Package | Status | Usage | GitHub | References |
---|---|---|---|---|
āWhat is to be sought in designs for the display of information is the clear portrayal of complexity. Not the complication of the simple; rather ā¦ the revelation of the complex.ā
- Edward R. Tufte
ggstatsplot
is an
extension of ggplot2
package
for creating graphics with details from statistical tests included in
the information-rich plots themselves. In a typical exploratory data
analysis workflow, data visualization and statistical modeling are two
different phases: visualization informs modeling, and modeling in its
turn can suggest a different visualization method, and so on and so
forth. The central idea of ggstatsplot
is simple: combine these two
phases into one in the form of graphics with statistical details, which
makes data exploration simpler and faster.
It, therefore, produces a limited kinds of plots for the supported analyses:
Function | Plot | Description |
---|---|---|
ggbetweenstats |
violin plots | for comparisons between groups/conditions |
ggwithinstats |
violin plots | for comparisons within groups/conditions |
gghistostats |
histograms | for distribution about numeric variable |
ggdotplotstats |
dot plots/charts | for distribution about labeled numeric variable |
ggpiestats |
pie charts | for categorical data |
ggbarstats |
bar charts | for categorical data |
ggscatterstats |
scatterplots | for correlations between two variables |
ggcorrmat |
correlation matrices | for correlations between multiple variables |
ggcoefstats |
dot-and-whisker plots | for regression models and meta-analysis |
In addition to these basic plots, ggstatsplot
also provides
grouped_
versions (see below) that makes it easy to repeat the
same analysis for any grouping variable.
Currently, it supports only the most common types of statistical tests: parametric, nonparametric, robust, and bayesian versions of t-test/anova, correlation analyses, contingency table analysis, meta-analysis, and regression analyses.
The table below summarizes all the different types of analyses currently supported in this package-
Functions | Description | Parametric | Non-parametric | Robust | Bayes Factor |
---|---|---|---|---|---|
ggbetweenstats |
Between group/condition comparisons | Yes | Yes | Yes | Yes |
ggwithinstats |
Within group/condition comparisons | Yes | Yes | Yes | Yes |
gghistostats , ggdotplotstats |
Distribution of a numeric variable | Yes | Yes | Yes | Yes |
ggcorrmat |
Correlation matrix | Yes | Yes | Yes | Yes |
ggscatterstats |
Correlation between two variables | Yes | Yes | Yes | Yes |
ggpiestats , ggbarstats |
Association between categorical variables | Yes | NA |
NA |
Yes |
ggpiestats , ggbarstats |
Equal proportions for categorical variable levels | Yes | NA |
NA |
Yes |
ggcoefstats |
Regression model coefficients | Yes | Yes | Yes | Yes |
ggcoefstats |
Random-effects meta-analysis | Yes | No | Yes | Yes |
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 Yuenās test for trimmed means (robust t-test):
Here is a summary table of all the statistical tests currently supported across various functions: https://indrajeetpatil.github.io/statsExpressions/articles/stats_details.html
To get the latest, stable CRAN
release:
install.packages("ggstatsplot")
Note: If you are on a linux machine, you will need to have OpenGL
libraries installed (specifically, libx11
, mesa
and Mesa OpenGL
Utility library - glu
) for the dependency package rgl
to work.
You can get the development version of the package from GitHub
. To
see what new changes (and bug fixes) have been made to the package since
the last release on CRAN
, you can check the detailed log of changes
here: https://indrajeetpatil.github.io/ggstatsplot/news/index.html
If you are in hurry and want to reduce the time of installation, prefer-
# needed package to download from GitHub repo
install.packages("remotes")
# downloading the package from GitHub (needs `remotes` package to be installed)
remotes::install_github(
repo = "IndrajeetPatil/ggstatsplot", # package path on GitHub
dependencies = FALSE, # assumes you have already installed needed packages
quick = TRUE # skips docs, demos, and vignettes
)
If time is not a constraint-
remotes::install_github(
repo = "IndrajeetPatil/ggstatsplot", # package path on GitHub
dependencies = TRUE, # installs packages which ggstatsplot depends on
upgrade_dependencies = TRUE # updates any out of date dependencies
)
If you are not using the RStudio IDE and you
get an error related to āpandocā 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
If you want to cite this package in a scientific journal or in any other
context, run the following code in your R
console:
citation("ggstatsplot")
#>
#> Patil, I. (2018). ggstatsplot: 'ggplot2' Based Plots with Statistical
#> Details. CRAN. Retrieved from
#> https://cran.r-project.org/web/packages/ggstatsplot/index.html
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Article{,
#> title = {{ggstatsplot}: 'ggplot2' Based Plots with Statistical Details},
#> author = {Indrajeet Patil},
#> year = {2018},
#> journal = {CRAN},
#> url = {https://CRAN.R-project.org/package=ggstatsplot},
#> doi = {10.5281/zenodo.2074621},
#> }
There is currently a publication in preparation corresponding to this package and the citation will be updated once itās published.
