You can cite this package/vignette as:
Patil, I. (2021). Visualizations with statistical details: The
'ggstatsplot' approach. Journal of Open Source Software, 6(61), 3167,
doi:10.21105/joss.03167
A BibTeX entry for LaTeX users is
@Article{,
doi = {10.21105/joss.03167},
url = {https://doi.org/10.21105/joss.03167},
year = {2021},
publisher = {{The Open Journal}},
volume = {6},
number = {61},
pages = {3167},
author = {Indrajeet Patil},
title = {{Visualizations with statistical details: The {'ggstatsplot'} approach}},
journal = {{Journal of Open Source Software}},
}
ggpiestats
The function ggpiestats
can be used for quick data exploration and/or to prepare publicationready pie charts to summarize the statistical relationship(s) among one or more categorical variables. We will see examples of how to use this function in this vignette.
To begin with, here are some instances where you would want to use ggpiestats

to check if the proportion of observations matches our hypothesized proportion, this is typically known as a “Goodness of Fit” test
to see if the frequency distribution of two categorical variables are independent of each other using the contingency table analysis
to check if the proportion of observations at each level of a categorical variable is equal
Note: The following demo uses the pipe operator (%>%
), if you are not familiar with this operator, here is a good explanation: http://r4ds.had.co.nz/pipes.html.
ggpiestats
works only with data organized in dataframes or tibbles. It will not work with other data structures like baseR tables or matrices. It can operate on dataframes that are organized with one row per observation or dataframes that have one column containing counts. This vignette provides examples of both (see examples below).
To help demonstrate how ggpiestats
can be used with categorical (also known as nominal) data, a modified version of the original Titanic
dataset (from the datasets
library) has been provided in the ggstatsplot
package with the name Titanic_full
. The Titanic Passenger Survival Dataset provides information “on the fate of passengers on the fatal maiden voyage of the ocean liner Titanic, including economic status (class), sex, age, and survival.”
Let’s have a look at the structure of both.
library(datasets)
library(dplyr)
library(ggstatsplot)
# looking at the original data in tabular format
dplyr::glimpse(x = Titanic)
#> 'table' num [1:4, 1:2, 1:2, 1:2] 0 0 35 0 0 0 17 0 118 154 ...
#>  attr(*, "dimnames")=List of 4
#> ..$ Class : chr [1:4] "1st" "2nd" "3rd" "Crew"
#> ..$ Sex : chr [1:2] "Male" "Female"
#> ..$ Age : chr [1:2] "Child" "Adult"
#> ..$ Survived: chr [1:2] "No" "Yes"
# looking at the dataset as a tibble or dataframe
dplyr::glimpse(x = ggstatsplot::Titanic_full)
#> Rows: 2,201
#> Columns: 5
#> $ id <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18…
#> $ Class <fct> 3rd, 3rd, 3rd, 3rd, 3rd, 3rd, 3rd, 3rd, 3rd, 3rd, 3rd, 3rd, 3…
#> $ Sex <fct> Male, Male, Male, Male, Male, Male, Male, Male, Male, Male, M…
#> $ Age <fct> Child, Child, Child, Child, Child, Child, Child, Child, Child…
#> $ Survived <fct> No, No, No, No, No, No, No, No, No, No, No, No, No, No, No, N…
ggpiestats
The simplest use case for ggpiestats
is that we want to display information about one categorical or nominal variable. As part of that display or plot, we may also choose to execute a chisquared goodness of fit test to see whether the proportions (or percentages) in categories of the single variable appear to line up with our hypothesis or model. To start simple and then expand, let’s say that we’d like to display a piechart with the percentages of passengers who did or did not survive. Our initial hypothesis is that it was no different than flipping a coin. People had a 50/50 chance of surviving.
# since effect size confidence intervals are computed using bootstrapping, let's
# set seed for reproducibility
set.seed(123)
# to speed up the process, let's use only half of the dataset
Titanic_full_50 < dplyr::sample_frac(ggstatsplot::Titanic_full, size = 0.5)
# plot
ggstatsplot::ggpiestats(
data = Titanic_full_50,
x = Survived,
title = "Passenger survival on the Titanic", # title for the entire plot
caption = "Source: Titanic survival dataset", # caption for the entire plot
legend.title = "Survived?"
)
Note: equal proportions per category are the default, e.g. 50/50, but you can specify any hypothesized ratio you like with ratio
so if our hypothesis was that 80% died and 20% survived we would add ratio = c(.80,.20)
when we entered the code.
ggpiestats
Let’s next investigate whether the passenger’s gender was independent of, or associated with, gender. The test is whether the proportion of people who survived was different between the sexes using ggpiestats
.
We’ll modify a number of arguments to change the appearance of this plot and showcase the flexibility of ggpiestats
. We will:
Change the plot theme to ggplot2::theme_grey()
Change our color palette to category10_d3
from ggsci
package
We’ll customize the subtitle by being more precise about which chi squared test this is stat.title = "chi squared test of independence: "
Finally, we’ll make a call to ggplot2
to modify the size of our plot title and to make it right justified
# since effect size confidence intervals are computed using bootstrapping, let's
# set seed for reproducibility
set.seed(123)
# plot
ggstatsplot::ggpiestats(
data = Titanic_full,
x = Survived,
y = Sex,
title = "Passenger survival on the Titanic by gender", # title for the entire plot
caption = "Source: Titanic survival dataset", # caption for the entire plot
legend.title = "Survived?", # legend title
ggtheme = ggplot2::theme_grey(), # changing plot theme
palette = "category10_d3", # choosing a different color palette
package = "ggsci", # package to which color palette belongs
k = 3, # decimal places in result
perc.k = 1 # decimal places in percentage labels
) + # further modification with `ggplot2` commands
ggplot2::theme(
plot.title = ggplot2::element_text(
color = "black",
size = 14,
hjust = 0
)
)
