ggscatterstats is meant to provide a publication-ready scatterplot with all statistical details included in the plot itself to show association between two continuous variables. This function is also helpful during the data exploration phase. We will see examples of how to use this function in this vignette with the
To begin with, here are some instances where you would want to use
Note before: The following demo uses the pipe operator (
%>%), so in case you are not familiar with this operator, here is a good explanation: http://r4ds.had.co.nz/pipes.html
To illustrate how this function can be used, we will rely on the
ggplot2movies dataset. This dataset provides information about movies scraped from IMDB. Specifically, we will be using cleaned version of this dataset included in the
ggstatsplot package itself.
library(ggstatsplot) # see the selected data (we have data from 1813 movies) dplyr::glimpse(movies_long) #> Rows: 1,579 #> Columns: 8 #> $ title <chr> "Shawshank Redemption, The", "Lord of the Rings: The Return of … #> $ year <int> 1994, 2003, 2001, 2002, 1994, 1993, 1977, 1980, 1968, 2002, 196… #> $ length <int> 142, 251, 208, 223, 168, 195, 125, 129, 158, 135, 93, 113, 108,… #> $ budget <dbl> 25.0, 94.0, 93.0, 94.0, 8.0, 25.0, 11.0, 18.0, 5.0, 3.3, 1.8, 5… #> $ rating <dbl> 9.1, 9.0, 8.8, 8.8, 8.8, 8.8, 8.8, 8.8, 8.7, 8.7, 8.7, 8.7, 8.6… #> $ votes <int> 149494, 103631, 157608, 114797, 132745, 97667, 134640, 103706, … #> $ mpaa <fct> R, PG-13, PG-13, PG-13, R, R, PG, PG, PG-13, R, PG, R, R, R, R,… #> $ genre <fct> Drama, Action, Action, Action, Drama, Drama, Action, Action, Dr…
Now that we have a clean dataset, we can start asking some interesting questions. For example, let’s see if the average IMDB rating for a movie has any relationship to its budget. Additionally, let’s also see which movies had a high budget but low IMDB rating by labeling those data points.
To reduce the processing time, let’s only work with 30% of the dataset.
# for reproducibility set.seed(123) # plot ggscatterstats( data = movies_long, # dataframe from which variables are taken x = budget, # predictor/independent variable y = rating, # dependent variable xlab = "Budget (in millions of US dollars)", # label for the x-axis ylab = "Rating on IMDB", # label for the y-axis label.var = title, # variable to use for labeling data points label.expression = rating < 5 & budget > 100, # expression for deciding which points to label point.label.args = list(alpha = 0.7, size = 4, color = "grey50"), xfill = "#CC79A7", # fill for marginals on the x-axis yfill = "#009E73", # fill for marginals on the y-axis title = "Relationship between movie budget and IMDB rating", caption = "Source: www.imdb.com" )
There is indeed a small, but significant, positive correlation between the amount of money studio invests in a movie and the ratings given by the audiences.
What if we want to do the same analysis do the same analysis for movies with different MPAA (Motion Picture Association of America) film ratings (NC-17, PG, PG-13, R)?
ggstatsplot provides a special helper function for such instances:
grouped_ggstatsplot. This is merely a wrapper function around
ggstatsplot::combine_plots. It applies
ggstatsplot 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 see how we can use this function to apply
ggscatterstats for all MPAA ratings. Also, let’s run a robust test this time.
# for reproducibility set.seed(123) # plot grouped_ggscatterstats( # arguments relevant for ggstatsplot::ggscatterstats data = movies_long, x = budget, y = rating, grouping.var = mpaa, label.var = title, label.expression = rating < 5 & budget > 80, type = "r", ggtheme = ggthemes::theme_tufte(), # arguments relevant for ggstatsplot::combine_plots annotation.args = list( title = "Relationship between movie budget and IMDB rating", caption = "Source: www.imdb.com" ), plotgrid.args = list(nrow = 3, ncol = 1) )
As seen from the plot, this analysis has revealed something interesting: The relationship we found between budget and IMDB rating holds only for PG-13 and R-rated movies.
Although this is a quick and dirty way to explore large amount of data with minimal effort, it does come with an important limitation: reduced flexibility. For example, if we wanted to add, let’s say, a separate type of marginal distribution plot for each MPAA rating or if we wanted to use different types of correlations across different levels of MPAA ratings (NC-17 has only 6 movies, so a robust correlation would be a good idea), this is not possible. But this can be easily done using
See the associated vignette here: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/purrr_examples.html
||argument for further modification|
|labels for raw data||
|marginal density plots||
Hypothesis testing and Effect size estimation
|Parametric||Pearson’s correlation coefficient||✅||
|Non-parametric||Spearman’s rank correlation coefficient||✅||
|Robust||Winsorized Pearson correlation coefficient||✅||
|Bayesian||Pearson’s correlation coefficient||✅||
If you wish to include statistical analysis results in a publication/report, the ideal reporting practice will be a hybrid of two approaches:
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:
ggscatterstats(mtcars, qsec, drat)
The narrative context (assuming
type = "parametric") can complement this plot either as a figure caption or in the main text-
Pearson’s correlation test revealed that, across 32 cars, a measure of acceleration (1/4 mile time;
qsec) was positively correlated with rear axle ratio (
drat), but this effect was not statistically significant. The effect size \((r = 0.09)\) was small, as per Cohen’s (1988) conventions. The Bayes Factor for the same analysis revealed that the data were 3.32 times more probable under the null hypothesis as compared to the alternative hypothesis. This can be considered moderate evidence (Jeffreys, 1961) in favor of the null hypothesis (of absence of any correlation between these two variables).
If you find any bugs or have any suggestions/remarks, please file an issue on GitHub: https://github.com/IndrajeetPatil/ggstatsplot/issues