ggstatsplot v0.0.12
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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 |
---|---|---|---|---|
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.
In a typical exploratory data analysis workflow, data visualization and
statistical modeling are two different phases: visualization informs
modeling, and modeling in its turn can suggest a different visualization
method, and so on and so forth. The central idea of ggstatsplot is
simple: combine these two phases into one in the form of graphics with
statistical details, which makes data exploration simpler and faster.
Summary of types of statistical analyses
Currently, it supports only the most common types of statistical tests: parametric, nonparametric, robust, and bayesian versions of t-test/anova, correlation analyses, contingency table analysis, and regression analyses.
It, therefore, produces a limited kinds of plots for the supported analyses:
- violin plots (for comparisons between groups or conditions),
- pie charts and bar charts (for categorical data),
- scatterplots (for correlations between two variables),
- correlation matrices (for correlations between multiple variables),
- histograms and dot plots/charts (for hypothesis about distributions),
- dot-and-whisker plots (for regression models).
In addition to these basic plots, ggstatsplot
also provides
grouped_
versions for most functions that makes it easy to repeat
the same analysis for any grouping variable.
Future versions will include other types of statistical analyses and plots as well.
The table below summarizes all the different types of analyses currently supported in this package-
Functions | Description | Parametric | Non-parametric | Robust | Bayes Factor |
---|---|---|---|---|---|
ggbetweenstats |
Between group/condition comparisons | Yes | Yes | Yes | Yes |
ggwithinstats |
Within group/condition comparisons | Yes | Yes | Yes | Yes |
gghistostats , ggdotplotstats |
Distribution of a numeric variable | Yes | Yes | Yes | Yes |
ggcorrmat |
Correlation matrix | Yes | Yes | Yes | No |
ggscatterstats |
Correlation between two variables | Yes | Yes | Yes | Yes |
ggpiestats , ggbarstats |
Association between categorical variables | Yes | NA |
NA |
Yes |
ggpiestats , ggbarstats |
Equal proportions for categorical variable levels | Yes | NA |
NA |
Yes |
ggcoefstats |
Regression model coefficients | Yes | No | Yes | No |
Statistical reporting
For all statistical tests reported in the plots, the default template abides by the APA 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 |
Parametric | Student’s and Welch’s t-test | Cohen’s d, Hedge’s g | |
ggbetweenstats |
Parametric | Fisher’s and Welch’s one-way ANOVA | ||
ggbetweenstats |
Non-parametric | Mann-Whitney U-test | r | |
ggbetweenstats |
Non-parametric | Kruskal-Wallis Rank Sum Test | ||
ggbetweenstats |
Robust | Yuen’s test for trimmed means | ||
ggbetweenstats |
Robust | Heteroscedastic one-way ANOVA for trimmed means | ||
ggwithinstats |
Parametric | Student’s t-test | Cohen’s d, Hedge’s g | |
ggwithinstats |
Parametric | Fisher’s one-way repeated measures ANOVA | ||
ggwithinstats |
Non-parametric | Wilcoxon signed-rank test | r | |
ggwithinstats |
Non-parametric | Friedman test | ||
ggwithinstats |
Robust | Yuen’s test on trimmed means for dependent samples | ||
ggwithinstats |
Robust | Heteroscedastic one-way repeated measures ANOVA for trimmed means | ||
ggpiestats |
Parametric | Cramér’s V | ||
ggpiestats |
Parametric | McNemar’s test | Cohen’s g | |
ggpiestats |
Parametric | One-sample proportion test | Cramér’s V | |
ggscatterstats /ggcorrmat |
Parametric | Pearson’s r | r | |
ggscatterstats /ggcorrmat |
Non-parametric | |||
ggscatterstats /ggcorrmat |
Robust | Percentage bend correlation | r | |
gghistostats /ggdotplotstats |
Parametric | One-sample t-test | Cohen’s d, Hedge’s g | |
gghistostats |
Non-parametric | One-sample Wilcoxon signed rank test | r | |
gghistostats /ggdotplotstats |
Robust | One-sample percentile bootstrap | robust estimator | |
gghistostats /ggdotplotstats |
Parametric | Regression models |
Work is in progress to add some of the currently missing functionality.
Installation
To get the latest, stable CRAN
release (0.0.12
):
utils::install.packages(pkgs = "ggstatsplot")
Note: If you are on a linux machine, you will need to have OpenGL
libraries installed (specifically, libx11
, mesa
and Mesa OpenGL
Utility library - glu
) for the dependency package rgl
to work.
