factor | $\delta_{posterior}$ | ✅ | `bayestestR::describe_posterior`
### `ggscatterstats`
This function creates a scatterplot with marginal distributions overlaid on the
axes and results from statistical tests in the subtitle:
```{r 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<br>
✅ raw data + distributions <br>
✅ marginal distributions <br>
✅ inferential statistics <br>
✅ effect size + CIs <br>
✅ Bayesian hypothesis-testing <br>
✅ Bayesian estimation <br>
There is also a `grouped_` variant of this function that makes it
easy to repeat the same operation across a **single** grouping variable.
```{r 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")
)
```
##### Summary of graphics
graphical element | `geom_` used | argument for further modification
--------- | ------- | --------------------------
raw data | `ggplot2::geom_point` | `point.args`
labels for raw data | `ggrepel::geom_label_repel` | `point.label.args`
smooth line | `ggplot2::geom_smooth` | `smooth.line.args`
marginal histograms | `ggside::geom_xsidehistogram`, `ggside::geom_ysidehistogram` | `xsidehistogram.args`, `ysidehistogram.args`
##### Summary of tests
**Hypothesis testing** and **Effect size estimation**
Type | Test | CI? | Function used
----------- | ------------------------- | --- | -----
Parametric | Pearson's correlation coefficient | ✅ | `correlation::correlation`
Non-parametric | Spearman's rank correlation coefficient | ✅ | `correlation::correlation`
Robust | Winsorized Pearson correlation coefficient | ✅ | `correlation::correlation`
Bayesian | Pearson's correlation coefficient | ✅ | `correlation::correlation`
For more, see the `ggscatterstats` vignette:
<https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggscatterstats.html>
### `ggcorrmat`
`ggcorrmat` makes a correlalogram (a matrix of correlation coefficients) with
minimal amount of code. Just sticking to the defaults itself produces
publication-ready correlation matrices. But, for the sake of exploring the
available options, let's change some of the defaults. For example, multiple
aesthetics-related arguments can be modified to change the appearance of the
correlation matrix.
```{r 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<br>
✅ effect size + significance<br>
✅ 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 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"
)
```
##### Summary of graphics
graphical element | `geom_` used | argument for further modification
--------- | ------- | --------------------------
correlation matrix | `ggcorrplot::ggcorrplot` | `ggcorrplot.args`
##### Summary of tests
**Hypothesis testing** and **Effect size estimation**
Type | Test | CI? | Function used
----------- | ------------------------- | --- | -----
Parametric | Pearson's correlation coefficient | ✅ | `correlation::correlation`
Non-parametric | Spearman's rank correlation coefficient | ✅ | `correlation::correlation`
Robust | Winsorized Pearson correlation coefficient | ✅ | `correlation::correlation`
Bayesian | Pearson's correlation coefficient | ✅ | `correlation::correlation`
For examples and more information, see the `ggcorrmat` vignette:
<https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggcorrmat.html>
### `ggpiestats`
This function creates a pie chart for categorical or nominal variables with
results from contingency table analysis (Pearson's chi-squared test for
between-subjects design and McNemar's chi-squared test for within-subjects
design) included in the subtitle of the plot. If only one categorical variable
is entered, results from one-sample proportion test (i.e., a chi-squared
goodness of fit test) will be displayed as a subtitle.
To study an interaction between two categorical variables:
```{r 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<br>
✅ descriptives (frequency + %s) <br>
✅ inferential statistics <br>
✅ effect size + CIs <br>
✅ Goodness-of-fit tests <br>
✅ Bayesian hypothesis-testing <br>
✅ Bayesian estimation <br>
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 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"
)
```
##### Summary of graphics
graphical element | `geom_` used | argument for further modification
--------- | ------- | --------------------------
pie slices | `ggplot2::geom_col` | ❌
descriptive labels | `ggplot2::geom_label`/`ggrepel::geom_label_repel` | `label.args`
##### Summary of tests
**two-way table**
**Hypothesis testing**
Type | Design | Test | Function used
----------- | ----- | ------------------------- | -----
Parametric/Non-parametric | Unpaired | Pearson's $\chi^2$ test | `stats::chisq.test`
Bayesian | Unpaired | Bayesian Pearson's $\chi^2$ test | `BayesFactor::contingencyTableBF`
Parametric/Non-parametric | Paired | McNemar's $\chi^2$ test | `stats::mcnemar.test`
Bayesian | Paired | ❌ | ❌
**Effect size estimation**
Type | Design | Effect size | CI? | Function used
----------- | ----- | ------------------------- | --- | -----
Parametric/Non-parametric | Unpaired | Cramer's $V$ | ✅ | `effectsize::cramers_v`
Bayesian | Unpaired | Cramer's $V$ | ✅ | `effectsize::cramers_v`
Parametric/Non-parametric | Paired | Cohen's $g$ | ✅ | `effectsize::cohens_g`
Bayesian | Paired | ❌ | ❌ | ❌
**one-way table**
**Hypothesis testing**
Type | Test | Function used
----------- | ------------------------- | -----
Parametric/Non-parametric | Goodness of fit $\chi^2$ test | `stats::chisq.test`
Bayesian | Bayesian Goodness of fit $\chi^2$ test | (custom)
**Effect size estimation**
Type | Effect size | CI? | Function used
----------- | ------------------------- | --- | -----
Parametric/Non-parametric | Pearson's $C$ | ✅ | `effectsize::pearsons_c`
Bayesian | ❌ | ❌ | ❌
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.
