You can cite this package/vignette as:


  Patil, I. (2018). Visualizations with statistical details: The
  'ggstatsplot' approach. PsyArxiv. doi:10.31234/osf.io/p7mku

A BibTeX entry for LaTeX users is

  @Article{,
    title = {Visualizations with statistical details: The 'ggstatsplot' approach},
    author = {Indrajeet Patil},
    year = {2021},
    journal = {PsyArxiv},
    url = {https://psyarxiv.com/p7mku/},
    doi = {10.31234/osf.io/p7mku},
  }

Lifecycle:

The function ggdotplotstats can be used for data exploration and to provide an easy way to make publication-ready dot plots/charts with appropriate and selected statistical details embedded in the plot itself. In this vignette, we will explore several examples of how to use it.

This function is a sister function of gghistostats with the difference being it expects a labeled numeric variable.

Distribution of a sample with `ggdotplotstats`

Let’s begin with a very simple example from the ggplot2 package (ggplot2::mpg), a subset of the fuel economy data that the EPA makes available on http://fueleconomy.gov.

# looking at the structure of the data using glimpse
dplyr::glimpse(ggplot2::mpg)
#> Rows: 234
#> Columns: 11
#> $ manufacturer <chr> "audi", "audi", "audi", "audi", "audi", "audi", "audi", "~
#> $ model        <chr> "a4", "a4", "a4", "a4", "a4", "a4", "a4", "a4 quattro", "~
#> $ displ        <dbl> 1.8, 1.8, 2.0, 2.0, 2.8, 2.8, 3.1, 1.8, 1.8, 2.0, 2.0, 2.~
#> $ year         <int> 1999, 1999, 2008, 2008, 1999, 1999, 2008, 1999, 1999, 200~
#> $ cyl          <int> 4, 4, 4, 4, 6, 6, 6, 4, 4, 4, 4, 6, 6, 6, 6, 6, 6, 8, 8, ~
#> $ trans        <chr> "auto(l5)", "manual(m5)", "manual(m6)", "auto(av)", "auto~
#> $ drv          <chr> "f", "f", "f", "f", "f", "f", "f", "4", "4", "4", "4", "4~
#> $ cty          <int> 18, 21, 20, 21, 16, 18, 18, 18, 16, 20, 19, 15, 17, 17, 1~
#> $ hwy          <int> 29, 29, 31, 30, 26, 26, 27, 26, 25, 28, 27, 25, 25, 25, 2~
#> $ fl           <chr> "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p~
#> $ class        <chr> "compact", "compact", "compact", "compact", "compact", "c~

Let’s say we want to visualize the distribution of mileage by car manufacturer.

# for reproducibility
set.seed(123)
library(ggstatsplot)

# removing factor level with very few no. of observations
df <- dplyr::filter(.data = ggplot2::mpg, cyl %in% c("4", "6"))

# creating a vector of colors using `paletteer` package
paletter_vector <-
  paletteer::paletteer_d(
    palette = "palettetown::venusaur",
    n = nlevels(as.factor(df$manufacturer)),
    type = "discrete"
  )

# plot
ggdotplotstats(
  data = df,
  x = cty,
  y = manufacturer,
  xlab = "city miles per gallon",
  ylab = "car manufacturer",
  test.value = 15.5,
  point.args = list(
    shape = 16,
    color = paletter_vector,
    size = 5
  ),
  title = "Distribution of mileage of cars",
  ggtheme = hrbrthemes::theme_ipsum_ps(),
  ggstatsplot.layer = FALSE
)

Grouped analysis with `grouped_ggdotplotstats`

What if we want to do the same analysis separately for different engines with different numbers of cylinders?

ggstatsplot provides a special helper function for such instances: grouped_ggdotplotstats. This is merely a wrapper function around ggstatsplot::combine_plots. It applies ggdotplotstats across all levels of a specified grouping variable and then combines the individual plots into a single plot.

Let’s see how we can use this function to apply ggdotplotstats to accomplish our task.

# for reproducibility
set.seed(123)

# removing factor level with very few no. of observations
df <- dplyr::filter(.data = ggplot2::mpg, cyl %in% c("4", "6"))

# plot
grouped_ggdotplotstats(
  # arguments relevant for ggstatsplot::ggdotplotstats
  data = df,
  x = cty,
  y = manufacturer,
  xlab = "city miles per gallon",
  ylab = "car manufacturer",
  grouping.var = cyl, # grouping variable
  type = "bayes", # Bayesian test
  test.value = 15.5,
  ggtheme = ggthemes::theme_fivethirtyeight(),
  # arguments relevant for `ggstatsplot::combine_plots`
  annotation.args = list(title = "Fuel economy data"),
  plotgrid.args = list(nrow = 2)
)

Grouped analysis with `purrr`

Although this is a quick and dirty way to explore a 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 test.value argument for each gender, this is not possible with grouped_ggdotplotstats. For cases like these, or to run separate kinds of tests (robust for some, parametric for other, while Bayesian for some other levels of the group) it would be better to use purrr.

See the associated vignette here: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/purrr_examples.html

Summary of tests

The central tendency measure displayed will depend on the statistics:

Type	Measure	Function used
Parametric	mean	`parameters::describe_distribution`
Non-parametric	median	`parameters::describe_distribution`
Robust	trimmed mean	`parameters::describe_distribution`
Bayesian	MAP estimate	`parameters::describe_distribution`

MAP: maximum a posteriori probability

Following tests are carried out for each type of analyses-

Type	Test	Function used
Parametric	One-sample Student’s t-test	`stats::t.test`
Non-parametric	One-sample Wilcoxon test	`stats::wilcox.test`
Robust	Bootstrap-t method for one-sample test	`trimcibt` (custom)
Bayesian	One-sample Student’s t-test	`BayesFactor::ttestBF`

Following effect sizes (and confidence intervals/CI) are available for each type of test-

Type	Effect size	CI?	Function used
Parametric	Cohen’s d, Hedge’s g	Yes	`effectsize::cohens_d`, `effectsize::hedges_g`
Non-parametric	r (rank-biserial correlation)	Yes	`effectsize::rank_biserial`
Robust	trimmed mean	Yes	`trimcibt` (custom)
Bayes Factor	\(\delta_{posterior}\)	Yes	`bayestestR::describe_posterior`

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 example, let’s see the following example:

The ggstatsplot reporting -

ggdotplotstats(morley, Speed, Expt, test.value = 800)

The narrative context (assuming type = "parametric") can complement this plot either as a figure caption or in the main text-

Student’s t-test revealed that, across 5 experiments, the speed of light was significantly different than posited speed. The effect size \((g = 1.22)\) was very large, as per Cohen’s (1988) conventions. The Bayes Factor for the same analysis revealed that the data were 3.46 times more probable under the alternative hypothesis as compared to the null hypothesis. This can be considered moderate evidence (Jeffreys, 1961) in favor of the alternative hypothesis.

Effect size interpretation

To see how the effect sizes displayed in these tests can be interpreted, see: https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/effsize_interpretation.html

Suggestions

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

Session Information

For details, see- https://indrajeetpatil.github.io/ggstatsplot/articles/web_only/session_info.html

ggdotplotstats

Indrajeet Patil

2021-04-19

Distribution of a sample with ggdotplotstats

Grouped analysis with grouped_ggdotplotstats

Grouped analysis with purrr