Visualizations with statistical details: The 'ggstatsplot' approach

Indrajeet Patil

doi:10.21105/joss.03167

Bar charts for categorical data with statistical details included in the plot as a subtitle.

Usage

ggbarstats(
  data,
  x,
  y = NULL,
  counts = NULL,
  type = "parametric",
  paired = FALSE,
  results.subtitle = TRUE,
  label = "percentage",
  label.args = list(alpha = 1, fill = "white"),
  sample.size.label.args = list(size = 4),
  digits = 2L,
  proportion.test = results.subtitle,
  digits.perc = 0L,
  bf.message = TRUE,
  ratio = NULL,
  alternative = "two.sided",
  conf.level = 0.95,
  p.adjust.method = "holm",
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  legend.title = NULL,
  xlab = NULL,
  ylab = NULL,
  ggtheme = ggstatsplot::theme_ggstatsplot(),
  palette = "ggthemes::gdoc",
  ggplot.component = NULL,
  ...
)

Arguments

data

A data frame (or a tibble) from which variables specified are to be taken. Other data types (e.g., matrix, table, array, etc.) will not be accepted. Additionally, grouped data frames from {dplyr} should be ungrouped before they are entered as data.

x

The variable to use as the rows in the contingency table. Note that empty factor levels are not dropped: they are shown in the legend and counted (with zero observations) in the statistical test. Use droplevels() beforehand if this is not intended.

y

The variable to use as the columns in the contingency table. If NULL (default), a one-sample proportion test (a goodness of fit test) is run for the x variable. Otherwise, an appropriate association test is run.

counts

The variable in data containing counts, or NULL if each row represents a single observation.

type

A character specifying the type of statistical approach:

"parametric"
"nonparametric"
"robust"
"bayes"

You can specify just the initial letter (e.g. "np" or "bf"). Matching is on the initial lowercase letter only, so any other value (including capitalized values such as "Bayes") falls back to "parametric" without a warning.

paired

Logical indicating whether data came from a within-subjects or repeated measures design study (Default: FALSE). Only relevant for two-way tables (i.e., when y is supplied), and paired designs are only supported for frequentist tests (McNemar's test). For a two-way table with type = "bayes", paired is ignored and the Bayesian test of independence for unpaired data is run instead. For one-way tables (y = NULL), paired has no effect.

results.subtitle

Logical that decides whether the results of statistical tests are displayed as a subtitle (Default: TRUE). Setting this to FALSE also skips the automatic Bayes Factor caption. Other plot details have their own arguments.

label

Character that decides what each label displays: "percentage" (default), "counts", or "both".

label.args

A list of additional aesthetic arguments passed to ggplot2::geom_label().

sample.size.label.args

A list of additional aesthetic arguments passed to ggplot2::geom_text().

digits

Number of digits for rounding or significant figures. May also be "signif" to return significant figures or "scientific" to return scientific notation. Control the number of digits by adding the value as suffix, e.g. digits = "scientific4" to have scientific notation with 4 decimal places, or digits = "signif5" for 5 significant figures (see also signif()).

proportion.test

Logical that decides whether a proportion test for x is carried out for each level of y. Defaults to results.subtitle. ggbarstats() displays only the p-values from this test. Always FALSE when type = "bayes", when y is NULL, or when x has only one level.

digits.perc

Number of decimal places for percentage labels (Default: 0L).

bf.message

Logical that decides whether to display the Bayes Factor in favor of the null hypothesis as a caption. Relevant only for non-Bayesian types and unpaired designs (paired = FALSE) (Default: TRUE).

ratio

A vector of proportions: the expected proportions for the proportion test (should sum to 1). Default is NULL, which means the null is equal theoretical proportions across the levels of the nominal variable. E.g., ratio = c(0.5, 0.5) for two levels, ratio = c(0.25, 0.25, 0.25, 0.25) for four levels, etc.

alternative

a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". You can specify just the initial letter.

conf.level

Scalar between 0 and 1 (default: 95% confidence/credible intervals, 0.95).

p.adjust.method

Adjustment method for p-values for multiple comparisons. Possible methods are: "holm" (default), "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none".

title

The text for the plot title.

subtitle

The text for the plot subtitle. Used only if results.subtitle = FALSE.

caption

The text for the plot caption. It will be replaced by the Bayes Factor caption whenever that is displayed (see bf.message).

legend.title

Title text for the legend. If NULL (default), the name of the x variable is used.

xlab

Label for x axis. If NULL (default), the name of the y variable is used (no label if y is NULL), since bars are drawn for each level of y.

ylab

Label for y axis. If NULL (default), no label is shown.

ggtheme

A {ggplot2} theme (Default: theme_ggstatsplot()). Any {ggplot2} theme (e.g., ggplot2::theme_bw()) or theme from an extension package (e.g., ggthemes::theme_fivethirtyeight()) can be used. Note that some themes remove plot details. For example, ggthemes::theme_fivethirtyeight() removes the secondary Y-axis, which ggbetweenstats() uses to show details about pairwise comparisons.

palette

Name of the discrete palette in "package::palette" format to be used for coloring (via {paletteer}). Run View(paletteer::palettes_d_names) to see all available options.

ggplot.component

A {ggplot2} component, or a list of components, to add to the plot (Default: NULL). This is mostly helpful for the grouped_ variants, where it is applied to each individual plot.

