# ggstatsplot v0.0.4

Monthly downloads

## 'ggplot2' Based Plots with Statistical Details

Extension of 'ggplot2', 'ggstatsplot' creates graphics with details from statistical tests
(parametric, non-parametric, or robust) included in the plots themselves. It is targeted primarily at
behavioral sciences community to provide a one-line code to generate information-rich plots for
statistical analysis of continuous (violin plots, scatterplots, histograms) or categorical (pie charts) data.

## Readme

# ggstatsplot: `ggplot2`

Based Plots with Statistical Details

## Overview

`ggstatsplot`

is an
extension of `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.
Currently, it supports only the most common types of statistical tests
(**parametric**, **nonparametric**, and **robust** versions of
**t-tets/anova**, **correlation**, and **contingency tables** analyses).

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

**violin plots**(for comparisons between groups or conditions),**pie charts**(for categorical data),**scatterplots**(for correlations between two variables),**correlation matrices**(for correlations between multiple variables),**histograms**(for hypothesis about distributions), and**dot-and-whisker plots**(for regression models).

In addition to these basic plots, `ggstatsplot`

also provides `grouped_`

versions of all functions that makes it easy to repeat the same anlysis
for any grouping variable.

Future versions will include other types of analyses and plots as well.

## Installation

To get the latest, stable CRAN release:

```
utils::install.packages(pkgs = "ggstatsplot")
```

You can get the **development** version from GitHub. If you are in hurry
and want to reduce the time of installation, prefer-

```
# needed package to download from GitHub repo
utils::install.packages(pkgs = "devtools")
# downloading the package from GitHub
devtools::install_github(
repo = "IndrajeetPatil/ggstatsplot", # package path on GitHub
dependencies = FALSE, # assumes that you already have all packages installed needed for this package to work
quick = TRUE # skips docs, demos, and vignettes
)
```

If time is not a constraint-

```
devtools::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:

```
utils::citation(package = "ggstatsplot")
```

## Help

There is a dedicated website to `ggstatplot`

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

In `R`

, documentation for any function can be accessed with the standard
`help`

command-

```
?ggbetweenstats
?ggscatterstats
?gghistostats
?ggpiestats
?ggcorrmat
?ggcoefstats
?combine_plots
?grouped_ggbetweenstats
?grouped_ggscatterstats
?grouped_gghistostats
?grouped_ggpiestats
?grouped_ggcorrmat
```

## Usage

`ggstatsplot`

relies on non-standard
evaluation,
which means you **shouldn’t** enter arguments in the following manner:
`data = NULL, x = data$x, y = data$y`

. You **must** always specify the
`data`

argument for all functions.

Additionally, `ggstatsplot`

is a very chatty package and will by default
output information about references for tests, notes on assumptions
about linear models, and warnings. If you don’t want your console to be
cluttered with such messages, they can be turned off by setting argument
`messages = FALSE`

in the function call.

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

. **Note**: 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**:
https://cran.r-project.org/web/packages/ggstatsplot/index.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-

```
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggbetweenstats(
data = datasets::iris,
x = Species,
y = Sepal.Length,
messages = FALSE
)
```

Number of other arguments can be specified to make this plot even more
informative and, additionally, this function returns a `ggplot2`

object
and thus any of the graphics layers can be further modified:

```
library(ggplot2)
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggbetweenstats(
data = datasets::iris,
x = Species,
y = Sepal.Length,
notch = TRUE, # show notched box plot
mean.plotting = TRUE, # whether mean for each group id to be displayed
type = "parametric", # which type of test is to be run
outlier.tagging = TRUE, # whether outliers need to be tagged
outlier.label = Sepal.Width, # variable to be used for the outlier tag
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
caption = expression( # caption text for the plot
paste(italic("Note"), ": this is a demo")
),
ggtheme = ggplot2::theme_grey(), # choosing a different theme
palette = "Set1", # choosing a different color palette
messages = FALSE
) + # further modification outside of ggstatsplot
ggplot2::coord_cartesian(ylim = c(3, 8)) +
ggplot2::scale_y_continuous(breaks = seq(3, 8, by = 1))
```

The `type`

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

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

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

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

, `"violin"`

, or `"boxviolin"`

).

Variant of this function `ggwithinstats`

is currently under work. You
*can* still use this function just to prepare the **plot** for
exploratory data analysis, but the statistical details displayed in the
subtitle will be incorrect. You can remove them by adding ```
+
ggplot2::labs(subtitle = NULL)
```

.

