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Plot a histogram or dotplot of an estimated magnitude with raw data

Source: R/plot_describe.R
plot_describe.Rd

plot_describe Takes an estimate produced from estimate_magnitude and produces a dotplot or histogram. It can mark various descriptive statistics on the plot, including mean, median, sd, quartiles, and z lines. If a percentile is passed, it color-codes data based on if it is above or below that percentile.

Usage

plot_describe(
  estimate,
  type = c("histogram", "dotplot"),
  mark_mean = FALSE,
  mark_median = FALSE,
  mark_sd = FALSE,
  mark_quartiles = FALSE,
  mark_z_lines = FALSE,
  mark_percentile = NULL,
  histogram_bins = 12,
  ylim = c(0, NA),
  ybreaks = NULL,
  xlim = c(NA, NA),
  xbreaks = NULL,
  fill_regular = "#008DF9",
  fill_highlighted = "#E20134",
  color = "black",
  marker_size = 5,
  ggtheme = NULL
)

Arguments

estimate

A esci_estimate object with raw data an es_mean

type

histogram or dotplot

mark_mean

should mean be marked?

mark_median

should median be marked?

mark_sd

should mean be marked?

mark_quartiles

should mean be marked?

mark_z_lines

should z lines be marked?

mark_percentile

a percentile (0 to 1) to be marked

histogram_bins

number of bins if a histogram

ylim

2-length numeric vector

ybreaks

numeric >= 1

xlim

2-length numeric vector

xbreaks

numeric >= 1

fill_regular

color for

fill_highlighted

color for

color

outline color

marker_size

Size of markers

ggtheme

theme to apply, if any

Value

Returns a ggplot object

Details

This function was developed primarily for student use within jamovi when learning along with the text book Introduction to the New Statistics, 2nd edition (Cumming & Calin-Jageman, 2024).

Expect breaking changes as this function is improved for general use. Work still do be done includes:

  • Revise to avoid deprecated ggplot features

  • Revise for consistent ability to control aesthetics and consistent layer names

Examples

# example code
# Generate an estimate on a single continuous variable
estimate <- esci::estimate_magnitude(esci::data_latimier_3groups, `Test%`)

# Now describe the result, with a histogram
myplot_hist <- plot_describe(estimate)

# Same, but as a dotplot and mark the mean
myplot_dots <- plot_describe(estimate, type = "dotplot", mark_mean = TRUE)
#> Warning: Ignoring unknown aesthetics: z