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/.
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
This function creates either a violin plot, a box plot, or a mix of two for between-group or between-condition comparisons with results from statistical tests in the subtitle. The simplest function call looks like this-
# loading needed libraries
library(ggstatsplot)
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggbetweenstats(
data = iris,
x = Species,
y = Sepal.Length,
title = "Distribution of sepal length across Iris species"
)
Note that this function returns object of class ggplot
and thus can be
further modified using ggplot2
functions.
A number of other arguments can be specified to make this plot even more informative or change some of the default options. Additionally, this time we will use a grouping variable that has only two levels. The function will automatically switch from carrying out an ANOVA analysis to a t-test.
The type
(of test) argument also accepts the following abbreviations:
"p"
(for parametric) or "np"
(for nonparametric) or "r"
(for
robust) or "bf"
(for Bayes Factor). Additionally, the type of plot
to be displayed can also be modified ("box"
, "violin"
, or
"boxviolin"
).
A number of other arguments can be specified to make this plot even more informative or change some of the default options.
# for reproducibility
set.seed(123)
library(ggplot2)
# plot
ggstatsplot::ggbetweenstats(
data = ToothGrowth,
x = supp,
y = len,
notch = TRUE, # show notched box plot
mean.ci = TRUE, # whether to display confidence interval for means
k = 3, # number of decimal places for statistical results
outlier.tagging = TRUE, # whether outliers need to be tagged
outlier.label = dose, # variable to be used for the outlier tag
xlab = "Supplement type", # label for the x-axis variable
ylab = "Tooth length", # label for the y-axis variable
title = "The Effect of Vitamin C on Tooth Growth", # title text for the plot
ggtheme = ggthemes::theme_fivethirtyeight(), # choosing a different theme
ggstatsplot.layer = FALSE, # turn off `ggstatsplot` theme layer
package = "wesanderson", # package from which color palette is to be taken
palette = "Darjeeling1" # choosing a different color palette
)
Additionally, there is also a grouped_
variant of this function that
makes it easy to repeat the same operation across a single grouping
variable:
# for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_ggbetweenstats(
data = dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
x = mpaa,
y = length,
grouping.var = genre, # grouping variable
pairwise.comparisons = TRUE, # display significant pairwise comparisons
p.adjust.method = "bonferroni", # method for adjusting p-values for multiple comparisons
# adding new components to `ggstatsplot` default
ggplot.component = list(ggplot2::scale_y_continuous(sec.axis = ggplot2::dup_axis())),
k = 3,
title.prefix = "Movie genre",
caption = substitute(paste(italic("Source"), ":IMDb (Internet Movie Database)")),
palette = "default_jama",
package = "ggsci",
plotgrid.args = list(nrow = 2),
title.text = "Differences in movie length by mpaa ratings for different genres"
)
Following (between-subjects) tests are carried out for each type of analyses-
Type | No. of groups | Test |
---|---|---|
Parametric | > 2 | Fisherās or Welchās one-way ANOVA |
Non-parametric | > 2 | Kruskal-Wallis one-way ANOVA |
Robust | > 2 | Heteroscedastic one-way ANOVA for trimmed means |
Bayes Factor | > 2 | Fisherās ANOVA |
Parametric | 2 | Studentās or Welchās t-test |
Non-parametric | 2 | Mann-Whitney U test |
Robust | 2 | Yuenās test for trimmed means |
Bayes Factor | 2 | Studentās 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 | Studentās t-test | Yes |
Non-parametric | No | Dunn test | Yes |
Robust | No | Yuenās trimmed means test | Yes |
Bayes Factor | NA |
Studentās t-test | NA |
For more, see the ggbetweenstats
vignette:
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggbetweenstats.html
ggbetweenstats
function has an identical twin function ggwithinstats
for repeated measures designs that behaves in the same fashion with a
few minor tweaks introduced to properly visualize the repeated measures
design. As can be seen from an example below, the only difference
between the plot structure is that now the group means are connected by
paths to highlight the fact that these data are paired with each other.
