`ggstatsplot`

: `ggplot2`

Based Plots with Statistical Details

Package | Status | Usage | GitHub | References |
---|---|---|---|---|

# Raison d’être

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

- Edward R. Tufte

`ggstatsplot`

is an
extension of `ggplot2`

package
for creating graphics with details from statistical tests included in
the information-rich plots themselves. In a typical exploratory data
analysis workflow, data visualization and statistical modeling are two
different phases: visualization informs modeling, and modeling in its
turn can suggest a different visualization method, and so on and so
forth. The central idea of `ggstatsplot`

is simple: combine these two
phases into one in the form of graphics with statistical details, which
makes data exploration simpler and faster.

# Summary of available plots

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

Function | Plot | Description |
---|---|---|

`ggbetweenstats` | violin plots | for comparisons between groups/conditions |

`ggwithinstats` | violin plots | for comparisons within groups/conditions |

`gghistostats` | histograms | for distribution about numeric variable |

`ggdotplotstats` | dot plots/charts | for distribution about labeled numeric variable |

`ggpiestats` | pie charts | for categorical data |

`ggbarstats` | bar charts | for categorical data |

`ggscatterstats` | scatterplots | for correlations between two variables |

`ggcorrmat` | correlation matrices | for correlations between multiple variables |

`ggcoefstats` | dot-and-whisker plots | for regression models 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

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

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

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 | Yes |

`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 | Yes | Yes | Yes |

`ggcoefstats` | Random-effects meta-analysis | Yes | No | Yes | Yes |

# Statistical reporting

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

# Summary of statistical tests and effect sizes

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

# Installation

To get the latest, stable `CRAN`

release:

`install.packages("ggstatsplot")`

*Note*: If you are on a linux machine, you will need to have OpenGL
libraries installed (specifically, `libx11`

, `mesa`

and Mesa OpenGL
Utility library - `glu`

) for the dependency package `rgl`

to work.

You can get the **development** version of the package from `GitHub`

. To
see what new changes (and bug fixes) have been made to the package since
the last release on `CRAN`

, you can check the detailed log of changes
here: https://indrajeetpatil.github.io/ggstatsplot/news/index.html

If you are in hurry and want to reduce the time of installation, prefer-

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

If time is not a constraint-

```
remotes::install_github(
repo = "IndrajeetPatil/ggstatsplot", # package path on GitHub
dependencies = TRUE, # installs packages which ggstatsplot depends on
upgrade_dependencies = TRUE # updates any out of date dependencies
)
```

If you are not using the RStudio IDE and you
get an error related to “pandoc” you will either need to remove the
argument `build_vignettes = TRUE`

(to avoid building the vignettes) or
install pandoc. If you have the `rmarkdown`

R
package installed then you can check if you have pandoc by running the
following in R:

```
rmarkdown::pandoc_available()
#> [1] TRUE
```

# Citation

If you want to cite this package in a scientific journal or in any other
context, run the following code in your `R`

console:

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

There is currently a publication in preparation corresponding to this package and the citation will be updated once it’s published.

# Documentation and Examples

To see the detailed documentation for each function in the stable
**CRAN** version of the package, see:

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

To see the documentation relevant for the **development** version of the
package, see the dedicated website for `ggstatplot`

, which is updated
after every new commit: https://indrajeetpatil.github.io/ggstatsplot/.

# Primary functions

Here are examples of the main functions currently supported in
`ggstatsplot`

.

**Note**: If you are reading this on `GitHub`

repository, the
documentation below is for the **development** version of the package.
So you may see some features available here that are not currently
present in the stable version of this package on **CRAN**. For
documentation relevant for the `CRAN`

version, see:
https://CRAN.R-project.org/package=ggstatsplot/readme/README.html

`ggbetweenstats`

This function creates either a violin plot, a box plot, or a mix of two
for **between**-group or **between**-condition comparisons with results
from statistical tests in the subtitle. The simplest function call looks
like this-

```
# loading needed libraries
library(ggstatsplot)
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggbetweenstats(
data = iris,
x = Species,
y = Sepal.Length,
title = "Distribution of sepal length across Iris species",
messages = FALSE
)
```

Note that this function returns object of class `ggplot`

and thus can be
further modified using `ggplot2`

functions.