For more, see the `ggbetweenstats`

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

`ggscatterstats`

This function creates a scatterplot with marginal histograms/boxplots/density/violin plots from and results from statistical tests in the subtitle:

```
ggstatsplot::ggscatterstats(
data = datasets::iris,
x = Sepal.Length,
y = Petal.Length,
title = "Dataset: Iris flower data set",
messages = FALSE
)
```

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

```
library(datasets)
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggscatterstats(
data = subset(datasets::iris, iris$Species == "setosa"),
x = Sepal.Length,
y = Petal.Length,
type = "robust", # type of test that needs to be run
xlab = "Attribute: Sepal Length", # label for x axis
ylab = "Attribute: Petal Length", # label for y axis
line.color = "black", # changing regression line color line
title = "Dataset: Iris flower data set", # title text for the plot
caption = expression( # caption text for the plot
paste(italic("Note"), ": this is a demo")
),
marginal.type = "density", # type of marginal distribution to be displayed
xfill = "blue", # color fill for x-axis marginal distribution
yfill = "red", # color fill for y-axis marginal distribution
centrality.para = "median", # which type of central tendency lines are to be displayed
width.jitter = 0.2, # amount of horizontal jitter for data points
height.jitter = 0.4, # amount of vertical jitter for data points
messages = FALSE # turn off messages and notes
)
```

For more, see the `ggscatterstats`

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

`ggpiestats`

This function creates a pie chart for categorical variables with results
from contingency table analysis included in the subtitle of the plot. If
only one categorical variable is entered, results from one-sample
**proportion test** will be displayed as a subtitle.

```
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggpiestats(
data = datasets::iris,
main = Species,
messages = FALSE
)
```

This function can also be used to study an interaction between two
categorical variables. Additionally, as with the other functions in
`ggstatsplot`

, this function returns a `ggplot2`

object and can further
be modified with `ggplot2`

syntax (e.g., we can change the color palette
*after* `ggstatsplot`

has produced the plot)-

```
library(ggplot2)
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggpiestats(
data = datasets::mtcars,
main = cyl,
condition = am,
title = "Dataset: Motor Trend Car Road Tests",
messages = FALSE
) + # further modification outside of ggstatsplot to change the default palette as an example
ggplot2::scale_fill_brewer(palette = "Set1")
```

As with the other functions, this basic plot can further be modified with additional arguments:

```
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggpiestats(
data = datasets::mtcars,
main = am,
condition = cyl,
title = "Dataset: Motor Trend Car Road Tests", # title for the plot
stat.title = "interaction: ", # title for the results from Pearson's chi-squared test
legend.title = "Transmission", # title for the legend
factor.levels = c("1 = manual", "0 = automatic"), # renaming the factor level names for 'main' variable
facet.wrap.name = "No. of cylinders", # name for the facetting variable
facet.proptest = FALSE, # turning of facetted proportion test results
caption = expression( # text for the caption
paste(italic("Note"), ": this is a demo")
),
messages = FALSE # turn off messages and notes
)
```

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

, see the `ggpiestats`

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

`gghistostats`

In case you would like to see the distribution of one variable and check if it is significantly different from a specified value with a one sample test, this function will let you do that.

```
library(datasets)
ggstatsplot::gghistostats(
data = datasets::iris,
x = Sepal.Length,
title = "Distribution of Iris sepal length",
type = "parametric", # one sample t-test
test.value = 3, # default value is 0
centrality.para = "mean", # which measure of central tendency is to be plotted
centrality.color = "darkred", # decides color of vertical line representing central tendency
binwidth = 0.10, # binwidth value (needs to be toyed around with until you find the best one)
messages = FALSE # turn off the messages
)
```

The `type`

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

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

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

(for
*Bayes Factor*).

```
ggstatsplot::gghistostats(
data = NULL,
title = "Distribution of variable x",
x = stats::rnorm(n = 1000, mean = 0, sd = 1),
test.value = 1,
test.value.line = TRUE,
test.value.color = "black",
centrality.para = "mean",
type = "bf",
bf.prior = 0.8,
messages = FALSE,
caption = expression(
paste(italic("Note"), ": black line - test value; blue line - observed mean")
)
)
```

As seen here, by default, Bayes Factor quantifies the support for the alternative hypothesis (H1) over the null hypothesis (H0) (i.e., BF10 is displayed).

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

, see the `gghistostats`

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

`ggcorrmat`

`ggcorrmat`

makes correlalograms with minimal amount of code. Just
sticking to the defaults itself produces publication-ready correlation
matrices. (Wrapper around
`ggcorrplot`

)

```
# as a default this function outputs a correlalogram plot
ggstatsplot::ggcorrmat(
data = datasets::iris,
corr.method = "spearman", # correlation method
sig.level = 0.005, # threshold of significance
cor.vars = Sepal.Length:Petal.Width, # a range of variables can be selected
cor.vars.names = c("Sepal Length", "Sepal Width", "Petal Length", "Petal Width"),
title = "Correlalogram for length measures for Iris species",
subtitle = "Iris dataset by Anderson",
caption = expression(
paste(
italic("Note"),
": X denotes correlation non-significant at ",
italic("p "),
"< 0.005; adjusted alpha"
)
)
)
```

Multiple arguments can be modified to change the appearance of the correlation matrix.