# for reproducibility and data
set.seed(123)
library(WRS2)
# plot
ggstatsplot::ggwithinstats(
data = WineTasting,
x = Wine,
y = Taste,
pairwise.comparisons = TRUE, # show pairwise comparison test results
title = "Wine tasting",
caption = "Data source: `WRS2` R package",
ggtheme = ggthemes::theme_fivethirtyeight(),
ggstatsplot.layer = FALSE,
messages = FALSE
)
As with the ggbetweenstats
, this function also has a grouped_
variant that makes repeating the same analysis across a single grouping
variable quicker. We will see an example with only repeated
measurements-
# common setup
set.seed(123)
# plot
ggstatsplot::grouped_ggwithinstats(
data = dplyr::filter(
.data = ggstatsplot::bugs_long,
region %in% c("Europe", "North America"),
condition %in% c("LDLF", "LDHF")
),
x = condition,
y = desire,
xlab = "Condition",
ylab = "Desire to kill an artrhopod",
grouping.var = region,
outlier.tagging = TRUE,
outlier.label = education,
ggtheme = hrbrthemes::theme_ipsum_tw(),
ggstatsplot.layer = FALSE,
messages = FALSE
)
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 | Friedmanās rank sum test |
Robust | > 2 | Heteroscedastic one-way repeated measures ANOVA for trimmed means |
Bayes Factor | > 2 | One-way repeated measures ANOVA |
Parametric | 2 | Studentās t-test |
Non-parametric | 2 | Wilcoxon signed-rank test |
Robust | 2 | Yuenās test on trimmed means for dependent samples |
Bayes Factor | 2 | Studentās 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 | Studentās t-test | Yes |
Non-parametric | Durbin-Conover test | Yes |
Robust | Yuenās trimmed means test | Yes |
Bayes Factor | Studentās t-test | NA |
For more, see the ggwithinstats
vignette:
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggwithinstats.html
This function creates a scatterplot with marginal distributions overlaid
on the axes (from ggExtra::ggMarginal
) and results from statistical
tests in the subtitle:
ggstatsplot::ggscatterstats(
data = ggplot2::msleep,
x = sleep_rem,
y = awake,
xlab = "REM sleep (in hours)",
ylab = "Amount of time spent awake (in hours)",
title = "Understanding mammalian sleep",
messages = FALSE
)
The available marginal distributions are-
- histograms
- boxplots
- density
- violin
- densigram (density + histogram)
Number of other arguments can be specified to modify this basic plot-
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggscatterstats(
data = dplyr::filter(.data = ggstatsplot::movies_long, genre == "Action"),
x = budget,
y = rating,
type = "robust", # type of test that needs to be run
xlab = "Movie budget (in million/ US$)", # label for x axis
ylab = "IMDB rating", # label for y axis
label.var = "title", # variable for labeling data points
label.expression = "rating < 5 & budget > 100", # expression that decides which points to label
title = "Movie budget and IMDB rating (action)", # title text for the plot
caption = expression(paste(italic("Note"), ": IMDB stands for Internet Movie DataBase")),
ggtheme = hrbrthemes::theme_ipsum_ps(), # choosing a different theme
ggstatsplot.layer = FALSE, # turn off `ggstatsplot` theme layer
marginal.type = "density", # type of marginal distribution to be displayed
xfill = "pink", # color fill for x-axis marginal distribution
yfill = "#009E73", # color fill for y-axis marginal distribution
centrality.parameter = "median", # central tendency lines to be displayed
messages = FALSE # turn off messages and notes
)
Additionally, there is also a grouped_
variant of this function that
makes it easy to repeat the same operation across a single grouping
variable. Also, note that, as opposed to the other functions, this
function does not return a ggplot
object and any modification you want
to make can be made in advance using ggplot.component
argument
(available for all functions, but especially useful for this particular
function):
# for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_ggscatterstats(
data = dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
x = rating,
y = length,
grouping.var = genre, # grouping variable
label.var = title,
label.expression = length > 200,
xfill = "#E69F00",
yfill = "#8b3058",
xlab = "IMDB rating",
title.prefix = "Movie genre",
ggtheme = ggplot2::theme_grey(),
ggplot.component = list(
ggplot2::scale_x_continuous(breaks = seq(2, 9, 1), limits = (c(2, 9)))
),
plotgrid.args = list(nrow = 2),
title.text = "Relationship between movie length by IMDB ratings for different genres"
)
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 | Pearsonās correlation coefficient | Yes |
Non-parametric | Spearmanās rank correlation coefficient | Yes |
Robust | Percentage bend correlation coefficient | Yes |
Bayes Factor | Pearsonās correlation coefficient | No |
For more, see the ggscatterstats
vignette:
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggscatterstats.html
This function creates a pie chart for categorical or nominal variables with results from contingency table analysis (Pearsonās chi-squared test for between-subjects design and McNemarās chi-squared test for within-subjects design) included in the subtitle of the plot. If only one categorical variable is entered, results from one-sample proportion test (i.e., a chi-squared goodness of fit test) will be displayed as a subtitle.