A number of other arguments can be specified to make this plot even more
informative or change some of the default options. Additionally, this
time we will use a grouping variable that has only two levels. The
function will automatically switch from carrying out an ANOVA analysis
to a *t*-test.

The `type`

(of test) argument also accepts the following abbreviations:
`"p"`

(for *parametric*) or `"np"`

(for *nonparametric*) or `"r"`

(for
*robust*) or `"bf"`

(for *Bayes Factor*). Additionally, the type of plot
to be displayed can also be modified (`"box"`

, `"violin"`

, or
`"boxviolin"`

).

A number of other arguments can be specified to make this plot even more informative or change some of the default options.

```
# for reproducibility
set.seed(123)
library(ggplot2)
# plot
ggstatsplot::ggbetweenstats(
data = ToothGrowth,
x = supp,
y = len,
notch = TRUE, # show notched box plot
mean.ci = TRUE, # whether to display confidence interval for means
k = 3, # number of decimal places for statistical results
outlier.tagging = TRUE, # whether outliers need to be tagged
outlier.label = dose, # variable to be used for the outlier tag
xlab = "Supplement type", # label for the x-axis variable
ylab = "Tooth length", # label for the y-axis variable
title = "The Effect of Vitamin C on Tooth Growth", # title text for the plot
ggtheme = ggthemes::theme_fivethirtyeight(), # choosing a different theme
ggstatsplot.layer = FALSE, # turn off `ggstatsplot` theme layer
package = "wesanderson", # package from which color palette is to be taken
palette = "Darjeeling1", # choosing a different color palette
messages = FALSE
)
```

Additionally, there is also a `grouped_`

variant of this function that
makes it easy to repeat the same operation across a **single** grouping
variable:

```
# for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_ggbetweenstats(
data = dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
x = mpaa,
y = length,
grouping.var = genre, # grouping variable
pairwise.comparisons = TRUE, # display significant pairwise comparisons
p.adjust.method = "bonferroni", # method for adjusting p-values for multiple comparisons
# adding new components to `ggstatsplot` default
ggplot.component = list(ggplot2::scale_y_continuous(sec.axis = ggplot2::dup_axis())),
k = 3,
title.prefix = "Movie genre",
caption = substitute(paste(italic("Source"), ":IMDb (Internet Movie Database)")),
palette = "default_jama",
package = "ggsci",
messages = FALSE,
plotgrid.args = list(nrow = 2),
title.text = "Differences in movie length by mpaa ratings for different genres"
)
```

### Summary of tests

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

Type | No. of groups | Test |
---|---|---|

Parametric | > 2 | Fisher’s or Welch’s one-way ANOVA |

Non-parametric | > 2 | Kruskal-Wallis one-way ANOVA |

Robust | > 2 | Heteroscedastic one-way ANOVA for trimmed means |

Bayes Factor | > 2 | Fisher’s ANOVA |

Parametric | 2 | Student’s or Welch’s t-test |

Non-parametric | 2 | Mann-Whitney U test |

Robust | 2 | Yuen’s test for trimmed means |

Bayes Factor | 2 | Student’s t-test |

The omnibus effect in one-way ANOVA design can also be followed up with
more focal pairwise comparison tests. Here is a summary of multiple
pairwise comparison tests supported in *ggbetweenstats*-

Type | Equal variance? | Test | p-value adjustment? |
---|---|---|---|

Parametric | No | Games-Howell test | Yes |

Parametric | Yes | Student’s t-test | Yes |

Non-parametric | No | Dunn test | Yes |

Robust | No | Yuen’s trimmed means test | Yes |

Bayes Factor | `NA` | Student’s t-test | `NA` |

For more, see the `ggbetweenstats`

vignette:
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggbetweenstats.html

