Visualizations with statistical details: The 'ggstatsplot' approach

Indrajeet Patil

doi:10.21105/joss.03167

Lifecycle:

The function 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 ggplot2movies dataset.

To begin with, here are some instances where you would want to use ggscatterstats-

to check linear association between two continuous variables
to check distribution of two continuous variables

Correlation plot with `ggscatterstats`

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 a cleaned version of this dataset included in the ggstatsplot package itself.

## see the selected data
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.

Note that the marginal histograms are drawn with the ggside package; set marginal = FALSE to turn them off.

ggscatterstats(
  data = movies_long, ## data frame 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"),
  xsidehistogram.args = list(fill = "#CC79A7"), ## fill for marginals on the x-axis
  ysidehistogram.args = list(fill = "#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 a studio invests in a movie and the ratings given by the audiences.

Grouped analysis with `grouped_ggscatterstats`

What if we want to do the same analysis for movies with different MPAA (Motion Picture Association of America) film ratings (PG, PG-13, R)?

ggstatsplot provides a special helper function for such instances: grouped_ggscatterstats. This is merely a wrapper function around combine_plots. It applies ggscatterstats 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.

grouped_ggscatterstats(
  ## arguments relevant for ggscatterstats
  data = movies_long,
  x = budget,
  y = rating,
  grouping.var = mpaa,
  label.var = title,
  label.expression = rating < 5 & budget > 80,
  type = "r",
  ## arguments relevant for 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.

Grouped analysis with `ggscatterstats` + `{purrr}`

For group-specific arguments or multiple grouping variables, see the programmatic plotting article. It explains how to split the data, customize each call, and combine the resulting plots.

Summary of graphics and tests

graphical element	`geom` used	argument for further modification
raw data	`ggplot2::geom_point()`	`point.args`
labels for raw data	`ggrepel::geom_label_repel()`	`point.label.args`
smooth line	`ggplot2::geom_smooth()`	`smooth.line.args`
marginal histograms	`ggside::geom_xsidehistogram()`, `ggside::geom_ysidehistogram()`	`xsidehistogram.args`, `ysidehistogram.args`

The statistical tests and effect sizes carried out for each type are listed in the function documentation: https://www.indrapatil.com/ggstatsplot/reference/ggscatterstats.html

Extracting statistical details

Use extract_stats() to retrieve the results as data frames; its reference page describes all returned components, including the lists returned for grouped_ plots.

p <- ggscatterstats(mtcars, qsec, drat)
p


extract_stats(p)$subtitle_data
#> # A tibble: 1 × 14
#>   parameter1 parameter2 effectsize          estimate conf.level conf.low
#>   <chr>      <chr>      <chr>                  <dbl>      <dbl>    <dbl>
#> 1 qsec       drat       Pearson correlation   0.0912       0.95   -0.266
#>   conf.high statistic df.error p.value method              n.obs conf.method
#>       <dbl>     <dbl>    <int>   <dbl> <chr>               <int> <chr>      
#> 1     0.426     0.502       30   0.620 Pearson correlation    32 normal     
#>   expression
#>   <list>    
#> 1 <language>

Reporting

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 the example above, the narrative context could read:

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).

Suggestions

If you find any bugs or have any suggestions/remarks, please file an issue on GitHub: https://github.com/IndrajeetPatil/ggstatsplot/issues

Citation

You can cite this package/vignette as:

To cite package 'ggstatsplot' in publications use:

  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}},
  }

ggscatterstats

Correlation plot with ggscatterstats

Grouped analysis with grouped_ggscatterstats

Grouped analysis with ggscatterstats + {purrr}