N.B. The *p*-values from one-sample proportion test are displayed on top of each
bar.
```{r 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",
ggtheme = hrbrthemes::theme_ipsum_pub(),
ggplot.component = list(ggplot2::scale_x_discrete(guide = ggplot2::guide_axis(n.dodge = 2))),
palette = "Set2"
)
```
**Defaults** return<br>
✅ descriptives (frequency + %s) <br>
✅ inferential statistics <br>
✅ effect size + CIs <br>
✅ Goodness-of-fit tests <br>
✅ Bayesian hypothesis-testing <br>
✅ Bayesian estimation <br>
And, needless to say, there is also a `grouped_` variant of this function-
```{r 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)
)
```
##### Summary of graphics
graphical element | `geom_` used | argument for further modification
--------- | ------- | --------------------------
bars | `ggplot2::geom_bar` | ❌
descriptive labels | `ggplot2::geom_label` | `label.args`
##### Summary of tests
**two-way table**
**Hypothesis testing**
Type | Design | Test | Function used
----------- | ----- | ------------------------- | -----
Parametric/Non-parametric | Unpaired | Pearson's $\chi^2$ test | `stats::chisq.test`
Bayesian | Unpaired | Bayesian Pearson's $\chi^2$ test | `BayesFactor::contingencyTableBF`
Parametric/Non-parametric | Paired | McNemar's $\chi^2$ test | `stats::mcnemar.test`
Bayesian | Paired | ❌ | ❌
**Effect size estimation**
Type | Design | Effect size | CI? | Function used
----------- | ----- | ------------------------- | --- | -----
Parametric/Non-parametric | Unpaired | Cramer's $V$ | ✅ | `effectsize::cramers_v`
Bayesian | Unpaired | Cramer's $V$ | ✅ | `effectsize::cramers_v`
Parametric/Non-parametric | Paired | Cohen's $g$ | ✅ | `effectsize::cohens_g`
Bayesian | Paired | ❌ | ❌ | ❌
**one-way table**
**Hypothesis testing**
Type | Test | Function used
----------- | ------------------------- | -----
Parametric/Non-parametric | Goodness of fit $\chi^2$ test | `stats::chisq.test`
Bayesian | Bayesian Goodness of fit $\chi^2$ test | (custom)
**Effect size estimation**
Type | Effect size | CI? | Function used
----------- | ------------------------- | --- | -----
Parametric/Non-parametric | Pearson's $C$ | ✅ | `effectsize::pearsons_c`
Bayesian | ❌ | ❌ | ❌
### `ggcoefstats`
The function `ggcoefstats` generates **dot-and-whisker plots** for
regression models saved in a tidy data frame. The tidy dataframes 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 ggcoefstats1, fig.height=5, fig.width=6}
set.seed(123)
## model
mod <- stats::lm(formula = mpg ~ am * cyl, data = mtcars)
ggcoefstats(mod, ggtheme = hrbrthemes::theme_ipsum_ps())
```
**Defaults** return<br>
✅ inferential statistics <br>
✅ estimate + CIs <br>
✅ model summary (AIC and BIC) <br>
##### Supported models
Most of the regression models that are supported in the underlying packages are
also supported by `ggcoefstats`.
```{r supported}
insight::supported_models()
```
Although not shown here, this function can also be used to carry out parametric,
robust, and Bayesian random-effects meta-analysis.
##### Summary of graphics
graphical element | `geom_` used | argument for further modification
--------- | ------- | --------------------------
regression estimate | `ggplot2::geom_point` | `point.args`
error bars | `ggplot2::geom_errorbarh` | `errorbar.args`
vertical line | `ggplot2::geom_vline` | `vline.args`
label with statistical details | `ggrepel::geom_label_repel` | `stats.label.args`
##### Summary of meta-analysis tests
**Hypothesis testing** and **Effect size estimation**
Type | Test | Effect size | CI? | Function used
----------- | -------------------- | -------- | --- | -----
Parametric | Meta-analysis via random-effects models | $\beta$ | ✅ | `metafor::metafor`
Robust | Meta-analysis via robust random-effects models | $\beta$ | ✅ | `metaplus::metaplus`
Bayes | Meta-analysis via Bayesian random-effects models | $\beta$ | ✅ | `metaBMA::meta_random`
For a more exhaustive account of this function, see the associated vignette-
<https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggcoefstats.html>
### Extracting dataframes with statistical details
`{ggstatsplot}` also offers a convenience function to extract dataframes with
statistical details that are used to create expressions displayed in
`{ggstatsplot}` plots.
```{r extract_stats}