ggstatsplot v0.1.4
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'ggplot2' Based Plots with Statistical Details
Extension of 'ggplot2', 'ggstatsplot' creates
graphics with details from statistical tests 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, dot plots, dot-and-whisker plots) or categorical (pie and
bar charts) data. Currently, it supports only the most common types of
statistical tests: parametric, nonparametric, robust, and bayesian
versions of t-test/anova, correlation analyses, contingency table
analysis, and regression analyses.
Readme
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 plots themselves and targeted primarily at behavioral sciences
community to provide a one-line code to produce information-rich plots.
In a typical exploratory data analysis workflow, data visualization and
statistical modeling are two different phases: visualization informs
modeling, and modeling in its turn can suggest a different visualization
method, and so on and so forth. The central idea of ggstatsplot
is
simple: combine these two phases into one in the form of graphics with
statistical details, which makes data exploration simpler and faster.
Summary of 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 |
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, 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 | No |
ggscatterstats |
Correlation between two variables | Yes | Yes | Yes | Yes |
ggpiestats , ggbarstats |
Association between categorical variables | Yes | NA |
NA |
Yes |
ggpiestats , ggbarstats |
Equal proportions for categorical variable levels | Yes | NA |
NA |
Yes |
ggcoefstats |
Regression model coefficients | Yes | No | Yes | No |
Statistical reporting
For all statistical tests reported in the plots, the default template abides by the APA 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:
Functions | Type | Test | Effect size | 95% CI available? |
---|---|---|---|---|
ggbetweenstats (2 groups) |
Parametric | Student’s and Welch’s t-test | Cohen’s d, Hedge’s g | |
ggbetweenstats (> 2 groups) |
Parametric | Fisher’s and Welch’s one-way ANOVA | ||
ggbetweenstats (2 groups) |
Non-parametric | Mann-Whitney U-test | r | |
ggbetweenstats (> 2 groups) |
Non-parametric | Kruskal-Wallis Rank Sum Test | ||
ggbetweenstats (2 groups) |
Robust | Yuen’s test for trimmed means | ||
ggbetweenstats (> 2 groups) |
Robust | Heteroscedastic one-way ANOVA for trimmed means | ||
ggwithinstats (2 groups) |
Parametric | Student’s t-test | Cohen’s d, Hedge’s g | |
ggwithinstats (> 2 groups) |
Parametric | Fisher’s one-way repeated measures ANOVA | ||
ggwithinstats (2 groups) |
Non-parametric | Wilcoxon signed-rank test | r | |
ggwithinstats (> 2 groups) |
Non-parametric | Friedman rank sum test | ||
ggwithinstats (2 groups) |
Robust | Yuen’s test on trimmed means for dependent samples | ||
ggwithinstats (> 2 groups) |
Robust | Heteroscedastic one-way repeated measures ANOVA for trimmed means | ||
ggpiestats and ggbarstats (unpaired) |
Parametric | Cramér’s V | ||
ggpiestats and ggbarstats (paired) |
Parametric | McNemar’s test | Cohen’s g | |
ggpiestats |
Parametric | One-sample proportion test | Cramér’s V | |
ggscatterstats and ggcorrmat |
Parametric | Pearson’s r | r | |
ggscatterstats and ggcorrmat |
Non-parametric | |||
ggscatterstats and ggcorrmat |
Robust | Percentage bend correlation | r | |
gghistostats and ggdotplotstats |
Parametric | One-sample t-test | Cohen’s d, Hedge’s g | |
gghistostats |
Non-parametric | One-sample Wilcoxon signed rank test | r | |
gghistostats and ggdotplotstats |
Robust | One-sample percentile bootstrap | robust estimator | |
ggcoefstats |
Parametric | Regression models |
Installation
To get the latest, stable CRAN
release (0.1.3
):
utils::install.packages(pkgs = "ggstatsplot")
Note: If you are on a linux machine, you will need to have OpenGL
libraries installed (specifically, libx11
, mesa
and Mesa OpenGL
Utility library - glu
) for the dependency package rgl
to work.