The plot clearly shows that survival rates were very different between males and females. The Pearson’s \(\chi^2\)test of independence is significant given our large sample size. Additionally, for both females and males, the survival rates were significantly different than 50% as indicated by a goodness of fit test for each gender.
grouped_ggpiestats
What if we want to do the same analysis of gender but also factor in the passenger’s age (Age)? We have information that classifies the passengers as Child or Adult, perhaps that makes a difference to their survival rate?
ggstatsplot
provides a special helper function for such instances: grouped_ggpiestats
. It is a convenient wrapper function around ggstatsplot::combine_plots
. It applies ggpiestats
across all levels of a specified grouping variable and then combines the list of individual plots into a single plot. Note that the grouping variable can be anything: conditions in a given study, groups in a study sample, different studies, etc.
# since effect size confidence intervals are computed using bootstrapping, let's
# set seed for reproducibility
set.seed(123)
# plot
ggstatsplot::grouped_ggpiestats(
# arguments relevant for ggstatsplot::gghistostats
data = ggstatsplot::Titanic_full,
x = Survived,
y = Sex,
grouping.var = Age,
perc.k = 1,
package = "ggsci",
palette = "category10_d3",
# arguments relevant for ggstatsplot::combine_plots
title.text = "Passenger survival on the Titanic by gender and age",
caption.text = "Asterisks denote results from proportion tests; \n***: p < 0.001, ns: nonsignificant",
plotgrid.args = list(nrow = 2)
)
The resulting pie charts and statistics make the story clear. For adults gender very much matters. Women survived at much higher rates than men. For children gender is not significantly associated with survival and both male and female children have a survival rate that is not significantly different from 50/50.
ggpiestats
+ purrr
Although grouped_ggpiestats
provides a quick way to explore the data, it leaves much to be desired. For example, we may want to add different captions, titles, themes, or palettes for each level of the grouping variable, etc. For cases like these, it would be better to use purrr
package.
See the associated vignette here: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/purrr_examples.html
counts
ggpiestats
can also work with dataframe containing counts (aka tabled data), i.e., when each row doesn’t correspond to a unique observation. For example, consider the following notional fishing
dataframe containing data from two boats (A
and B
) about the number of different types fish they caught in the months of February
and March
. In this dataframe, each row corresponds to a unique combination of Boat
and Month
.
# for reproducibility
set.seed(123)
# creating a dataframe
# (this is completely fictional; I don't know first thing about fishing!)
(
fishing < data.frame(
Boat = c(rep("B", 4), rep("A", 4), rep("A", 4), rep("B", 4)),
Month = c(rep("February", 2), rep("March", 2), rep("February", 2), rep("March", 2)),
Fish = c(
"Bass",
"Catfish",
"Cod",
"Haddock",
"Cod",
"Haddock",
"Bass",
"Catfish",
"Bass",
"Catfish",
"Cod",
"Haddock",
"Cod",
"Haddock",
"Bass",
"Catfish"
),
SumOfCaught = c(25, 20, 35, 40, 40, 25, 30, 42, 40, 30, 33, 26, 100, 30, 20, 20)
) %>% # converting to a tibble dataframe
tibble::as_data_frame(x = .)
)
#> # A tibble: 16 × 4
#> Boat Month Fish SumOfCaught
#> <chr> <chr> <chr> <dbl>
#> 1 B February Bass 25
#> 2 B February Catfish 20
#> 3 B March Cod 35
#> 4 B March Haddock 40
#> 5 A February Cod 40
#> 6 A February Haddock 25
#> 7 A March Bass 30
#> 8 A March Catfish 42
#> 9 A February Bass 40
#> 10 A February Catfish 30
#> 11 A March Cod 33
#> 12 A March Haddock 26
#> 13 B February Cod 100
#> 14 B February Haddock 30
#> 15 B March Bass 20
#> 16 B March Catfish 20
When the data is organized this way, we make a slightly different call to the ggpiestats
function: we use the counts
argument. If we want to investigate the relationship of type of fish by month (a test of independence), our command would be:
# running `ggpiestats` with counts information
ggstatsplot::ggpiestats(
data = fishing,
x = Fish,
y = Month,
counts = SumOfCaught,
label = "both",
package = "ggsci",
palette = "default_jama",
title = "Type fish caught by month",
caption = "Source: completely made up",
legend.title = "Type fish caught: "
)
The results support our hypothesis that the type of fish caught is related to the month in which we’re fishing. The \(\chi^2\) independence test results at the top of the plot. In February we catch significantly more Haddock than we would hypothesize for an equal distribution. Whereas in March our results indicate there’s no strong evidence that the distribution isn’t equal.
For our final example let’s imagine we’re conducting clinical trials for some new imaginary wonder drug. We have 134 subjects entering the trial. Some of them enter healthy (n = 96), some of them enter the trial already being sick (n = 38). All of them receive our treatment or intervention. Then we check back in a month to see if they are healthy or sick. A classic pre/post experimental design. We’re interested in seeing the change in both groupings. In the case of withinsubjects designs, you can set paired = TRUE
, which will display results from McNemar test in the subtitle.
(Note: If you forget to set paired = TRUE
, the results you get will be inaccurate.)
# seed for reproducibility
set.seed(123)
# create our imaginary data
clinical_trial <
tibble::tribble(
~SickBefore, ~SickAfter, ~Counts,
"No", "Yes", 4,
"Yes", "No", 25,
"Yes", "Yes", 13,
"No", "No", 92
)
# plot
ggstatsplot::ggpiestats(
data = clinical_trial,
x = SickAfter,
y = SickBefore,
counts = Counts,
paired = TRUE,
label = "both",
title = "Results from imaginary clinical trial",
package = "ggsci",
palette = "default_ucscgb"
)
The results bode well for our experimental wonder drug. Of the 96 who started out healthy only 4% were sick after a month. Ideally, we would have hoped for zero but reality is seldom perfect. On the other side of the 38 who started out sick that number has reduced to just 13 or 34% which is a marked improvement.
graphical element 
geom_ used 
argument for further modification 