Alternatively, you can use it just to get the correlation matrices and
their corresponding *p*-values (in a
tibble format). This is especially
useful for robust correlation coefficient, which is not currently
supported in `ggcorrmat`

plot.

```
# getting the correlation coefficient matrix
ggstatsplot::ggcorrmat(
data = datasets::iris,
cor.vars = Sepal.Length:Petal.Width,
corr.method = "robust",
output = "correlations", # specifying the needed output
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
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 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 = datasets::iris,
cor.vars = Sepal.Length:Petal.Width,
corr.method = "robust",
output = "p-values"
)
#> # A tibble: 4 x 5
#> variable Sepal.Length Sepal.Width Petal.Length Petal.Width
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Sepal.Length 0 0.0818 0 0
#> 2 Sepal.Width 0.0818 0 0.0000000529 0.00000252
#> 3 Petal.Length 0 0.0000000529 0 0
#> 4 Petal.Width 0 0.00000252 0 0
```

For examples and more information, see the `ggcorrmat`

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

`ggcoefstats`

`ggcoefstats`

creates a lot with the regression coefficients’ point
estimates as dots with confidence interval whiskers. This is a wrapper
function around `GGally::ggcoef`

.

```
ggstatsplot::ggcoefstats(x = stats::lm(formula = mpg ~ am * cyl,
data = mtcars))
```

The basic can be further modified to one’s liking with additional arguments:

```
ggstatsplot::ggcoefstats(
x = stats::lm(formula = mpg ~ am * cyl,
data = mtcars),
point.color = "red",
vline.color = "#CC79A7",
vline.linetype = "dotdash",
stats.label.size = 3.5,
stats.label.color = c("#0072B2", "#D55E00", "darkgreen"),
title = "Car performance predicted by transmission and cylinder count",
subtitle = "Source: 1974 Motor Trend US magazine"
) +
# 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)
```

All the regression model classes that are supported in the `broom`

package with `tidy`

and `glance`

methods
(https://broom.tidyverse.org/articles/available-methods.html) are also
supported by `ggcoefstats`

. Let’s see few examples:

```
library(dplyr)
library(lme4)
# for reproducibility
set.seed(200)
# creating dataframes needed for the analysis below
d <- as.data.frame(Titanic)
# combining plots together
ggstatsplot::combine_plots(
# generalized linear model
ggstatsplot::ggcoefstats(
x = stats::glm(
formula = Survived ~ Sex + Age,
data = d,
weights = d$Freq,
family = "binomial"
),
exponentiate = TRUE,
exclude.intercept = FALSE,
title = "generalized linear model"
),
# nonlinear least squares
ggstatsplot::ggcoefstats(
x = stats::nls(
formula = mpg ~ k / wt + b,
data = mtcars,
start = list(k = 1, b = 0)
),
point.color = "darkgreen",
title = "non-linear least squares"
),
# linear mmodel
ggstatsplot::ggcoefstats(
x = lme4::lmer(
formula = Reaction ~ Days + (Days | Subject),
data = lme4::sleepstudy
),
point.color = "red",
exclude.intercept = TRUE,
title = "linear mixed-effects model"
),
# generalized linear mixed-effects model
ggstatsplot::ggcoefstats(
x = lme4::glmer(
formula = cbind(incidence, size - incidence) ~ period + (1 | herd),
data = lme4::cbpp,
family = binomial
),
exclude.intercept = FALSE,
title = "generalized linear mixed-effects model"
),
labels = c("(a)", "(b)", "(c)", "(d)"),
nrow = 2,
ncol = 2
)
```

This is by no means an exhaustive list of models supported by
`ggcoefstats`

. For more, see the associated vignette-
https://indrajeetpatil.github.io/ggstatsplot/articles/ggcoefstats.html

`combine_plots`

`ggstatsplot`

also contains a helper function `combine_plots`

to combine
multiple plots. This is a wrapper around and lets you combine multiple
plots and add combination of title, caption, and annotation texts with
suitable default parameters.

The full power of `ggstatsplot`

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

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

should be preferrred.