`ggwithinstats`

`ggbetweenstats`

function has an identical twin function `ggwithinstats`

for repeated measures designs that behaves in the same fashion with a
few minor tweaks introduced to properly visualize the repeated measures
design. As can be seen from an example below, the only difference
between the plot structure is that now the group means are connected by
paths to highlight the fact that these data are paired with each other.

```
# for reproducibility and data
set.seed(123)
library(WRS2)
# plot
ggstatsplot::ggwithinstats(
data = WineTasting,
x = Wine,
y = Taste,
pairwise.comparisons = TRUE, # show pairwise comparison test results
title = "Wine tasting",
caption = "Data source: `WRS2` R package",
ggtheme = ggthemes::theme_fivethirtyeight(),
ggstatsplot.layer = FALSE,
messages = FALSE
)
```

As with the `ggbetweenstats`

, this function also has a `grouped_`

variant that makes repeating the same analysis across a single grouping
variable quicker. We will see an example with only repeated
measurements-

```
# common setup
set.seed(123)
# plot
ggstatsplot::grouped_ggwithinstats(
data = dplyr::filter(
.data = ggstatsplot::bugs_long,
region %in% c("Europe", "North America"),
condition %in% c("LDLF", "LDHF")
),
x = condition,
y = desire,
xlab = "Condition",
ylab = "Desire to kill an artrhopod",
grouping.var = region,
outlier.tagging = TRUE,
outlier.label = education,
ggtheme = hrbrthemes::theme_ipsum_tw(),
ggstatsplot.layer = FALSE,
messages = FALSE
)
```

### Summary of tests

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

Type | No. of groups | Test |
---|---|---|

Parametric | > 2 | One-way repeated measures ANOVA |

Non-parametric | > 2 | Friedman’s rank sum test |

Robust | > 2 | Heteroscedastic one-way repeated measures ANOVA for trimmed means |

Bayes Factor | > 2 | One-way repeated measures ANOVA |

Parametric | 2 | Student’s t-test |

Non-parametric | 2 | Wilcoxon signed-rank test |

Robust | 2 | Yuen’s test on trimmed means for dependent samples |

Bayes Factor | 2 | Student’s t-test |

The omnibus effect in one-way ANOVA design can also be followed up with
more focal pairwise comparison tests. Here is a summary of multiple
pairwise comparison tests supported in *ggwithinstats*-

Type | Test | p-value adjustment? |
---|---|---|

Parametric | Student’s t-test | Yes |

Non-parametric | Durbin-Conover test | Yes |

Robust | Yuen’s trimmed means test | Yes |

Bayes Factor | Student’s t-test | `NA` |

For more, see the `ggwithinstats`

vignette:
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggwithinstats.html

`ggscatterstats`

This function creates a scatterplot with marginal distributions overlaid
on the axes (from `ggExtra::ggMarginal`

) and results from statistical
tests in the subtitle:

```
ggstatsplot::ggscatterstats(
data = ggplot2::msleep,
x = sleep_rem,
y = awake,
xlab = "REM sleep (in hours)",
ylab = "Amount of time spent awake (in hours)",
title = "Understanding mammalian sleep",
messages = FALSE
)
```

The available marginal distributions are-

- histograms
- boxplots
- density
- violin
- densigram (density + histogram)

Number of other arguments can be specified to modify this basic plot-

```
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggscatterstats(
data = dplyr::filter(.data = ggstatsplot::movies_long, genre == "Action"),
x = budget,
y = rating,
type = "robust", # type of test that needs to be run
xlab = "Movie budget (in million/ US$)", # label for x axis
ylab = "IMDB rating", # label for y axis
label.var = "title", # variable for labeling data points
label.expression = "rating < 5 & budget > 100", # expression that decides which points to label
title = "Movie budget and IMDB rating (action)", # title text for the plot
caption = expression(paste(italic("Note"), ": IMDB stands for Internet Movie DataBase")),
ggtheme = hrbrthemes::theme_ipsum_ps(), # choosing a different theme
ggstatsplot.layer = FALSE, # turn off `ggstatsplot` theme layer
marginal.type = "density", # type of marginal distribution to be displayed
xfill = "pink", # color fill for x-axis marginal distribution
yfill = "#009E73", # color fill for y-axis marginal distribution
centrality.parameter = "median", # central tendency lines to be displayed
messages = FALSE # turn off messages and notes
)
```