To study an interaction between two categorical variables:
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggpiestats(
data = mtcars,
x = am,
y = cyl,
title = "Dataset: Motor Trend Car Road Tests", # title for the plot
legend.title = "Transmission", # title for the legend
caption = substitute(paste(italic("Source"), ": 1974 Motor Trend US magazine")),
messages = FALSE
)
In case of repeated measures designs, setting paired = TRUE
will
produce results from McNemarās chi-squared test-
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggpiestats(
data = data.frame(
"before" = c("Approve", "Approve", "Disapprove", "Disapprove"),
"after" = c("Approve", "Disapprove", "Approve", "Disapprove"),
counts = c(794, 150, 86, 570),
check.names = FALSE
),
x = before,
y = after,
counts = counts,
title = "Survey results before and after the intervention",
label = "both",
paired = TRUE, # within-subjects design
package = "wesanderson",
palette = "Royal1"
)
Additionally, there is also a grouped_
variant of this function that
makes it easy to repeat the same operation across a single grouping
variable. Following example is a case where the theoretical question is
about proportions for different levels of a single nominal variable:
# for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_ggpiestats(
dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
x = mpaa,
grouping.var = genre, # grouping variable
title.prefix = "Movie genre", # prefix for the facetted title
package = "ggsci", # package from which color palette is to be taken
palette = "default_jama", # choosing a different color palette
title.text = "Composition of MPAA ratings for different genres"
)
Following tests are carried out for each type of analyses-
Type of data | Design | Test |
---|---|---|
Unpaired | Pearsonās |
|
Paired | McNemarās |
|
Frequency | Goodness of fit ( |
Following effect sizes (and confidence intervals/CI) are available for each type of test-
Type | Effect size | CI? |
---|---|---|
Pearsonās chi-squared test | CramĆ©rās V | Yes |
McNemarās test | Cohenās g | Yes |
Goodness of fit | CramĆ©rās V | Yes |
For more, see the ggpiestats
vignette:
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggpiestats.html
In case you are not a fan of pie charts (for very good reasons), you can
alternatively use ggbarstats
function which has a similar syntax-
# for reproducibility
set.seed(123)
library(ggplot2)
# plot
ggstatsplot::ggbarstats(
data = ggstatsplot::movies_long,
x = mpaa,
y = genre,
sampling.plan = "jointMulti",
title = "MPAA Ratings by Genre",
xlab = "movie genre",
legend.title = "MPAA rating",
ggtheme = hrbrthemes::theme_ipsum_pub(),
ggplot.component = list(scale_x_discrete(guide = guide_axis(n.dodge = 2))),
palette = "Set2",
messages = FALSE
)
And, needless to say, there is also a grouped_
variant of this
function-
# setup
set.seed(123)
# smaller dataset
df <-
dplyr::filter(
.data = forcats::gss_cat,
race %in% c("Black", "White"),
relig %in% c("Protestant", "Catholic", "None"),
!partyid %in% c("No answer", "Don't know", "Other party")
)
# plot
ggstatsplot::grouped_ggbarstats(
data = df,
x = relig,
y = partyid,
grouping.var = race,
title.prefix = "Race",
xlab = "Party affiliation",
ggtheme = ggthemes::theme_tufte(base_size = 12),
ggstatsplot.layer = FALSE,
title.text = "Race, religion, and political affiliation",
plotgrid.args = list(nrow = 2)
)
This is identical to the ggpiestats
function summary of tests.
To visualize the distribution of a single variable and check if its mean
is significantly different from a specified value with a one-sample
test, gghistostats
can be used.