pie slices  ggplot2::geom_col 
❌ 
descriptive labels 
ggplot2::geom_label /ggrepel::geom_label_repel

label.args 
twoway table
Hypothesis testing
Type  Design  Test  Function used 

Parametric/Nonparametric  Unpaired  Pearson’s \(\chi^2\) test  stats::chisq.test 
Bayesian  Unpaired  Bayesian Pearson’s \(\chi^2\) test  BayesFactor::contingencyTableBF 
Parametric/Nonparametric  Paired  McNemar’s \(\chi^2\) test  stats::mcnemar.test 
Bayesian  Paired  ❌  ❌ 
Effect size estimation
Type  Design  Effect size  CI?  Function used 

Parametric/Nonparametric  Unpaired  Cramer’s \(V\)  ✅  effectsize::cramers_v 
Bayesian  Unpaired  Cramer’s \(V\)  ✅  effectsize::cramers_v 
Parametric/Nonparametric  Paired  Cohen’s \(g\)  ✅  effectsize::cohens_g 
Bayesian  Paired  ❌  ❌  ❌ 
oneway table
Hypothesis testing
Type  Test  Function used 

Parametric/Nonparametric  Goodness of fit \(\chi^2\) test  stats::chisq.test 
Bayesian  Bayesian Goodness of fit \(\chi^2\) test  (custom) 
Effect size estimation
Type  Effect size  CI?  Function used 

Parametric/Nonparametric  Cramer’s \(V\)  ✅  bayestestR::describe_posterior 
Bayesian  ❌  ❌  ❌ 
If you wish to include statistical analysis results in a publication/report, the ideal reporting practice will be a hybrid of two approaches:
the ggstatsplot
approach, where the plot contains both the visual and numerical summaries about a statistical model, and
the standard narrative approach, which provides interpretive context for the reported statistics.
For example, let’s see the following example:
ggpiestats(mtcars, am, cyl)
The narrative context (assuming type = "parametric"
) can complement this plot either as a figure caption or in the main text
Pearson’s \(\chi^2\)test of independence revealed that, across 32 automobiles, showed that there was a significant association between transmission engine and number of cylinders. The Bayes Factor for the same analysis revealed that the data were 16.78 times more probable under the alternative hypothesis as compared to the null hypothesis. This can be considered strong evidence (Jeffreys, 1961) in favor of the alternative hypothesis.
Similar reporting style can be followed when the function performs onesample goodnessoffit test instead of a \(\chi^2\)test.
Same holds true for ggbarstats
.
If you find any bugs or have any suggestions/remarks, please file an issue on GitHub: https://github.com/IndrajeetPatil/ggstatsplot/issues