knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
ggstatsplot::ggbetweenstats is designed to facilitate
data exploration, and for making highly customizable publication-ready plots,
with relevant statistical details included in the plot itself if desired. 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
to check if a continuous variable differs across multiple groups/conditions
to compare distributions visually and check for outliers
Note: This vignette uses the pipe operator (
%>%), if you are not
familiar with this operator, here is a good explanation:
Comparisons between groups with
To illustrate how this function can be used, we will use the
throughout this vignette. This dataset provides values for life expectancy, GDP
per capita, and population, at 5 year intervals, from 1952 to 2007, for each of
142 countries (courtesy Gapminder Foundation).
Let's have a look at the data-
library(gapminder) dplyr::glimpse(x = gapminder::gapminder)
Note: for the remainder of the vignette we're going to exclude Oceania from the analysis simply because there are so few observations (countries).
Suppose the first thing we want to inspect is the distribution of life expectancy for the countries of a continent in 2007. We also want to know if the mean differences in life expectancy between the continents is statistically significant.
The simplest form of the function call is-
# since the confidence intervals for the effect sizes are computed using # bootstrapping, important to set a seed for reproducibility set.seed(123) # function call ggstatsplot::ggbetweenstats( data = dplyr::filter(.data = gapminder::gapminder, year == 2007, continent != "Oceania"), x = continent, y = lifeExp, nboot = 10, messages = FALSE )
- The function automatically decides whether an independent samples t-test is preferred (for 2 groups) or a Oneway ANOVA (3 or more groups). based on the number of levels in the grouping variable.
- The output of the function is a
ggplotobject which means that it can be further modified with
We can make the output much more aesthetically pleasing as well as informative
by making use of the many optional parameters in
ggbetweenstats. We'll add a
title and caption, better
y axis labels, and tag and label the
outliers in the data. We can and will change the overall theme as well as the
color palette in use.
library(ggstatsplot) library(gapminder) # for reproducibility set.seed(123) # plot ggstatsplot::ggbetweenstats( data = dplyr::filter(.data = gapminder, year == 2007, continent != "Oceania"), # dataframe x = continent, # grouping/independent variable y = lifeExp, # dependent variables xlab = "Continent", # label for the x-axis ylab = "Life expectancy", # label for the y-axis plot.type = "boxviolin", # type of plot type = "parametric", # type of statistical test effsize.type = "biased", # type of effect size nboot = 10, # number of bootstrap samples used bf.message = TRUE, # display bayes factor in favor of null hypothesis outlier.tagging = TRUE, # whether outliers should be flagged outlier.coef = 1.5, # coefficient for Tukey's rule outlier.label = country, # label to attach to outlier values outlier.label.color = "red", # outlier point label color mean.plotting = TRUE, # whether the mean is to be displayed mean.color = "darkblue", # color for mean messages = FALSE, # turn off messages ggtheme = ggplot2::theme_gray(), # a different theme package = "yarrr", # package from which color palette is to be taken palette = "info2", # choosing a different color palette title = "Comparison of life expectancy across continents (Year: 2007)", caption = "Source: Gapminder Foundation" ) + # modifying the plot further ggplot2::scale_y_continuous(limits = c(35, 85), breaks = seq(from = 35, to = 85, by = 5))
As can be appreciated from the effect size (partial eta squared) of 0.635, there are large differences in the mean life expectancy across continents. Importantly, this plot also helps us appreciate the distributions within any given continent. For example, although Asian countries are doing much better than African countries, on average, Afghanistan has a particularly grim average for the Asian continent, possibly reflecting the war and the political turmoil.
So far we have only used a classic parametric test and a boxviolin plot, but we can also use other available options:
type(of test) argument also accepts the following abbreviations:
The type of plot to be displayed can also be modified (
The color palettes can be modified.
Let's use the
combine_plots function to make one plot from three separate
plots that demonstrates all of these options. Let's compare life expectancy for
all countries for the first and last year of available data 1957 and 2007. We
will generate the plots one by one and then use
combine_plots to merge them
into one plot with some common labeling. It is possible, but not necessarily
recommended, to make each plot have different colors or themes.
