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Summarising stat for geom_error()

Source: R/stat-error.R
stat_error.Rd

stat_error() computes the error bounds from raw observation-level data using ggplot2's fun.data contract. Where geom_error() expects pre- computed error columns, stat_error() summarises y (or x, when orientation is horizontal) within each group via the function supplied to fun.

Usage

stat_error(
  mapping = NULL,
  data = NULL,
  geom = NULL,
  position = "identity",
  ...,
  fun = "mean_se",
  fun.args = list(),
  error_geom = "errorbar",
  orientation = NA,
  na.rm = FALSE,
  conf.int = 0.95,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping, data, position, show.legend, inherit.aes

Standard ggplot2 layer arguments.

geom

The geom to render the summary with. Defaults to GeomErrorStat, which reuses geom_error()'s draw path.

...

Additional parameters. Names that match fun's formals (or any name, when fun accepts ...) are forwarded to fun; the remainder go to geom_error() as per-side styling (colour_neg, width_pos, …) or standard aesthetics.

fun

One of "mean_se" (default, uses ggplot2::mean_se()), "mean_ci" (mean with 95% normal-theory CI via stats::qt(); no Hmisc dependency), or a function taking a numeric vector and returning a single-row data.frame with columns y, ymin, ymax.

fun.args

Named list of extra arguments to pass to fun. Merged with any ... arguments whose names match fun's formals; fun.args wins on collision.

error_geom

One of "errorbar" (default), "linerange", "crossbar", "pointrange".

orientation

NA (default, inferred), "x", or "y".

na.rm

If TRUE, drop NA values from the summarised axis before applying fun.

conf.int

Confidence level forwarded to fun when the function accepts a conf.int argument (e.g. fun = "mean_ci" or a custom fun.data with that formal). Ignored for funs that don't declare it, so it's safe to leave at the default when using fun = "mean_se".

Examples

library(ggplot2)

ggplot(mtcars, aes(factor(cyl), mpg)) + stat_error()
#> `stat_error()` using fun = "mean_se".


ggplot(mtcars, aes(factor(cyl), mpg)) +
  stat_error(fun = "mean_ci", error_geom = "pointrange")
#> `stat_error()` using fun = "mean_ci" and conf.int = 0.95.


# 90% CI with NA-tolerant summarising:
ggplot(mtcars, aes(factor(cyl), mpg)) +
  stat_error(fun = "mean_ci", conf.int = 0.9, na.rm = TRUE)