You can get the development version of the package from GitHub
(0.1.3.9000
). To see what new changes (and bug fixes) have been made
to the package since the last release on CRAN
, you can check the
detailed log of changes here:
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
utils::install.packages(pkgs = "remotes")
# downloading the package from GitHub
remotes::install_github(
repo = "IndrajeetPatil/ggstatsplot", # package path on GitHub
dependencies = FALSE, # assumes you have already installed needed packages
quick = TRUE # skips docs, demos, and vignettes
)
If time is not a constraint-
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")
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/.
Help
In R
, documentation for any function can be accessed with the standard
help
command (e.g., ?ggbetweenstats
).
Another handy tool to see arguments to any of the functions is args
.
For example-
args(name = ggstatsplot::specify_decimal_p)
#> function (x, k = 3, p.value = FALSE)
#> NULL
In case you want to look at the function body for any of the functions, just type the name of the function without the parentheses:
# function to convert class of any object to `ggplot` class
ggstatsplot::ggplot_converter
#> function(plot) {
#> cowplot::ggdraw() + cowplot::draw_grob(grid::grobTree(plot))
#> }
#> <bytecode: 0x000000002d4a7830>
#> <environment: namespace:ggstatsplot>
If you are not familiar either with what the namespace ::
does or how
to use pipe operator %>%
, something this package and its documentation
relies a lot on, you can check out these links-
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,
messages = FALSE
) + # further modification outside of ggstatsplot
ggplot2::coord_cartesian(ylim = c(3, 8)) +
ggplot2::scale_y_continuous(breaks = seq(3, 8, by = 1))
Note that this function returns 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.
library(ggplot2)
# for reproducibility
set.seed(123)
# let's leave out one of the factor levels and see if instead of anova, a t-test will be run
iris2 <- dplyr::filter(.data = iris, Species != "setosa")
# let's change the levels of our factors, a common routine in data analysis
# pipeline, to see if this function respects the new factor levels
iris2$Species <- factor(x = iris2$Species, levels = c("virginica", "versicolor"))
# plot
ggstatsplot::ggbetweenstats(
data = iris2,
x = Species,
y = Sepal.Length,
notch = TRUE, # show notched box plot
mean.plotting = TRUE, # whether mean for each group is to be displayed
mean.ci = TRUE, # whether to display confidence interval for means
mean.label.size = 2.5, # size of the label for mean
type = "parametric", # which type of test is to be run
k = 3, # number of decimal places for statistical results
outlier.tagging = TRUE, # whether outliers need to be tagged
outlier.label = Sepal.Width, # variable to be used for the outlier tag
outlier.label.color = "darkgreen", # changing the color for the text label
xlab = "Type of Species", # label for the x-axis variable
ylab = "Attribute: Sepal Length", # label for the y-axis variable
title = "Dataset: Iris flower data set", # title text for the plot
ggtheme = ggthemes::theme_fivethirtyeight(), # choosing a different theme
ggstatsplot.layer = FALSE, # turn off ggstatsplot theme layer
package = "wesanderson", # package from which color palette is to be taken
palette = "Darjeeling1", # choosing a different color palette
messages = FALSE
)
As can be seen from the plot, the function by default returns Bayes
Factor for the test (here, Student’s t-test). If the null hypothesis
can’t be rejected with the null hypothesis significance testing (NHST)
approach, the Bayesian approach can help index evidence in favor of the
null hypothesis (i.e.,
).
By default, natural logarithms are shown because Bayes Factor values can sometimes be pretty large. Having values on logarithmic scale also makes it easy to compare evidence in favor alternative () versus null () hypotheses (since ).