Additionally, there is also a `grouped_`

variant of this function that
makes it easy to repeat the same operation across a **single** grouping
variable. Also, note that, as opposed to the other functions, this
function does not return a `ggplot`

object and any modification you want
to make can be made in advance using `ggplot.component`

argument
(available for all functions, but especially useful for this particular
function):

```
# for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_ggscatterstats(
data = dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
x = rating,
y = length,
grouping.var = genre, # grouping variable
label.var = title,
label.expression = length > 200,
xfill = "#E69F00",
yfill = "#8b3058",
xlab = "IMDB rating",
title.prefix = "Movie genre",
ggtheme = ggplot2::theme_grey(),
ggplot.component = list(
ggplot2::scale_x_continuous(breaks = seq(2, 9, 1), limits = (c(2, 9)))
),
messages = FALSE,
plotgrid.args = list(nrow = 2),
title.text = "Relationship between movie length by IMDB ratings for different genres"
)
```

### Summary of tests

Following tests are carried out for each type of analyses. Additionally, the correlation coefficients (and their confidence intervals) are used as effect sizes-

Type | Test | CI? |
---|---|---|

Parametric | Pearson’s correlation coefficient | Yes |

Non-parametric | Spearman’s rank correlation coefficient | Yes |

Robust | Percentage bend correlation coefficient | Yes |

Bayes Factor | Pearson’s correlation coefficient | No |

For more, see the `ggscatterstats`

vignette:
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggscatterstats.html

`ggpiestats`

This function creates a pie chart for categorical or nominal variables with results from contingency table analysis (Pearson’s chi-squared test for between-subjects design and McNemar’s chi-squared test for within-subjects design) included in the subtitle of the plot. If only one categorical variable is entered, results from one-sample proportion test (i.e., a chi-squared goodness of fit test) will be displayed as a subtitle.

To study an interaction between two categorical variables:

```
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggpiestats(
data = mtcars,
x = am,
y = cyl,
title = "Dataset: Motor Trend Car Road Tests", # title for the plot
legend.title = "Transmission", # title for the legend
caption = substitute(paste(italic("Source"), ": 1974 Motor Trend US magazine")),
messages = FALSE
)
```

In case of repeated measures designs, setting `paired = TRUE`

will
produce results from McNemar’s chi-squared test-

```
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggpiestats(
data = data.frame(
"before" = c("Approve", "Approve", "Disapprove", "Disapprove"),
"after" = c("Approve", "Disapprove", "Approve", "Disapprove"),
counts = c(794, 150, 86, 570),
check.names = FALSE
),
x = before,
y = after,
counts = counts,
title = "Survey results before and after the intervention",
label = "both",
paired = TRUE, # within-subjects design
package = "wesanderson",
palette = "Royal1"
)
#> Note: 95% CI for effect size estimate was computed with 100 bootstrap samples.
#> # A tibble: 2 x 11
#> after counts perc N Approve Disapprove statistic p.value
#> <fct> <int> <dbl> <chr> <chr> <chr> <dbl> <dbl>
#> 1 Disapprove 720 45 (n = 720) 20.83% 79.17% 245 3.20e- 55
#> 2 Approve 880 55. (n = 880) 90.23% 9.77% 570. 6.80e-126
#> parameter method significance
#> <dbl> <chr> <chr>
#> 1 1 Chi-squared test for given probabilities ***
#> 2 1 Chi-squared test for given probabilities ***
```

Additionally, there is also a `grouped_`

variant of this function that
makes it easy to repeat the same operation across a **single** grouping
variable. Following example is a case where the theoretical question is
about proportions for different levels of a single nominal variable:

```
# for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_ggpiestats(
dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
x = mpaa,
grouping.var = genre, # grouping variable
title.prefix = "Movie genre", # prefix for the facetted title
messages = FALSE,
package = "ggsci", # package from which color palette is to be taken
palette = "default_jama", # choosing a different color palette
title.text = "Composition of MPAA ratings for different genres"
)
```

