ggstatsplot: 'ggplot2' Based Plots with Statistical Details

ggdotplotstats

R Documentation

Dot plot/chart for labeled numeric data.

Description

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

Usage

ggdotplotstats(
  data,
  x,
  y,
  xlab = NULL,
  ylab = NULL,
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  type = "parametric",
  test.value = 0,
  bf.prior = 0.707,
  bf.message = TRUE,
  effsize.type = "g",
  conf.level = 0.95,
  tr = 0.2,
  digits = 2L,
  results.subtitle = TRUE,
  point.args = list(color = "black", size = 3, shape = 16),
  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.
`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.
`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`.
`type`	A character specifying the type of statistical approach: `"parametric"` `"nonparametric"` `"robust"` `"bayes"` You can specify just the initial letter.
`test.value`	A number indicating the true value of the mean (Default: `0`).
`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 Bayes Factor in favor of the null hypothesis. This argument is relevant only for parametric test (Default: `TRUE`).
`effsize.type`	Type of effect size needed for parametric tests. The argument can be `"d"` (for Cohen's d) or `"g"` (for Hedge's g).
`conf.level`	Scalar between `0` and `1` (default: `⁠95%⁠` confidence/credible intervals, `0.95`). If `NULL`, no confidence intervals will be computed.
`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`	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.
`point.args`	A list of additional aesthetic arguments passed to `geom_point`.
`centrality.plotting`	Logical that decides whether centrality 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 If you want default centrality parameter, you can specify this using `centrality.type` argument.
`centrality.type`	Decides which centrality parameter is to be displayed. The default is to choose the same as `type` argument. You can specify this to be: `"parameteric"` (for mean) `"nonparametric"` (for median) `robust` (for trimmed mean) `bayes` (for MAP estimator) Just as `type` argument, abbreviations are also accepted.
`centrality.line.args`	A list of additional aesthetic arguments to be passed to the `geom_line` used to display the lines corresponding to the centrality parameter.
`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.
`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.
`...`	Currently ignored.

Details

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

Summary of graphics

graphical element	`geom` used	argument for further modification
histogram bin	`ggplot2::stat_bin()`	`bin.args`
centrality measure line	`ggplot2::geom_vline()`	`centrality.line.args`
normality curve	`ggplot2::stat_function()`	`normal.curve.args`

One-sample tests

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

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, Hedge's g	Yes	`effectsize::cohens_d()`, `effectsize::hedges_g()`
Non-parametric	r (rank-biserial correlation)	Yes	`effectsize::rank_biserial()`
Robust	trimmed mean	Yes	`WRS2::trimcibt()`
Bayes Factor	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)

IndrajeetPatil/ggstatsplot documentation built on April 18, 2024, 9:48 p.m.