You can get the development version of the package from GitHub
(0.0.12.9000
). To see what new changes (and bug fixes) have been made
to the package since the last release on CRAN
, you can check the
detailed log of changes here:
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) {
#> # convert the saved plot
#> p <- cowplot::ggdraw() +
#> cowplot::draw_grob(grid::grobTree(plot))
#>
#> # returning the converted plot
#> return(p)
#> }
#> <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-
Usage and syntax
ggstatsplot
relies on non-standard evaluation (NSE), i.e., rather than
looking at the values of arguments (x
, y
), it instead looks at their
expressions. This means that you shouldn’t enter arguments with the
$
operator and set data = NULL
(e.g., data = NULL, x = data$x, y =
data$y
). You must always specify the data
argument for all
functions. On the plus side, you can enter arguments either as a string
(x = "x", y = "y"
) or as a bare expression (x = x, y = y
) and it
wouldn’t matter. To read more about NSE, see-
http://adv-r.had.co.nz/Computing-on-the-language.html
ggstatsplot
is a very chatty package and will by default print helpful
notes on assumptions about statistical tests, warnings, etc. 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.
Most functions share a type
(of test) argument that is helpful to
specify the type of statistical analysis:
"p"
(for parametric)"np"
(for non-parametric)"r"
(for robust)"bf"
(for Bayes Factor)
All relevant functions in ggstatsplot
have a return
argument which
can be used to not only return plots (which is the default), but also to
return a subtitle
or caption
, which are objects of type call
and
can be used to display statistical details in conjunction with a custom
plot and at a custom location in the plot.
Additionally, all functions share the ggtheme
and palette
arguments
that can be used to specify your favorite ggplot
theme and color
palette.
Primary functions
Here are examples of the main functions currently supported in
ggstatsplot
.
Note: If you are reading this on GitHub
repository, the
documentation below is for the development version of the package.
So you may see some features available here that are not currently
present in the stable version of this package on CRAN. For
documentation relevant for the CRAN
version, see:
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 a ggplot2
object and thus any of the
graphics layers can be further modified.
The type
(of test) argument also accepts the following abbreviations:
"p"
(for parametric) or "np"
(for nonparametric) or "r"
(for
robust) or "bf"
(for Bayes Factor). Additionally, the type of plot
to be displayed can also be modified ("box"
, "violin"
, or
"boxviolin"
).
A number of other arguments can be specified to make this plot even more informative or change some of the default options.
library(ggplot2)
# for reproducibility
set.seed(123)
# let's leave out one of the factor levels and see if instead of anova, a t-test will be run
iris2 <- dplyr::filter(.data = iris, Species != "setosa")
# let's change the levels of our factors, a common routine in data analysis
# pipeline, to see if this function respects the new factor levels
iris2$Species <-
base::factor(
x = iris2$Species,
levels = c("virginica", "versicolor")
)
# plot
ggstatsplot::ggbetweenstats(
data = iris2,
x = Species,
y = Sepal.Length,
notch = TRUE, # show notched box plot
mean.plotting = TRUE, # whether mean for each group is to be displayed
mean.ci = TRUE, # whether to display confidence interval for means
mean.label.size = 2.5, # size of the label for mean
type = "p", # which type of test is to be run
k = 3, # number of decimal places for statistical results
outlier.tagging = TRUE, # whether outliers need to be tagged
outlier.label = Sepal.Width, # variable to be used for the outlier tag
outlier.label.color = "darkgreen", # changing the color for the text label
xlab = "Type of Species", # label for the x-axis variable
ylab = "Attribute: Sepal Length", # label for the y-axis variable
title = "Dataset: Iris flower data set", # title text for the plot
ggtheme = ggthemes::theme_fivethirtyeight(), # choosing a different theme
ggstatsplot.layer = FALSE, # turn off ggstatsplot theme layer
package = "wesanderson", # package from which color palette is to be taken
palette = "Darjeeling1", # choosing a different color palette
messages = FALSE
)