# for reproducibility
set.seed(123)
# plot
ggstatsplot::gghistostats(
data = iris, # dataframe from which variable is to be taken
x = Sepal.Length, # numeric variable whose distribution is of interest
title = "Distribution of Iris sepal length", # title for the plot
caption = substitute(paste(italic("Source:"), "Ronald Fisher's Iris data set")),
bar.measure = "both",
test.value = 5, # default value is 0
test.value.line = TRUE, # display a vertical line at test value
centrality.parameter = "mean", # which measure of central tendency is to be plotted
centrality.line.args = list(color = "darkred"), # aesthetics for central tendency line
binwidth = 0.10, # binwidth value (experiment)
ggtheme = hrbrthemes::theme_ipsum_tw(), # choosing a different theme
ggstatsplot.layer = FALSE # turn off ggstatsplot theme layer
)
As can be seen from the plot, Bayes Factor can be attached (bf.message = TRUE
) to assess evidence in favor of the null hypothesis.
Additionally, there is also a grouped_
variant of this function that
makes it easy to repeat the same operation across a single grouping
variable:
# for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_gghistostats(
data = dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
x = budget,
xlab = "Movies budget (in million US$)",
type = "robust", # use robust location measure
grouping.var = genre, # grouping variable
normal.curve = TRUE, # superimpose a normal distribution curve
normal.curve.args = list(color = "red", size = 1),
title.prefix = "Movie genre",
ggtheme = ggthemes::theme_tufte(),
ggplot.component = list( # modify the defaults from `ggstatsplot` for each plot
ggplot2::scale_x_continuous(breaks = seq(0, 200, 50), limits = (c(0, 200)))
),
plotgrid.args = list(nrow = 2),
title.text = "Movies budgets for different genres"
)
Following tests are carried out for each type of analyses-
Type | Test |
---|---|
Parametric | One-sample Studentās t-test |
Non-parametric | One-sample Wilcoxon test |
Robust | One-sample percentile bootstrap |
Bayes Factor | One-sample Studentās t-test |
Following effect sizes (and confidence intervals/CI) are available for each type of test-
Type | Effect size | CI? |
---|---|---|
Parametric | Cohenās d, Hedgeās g (central-and noncentral-t distribution based) | Yes |
Non-parametric | r | Yes |
Robust | robust location measure | Yes |
Bayes Factor | No | No |
For more, including information about the variant of this function
grouped_gghistostats
, see the gghistostats
vignette:
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/gghistostats.html
This function is similar to gghistostats
, but is intended to be used
when the numeric variable also has a label.
# for reproducibility
set.seed(123)
# plot
ggdotplotstats(
data = dplyr::filter(.data = gapminder::gapminder, continent == "Asia"),
y = country,
x = lifeExp,
test.value = 55,
test.value.line = TRUE,
centrality.parameter = "median",
centrality.k = 0,
title = "Distribution of life expectancy in Asian continent",
xlab = "Life expectancy",
caption = substitute(
paste(
italic("Source"),
": Gapminder dataset from https://www.gapminder.org/"
)
)
)
As with the rest of the functions in this package, there is also a
grouped_
variant of this function to facilitate looping the same
operation for all levels of a single grouping variable.
# for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_ggdotplotstats(
data = dplyr::filter(.data = ggplot2::mpg, cyl %in% c("4", "6")),
x = cty,
y = manufacturer,
xlab = "city miles per gallon",
ylab = "car manufacturer",
type = "nonparametric", # non-parametric test
grouping.var = cyl, # grouping variable
test.value = 15.5,
test.value.line = TRUE,
title.prefix = "cylinder count",
point.args = list(color = "red", size = 5, shape = 13),
title.text = "Fuel economy data"
)
This is identical to summary of tests for gghistostats
.
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, letās change some of the defaults. For
example, multiple aesthetics-related arguments can be modified to change
the appearance of the correlation matrix.
# for reproducibility
set.seed(123)
# as a default this function outputs a correlation matrix plot
ggstatsplot::ggcorrmat(
data = ggplot2::msleep,
type = "robust", # correlation method
p.adjust.method = "holm", # p-value adjustment method for multiple comparisons
cor.vars = c(sleep_rem, awake:bodywt), # a range of variables can be selected
cor.vars.names = c(
"REM sleep", # variable names
"time awake",
"brain weight",
"body weight"
),
matrix.type = "upper", # type of visualization matrix
colors = c("#B2182B", "white", "#4D4D4D"),
title = "Correlalogram for mammals sleep dataset",
subtitle = "sleep units: hours; weight units: kilograms",
caption = "Source: `ggplot2` R package"
)
Two things to note:
-
If there are
NA
s present in the selected variables, the legend will display minimum, median, and maximum number of pairs used for correlation tests. -
If
cor.vars
are not specified, all numeric variables will be used.