Additionally, there is also a grouped_
variant of this function that
makes it easy to repeat the same operation across a single grouping
variable:
# for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_ggbetweenstats(
data = dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
x = mpaa,
y = length,
grouping.var = genre, # grouping variable
pairwise.comparisons = TRUE, # display significant pairwise comparisons
pairwise.annotation = "p.value", # how do you want to annotate the pairwise comparisons
p.adjust.method = "bonferroni", # method for adjusting p-values for multiple comparisons
conf.level = 0.99, # changing confidence level to 99%
ggplot.component = list( # adding new components to `ggstatsplot` default
ggplot2::scale_y_continuous(sec.axis = ggplot2::dup_axis())
),
k = 3,
title.prefix = "Movie genre",
caption = substitute(paste(
italic("Source"),
":IMDb (Internet Movie Database)"
)),
palette = "default_jama",
package = "ggsci",
messages = FALSE,
nrow = 2,
title.text = "Differences in movie length by mpaa ratings for different genres"
)
Summary of tests
Following (between-subjects) tests are carried out for each type of analyses-
Type | No. of groups | Test |
---|---|---|
Parametric | > 2 | 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 | Dwass-Steel-Crichtlow-Fligner test | Yes |
Robust | No | Yuen’s trimmed means test | Yes |
Bayes Factor | No | No | No |
Bayes Factor | Yes | No | No |
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 = WRS2::WineTasting,
x = Wine,
y = Taste,
sort = "descending", # ordering groups along the x-axis based on
sort.fun = median, # values of `y` variable
pairwise.comparisons = TRUE,
pairwise.display = "s",
pairwise.annotation = "p",
title = "Wine tasting",
caption = "Data from: `WRS2` R package",
ggtheme = ggthemes::theme_fivethirtyeight(),
ggstatsplot.layer = FALSE,
messages = FALSE
)
As with the ggbetweenstats
, this function also has a grouped_
variant that makes repeating the same analysis across a single grouping
variable quicker. We will see an example with only repeated
measurements-
# common setup
set.seed(123)
# getting data in tidy format
data_bugs <- ggstatsplot::bugs_long %>%
dplyr::filter(.data = ., region %in% c("Europe", "North America"))
# plot
ggstatsplot::grouped_ggwithinstats(
data = dplyr::filter(data_bugs, 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 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 | No | No |
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
conf.level = 0.99, # confidence level
xlab = "Movie budget (in million/ US$)", # label for x axis
ylab = "IMDB rating", # label for y axis
label.var = "title", # variable for labeling data points
label.expression = "rating < 5 & budget > 100", # expression that decides which points to label
line.color = "yellow", # changing regression line color line
title = "Movie budget and IMDB rating (action)", # title text for the plot
caption = expression( # caption text for the plot
paste(italic("Note"), ": IMDB stands for Internet Movie DataBase")
),
ggtheme = hrbrthemes::theme_ipsum_ps(), # choosing a different theme
ggstatsplot.layer = FALSE, # turn off ggstatsplot theme layer
marginal.type = "density", # type of marginal distribution to be displayed
xfill = "#0072B2", # color fill for x-axis marginal distribution
yfill = "#009E73", # color fill for y-axis marginal distribution
xalpha = 0.6, # transparency for x-axis marginal distribution
yalpha = 0.6, # transparency for y-axis marginal distribution
centrality.para = "median", # central tendency lines to be displayed
messages = FALSE # turn off messages and notes
)
Additionally, there is also a grouped_
variant of this function that
makes it easy to repeat the same operation across a single grouping
variable. Also, note that, as opposed to the other functions, this
function does not return a ggplot
object and any modification you want
to make can be made in advance using ggplot.component
argument
(available for all functions, but especially useful for this particular
function):
# 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,
label.var = title,
label.expression = length > 200,
conf.level = 0.99,
k = 3, # no. of decimal places in the results
xfill = "#E69F00",
yfill = "#8b3058",
xlab = "IMDB rating",
grouping.var = genre, # grouping variable
title.prefix = "Movie genre",
ggtheme = ggplot2::theme_grey(),
ggplot.component = list(
ggplot2::scale_x_continuous(breaks = seq(2, 9, 1), limits = (c(2, 9)))
),
messages = FALSE,
nrow = 2,
title.text = "Relationship between movie length by IMDB ratings for different genres"
)
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 test for between-subjects design and McNemar’s test for within-subjects design) included in the subtitle of the plot. If only one categorical variable is entered, results from one-sample proportion test (i.e., a goodness of fit test) will be displayed as a subtitle.