As can be seen from the plot, the function by default returns Bayes Factor for the test (here, 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 | Student’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 | Student’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)
library(jmv)
data("bugs", package = "jmv")
# getting data in tidy format
data_bugs <- bugs %>%
tibble::as_tibble(x = .) %>%
tidyr::gather(data = ., key, value, LDLF:HDHF) %>%
dplyr::filter(.data = ., Region %in% c("Europe", "North America"))
# plot
ggstatsplot::grouped_ggwithinstats(
data = dplyr::filter(data_bugs, key %in% c("LDLF", "LDHF")),
x = key,
y = value,
xlab = "Condition",
ylab = "Desire to kill an artrhopod",
grouping.var = Region,
outlier.tagging = TRUE,
outlier.label = Education,
ggtheme = hrbrthemes::theme_ipsum_tw(),
ggstatsplot.layer = FALSE,
messages = FALSE
)
Summary of tests
Following (within-subjects) tests are carried out for each type of analyses-
Type | No. of groups | Test |
---|---|---|
Parametric | > 2 | One-way repeated measures ANOVA |
Non-parametric | > 2 | Friedman test |
Robust | > 2 | Heteroscedastic one-way repeated measures ANOVA for trimmed means |
Bayes Factor | > 2 | One-way repeated measures ANOVA |
Parametric | 2 | 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,
conf.level = 0.99,
k = 3, # no. of decimal places in the results
xfill = "#E69F00",
yfill = "#8b3058",
xlab = "IMDB rating",
grouping.var = genre, # grouping variable
title.prefix = "Movie genre",
ggtheme = ggplot2::theme_grey(),
ggplot.component = list(
ggplot2::scale_x_continuous(breaks = seq(2, 9, 1), limits = (c(2, 9)))
),
messages = FALSE,
nrow = 2,
title.text = "Relationship between movie length by IMDB ratings for different genres"
)
Using ggscatterstats()
in R Notebooks or R Markdown
If you include a ggscatterstats()
plot inside an R Notebook
or R
Markdown
code chunk, you will notice that running the chunk doesn’t
return any output nor does it give any error. In order to get a
ggscatterstats()
to show up in these contexts, you need to save the
ggscatterstats
plot as a variable in one code chunk, and explicitly
print it using the grid
package in another chunk, like this:
# include the following code in your code chunk inside R Notebook or Markdown
grid::grid.newpage()
grid::grid.draw(
ggstatsplot::ggscatterstats(
data = ggstatsplot::movies_wide,
x = budget,
y = rating,
marginal = TRUE,
messages = FALSE
)
)
Another option - or rather a compromise - is not to include marginal
distribution at all by setting marginal = FALSE
.
Summary of tests
Following tests are carried out for each type of analyses. Additionally, the correlation coefficients (and their confidence intervals) are used as effect sizes-
Type | Test | CI? |
---|---|---|
Parametric | 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,
main = vore,
title = "Composition of vore types among mammals",
messages = FALSE
)
This function can also be used to study an interaction between two categorical variables:
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggpiestats(
data = mtcars,
main = am,
condition = cyl,
conf.level = 0.99, # confidence interval for effect size measure
title = "Dataset: Motor Trend Car Road Tests", # title for the plot
stat.title = "interaction: ", # title for the results
legend.title = "Transmission", # title for the legend
factor.levels = c("1 = manual", "0 = automatic"), # renaming the factor level names (`main`)
facet.wrap.name = "No. of cylinders", # name for the facetting variable
slice.label = "counts", # show counts data instead of percentages
package = "ggsci", # package from which color palette is to be taken
palette = "default_jama", # choosing a different color palette
caption = substitute( # text for the caption
paste(italic("Source"), ": 1974 Motor Trend US magazine")
),
messages = FALSE # turn off messages and notes
)
In case of repeated measures designs, setting paired = TRUE
will
produce results from 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,
main = `1st survey`,
condition = `2nd survey`,
counts = Counts,
paired = TRUE, # within-subjects design
conf.level = 0.99, # confidence interval for effect size measure
stat.title = "McNemar's Test: ",
package = "wesanderson",
palette = "Royal1"
)
#> Note: 99% CI for effect size estimate was computed with 100 bootstrap samples.
#> Note: Results from one-sample proportion tests for each level of the variable
#> 2nd survey testing for equal proportions of the variable 1st survey.