There is also a grouped_
variant of this function that makes it easy
to repeat the same operation across a single grouping variable:
# for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_ggcorrmat(
data = dplyr::filter(
.data = ggstatsplot::movies_long,
genre %in% c("Action", "Action Comedy", "Action Drama", "Comedy")
),
cor.vars = length:votes,
colors = c("#cbac43", "white", "#550000"),
grouping.var = genre, # grouping variable
k = 3L, # number of digits after decimal point
title.prefix = "Movie genre",
plotgrid.args = list(nrow = 2)
)
You can also get a dataframe containing all relevant details from the statistical tests:
# setup
set.seed(123)
# dataframe in long format
ggcorrmat(
data = ggplot2::msleep,
type = "bayes",
output = "dataframe"
)
#> # A tibble: 15 x 12
#> parameter1 parameter2 rho ci_low ci_high pd rope_percentage
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 sleep_total sleep_rem 0.735 0.617 0.810 1 0
#> 2 sleep_total sleep_cycle -0.436 -0.645 -0.194 0.998 0.0225
#> 3 sleep_total awake -1.00 -1.00 -1.00 1 0
#> 4 sleep_total brainwt -0.344 -0.525 -0.157 0.997 0.0222
#> 5 sleep_total bodywt -0.295 -0.456 -0.142 0.997 0.0318
#> 6 sleep_rem sleep_cycle -0.308 -0.539 -0.0463 0.969 0.0985
#> 7 sleep_rem awake -0.733 -0.827 -0.640 1 0
#> 8 sleep_rem brainwt -0.206 -0.413 0.0106 0.924 0.208
#> 9 sleep_rem bodywt -0.313 -0.492 -0.132 0.994 0.0368
#> 10 sleep_cycle awake 0.440 0.213 0.659 0.992 0.0205
#> 11 sleep_cycle brainwt 0.823 0.716 0.910 1 0
#> 12 sleep_cycle bodywt 0.379 0.133 0.607 0.988 0.0385
#> 13 awake brainwt 0.341 0.160 0.520 0.996 0.03
#> 14 awake bodywt 0.302 0.144 0.463 0.998 0.0295
#> 15 brainwt bodywt 0.925 0.892 0.955 1 0
#> prior_distribution prior_location prior_scale bf nobs
#> <chr> <dbl> <dbl> <dbl> <int>
#> 1 cauchy 0 0.707 3.00e+ 9 61
#> 2 cauchy 0 0.707 8.85e+ 0 32
#> 3 cauchy 0 0.707 NA 83
#> 4 cauchy 0 0.707 7.29e+ 0 56
#> 5 cauchy 0 0.707 9.28e+ 0 83
#> 6 cauchy 0 0.707 1.42e+ 0 32
#> 7 cauchy 0 0.707 3.01e+ 9 61
#> 8 cauchy 0 0.707 6.54e- 1 48
#> 9 cauchy 0 0.707 4.80e+ 0 61
#> 10 cauchy 0 0.707 8.85e+ 0 32
#> 11 cauchy 0 0.707 3.80e+ 6 30
#> 12 cauchy 0 0.707 3.76e+ 0 32
#> 13 cauchy 0 0.707 7.29e+ 0 56
#> 14 cauchy 0 0.707 9.27e+ 0 83
#> 15 cauchy 0 0.707 1.58e+22 56
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 | Pearsonās correlation coefficient | Yes |
Non-parametric | Spearmanās rank correlation coefficient | Yes |
Robust | Percentage bend correlation coefficient | Yes |
Bayes Factor | Pearsonās correlation coefficient | Yes |
For examples and more information, see the ggcorrmat
vignette:
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggcorrmat.html
The function ggstatsplot::ggcoefstats
generates dot-and-whisker
plots for regression models saved in a tidy data frame. The tidy
dataframes are prepared using the following packages: broom
,
broom.mixed
, and parameters
. Additionally, if available, the model
summary indices are also extracted from the following packages: broom
,
broom.mixed
, and performance
.
Although the statistical models displayed in the plot may differ based on the class of models being investigated, there are few aspects of the plot that will be invariant across models:
-
The dot-whisker plot contains a dot representing the estimate and their confidence intervals (
95%
is the default). The estimate can either be effect sizes (for tests that depend on theF
statistic) or regression coefficients (for tests witht
andz
statistic), etc. The function will, by default, display a helpfulx
-axis label that should clear up what estimates are being displayed. The confidence intervals can sometimes be asymmetric if bootstrapping was used. -
The caption will always contain diagnostic information, if available, about models that can be useful for model selection: The smaller the Akaikeās Information Criterion (AIC) and the Bayesian Information Criterion (BIC) values, the ābetterā the model is.