library(ggstatsplot) library(gapminder) # selecting subset of the data df_year <- dplyr::filter(.data = gapminder::gapminder, year == 2007 | year == 1957) # for reproducibility set.seed(123) # parametric ANOVA and box plot p1 <- ggstatsplot::ggbetweenstats( data = df_year, x = year, y = lifeExp, plot.type = "box", type = "p", effsize.type = "d", conf.level = 0.99, title = "parametric test", package = "ggsci", palette = "nrc_npg", k = 2, messages = FALSE ) # Kruskal-Wallis test (nonparametric ANOVA) and violin plot p2 <- ggstatsplot::ggbetweenstats( data = df_year, x = year, y = lifeExp, xlab = "Year", ylab = "Life expectancy", plot.type = "violin", type = "np", conf.level = 0.99, title = "Non-parametric Test (violin plot)", package = "ggsci", palette = "uniform_startrek", k = 2, messages = FALSE ) # robust ANOVA and boxviolin plot p3 <- ggstatsplot::ggbetweenstats( data = df_year, x = year, y = lifeExp, xlab = "Year", ylab = "Life expectancy", plot.type = "boxviolin", type = "r", conf.level = 0.99, title = "Robust Test (box & violin plot)", tr = 0.005, package = "wesanderson", palette = "Royal2", nboot = 15, k = 2, messages = FALSE ) # robust ANOVA and boxviolin plot p4 <- ggstatsplot::ggbetweenstats( data = df_year, x = year, y = lifeExp, xlab = "Year", ylab = "Life expectancy", type = "bf", plot.type = "box", title = "Bayesian Test (box plot)", package = "ggsci", palette = "nrc_npg", k = 2, messages = FALSE ) # combining the individual plots into a single plot ggstatsplot::combine_plots( p1, p2, p3, p4, nrow = 2, ncol = 2, labels = c("(a)", "(b)", "(c)", "(d)"), title.text = "Comparison of life expectancy between 1957 and 2007", caption.text = "Source: Gapminder Foundation", title.size = 14, caption.size = 12 )
Grouped analysis with
What if we want to analyze both by continent and between 1957 and 2007? A
combination of our two previous efforts. In that case, we could write a
loop or use
purrr, both of which are time consuming and can be a bit of a
ggstatsplot provides a special helper function for such instances:
grouped_ggbetweenstats. This is merely a wrapper function around
ggstatsplot::combine_plots. It applies
ggbetweenstats across all levels
of a specified grouping variable and then combines 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.
Let's focus on the same 4 continents and for years: 1967, 1987, 2007. Also, let's carry out pairwise comparisons to see if there differences between every pair of continents.
# for reproducibility set.seed(123) ggstatsplot::grouped_ggbetweenstats( # arguments relevant for ggstatsplot::ggbetweenstats data = dplyr::filter( .data = gapminder::gapminder, year == 1967 | year == 1987 | year == 2007, continent != "Oceania" ), x = continent, y = lifeExp, xlab = "Continent", ylab = "Life expectancy", k = 2, nboot = 10, effsize.type = "unbiased", # type of effect size (unbiased = omega) partial = FALSE, # partial omega or omega? pairwise.comparisons = TRUE, # display results from pairwise comparisons pairwise.display = "significant", # display only significant pairwise comparisons pairwise.annotation = "p.value", # annotate the pairwise comparisons using p-values p.adjust.method = "fdr", # adjust p-values for multiple tests using this method ggtheme = ggthemes::theme_tufte(), package = "ggsci", palette = "default_jco", outlier.tagging = TRUE, ggstatsplot.layer = FALSE, outlier.label = country, grouping.var = year, title.prefix = "Year", messages = FALSE, # arguments relevant for ggstatsplot::combine_plots title.text = "Changes in life expectancy across continents (1967-2007)", nrow = 3, ncol = 1, labels = c("(a)","(b)","(c)") )
As seen from the plot, although the life expectancy has been improving steadily across all continents as we go from 1967 to 2007, this improvement has not been happening at the same rate for all continents. Additionally, irrespective of which year we look at, we still find significant differences in life expectancy across continents which have been surprisingly consistent across five decades (based on the observed effect sizes).
Grouped analysis with
Although this grouping function provides a quick way to explore the data, it
leaves much to be desired. For example, the same type of plot and test is
applied for all years, but maybe we want to change this for different years, or
maybe we want to gave different effect sizes for different years. This type of
customization for different levels of a grouping variable is not possible with
grouped_ggbetweenstats, but this can be easily achieved using the
See the associated vignette here: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/purrr_examples.html
A variant of this function, called
ggwithinstats, is currently under
development. You can still use this function just to prepare the plot for,
but the statistical details displayed in the subtitle will be incorrect. You can
remove them by adding
+ ggplot2::labs(subtitle = NULL) and add a new subtitle
for the within-subjects test using an appropriate helper function-
Pairwise comparison tests in ggbetweenstats
|Type||Design||Equal variance?||Test||p-value adjustment?|
|Robust||between||No||Yuen's trimmed means test||Yes|
||Yuen's trimmed means test||Yes|
If you find any bugs or have any suggestions/remarks, please file an issue on GitHub: https://github.com/IndrajeetPatil/ggstatsplot/issues