#> # A tibble: 2 x 8
#> condition N Approve Disapprove `Chi-squared` df `p-value`
#> <fct> <chr> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 Approve (n =~ 90.23% 9.77% 570. 1 0
#> 2 Disappro~ (n =~ 20.83% 79.17% 245 1 0
#> # ... with 1 more variable: significance <chr>
Note that when a two-way table is present (i.e., when both main
and
condition
arguments are specified), p-values for results from
one-sample proportion tests are displayed in each facet in the form of
asterisks with the following convention:
- :
- :
- :
- :
Additionally, there is also a grouped_
variant of this function that
makes it easy to repeat the same operation across a single grouping
variable:
# for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_ggpiestats(
dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
main = mpaa,
grouping.var = genre, # grouping variable
title.prefix = "Movie genre", # prefix for the facetted title
label.text.size = 3, # text size for slice labels
slice.label = "both", # show both counts and percentage data
perc.k = 1, # no. of decimal places for percentages
palette = "brightPastel",
package = "quickpalette",
messages = FALSE,
nrow = 2,
title.text = "Composition of MPAA ratings for different genres"
)
Summary of tests
Following tests are carried out for each type of analyses-
Type of data | Design | Test |
---|---|---|
Unpaired | contingency table | 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 | Cramer’s V | Yes |
McNemar’s test | g | Yes |
Goodness of fit | 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,
main = mpaa,
condition = genre,
sampling.plan = "jointMulti",
title = "MPAA Ratings by Genre",
xlab = "movie genre",
perc.k = 1,
x.axis.orientation = "slant",
ggtheme = hrbrthemes::theme_modern_rc(),
ggstatsplot.layer = FALSE,
ggplot.component = ggplot2::theme(axis.text.x = ggplot2::element_text(face = "italic")),
palette = "Set2",
messages = FALSE
)
And, needless to say, there is also a grouped_
variant of this
function-
# setup
library(ggstatsplot)
set.seed(123)
# let's create a smaller dataframe
diamonds_short <- ggplot2::diamonds %>%
dplyr::filter(.data = ., cut %in% c("Very Good", "Ideal")) %>%
dplyr::filter(.data = ., clarity %in% c("SI1", "SI2", "VS1", "VS2", "VVS1")) %>%
dplyr::sample_frac(tbl = ., size = 0.05)
# plot
ggstatsplot::grouped_ggbarstats(
data = diamonds_short,
main = color,
condition = clarity,
grouping.var = cut,
sampling.plan = "poisson",
title.prefix = "Quality",
data.label = "both",
label.text.size = 3,
perc.k = 1,
package = "palettetown",
palette = "charizard",
ggtheme = ggthemes::theme_tufte(base_size = 12),
ggstatsplot.layer = FALSE,
messages = FALSE,
title.text = "Diamond quality and color combination",
nrow = 2
)
Summary of tests
This is identical to the ggpiestats
function summary of tests.
gghistostats
In case you would like to see the distribution of a single variable and check if it is significantly different from a specified value with a one sample test, this function will let you do that.
ggstatsplot::gghistostats(
data = ToothGrowth, # dataframe from which variable is to be taken
x = len, # numeric variable whose distribution is of interest
title = "Distribution of Sepal.Length", # title for the plot
fill.gradient = TRUE, # use color gradient
test.value = 10, # the comparison value for t-test
test.value.line = TRUE, # display a vertical line at test value
type = "bf", # bayes factor for one sample t-test
bf.prior = 0.8, # prior width for calculating the bayes factor
messages = FALSE # turn off the messages
)
The aesthetic defaults can be easily modified-
# 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 |
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 = "np", # non-parametric test
grouping.var = cyl, # grouping variable
test.value = 15.5,
title.prefix = "cylinder count",
point.color = "red",
point.size = 5,
point.shape = 13,
test.value.line = TRUE,
ggtheme = ggthemes::theme_par(),
messages = FALSE,
title.text = "Fuel economy data"
)
Summary of tests
This is identical to summary of tests for gghistostats
.
ggcorrmat
ggcorrmat
makes a correlalogram (a matrix of correlation coefficients)
with minimal amount of code. Just sticking to the defaults itself
produces publication-ready correlation matrices. But, for the sake of
exploring the available options, 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_to~ 0. 5.291e-12 9.138e- 3 0. 3.170e- 5 2.568e- 6
#> 2 sleep_rem 4.070e-13 0. 1.978e- 2 5.291e-12 9.698e- 3 3.762e- 3
#> 3 sleep_cy~ 2.285e- 3 1.978e- 2 0. 9.138e- 3 1.637e- 9 1.696e- 5
#> 4 awake 0. 4.070e-13 2.285e- 3 0. 3.170e- 5 2.568e- 6
#> 5 brainwt 4.528e- 6 4.849e- 3 1.488e-10 4.528e- 6 0. 4.509e-17
#> 6 bodywt 2.568e- 7 7.524e- 4 2.120e- 6 2.568e- 7 3.221e-18 0.