For more, see the associated vignette- https://indrajeetpatil.github.io/ggstatsplot/articles/combine_plots.html

`theme_mprl`

All plots from `ggstatsplot`

have a default theme: `theme_mprl`

. For
more on how to modify it, see the associated vignette-
https://indrajeetpatil.github.io/ggstatsplot/articles/theme_mprl.html

## Functions in ggstatsplot

Name | Description | |

check_outlier | Finding the outliers in the dataframe using Tukey's interquartile range rule | |

combine_plots | Combining multiple plots using cowplot::plot_grid() with a combination of title, caption, and annotation label | |

grouped_ggpiestats | Grouped pie charts with statistical tests | |

cor_tets_ci | A correlation test with confidence interval for effect size. | |

grouped_ggscatterstats | Scatterplot with marginal distributions for all levels of a grouping variable | |

normality_message | Display normality test result as a message. | |

%>% | Pipe operator | |

lm_effsize_ci | Confidence intervals for partial eta-squared and omega-squared for linear models. | |

theme_mprl | Default theme used in all ggstatsplot package plots | |

theme_corrmat | Default theme used for correlation matrix | |

ggbetweenstats | violin plots for group or condition comparisons | |

untable | Untable a dataset | |

ggcoefstats | Model coefficients for fitted models with the model summary as a caption. | |

grouped_proptest | Function to run proportion test on grouped data. | |

ggcorrmat | Visualization of a correlalogram (or correlation matrix) using 'ggplot2'/'ggcorrplot' | |

gghistostats | Histogram for distribution of a numeric variable | |

intent_morality | Moral judgments about third-party moral behavior. | |

robcor_ci | Robust correlation coefficient and its confidence interval | |

signif_column | Creating a new character type column with significance labels | |

Titanic_full | Titanic dataset. | |

bartlett_message | Display homogeneity of variance test as a message | |

specify_decimal_p | Custom function for getting specified number of decimal places in results for p-value | |

ggpiestats | Pie charts with statistical tests | |

t1way_ci | A heteroscedastic one-way ANOVA for trimmed means with confidence interval for effect size. | |

ggscatterstats | Scatterplot with marginal distributions | |

movies_long | Movie information and user ratings from IMDB.com (long format). | |

movies_wide | Movie information and user ratings from IMDB.com (wide format). | |

theme_pie | Default theme used for pie chart | |

tibble | Anticipate use of tibbles | |

chisq_v_ci | Chi-squared test of association with confidence interval for effect size (Cramer's V). | |

grouped_gghistostats | Grouped histograms for distribution of a numeric variable | |

grouped_ggbetweenstats | Violin plots for group or condition comparisons repeated across all levels of a grouping variable. | |

grouped_ggcorrmat | Visualization of a correlalogram (or correlation matrix) using 'ggplot2'/'ggcorrplot' for all levels of a grouping variable | |

ggstatsplot-package | ggstatsplot | |

legend_title_margin | Custom function to set upper and lower margins to legend title in ggplot2 | |

No Results! |

## Vignettes of ggstatsplot

Name | ||

combine_plots.Rmd | ||

ggbetweenstats.Rmd | ||

ggcoefstats.Rmd | ||

ggcorrmat.Rmd | ||

gghistostats.Rmd | ||

ggpiestats.Rmd | ||

ggscatterstats.Rmd | ||

theme_mprl.Rmd | ||

No Results! |

## Last month downloads

## Details

Type | Package |

License | GPL-3 | file LICENSE |

URL | https://indrajeetpatil.github.io/ggstatsplot/ |

BugReports | https://github.com/IndrajeetPatil/ggstatsplot/issues |

Encoding | UTF-8 |

LazyData | true |

RoxygenNote | 6.0.1.9000 |

VignetteBuilder | knitr |

Language | en-US |

NeedsCompilation | no |

Packaged | 2018-07-05 06:18:26 UTC; inp099 |

Repository | CRAN |

Date/Publication | 2018-07-05 12:50:02 UTC |

imports | apaTables (>= 2.0.4) , boot (>= 1.3-20) , broom (>= 0.4.5) , coin (>= 1.2-2) , cowplot (>= 0.9.2) , crayon (>= 1.3.4) , dplyr (>= 0.7.5) , effsize (>= 0.7.1) , ggcorrplot (>= 0.1.1) , ggExtra (>= 0.8) , ggplot2 (>= 3.0.0) , ggrepel (>= 0.8.0) , glue (>= 1.2.0) , grid , gtable (>= 0.2.0) , jmv (>= 0.8.6.2) , lmerTest (>= 3.0-1) , magrittr , MBESS (>= 4.4.3) , purrr (>= 0.2.4) , purrrlyr (>= 0.0.3) , rlang (>= 0.2.0) , scales (>= 0.5.0) , sjstats (>= 0.15.0) , tibble (>= 1.4.2) , tidyr (>= 0.8.1) , WRS2 (>= 0.10-0) |

depends | datasets , grDevices , R (>= 3.3.0) , stats |

suggests | gapminder (>= 0.3.0) , ggplot2movies , knitr , lme4 , ordinal , rmarkdown , spelling , testthat , vdiffr |

Contributors |

#### Include our badge in your README

```
[![Rdoc](http://www.rdocumentation.org/badges/version/ggstatsplot)](http://www.rdocumentation.org/packages/ggstatsplot)
```