-
The output of this function will be a
ggplot2
object and, thus, it can be further modified (e.g., change themes, etc.) withggplot2
functions.
# for reproducibility
set.seed(123)
# model
mod <- stats::lm(formula = mpg ~ am * cyl, data = mtcars)
# plot
ggstatsplot::ggcoefstats(mod)
This default plot can be further modified to oneās liking with additional arguments (also, letās use a different model now):
# for reproducibility
set.seed(123)
# plot
ggstatsplot::ggcoefstats(
x = MASS::rlm(formula = mpg ~ am * cyl, data = mtcars),
point.args = list(color = "red", size = 3, shape = 15),
vline.args = list(size = 1, color = "#CC79A7", linetype = "dotdash"),
stats.label.color = c("#0072B2", "#D55E00", "darkgreen"),
title = "Car performance predicted by transmission & cylinder count",
subtitle = "Source: 1974 Motor Trend US magazine",
ggtheme = hrbrthemes::theme_ipsum_ps(),
ggstatsplot.layer = FALSE
) + # note the order in which the labels are entered
ggplot2::scale_y_discrete(labels = c("transmission", "cylinders", "interaction")) +
ggplot2::labs(x = "regression coefficient", y = NULL)
Most of the regression models that are supported in the underlying
packages are also supported by ggcoefstats
. For example-
aareg
, anova
, aov
, aovlist
, Arima
, bayesx
, bayesGARCH
,
BBmm
, BBreg
, bcplm
, betamfx
, betaor
, bglmerMod
, bife
,
bigglm
, biglm
, blavaan
, bmlm
, blmerMod
, bracl
, brglm2
,
brmsfit
, brmultinom
, btergm
, cch
, cgam
, cgamm
, cglm
,
clm
, clm2
, clmm
, clmm2
, coeftest
, complmrob
,
confusionMatrix
, coxme
, coxph
, cpglm
, cpglmm
, crch
,
DirichReg
, drc
, emmGrid
, epi.2by2
, ergm
, feis
, felm
,
fitdistr
, flexsurvreg
, glmc
, glmerMod
, glmmTMB
, gls
, gam
,
Gam
, gamlss
, garch
, glm
, glmmadmb
, glmmPQL
, glmRob
,
glmrob
, glmx
, gmm
, hurdle
, ivreg
, iv_robust
, lavaan
, lm
,
lm.beta
, lmerMod
, lmerModLmerTest
, lmodel2
, lmRob
, lmrob
,
logitmfx
, logitor
, logitsf
, LORgee
, lrm
, mcmc
, mcmc.list
,
MCMCglmm
, mclogit
, mmclogit
, mediate
, mixor
, mjoint
, mle2
,
mlm
, multinom
, negbin
, negbinmfx
, negbinirr
, nlmerMod
,
nlrq
, nlreg
, nls
, orcutt
, plm
, poissonmfx
, poissonirr
,
polr
, ridgelm
, rjags
, rlm
, rlmerMod
, robmixglm
, rq
,
rqss
, slm
, speedglm
, speedlm
, stanfit
, stanreg
, survreg
,
svyglm
, svyolr
, svyglm
, tobit
, truncreg
, vgam
, wbgee
,
wblm
, zcpglm
, zeroinfl
, etc.
Although not shown here, this function can also be used to carry out both frequentist and Bayesian random-effects meta-analysis.
For a more exhaustive account of this function, see the associated vignette- https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/ggcoefstats.html
The full power of ggstatsplot
can be leveraged with a functional
programming package like purrr
that
replaces for
loops with code that is both more succinct and easier to
read and, therefore, purrr
should be preferrred š». (Another old school
option to do this effectively is using the plyr
package.)
In such cases, ggstatsplot
contains a helper function combine_plots
to combine multiple plots, which can be useful for combining a list of
plots produced with purrr
. This is a wrapper around
cowplot::plot_grid
and lets you combine multiple plots and add a
combination of title, caption, and annotation texts with suitable
defaults.
For examples (both with plyr
and purrr
), see the associated
vignette-
https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/combine_plots.html
Sometimes you may not like the default plots produced by ggstatsplot
.
In such cases, you can use other custom plots (from ggplot2
or
other plotting packages) and still use ggstatsplot
functions to
display results from relevant statistical test.
