Stacked bar charts with statistical tests

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

Usage

ggbarstats(
  data,
  x,
  y,
  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,
  conf.level = 0.95,
  sampling.plan = "indepMulti",
  fixed.margin = "rows",
  prior.concentration = 1,
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  legend.title = NULL,
  xlab = NULL,
  ylab = NULL,
  ggtheme = ggstatsplot::theme_ggstatsplot(),
  package = "RColorBrewer",
  palette = "Dark2",
  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. Please note that if there are empty factor levels in your variable, they will be dropped.

y

The variable to use as the columns in the contingency table. Please note that if there are empty factor levels in your variable, they will be dropped. Default is NULL. If NULL, one-sample proportion test (a goodness of fit test) will be run for the x variable. Otherwise an appropriate association test will be run. This argument can not be NULL for ggbarstats function.

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.

paired

Logical indicating whether data came from a within-subjects or repeated measures design study (Default: FALSE).

results.subtitle

Decides whether the results of statistical tests are to be displayed as a subtitle (Default: TRUE). If set to FALSE, only the plot will be returned.

label

Character decides what information needs to be displayed on the label in each pie slice. Possible options are "percentage" (default), "counts", "both".

label.args

Additional aesthetic arguments that will be passed to ggplot2::geom_label().

sample.size.label.args

Additional aesthetic arguments that will be 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

Decides whether proportion test for x variable is to be carried out for each level of y. Defaults to results.subtitle. In ggbarstats, only p-values from this test will be displayed.

digits.perc

Numeric that decides number of decimal places for percentage labels (Default: 0L).

bf.message

Logical that decides whether to display Bayes Factor in favor of the null hypothesis. This argument is relevant only for parametric test (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.

conf.level

Scalar between 0 and 1 (default: 95% confidence/credible intervals, 0.95). If NULL, no confidence intervals will be computed.

sampling.plan

Character describing the sampling plan. Possible options are "indepMulti" (independent multinomial; default), "poisson", "jointMulti" (joint multinomial), "hypergeom" (hypergeometric). For more, see ?BayesFactor::contingencyTableBF().

fixed.margin

For the independent multinomial sampling plan, which margin is fixed ("rows" or "cols"). Defaults to "rows".

prior.concentration

Specifies the prior concentration parameter, set to 1 by default. It indexes the expected deviation from the null hypothesis under the alternative, and corresponds to Gunel and Dickey's (1974) "a" parameter.

title

The text for the plot title.

subtitle

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

caption

The text for the plot caption. This argument is relevant only if bf.message = FALSE.

legend.title

Title text for the legend.

xlab

Label for x axis variable. If NULL (default), variable name for x will be used.

ylab

Labels for y axis variable. If NULL (default), variable name for y will be used.

ggtheme

A {ggplot2} theme. Default value is ggstatsplot::theme_ggstatsplot(). Any of the {ggplot2} themes (e.g., theme_bw()), or themes from extension packages are allowed (e.g., ggthemes::theme_fivethirtyeight(), hrbrthemes::theme_ipsum_ps(), etc.). But note that sometimes these themes will remove some of the details that {ggstatsplot} plots typically contains. For example, if relevant, ggbetweenstats() shows details about multiple comparison test as a label on the secondary Y-axis. Some themes (e.g. ggthemes::theme_fivethirtyeight()) will remove the secondary Y-axis and thus the details as well.

package, palette

Name of the package from which the given palette is to be extracted. The available palettes and packages can be checked by running View(paletteer::palettes_d_names).

ggplot.component

A ggplot component to be added to the plot prepared by {ggstatsplot}. This argument is primarily helpful for grouped_ variants of all primary functions. Default is NULL. The argument should be entered as a {ggplot2} function or a list of {ggplot2} functions.

...

Currently ignored.

Details

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

Summary of graphics

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

Contingency table analyses

The table below provides summary about:

• statistical test carried out for inferential statistics

• type of effect size estimate and a measure of uncertainty for this estimate

• functions used internally to compute these details

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

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

See also

grouped_ggbarstats, ggpiestats, grouped_ggpiestats

Examples

# for reproducibility
set.seed(123)

# creating a plot
p <- ggbarstats(mtcars, x = vs, y = cyl)

# 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> <int>     <dbl> <chr>                      <chr>            
#> 1      21.3     2 0.0000232 Pearson's Chi-squared test Cramer's V (adj.)
#>   estimate conf.level conf.low conf.high conf.method conf.distribution n.obs
#>      <dbl>      <dbl>    <dbl>     <dbl> <chr>       <chr>             <int>
#> 1    0.789       0.95    0.371         1 ncp         chisq                32
#>   expression
#>   <list>    
#> 1 <language>
#> 
#> $caption_data
#> # A tibble: 1 × 15
#>   term  conf.level effectsize estimate conf.low conf.high
#>   <chr>      <dbl> <chr>         <dbl>    <dbl>     <dbl>
#> 1 Ratio       0.95 Cramers_v     0.683    0.436     0.840
#>   prior.distribution      prior.location prior.scale   bf10
#>   <chr>                            <dbl>       <dbl>  <dbl>
#> 1 independent multinomial              0           1 30129.
#>   method                              conf.method log_e_bf10 n.obs expression
#>   <chr>                               <chr>            <dbl> <int> <list>    
#> 1 Bayesian contingency table analysis ETI               10.3    32 <language>
#> 
#> $pairwise_comparisons_data
#> NULL
#> 
#> $descriptive_data
#> # A tibble: 5 × 5
#>   cyl   vs    counts   perc .label
#>   <fct> <fct>  <int>  <dbl> <chr> 
#> 1 4     1         10  90.9  91%   
#> 2 6     1          4  57.1  57%   
#> 3 4     0          1   9.09 9%    
#> 4 6     0          3  42.9  43%   
#> 5 8     0         14 100    100%  
#> 
#> $one_sample_data
#> # A tibble: 3 × 10
#>   cyl   counts  perc N        statistic    df  p.value method    .label .p.label
#>   <fct>  <int> <dbl> <chr>        <dbl> <dbl>    <dbl> <chr>     <glue> <glue>  
#> 1 8         14  43.8 (n = 14)    14         1 0.000183 Chi-squa… list(… list(~i…
#> 2 6          7  21.9 (n = 7)      0.143     1 0.705    Chi-squa… list(… list(~i…
#> 3 4         11  34.4 (n = 11)     7.36      1 0.00666  Chi-squa… list(… list(~i…
#> 
#> $tidy_data
#> NULL
#> 
#> $glance_data
#> NULL
#>