getting the confidence intervals for correlat
Functions in ggstatsplot
Name | Description | |
bf_corr_test | Bayesian correlation test. | |
bf_extractor | Extract Bayes Factors from BayesFactor model object. | |
Titanic_full | Titanic dataset. | |
bf_contingency_tab | Bayesian contingency table analysis. | |
bf_oneway_anova | Bayesian one-way analysis of variance. | |
aesthetic_addon | Making aesthetic modifications to the plot. | |
bf_caption_maker | Prepare caption with bayes factor in favor of null | |
bf_meta_message | Bayes factor message for random-effects meta-analysis | |
VR_dilemma | Virtual reality moral dilemmas. | |
bartlett_message | Display homogeneity of variance test as a message | |
games_howell | Games-Howell post-hoc test | |
ggdotplotstats | Dot plot/chart for labeled numeric data. | |
gghistostats | Histogram for distribution of a numeric variable | |
ggcorrmat_matrix_message | Message to display when adjusted p-values are displayed in correlation matrix. | |
effsize_type_switch | Switch function to determine which effect size is to computed. | |
effsize_t_parametric | Calculating Cohen's d or Hedge's g (for between-/within- or one sample designs). | |
ggcorrmat | Visualization of a correlalogram (or correlation matrix) | |
ggbarstats | Bar (column) charts with statistical tests | |
ggbetweenstats_switch | Switch function to use helper function to create subtitle for the ggbetweenstats plot. | |
ggbetweenstats | Box/Violin plots for group or condition comparisons in between-subjects designs. | |
bf_ttest | Bayes Factor for t-test | |
ggpiestats | Pie charts with statistical tests | |
cat_counter | Preparing dataframe with counts and percentages for categorical variables. | |
combine_plots | Combining and arranging multiple plots in a grid | |
cat_label_df | Summary dataframe for categorical variables. | |
ggplot_converter | Transform object of any other class to an object of class ggplot. | |
grouped_ggdotplotstats | Grouped histograms for distribution of a labeled numeric variable | |
grouped_ggcorrmat | Visualization of a correlalogram (or correlation matrix) for all levels of a grouping variable | |
grouped_ggbetweenstats | Violin plots for group or condition comparisons in between-subjects designs repeated across all levels of a grouping variable. | |
ggsignif_position_calculator | Calculating y coordinates for the ggsignif comparison bars. | |
grouped_ggpiestats | Grouped pie charts with statistical tests | |
ggstatsplot-package | ggstatsplot: 'ggplot2' Based Plots with Statistical Details | |
grouped_ggscatterstats | Scatterplot with marginal distributions for all levels of a grouping variable | |
long_to_wide_converter | Converts long-format dataframe to wide-format dataframe | |
mean_ggrepel | Adding labels for mean values. | |
check_outlier | Finding the outliers in the dataframe using Tukey's interquartile range rule | |
ggwithinstats | Box/Violin plots for group or condition comparisons in within-subjects (or repeated measures) designs. | |
ggcoefstats | Model coefficients for fitted models with the model summary as a caption. | |
ggcoefstats_label_maker | Create labels with statistical details for ggcoefstats. | |
subtitle_anova_robust | Making text subtitle for the robust ANOVA | |
outlier_df | Adding a column to dataframe describing outlier status. | |
grouped_ggbarstats | Grouped bar (column) charts with statistical tests | |
histo_labeller | Custom function for adding labelled lines for x-axis variable. | |
grouped_message | grouped_message | |
pairwise_p | Pairwise comparison tests | |
grouped_gghistostats | Grouped histograms for distribution of a numeric variable | |
subtitle_anova_nonparametric | Making text subtitle for nonparametric ANOVA. | |
grouped_list | Split dataframe into a list by grouping variable. | |
grouped_ggwithinstats | Violin plots for group or condition comparisons in within-subjects designs repeated across all levels of a grouping variable. | |
subtitle_anova_parametric | Making text subtitle for parametric ANOVA. | |
