seq_range: Sequence Generation Based on the Values of a Vector

In mattansb/MSBMisc: Some functions I wrote that I find useful

Sequence Generation Based on the Values of a Vector

Description

Sequence Generation Based on the Values of a Vector

Usage

seq_range(
  x,
  length.out = NULL,
  by = NULL,
  along.with = NULL,
  na.rm = TRUE,
  padding = 0.05
)

seq_quantile(
  x,
  probs,
  length.out = NULL,
  by = NULL,
  along.with = NULL,
  na.rm = TRUE
)

seq_IQR(x, length.out = NULL, by = NULL, along.with = NULL, na.rm = TRUE)

mean_sd(x, na.rm = TRUE, out = c("vector", "data.frame"))

median_mad(x, na.rm = TRUE, out = c("vector", "data.frame"))

Arguments

x	A numeric vector
length.out	desired length of the sequence. If no other arguments are valued, defaults to 20.
by	number: increment of the sequence.
along.with	take the length from the length of this argument.
na.rm	a logical evaluating to TRUE or FALSE indicating whether NA values should be stripped before the computation proceeds.
padding	Padding factor for the range.
probs	numeric vector of probabilities with values in [0,1]. (Values up to ‘⁠2e-14⁠’ outside that range are accepted and moved to the nearby endpoint.)
out	If "data.frame" can be used as a summary function in ggplot2.

Examples

set.seed(1)
x <- rt(100, df = 3)
seq_range(x, length.out = 5)
seq_IQR(x, length.out = 5)
seq_quantile(x, c(.05, .95), length.out = 5)

mean_sd(x)


library(ggplot2)
ggplot(mtcars, aes(cyl, mpg)) +
  stat_summary(aes(color = "Mean (SD)"),
    fun.data = mean_sd,
    fun.args = list(out = "data.frame")
  ) +
  stat_summary(aes(color = "Median (MAD)"),
    fun.data = median_mad,
    fun.args = list(out = "data.frame"),
    position = position_nudge(x = 0.5)
  )

mattansb/MSBMisc documentation built on March 22, 2023, 6:02 p.m.