Here is an example of a case where the theoretical question is about proportions for different levels of a single nominal variable:
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggpiestats(
data = ggplot2::msleep,
x = vore,
title = "Composition of vore types among mammals",
messages = FALSE
)
This function can also be used to study an interaction between two categorical variables:
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggpiestats(
data = mtcars,
x = am,
y = cyl,
conf.level = 0.99, # confidence interval for effect size measure
title = "Dataset: Motor Trend Car Road Tests", # title for the plot
stat.title = "interaction: ", # title for the results
legend.title = "Transmission", # title for the legend
factor.levels = c("1 = manual", "0 = automatic"), # renaming the factor level names (`x`)
facet.wrap.name = "No. of cylinders", # name for the facetting variable
slice.label = "counts", # show counts data instead of percentages
package = "ggsci", # package from which color palette is to be taken
palette = "default_jama", # choosing a different color palette
caption = substitute( # text for the caption
paste(italic("Source"), ": 1974 Motor Trend US magazine")
),
messages = FALSE # turn off messages and notes
)
In case of repeated measures designs, setting paired = TRUE
will
produce results from McNemar’s
test-
# for reproducibility
set.seed(123)
# data
survey.data <- data.frame(
`1st survey` = c("Approve", "Approve", "Disapprove", "Disapprove"),
`2nd survey` = c("Approve", "Disapprove", "Approve", "Disapprove"),
`Counts` = c(794, 150, 86, 570),
check.names = FALSE
)
# plot
ggstatsplot::ggpiestats(
data = survey.data,
x = `1st survey`,
y = `2nd survey`,
counts = Counts,
paired = TRUE, # within-subjects design
conf.level = 0.99, # confidence interval for effect size measure
package = "wesanderson",
palette = "Royal1"
)
#> Note: 99% CI for effect size estimate was computed with 100 bootstrap samples.
#> # A tibble: 2 x 11
#> `2nd survey` 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:
# 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
label.text.size = 3, # text size for slice labels
slice.label = "both", # show both counts and percentage data
perc.k = 1, # no. of decimal places for percentages
messages = FALSE,
nrow = 2,
title.text = "Composition of MPAA ratings for different genres"
)
Summary of tests
Following tests are carried out for each type of analyses-
Type of data | Design | Test |
---|---|---|
Unpaired | contingency table | 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)
# plot
ggstatsplot::ggbarstats(
data = ggstatsplot::movies_long,
x = mpaa,
y = genre,
sampling.plan = "jointMulti",
title = "MPAA Ratings by Genre",
xlab = "movie genre",
perc.k = 1,
x.axis.orientation = "slant",
ggtheme = hrbrthemes::theme_modern_rc(),
ggstatsplot.layer = FALSE,
ggplot.component = ggplot2::theme(axis.text.x = ggplot2::element_text(face = "italic")),
palette = "Set2",
messages = FALSE
)
Note that p-values for results from one-sample proportion tests are
displayed for each bar in the form of asterisks with the following
convention:
- :
- :
- :
- :
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",
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. We will again demonstrate here that
the outputs are ggplot
objects and can be further modified using any
of the ggplot
extension packages:
# for reproducibility
set.seed(123)
library(firatheme)
# plot
ggstatsplot::gghistostats(
data = ToothGrowth, # dataframe from which variable is to be taken
x = len, # numeric variable whose distribution is of interest
xlab = "Tooth length", # `x`-axis label
title = "Distribution of Tooth Length", # title for the plot
fill.gradient = TRUE, # use color gradient
test.value = 10, # the comparison value for one-sample test
test.value.line = TRUE, # display a vertical line at test value
type = "bayes", # bayes factor for one sample t-test
bf.prior = 0.8, # prior width for calculating the bayes factor
messages = FALSE # turn off the messages
) + # modification outside of `ggstatsplot` using extension packages
theme_fira() +
scale_colour_fira()
The aesthetic defaults can be easily modified internally as well-
# for reproducibility
set.seed(123)
# plot
ggstatsplot::gghistostats(
data = iris, # dataframe from which variable is to be taken
x = Sepal.Length, # numeric variable whose distribution is of interest
title = "Distribution of Iris sepal length", # title for the plot
caption = substitute(paste(italic("Source:"), "Ronald Fisher's Iris data set")),
type = "parametric", # one sample t-test
conf.level = 0.99, # changing confidence level for effect size
bar.measure = "mix", # what does the bar length denote
test.value = 5, # default value is 0
test.value.line = TRUE, # display a vertical line at test value
test.value.color = "#0072B2", # color for the line for test value
centrality.para = "mean", # which measure of central tendency is to be plotted
centrality.color = "darkred", # decides color for central tendency line
binwidth = 0.10, # binwidth value (experiment)
bf.prior = 0.8, # prior width for computing bayes factor
messages = FALSE, # turn off the messages
ggtheme = hrbrthemes::theme_ipsum_tw(), # choosing a different theme
ggstatsplot.layer = FALSE # turn off ggstatsplot theme layer
)
As can be seen from the plot, bayes factor can be attached (bf.message
= TRUE
) to assess evidence in favor of the null hypothesis.
