Visualizations with statistical details: The 'ggstatsplot' approach

Indrajeet Patil

doi:10.21105/joss.03167

A dot chart (as described by William S. Cleveland) with statistical details from one-sample test.

The point estimate (and associated uncertainty) displayed depends on the type of statistics selected:

mean for parametric statistics
median for non-parametric statistics
trimmed mean for robust statistics
MAP estimator for Bayesian statistics

Usage

ggdotplotstats(
  data,
  x,
  y,
  xlab = NULL,
  ylab = NULL,
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  type = "parametric",
  test.value = 0,
  alternative = "two.sided",
  bf.prior = 0.707,
  bf.message = TRUE,
  conf.int = TRUE,
  conf.level = 0.95,
  tr = 0.2,
  digits = 2L,
  results.subtitle = TRUE,
  point.args = list(color = "black", size = 3, shape = 16),
  errorbar.args = list(width = 0, na.rm = TRUE),
  centrality.plotting = TRUE,
  centrality.type = type,
  centrality.line.args = list(color = "blue", linewidth = 1, linetype = "dashed"),
  ggplot.component = NULL,
  ggtheme = ggstatsplot::theme_ggstatsplot(),
  ...
)

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

A numeric variable from the data frame data.

y

Label or grouping variable. Each level of y is shown as a single dot: if a level has several rows, x is summarized using the centrality measure for the chosen type (see above), and the one-sample test is then carried out on these per-level summaries (i.e., the sample size is the number of levels of y).

xlab

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

ylab

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

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).

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.

test.value

A number indicating the true value of the mean (Default: 0).

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.

bf.prior

A number between 0.5 and 2 (default 0.707), the prior width to use in calculating Bayes factors and posterior estimates. In addition to numeric arguments, several named values are also recognized: "medium", "wide", and "ultrawide", corresponding to r scale values of 1/2, sqrt(2)/2, and 1, respectively. In case of an ANOVA, this value corresponds to scale for fixed effects.

bf.message

Logical that decides whether to display the Bayes Factor in favor of the null hypothesis as a caption. Relevant only for parametric tests (Default: TRUE).

conf.int

Logical that decides whether to display confidence intervals as error bars (Default: TRUE).

conf.level

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

tr

Trim level for the mean when carrying out robust tests. In case of an error, try reducing the value of tr, which is by default set to 0.2. Lowering the value might help.

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()).

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.

point.args

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

errorbar.args

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

centrality.plotting

Logical that decides whether central tendency measure is to be displayed as a point with a label (Default: TRUE). Function decides which central tendency measure to show depending on the type argument.

mean for parametric statistics
median for non-parametric statistics
trimmed mean for robust statistics
MAP estimator for Bayesian statistics

Use centrality.type to choose a different centrality measure.

centrality.type

Decides which centrality measure is displayed. Defaults to the same value as type. Can be one of:

"parametric" (for mean)
"nonparametric" (for median)
"robust" (for trimmed mean)
"bayes" (for MAP estimator)

As with type, abbreviations are accepted.

centrality.line.args

A list of additional aesthetic arguments passed to ggplot2::geom_vline(), which displays the line for the centrality measure.

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.

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.

...

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 either x or y are removed before plotting and analysis.

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

Summary of graphics

graphical element	`geom` used	argument for further modification
point estimates	`ggplot2::geom_point()`	`point.args`
error bars	`ggplot2::geom_errorbar()`	`errorbar.args`
centrality measure line	`ggplot2::geom_vline()`	`centrality.line.args`

One-sample tests

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

Hypothesis testing

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	`WRS2::trimcibt()`
Bayesian	One-sample Student's t-test	`BayesFactor::ttestBF()`

Effect size estimation

Type	Effect size	CI available?	Function used
Parametric	Cohen's d, Hedges' g	Yes	`effectsize::cohens_d()`, `effectsize::hedges_g()`
Non-parametric	r (rank-biserial correlation)	Yes	`effectsize::rank_biserial()`
Robust	trimmed mean	Yes	`WRS2::trimcibt()`
Bayesian	difference	Yes	`bayestestR::describe_posterior()`

Examples

# for reproducibility
set.seed(123)

# creating a plot
p <- ggdotplotstats(
  data = ggplot2::mpg,
  x = cty,
  y = manufacturer,
  title = "Fuel economy data",
  xlab = "city miles per gallon"
)

# looking at the plot
p


# extracting details from statistical tests
extract_stats(p)
#> $subtitle_data
#> # A tibble: 1 × 15
#>      mu statistic df.error  p.value method            alternative effectsize
#>   <dbl>     <dbl>    <dbl>    <dbl> <chr>             <chr>       <chr>     
#> 1     0      17.1       14 9.07e-11 One Sample t-test two.sided   Hedges' g 
#>   estimate conf.level conf.low conf.high conf.method conf.distribution n.obs
#>      <dbl>      <dbl>    <dbl>     <dbl> <chr>       <chr>             <int>
#> 1     4.17       0.95     2.56      5.76 ncp         t                    15
#>   expression
#>   <list>    
#> 1 <language>
#> 
#> $caption_data
#> # A tibble: 1 × 16
#>   term       effectsize      estimate conf.level conf.low conf.high    pd
#>   <chr>      <chr>              <dbl>      <dbl>    <dbl>     <dbl> <dbl>
#> 1 Difference Bayesian t-test     16.3       0.95     14.1      18.4     1
#>   prior.distribution prior.location prior.scale      bf10 method         
#>   <chr>                       <dbl>       <dbl>     <dbl> <chr>          
#> 1 cauchy                          0       0.707 87122783. Bayesian t-test
#>   conf.method log_e_bf10 n.obs expression
#>   <chr>            <dbl> <int> <list>    
#> 1 ETI               18.3    15 <language>
#> 
#> $pairwise_comparisons_data
#> NULL
#> 
#> $descriptive_data
#> NULL
#> 
#> $one_sample_data
#> NULL
#> 
#> $tidy_data
#> NULL
#> 
#> $glance_data
#> NULL
#> 
#> attr(,"class")
#> [1] "ggstatsplot_stats" "list"

Dot plot/chart for labeled numeric data