...

Currently ignored.

Value

A ggplot object, which can be further modified with {ggplot2} functions. Use extract_stats() to get the statistical details shown in the plot as data frames.

Details

Rows with a missing value in x, y, or counts are removed before plotting and analysis.

For details, see: https://www.indrapatil.com/ggstatsplot/articles/web_only/ggbarstats.html

Summary of graphics

graphical element	`geom` used	argument for further modification
bars	`ggplot2::geom_col()`	`NA`
descriptive labels	`ggplot2::geom_label()`	`label.args`
sample size labels	`ggplot2::geom_text()`	`sample.size.label.args`

Contingency table analyses

Below is a summary of:

the statistical test carried out for inferential statistics
the type of effect size estimate and a measure of uncertainty for this estimate
the functions used internally to compute these details

There are no dedicated non-parametric or robust contingency table analyses. type = "nonparametric" and type = "robust" are accepted, but run the same frequentist tests as type = "parametric" (the "Parametric/Non-parametric" rows below).

Two-way table

Hypothesis testing

Type	Design	Test	Function used
Parametric/Non-parametric	Unpaired	Pearson's chi-squared test	`stats::chisq.test()`
Bayesian	Unpaired	Bayesian Pearson's chi-squared test	`BayesFactor::contingencyTableBF()`
Parametric/Non-parametric	Paired	McNemar's chi-squared test	`stats::mcnemar.test()`
Bayesian	Paired	No	No

Effect size estimation

Type	Design	Effect size	CI available?	Function used
Parametric/Non-parametric	Unpaired	Cramer's V	Yes	`effectsize::cramers_v()`
Bayesian	Unpaired	Cramer's V	Yes	`effectsize::cramers_v()`
Parametric/Non-parametric	Paired	Cohen's g	Yes	`effectsize::cohens_g()`
Bayesian	Paired	No	No	No

Paired Bayesian analysis is not supported: for a two-way table with type = "bayes", the paired argument is ignored and the unpaired Bayesian test is run instead.

One-way table

Hypothesis testing

Type	Test	Function used
Parametric/Non-parametric	Goodness of fit chi-squared test	`stats::chisq.test()`
Bayesian	Bayesian Goodness of fit chi-squared test	(custom)

Effect size estimation

Type	Effect size	CI available?	Function used
Parametric/Non-parametric	Pearson's C	Yes	`effectsize::pearsons_c()`
Bayesian	No	No	No

Pairwise comparisons

When there is a two-way table, x has more than two levels, and paired = FALSE, pairwise contingency table analyses (Fisher's exact tests) are computed using statsExpressions::pairwise_contingency_table(). These results are not displayed in the plot: unlike the brackets in box/violin plots, pie and bar charts (which are designed to stay visually congruent) have no natural or established way to show pairwise comparisons. Retrieve them as a data frame with extract_stats(plot)$pairwise_comparisons_data.

Examples

# for reproducibility
set.seed(123)

# one sample goodness of fit proportion test
p <- ggbarstats(mtcars, vs)

# looking at the plot
p


# extracting details from statistical tests
extract_stats(p)
#> $subtitle_data
#> # A tibble: 1 × 13
#>   statistic    df p.value method                                   effectsize 
#>       <dbl> <dbl>   <dbl> <chr>                                    <chr>      
#> 1       0.5     1   0.480 Chi-squared test for given probabilities Pearson's C
#>   estimate conf.level conf.low conf.high conf.method conf.distribution n.obs
#>      <dbl>      <dbl>    <dbl>     <dbl> <chr>       <chr>             <int>
#> 1    0.124       0.95        0     0.426 ncp         chisq                32
#>   expression
#>   <list>    
#> 1 <language>
#> 
#> $caption_data
#> # A tibble: 1 × 4
#>    bf10 prior.scale method                                      expression
#>   <dbl>       <dbl> <chr>                                       <list>    
#> 1 0.180           1 Bayesian one-way contingency table analysis <language>
#> 
#> $pairwise_comparisons_data
#> NULL
#> 
#> $descriptive_data
#> # A tibble: 2 × 4
#>   vs    counts  perc .label
#>   <fct>  <int> <dbl> <chr> 
#> 1 0         18  56.2 56%   
#> 2 1         14  43.8 44%   
#> 
#> $one_sample_data
#> NULL
#> 
#> $tidy_data
#> NULL
#> 
#> $glance_data
#> NULL
#> 
#> attr(,"class")
#> [1] "ggstatsplot_stats" "list"             

# association test (or contingency table analysis)
ggbarstats(mtcars, vs, cyl)


# with 3+ x levels, pairwise comparisons are available
ggbarstats(mtcars, cyl, am)


# Bayesian test
ggbarstats(mtcars, vs, cyl, type = "bayes")


# using pre-aggregated data with counts
ggbarstats(as.data.frame(Titanic), x = Survived, y = Sex, counts = Freq)

Stacked bar charts with statistical tests

Usage

Arguments

Value

Details

Summary of graphics

Contingency table analyses

Two-way table

One-way table

Pairwise comparisons

See also

Examples