Additionally, there is also a grouped_
variant of this function that
makes it easy to repeat the same operation across a single grouping
variable:
# for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_gghistostats(
data = dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
x = budget,
xlab = "Movies budget (in million US$)",
type = "robust", # use robust location measure
grouping.var = genre, # grouping variable
normal.curve = TRUE, # superimpose a normal distribution curve
normal.curve.color = "red",
title.prefix = "Movie genre",
ggtheme = ggthemes::theme_tufte(),
ggplot.component = list( # modify the defaults from `ggstatsplot` for each plot
ggplot2::scale_x_continuous(breaks = seq(0, 200, 50), limits = (c(0, 200)))
),
messages = FALSE,
nrow = 2,
title.text = "Movies budgets for different genres"
)
Summary of tests
Following tests are carried out for each type of analyses-
Type | Test |
---|---|
Parametric | One-sample 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 (computed as ) | 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,
test.line.labeller = TRUE,
test.value.color = "red",
centrality.para = "median",
centrality.k = 0,
title = "Distribution of life expectancy in Asian continent",
xlab = "Life expectancy",
messages = FALSE,
caption = substitute(
paste(
italic("Source"),
": Gapminder dataset from https://www.gapminder.org/"
)
)
)
As with the rest of the functions in this package, there is also a
grouped_
variant of this function to facilitate looping the same
operation for all levels of a single grouping variable.
# for reproducibility
set.seed(123)
# removing factor level with very few no. of observations
df <- dplyr::filter(.data = ggplot2::mpg, cyl %in% c("4", "6"))
# plot
ggstatsplot::grouped_ggdotplotstats(
data = df,
x = cty,
y = manufacturer,
xlab = "city miles per gallon",
ylab = "car manufacturer",
type = "nonparametric", # non-parametric test
grouping.var = cyl, # grouping variable
test.value = 15.5,
title.prefix = "cylinder count",
point.color = "red",
point.size = 5,
point.shape = 13,
test.value.line = TRUE,
ggtheme = ggthemes::theme_par(),
messages = FALSE,
title.text = "Fuel economy data"
)
Summary of tests
This is identical to summary of tests for gghistostats
.
ggcorrmat
ggcorrmat
makes a correlalogram (a matrix of correlation coefficients)
with minimal amount of code. Just sticking to the defaults itself
produces publication-ready correlation matrices. But, for the sake of
exploring the available options, 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 correlalogram plot
ggstatsplot::ggcorrmat(
data = ggplot2::msleep,
corr.method = "robust", # correlation method
sig.level = 0.001, # threshold of significance
p.adjust.method = "holm", # p-value adjustment method for multiple comparisons
cor.vars = c(sleep_rem, awake:bodywt), # a range of variables can be selected
cor.vars.names = c(
"REM sleep", # variable names
"time awake",
"brain weight",
"body weight"
),
matrix.type = "upper", # type of visualization matrix
colors = c("#B2182B", "white", "#4D4D4D"),
title = "Correlalogram for mammals sleep dataset",
subtitle = "sleep units: hours; weight units: kilograms"
)
Note that if there are NA
s present in the selected variables, the
legend will display minimum, median, and maximum number of pairs used
for correlation tests.