effsize_ci_message | Message to display when bootstrapped confidence intervals are shown for effect size measure. | |
subtitle_t_robust | Making text subtitle for the robust t-test (between- and within-subjects designs). | |
subtitle_contingency_tab | Subtitle for categorical tests | |
subtitle_template | Template for subtitles with statistical details for tests with a single parameter (e.g., t, chi-squared, etc.) | |
intent_morality | Moral judgments about third-party moral behavior. | |
subtitle_meta_ggcoefstats | Prepare subtitle with meta-analysis results | |
subtitle_t_bayes | Making text subtitle for the bayesian t-test. | |
pairwise_p_caption | Preparing caption in case pairwise comparisons are displayed. | |
ggsignif_adder | Adding geom_signif to the plot. | |
ggscatterstats | Scatterplot with marginal distributions | |
iris_long | Edgar Anderson's Iris Data in long format. | |
theme_corrmat | Default theme used for correlation matrix | |
theme_ggstatsplot | Default theme used in all ggstatsplot package plots | |
tfz_labeller | Prepare labels with statistic for ggcoefstats function. | |
t1way_ci | #' @title A heteroscedastic one-way ANOVA for trimmed means with confidence interval for effect size. | |
tibble | Anticipate use of tibbles | |
theme_pie | Default theme used for pie chart | |
kendall_w_ci | Computing confidence intervals for the Kendall's coefficient of concordance (aka Kendall's W). | |
movies_wide | Movie information and user ratings from IMDB.com (wide format). | |
palette_message | Message if palette doesn't have enough number of colors. | |
normality_message | Display normality test result as a message. | |
proptest_message | Message about results from a single-sample proportion test. | |
line_labeller | Adds a label to the horizontal or vertical line. | |
mean_labeller | @title Create a dataframe with mean per group and a formatted label for display in ggbetweenstats plot. | |
movies_long | Movie information and user ratings from IMDB.com (long format). | |
subtitle_anova_bayes | Making text subtitle for the between-subject one-way anova designs. | |
stats_type_switch | Switch function to determine which type of statistics is to be run. | |
p.adjust.method.description | Preparing text to describe which p-value adjustment method was used. | |
subtitle_t_onesample | Making text subtitle for one sample t-test and its nonparametric and robust equivalents. | |
reexports | Objects exported from other packages | |
subtitle_t_parametric | Making text subtitle for the t-test (between-/within-subjects designs). | |
yuend_ci | Paired samples robust t-tests with confidence interval for effect size. | |
subtitle_mann_nonparametric | Making text subtitle for the Mann-Whitney U-test (between-subjects designs). | |
robcor_ci | Robust correlation coefficient and its confidence interval | |
subtitle_ggscatterstats | Making text subtitle for the correlation test. | |
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Vignettes of ggstatsplot
Last month downloads
Details
imports | BayesFactor (>= 0.9.12-4.2) , boot (>= 1.3-22) , broomExtra (>= 0.0.4) , cowplot (>= 1.0.0) , crayon (>= 1.3.4) , DescTools (>= 0.99.28) , dplyr (>= 0.8.3) , ellipsis (>= 0.2.0) , ez (>= 4.4-0) , forcats (>= 0.4.0) , ggcorrplot (>= 0.1.3) , ggExtra (>= 0.8) , ggplot2 (>= 3.2.0) , ggrepel (>= 0.8.1) , ggsignif (>= 0.5.0) , glue (>= 1.3.1) , grid , groupedstats (>= 0.0.7) , jmv (>= 0.9.6.1) , magrittr (>= 1.5) , MCMCpack , metaBMA (>= 0.6.1) , metafor (>= 2.1-0) , methods , paletteer (>= 0.2.1) , psych (>= 1.8.12) , purrr (>= 0.3.2) , purrrlyr (>= 0.0.5) , rcompanion (>= 2.2.1) , rlang (>= 0.4.0) , scales (>= 1.0.0) , sjstats (>= 0.17.5) , stats , stringr (>= 1.4.0) , tibble (>= 2.1.3) , tidyr (>= 0.8.3) , utils , WRS2 (>= 1.0-0) |
suggests | broom , broom.mixed , glmmTMB , knitr , lme4 , MASS , MCMCglmm , ordinal , readr , rmarkdown , robust , spelling , survival , testthat |
depends | R (>= 3.5.0) |
Contributors | Chuck Powell |
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