For example, in the following chunk, we will create plot (ridgeplot)
using ggridges
package and use ggstatsplot
function for extracting
results.
set.seed(123)
# loading the needed libraries
library(ggridges)
library(ggplot2)
library(ggstatsplot)
# using `ggstatsplot` to get call with statistical results
stats_results <-
ggstatsplot::ggbetweenstats(
data = morley,
x = Expt,
y = Speed,
output = "subtitle"
)
# using `ggridges` to create plot
ggplot(morley, aes(x = Speed, y = as.factor(Expt), fill = as.factor(Expt))) +
geom_density_ridges(
jittered_points = TRUE,
quantile_lines = TRUE,
scale = 0.9,
alpha = 0.7,
vline_size = 1,
vline_color = "red",
point_size = 0.4,
point_alpha = 1,
position = position_raincloud(adjust_vlines = TRUE)
) + # adding annotations
labs(
title = "Michelson-Morley experiments",
subtitle = stats_results,
x = "Speed of light",
y = "Experiment number"
) + # remove the legend
theme(legend.position = "none")
As seen from these examples, ggstatsplot
relies on non-standard
evaluation (NSE) - implemented via rlang
- i.e., rather than looking
at the values of arguments (x
, y
), it instead looks at their
expressions. Therefore, the syntax is simpler and follows the following
principles-
- When a given function depends on variables in a dataframe,
data
argument must always be specified. - The
$
operator cannot be used to specify variables in a dataframe. - All functions accept both string (
x = "var1"
) and symbol (x = var1
) arguments.
These set principles combined with the fact that almost all functions produce publication-ready plots that require very few arguments if one finds the aesthetic and statistical defaults satisfying make the syntax much less cognitively demanding and easy to remember/reconstruct.
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 output
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.
To make the maintenance and development of ggstatsplot
more
manageable, it is being broken into smaller pieces. Currently, the
package internally relies on the following packages that manage
different aspects of statistical analyses:
The statsExpressions
package forms the statistical backend that
processes data and creates expressions containing results from
statistical tests and are by default displayed in as plot subtitle.
For more exhaustive documentation for this package, see: https://indrajeetpatil.github.io/statsExpressions/
The tidyBF
package forms the backend that processes data and creates
expressions containing results from Bayes Factor tests and are by
default displayed in as plot caption.
For more exhaustive documentation for this package, see: https://indrajeetpatil.github.io/tidyBF/
The pairwiseComparisons
package forms the pairwise comparison backend
for creating results that are used to display post hoc multiple
comparisons displayed in ggbetweenstats
and ggwithinstats
functions.
For more exhaustive documentation for this package, see: https://indrajeetpatil.github.io/pairwiseComparisons/
The ipmisc
package contains some of the data wrangling/cleaning
functions and a few other miscellaneous functions.
For more exhaustive documentation for this package, see: https://indrajeetpatil.github.io/ipmisc/
I would like to thank all the contributors to ggstatsplot
who pointed
out bugs or requested features I hadnāt considered. I would especially
like to thank Chuck Powell for his initial contributions to the package.
I also want to thank other package maintainers (like Daniel LĆ¼decke,
Dominique Makowski, Patrick Mair, Salvatore Mangiafico, etc.) who have
patiently and diligently answered my relentless number of questions and
added feature requests I wanted.
The hexsticker was generously designed by Sarah Otterstetter (Max Planck
Institute for Human Development, Berlin). This package has also
benefited from the larger rstats
community on Twitter and
StackOverflow.
Thanks are also due to my postdoc advisers (Mina Cikara and Fiery Cushman at Harvard; Iyad Rahwan at Max Planck Institute for Human Development) who patiently supported me spending hundreds of hours working on this package rather than what I was paid to do. š
As the code stands right now, here is the code coverage for all primary functions involved: https://codecov.io/gh/IndrajeetPatil/ggstatsplot/tree/master/R
Iām happy to receive bug reports, suggestions, questions, and (most of
all) contributions to fix problems and add features. I personally prefer
using the GitHub
issues system over trying to reach out to me in other
ways (personal e-mail, Twitter, etc.). Pull Requests for contributions
are encouraged.
Here are some simple ways in which you can contribute (in the increasing order of commitment):
-
Read and correct any inconsistencies in the documentation
-
Raise issues about bugs or wanted features
-
Review code
-
Add new functionality (in the form of new plotting functions or helpers for preparing subtitles)
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
For reproducibility purposes, the details about the session information in which this document was rendered, see- https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/session_info.html