Alternatively, you can use it just to get the correlation matrices and
their corresponding p-values (in a tibble
format).
``` r
for reproducibility
set.seed(123)
show four digits in a tibble
options(pillar.sigfig = 4)
getting the correlation coefficient matrix
ggstatsplot::ggcorrmat( data = iris, # all numeric variables from data will be used corr.method = "robust", output = "correlations", # specifying the needed output ("r" or "corr" will also work) digits = 3 # number of digits to be dispayed for correlation coefficient )
#> # A tibble: 4 x 5
#> variable Sepal.Length Sepal.Width Petal.Length Petal.Width
#>
#> 1 Sepal.Length 1 -0.143 0.878 0.837
#> 2 Sepal.Width -0.143 1 -0.426 -0.373
#> 3 Petal.Length 0.878 -0.426 1 0.966
#> 4 Petal.Width 0.837 -0.373 0.966 1
getting the p-value matrix
ggstatsplot::ggcorrmat( data = ggplot2::msleep, cor.vars = sleep_total:bodywt, corr.method = "robust", output = "p.values", # only "p" or "p-values" will also work p.adjust.method = "holm" )
#> # A tibble: 6 x 7
#> variable sleep_total sleep_rem sleep_cycle awake brainwt bodywt
#>
#> 1 sleep_total 0. 5.291e-12 9.138e- 3 0. 3.170e- 5 2.568e- 6
#> 2 sleep_rem 4.070e-13 0. 1.978e- 2 5.291e-12 9.698e- 3 3.762e- 3
#> 3 sleep_cycle 2.285e- 3 1.978e- 2 0. 9.138e- 3 1.637e- 9 1.696e- 5
#> 4 awake 0. 4.070e-13 2.285e- 3 0. 3.170e- 5 2.568e- 6
#> 5 brainwt 4.528e- 6 4.849e- 3 1.488e-10 4.528e- 6 0. 4.509e-17
#> 6 bodywt 2.568e- 7 7.524e- 4 2.120e- 6 2.568e- 7 3.221e-18 0.
getting the confidence intervals for correlations
ggstatsplot::ggcorrmat( data = ggplot2::msleep, cor.vars = sleep_total:bodywt, corr.method = "spearman", output = "ci", p.adjust.method = "holm" )
#> Note: In the correlation matrix,
#> the upper triangle: p-values adjusted for multiple comparisons
#> the lower triangle: unadjusted p-values.
#> # A tibble: 15 x 7
#> pair r lower upper p lower.adj
#>
#> 1 sleep_total-sleep_rem 0.7641 0.6344 0.8520 7.806e-13 0.5632
#> 2 sleep_total-sleep_cycle -0.4888 -0.7155 -0.1689 4.530e- 3 -0.7609
#> 3 sleep_total-awake -1 -1 -1 0. -1
#> 4 sleep_total-brainwt -0.5935 -0.7408 -0.3918 1.426e- 6 -0.7852
#> 5 sleep_total-bodywt -0.5346 -0.6727 -0.3605 1.931e- 7 -0.7121
#> 6 sleep_rem-sleep_cycle -0.3344 -0.6118 0.01617 6.1
Functions in ggstatsplot
Name | Description | |
cat_label_df | Summary dataframe for categorical variables. | |
aesthetic_addon | Making aesthetic modifications to the plot | |
bartlett_message | Display homogeneity of variance test as a message | |
VR_dilemma | Virtual reality moral dilemmas. | |
cat_counter | Counts and percentages across grouping variables. | |
ggbarstats | Bar (column) charts with statistical tests | |
bf_meta_message | Bayes factor message for random-effects meta-analysis | |
bugs_long | Tidy version of the "Bugs" dataset. | |
combine_plots | Combining and arranging multiple plots in a grid | |
Titanic_full | Titanic dataset. | |
ggbetweenstats | Box/Violin plots for group or condition comparisons in between-subjects designs. | |
ggcoefstats_label_maker | Create labels with statistical details for ggcoefstats | |
ggcoefstats | Dot-and-whisker plots for regression analyses | |
ggbetweenstats_switch | Switch function to use helper function to create subtitle for the ggbetweenstats plot. | |
ggplot_converter | Transform object of any other class to an object of class ggplot. | |
ggdotplotstats | Dot plot/chart for labeled numeric data. | |