### Summary of tests

Following tests are carried out for each type of analyses-

Type of data | Design | Test |
---|---|---|

Unpaired | contingency table | Pearson’s test |

Paired | contingency table | McNemar’s 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? |
---|---|---|

Pearson’s chi-squared test | Cramér’s V | Yes |

McNemar’s test | Cohen’s g | Yes |

Goodness of fit | Cramér’s V | Yes |

For more, see the `ggpiestats`

vignette:
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggpiestats.html

`ggbarstats`

In case you are not a fan of pie charts (for very good reasons), you can
alternatively use `ggbarstats`

function which has a similar syntax-

```
# for reproducibility
set.seed(123)
library(ggplot2)
# plot
ggstatsplot::ggbarstats(
data = ggstatsplot::movies_long,
x = mpaa,
y = genre,
sampling.plan = "jointMulti",
title = "MPAA Ratings by Genre",
xlab = "movie genre",
legend.title = "MPAA rating",
ggtheme = hrbrthemes::theme_ipsum_pub(),
ggplot.component = list(scale_x_discrete(guide = guide_axis(n.dodge = 2))),
palette = "Set2",
messages = FALSE
)
```

And, needless to say, there is also a `grouped_`

variant of this
function-

```
# setup
set.seed(123)
# smaller dataset
df <-
dplyr::filter(
.data = forcats::gss_cat,
race %in% c("Black", "White"),
relig %in% c("Protestant", "Catholic", "None"),
!partyid %in% c("No answer", "Don't know", "Other party")
)
# plot
ggstatsplot::grouped_ggbarstats(
data = df,
x = relig,
y = partyid,
grouping.var = race,
title.prefix = "Race",
xlab = "Party affiliation",
ggtheme = ggthemes::theme_tufte(base_size = 12),
ggstatsplot.layer = FALSE,
messages = FALSE,
title.text = "Race, religion, and political affiliation",
plotgrid.args = list(nrow = 2)
)
```

### Summary of tests

This is identical to the `ggpiestats`

function summary of tests.

`gghistostats`

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

can be used.

```
# for reproducibility
set.seed(123)
# plot
ggstatsplot::gghistostats(
data = iris, # dataframe from which variable is to be taken
x = Sepal.Length, # numeric variable whose distribution is of interest
title = "Distribution of Iris sepal length", # title for the plot
caption = substitute(paste(italic("Source:"), "Ronald Fisher's Iris data set")),
bar.measure = "both",
test.value = 5, # default value is 0
test.value.line = TRUE, # display a vertical line at test value
centrality.parameter = "mean", # which measure of central tendency is to be plotted
centrality.line.args = list(color = "darkred"), # aesthetics for central tendency line
binwidth = 0.10, # binwidth value (experiment)
messages = FALSE, # turn off the messages
ggtheme = hrbrthemes::theme_ipsum_tw(), # choosing a different theme
ggstatsplot.layer = FALSE # turn off ggstatsplot theme layer
)
```

As can be seen from the plot, Bayes Factor can be attached (```
bf.message
= TRUE
```

) to assess evidence in favor of the null hypothesis.

Additionally, there is also a `grouped_`

variant of this function that
makes it easy to repeat the same operation across a **single** grouping
variable:

```
# for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_gghistostats(
data = dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
x = budget,
xlab = "Movies budget (in million US$)",
type = "robust", # use robust location measure
grouping.var = genre, # grouping variable
normal.curve = TRUE, # superimpose a normal distribution curve
normal.curve.args = list(color = "red", size = 1),
title.prefix = "Movie genre",
ggtheme = ggthemes::theme_tufte(),
ggplot.component = list( # modify the defaults from `ggstatsplot` for each plot
ggplot2::scale_x_continuous(breaks = seq(0, 200, 50), limits = (c(0, 200)))
),
messages = FALSE,
plotgrid.args = list(nrow = 2),
title.text = "Movies budgets for different genres"
)
```

### Summary of tests

Following tests are carried out for each type of analyses-

Type | Test |
---|---|

Parametric | One-sample Student’s t-test |

Non-parametric | One-sample Wilcoxon test |

Robust | One-sample percentile bootstrap |

Bayes Factor | One-sample Student’s t-test |

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

Type | Effect size | CI? |
---|---|---|

Parametric | Cohen’s d, Hedge’s g (central-and noncentral-t distribution based) | Yes |

Non-parametric | r | Yes |

Robust | robust location measure | Yes |

Bayes Factor | No | No |

For more, including information about the variant of this function
`grouped_gghistostats`

, see the `gghistostats`

vignette:
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/gghistostats.html

`ggdotplotstats`

This function is similar to `gghistostats`

, but is intended to be used
when the numeric variable also has a label.

```
# for reproducibility
set.seed(123)
# plot
ggdotplotstats(
data = dplyr::filter(.data = gapminder::gapminder, continent == "Asia"),
y = country,
x = lifeExp,
test.value = 55,
test.value.line = TRUE,
centrality.parameter = "median",
centrality.k = 0,
title = "Distribution of life expectancy in Asian continent",
xlab = "Life expectancy",
messages = FALSE,
caption = substitute(
paste(
italic("Source"),
": Gapminder dataset from https://www.gapminder.org/"
)
)
)
```

As with the rest of the functions in this package, there is also a
`grouped_`

variant of this function to facilitate looping the same
operation for all levels of a single grouping variable.

```
# for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_ggdotplotstats(
data = dplyr::filter(.data = ggplot2::mpg, cyl %in% c("4", "6")),
x = cty,
y = manufacturer,
xlab = "city miles per gallon",
ylab = "car manufacturer",
type = "nonparametric", # non-parametric test
grouping.var = cyl, # grouping variable
test.value = 15.5,
test.value.line = TRUE,
title.prefix = "cylinder count",
point.args = list(color = "red", size = 5, shape = 13),
messages = FALSE,
title.text = "Fuel economy data"
)
```

### Summary of tests

This is identical to summary of tests for `gghistostats`

.

`ggcorrmat`

`ggcorrmat`

makes a correlalogram (a matrix of correlation coefficients)
with minimal amount of code. Just sticking to the defaults itself
produces publication-ready correlation matrices. But, for the sake of
exploring the available options, let’s change some of the defaults. For
example, multiple aesthetics-related arguments can be modified to change
the appearance of the correlation matrix.

```
# for reproducibility
set.seed(123)
# as a default this function outputs a correlation matrix plot
ggstatsplot::ggcorrmat(
data = ggplot2::msleep,
type = "robust", # correlation method
p.adjust.method = "holm", # p-value adjustment method for multiple comparisons
cor.vars = c(sleep_rem, awake:bodywt), # a range of variables can be selected
cor.vars.names = c(
"REM sleep", # variable names
"time awake",
"brain weight",
"body weight"
),
matrix.type = "upper", # type of visualization matrix
colors = c("#B2182B", "white", "#4D4D4D"),
title = "Correlalogram for mammals sleep dataset",
subtitle = "sleep units: hours; weight units: kilograms",
caption = "Source: `ggplot2` R package"
)
```

Two things to note:

If there are

`NA`

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

`cor.vars`

are not specified, all numeric variables will be used.

There is also a `grouped_`

variant of this function that makes it easy
to repeat the same operation across a **single** grouping variable:

```
# for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_ggcorrmat(
data = dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
cor.vars = length:votes,
colors = c("#cbac43", "white", "#550000"),
grouping.var = genre, # grouping variable
k = 3L, # number of digits after decimal point
title.prefix = "Movie genre",
messages = FALSE,
plotgrid.args = list(nrow = 2)
)
```

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