ggcorrmat | Visualization of a correlation matrix | |
ggscatterstats | Scatterplot with marginal distributions and statistical results | |
gghistostats | Histogram for distribution of a numeric variable | |
ggpiestats | Pie charts with statistical tests | |
ggwithinstats | Box/Violin plots for group or condition comparisons in within-subjects (or repeated measures) designs. | |
grouped_ggbetweenstats | Violin plots for group or condition comparisons in between-subjects designs repeated across all levels of a grouping variable. | |
grouped_gghistostats | Grouped histograms for distribution of a numeric variable | |
grouped_ggdotplotstats | Grouped histograms for distribution of a labeled numeric variable | |
ggsignif_adder | Adding geom_signif to ggplot | |
grouped_ggbarstats | Grouped bar (column) charts with statistical tests | |
movies_long | Movie information and user ratings from IMDB.com (long format). | |
movies_wide | Movie information and user ratings from IMDB.com (wide format). | |
grouped_ggcorrmat | Visualization of a correlalogram (or correlation matrix) for all levels of a grouping variable | |
ggstatsplot-package | ggstatsplot: 'ggplot2' Based Plots with Statistical Details | |
grouped_ggpiestats | Grouped pie charts with statistical tests | |
subtitle_meta_parametric | Making expression with frequentist random-effects meta-analysis results | |
theme_corrmat | Default theme used for correlation matrix | |
grouped_ggscatterstats | Scatterplot with marginal distributions for all levels of a grouping variable | |
grouped_ggwithinstats | Violin plots for group or condition comparisons in within-subjects designs repeated across all levels of a grouping variable. | |
mean_ggrepel | Adding labels for mean values. | |
mean_labeller | Dataframe with mean per group and a formatted label for display in ggbetweenstats plot. | |
grouped_list | Split dataframe into a list by grouping variable. | |
histo_labeller | Custom function for adding labeled lines for x-axis variable. | |
intent_morality | Moral judgments about third-party moral behavior. | |
normality_message | Display normality test result as a message. | |
iris_long | Edgar Anderson's Iris Data in long format. | |
reexports | Objects exported from other packages | |
line_labeller | Adds a label to the horizontal or vertical line. | |
theme_ggstatsplot | Default theme used in all ggstatsplot package plots | |
theme_pie | Default theme used for pie chart | |
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Vignettes of ggstatsplot
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suggests | broom , broom.mixed , jmv , knitr , lme4 , MASS , MCMCglmm , ordinal , rmarkdown , spelling , stringr , survival , testthat |
imports | broomExtra (>= 0.0.6) , cowplot (>= 1.0.0) , crayon (>= 1.3.4) , dplyr (>= 0.8.3) , forcats (>= 0.4.0) , ggcorrplot (>= 0.1.3) , ggExtra (>= 0.9) , ggplot2 (>= 3.2.1) , ggrepel (>= 0.8.1) , ggsignif (>= 0.6.0) , grid , groupedstats (>= 0.1.0) , insight (>= 0.7.1) , metaBMA (>= 0.6.2) , metafor (>= 2.1-0) , pairwiseComparisons (>= 0.1.2) , paletteer (>= 1.0.0) , parameters (>= 0.3.0) , psych (>= 1.8.12) , purrr (>= 0.3.3) , rlang (>= 0.4.2) , scales (>= 1.1.0) , stats , statsExpressions (>= 0.1.3) , tibble (>= 2.1.3) , tidyr (>= 1.0.0) , WRS2 (>= 1.0-0) |
depends | R (>= 3.5.0) |
Contributors | Chuck Powell |
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