```
# setup
set.seed(123)
# dataframe in long format
ggcorrmat(
data = ggplot2::msleep,
type = "bayes",
output = "dataframe"
)
#> # A tibble: 15 x 12
#> parameter1 parameter2 rho ci_low ci_high pd rope_percentage
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 sleep_total sleep_rem 0.735 0.617 0.810 1 0
#> 2 sleep_total sleep_cycle -0.436 -0.645 -0.194 0.998 0.0225
#> 3 sleep_total awake -1.00 -1.00 -1.00 1 0
#> 4 sleep_total brainwt -0.344 -0.525 -0.157 0.997 0.0222
#> 5 sleep_total bodywt -0.295 -0.456 -0.142 0.997 0.0318
#> 6 sleep_rem sleep_cycle -0.308 -0.539 -0.0463 0.969 0.0985
#> 7 sleep_rem awake -0.733 -0.827 -0.640 1 0
#> 8 sleep_rem brainwt -0.206 -0.413 0.0106 0.924 0.208
#> 9 sleep_rem bodywt -0.313 -0.492 -0.132 0.994 0.0368
#> 10 sleep_cycle awake 0.440 0.213 0.659 0.992 0.0205
#> 11 sleep_cycle brainwt 0.823 0.716 0.910 1 0
#> 12 sleep_cycle bodywt 0.379 0.133 0.607 0.988 0.0385
#> 13 awake brainwt 0.341 0.160 0.520 0.996 0.03
#> 14 awake bodywt 0.302 0.144 0.463 0.998 0.0295
#> 15 brainwt bodywt 0.925 0.892 0.955 1 0
#> prior_distribution prior_location prior_scale bf nobs
#> <chr> <dbl> <dbl> <dbl> <int>
#> 1 cauchy 0 0.707 3.00e+ 9 61
#> 2 cauchy 0 0.707 8.85e+ 0 32
#> 3 cauchy 0 0.707 NA 83
#> 4 cauchy 0 0.707 7.29e+ 0 56
#> 5 cauchy 0 0.707 9.28e+ 0 83
#> 6 cauchy 0 0.707 1.42e+ 0 32
#> 7 cauchy 0 0.707 3.01e+ 9 61
#> 8 cauchy 0 0.707 6.54e- 1 48
#> 9 cauchy 0 0.707 4.80e+ 0 61
#> 10 cauchy 0 0.707 8.85e+ 0 32
#> 11 cauchy 0 0.707 3.80e+ 6 30
#> 12 cauchy 0 0.707 3.76e+ 0 32
#> 13 cauchy 0 0.707 7.29e+ 0 56
#> 14 cauchy 0 0.707 9.27e+ 0 83
#> 15 cauchy 0 0.707 1.58e+22 56
```

### Summary of tests

Following tests are carried out for each type of analyses. Additionally, the correlation coefficients (and their confidence intervals) are used as effect sizes-

Type | Test | CI? |
---|---|---|

Parametric | Pearson’s correlation coefficient | Yes |

Non-parametric | Spearman’s rank correlation coefficient | Yes |

Robust | Percentage bend correlation coefficient | Yes |

Bayes Factor | Pearson’s correlation coefficient | Yes |

For examples and more information, see the `ggcorrmat`

vignette:
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggcorrmat.html

`ggcoefstats`

The function `ggstatsplot::ggcoefstats`

generates **dot-and-whisker
plots** for regression models saved in a tidy data frame. The tidy
dataframes are prepared using the following packages: `broom`

,
`broom.mixed`

, and `parameters`

. Additionally, if available, the model
summary indices are also extracted from the following packages: `broom`

,
`broom.mixed`

, and `performance`

.

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

The dot-whisker plot contains a dot representing the

**estimate**and their**confidence intervals**(`95%`

is the default). The estimate can either be effect sizes (for tests that depend on the`F`

statistic) or regression coefficients (for tests with`t`

and`z`

statistic), etc. The function will, by default, display a helpful`x`

-axis label that should clear up what estimates are being displayed. The confidence intervals can sometimes be asymmetric if bootstrapping was used.The caption will always contain diagnostic information, if available, about models that can be useful for model selection: The smaller the Akaike’s Information Criterion (

**AIC**) and the Bayesian Information Criterion (**BIC**) values, the “better” the model is.The output of this function will be a

`ggplot2`

object and, thus, it can be further modified (e.g., change themes, etc.) with`ggplot2`

functions.

```
# for reproducibility
set.seed(123)
# model
mod <- stats::lm(formula = mpg ~ am * cyl, data = mtcars)
# plot
ggstatsplot::ggcoefstats(mod)
```

This default plot can be further modified to one’s liking with additional arguments (also, let’s use a different model now):

```
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggcoefstats(
x = MASS::rlm(formula = mpg ~ am * cyl, data = mtcars),
point.args = list(color = "red", size = 3, shape = 15),
vline.args = list(size = 1, color = "#CC79A7", linetype = "dotdash"),
stats.label.color = c("#0072B2", "#D55E00", "darkgreen"),
title = "Car performance predicted by transmission & cylinder count",
subtitle = "Source: 1974 Motor Trend US magazine",
ggtheme = hrbrthemes::theme_ipsum_ps(),
ggstatsplot.layer = FALSE
) + # note the order in which the labels are entered
ggplot2::scale_y_discrete(labels = c("transmission", "cylinders", "interaction")) +
ggplot2::labs(x = "regression coefficient", y = NULL)
```

Most of the regression models that are supported in the underlying
packages are also supported by `ggcoefstats`

. For example-

`aareg`

, `anova`

, `aov`

, `aovlist`

, `Arima`

, `bayesx`

, `bayesGARCH`

,
`BBmm`

, `BBreg`

, `bcplm`

, `bglmerMod`

, `bife`

, `bigglm`

, `biglm`

,
`blavaan`

, `bmlm`

, `blmerMod`

, `bracl`

, `brglm2`

, `brmsfit`

,
`brmultinom`

, `btergm`

, `cch`

, `cgam`

, `cgamm`

, `cglm`

, `clm`

, `clm2`

,
`clmm`

, `clmm2`

, `coeftest`

, `complmrob`

, `confusionMatrix`

, `coxme`

,
`coxph`

, `cpglm`

, `cpglmm`

, `crch`

, `DirichReg`

, `drc`

, `emmGrid`

,
`epi.2by2`

, `ergm`

, `feis`

, `felm`

, `fitdistr`

, `flexsurvreg`

, `glmc`

,
`glmerMod`

, `glmmTMB`

, `gls`

, `gam`

, `Gam`

, `gamlss`

, `garch`

, `glm`

,
`glmmadmb`

, `glmmPQL`

, `glmRob`

, `glmrob`

, `glmx`

, `gmm`

, `hurdle`

,
`ivreg`

, `iv_robust`

, `lavaan`

, `lm`

, `lm.beta`

, `lmerMod`

,
`lmerModLmerTest`

, `lmodel2`

, `lmRob`

, `lmrob`

, `LORgee`

, `lrm`

, `mcmc`

,
`mcmc.list`

, `MCMCglmm`

, `mclogit`

, `mmclogit`

, `mediate`

, `mixor`

,
`mjoint`

, `mle2`

, `mlm`

, `multinom`

, `negbin`

, `nlmerMod`

, `nlrq`

,
`nlreg`

, `nls`

, `orcutt`

, `plm`

, `polr`

, `ridgelm`

, `rjags`

, `rlm`

,
`rlmerMod`

, `robmixglm`

, `rq`

, `rqss`

, `slm`

, `speedglm`

, `speedlm`

,
`stanfit`

, `stanreg`

, `survreg`

, `svyglm`

, `svyolr`

, `svyglm`

, `tobit`

,
`truncreg`

, `vgam`

, `wbgee`

, `wblm`

, `zcpglm`

, `zeroinfl`

, etc.

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

For a more exhaustive account of this function, see the associated vignette- https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggcoefstats.html

`combine_plots`

The full power of `ggstatsplot`

can be leveraged with a functional
programming package like `purrr`

that
replaces `for`

loops with code that is both more succinct and easier to
read and, therefore, `purrr`

should be preferrred