ggstudent (version 0.1.2)

geom_student: Student CI plot

Description

A Student CI plot (or Violin CI plot) is a mirrored density plot similar to violin plot but instead of kernel density estimate it is based on the density of the t-distribution. It can be though of as a continuous "confidence interval density" (hence the name), which could reduce the dichotomous interpretations due to a fixed confidence level. geom_student can also be used to draw Gradient CI plots (using argument type), which replaces the violin shaped density with a rectangle.

Usage

geom_student(
  mapping = NULL,
  data = NULL,
  position = "identity",
  width = 0.25,
  type = "density",
  scale = TRUE,
  draw_lines = NULL,
  draw_mean = TRUE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

Value

A ggplot object.

Arguments

mapping

Set of aesthetic mappings. See [ggplot2::layer()] for details.

data

The data to be displayed in this layer. See [ggplot2::layer()] for details.

position

A position adjustment to use on the data for this layer. See [ggplot2::layer()] for details.

width

Scaling parameter for the width of the violin/rectangle.

type

Type of the plot. The default is "density" which draws violin style density plot, whereas "box" draws a rectangle shaped gradient plot.

scale

If "TRUE" (default), violins/rectangles are scaled according to the maximum width of the groups (max(dt(0, df) / se)).

draw_lines

If not NULL (default), draw horizontal lines at the given quantiles of the density estimate.

draw_mean

If TRUE (default), draw horizontal line at mean.

show.legend

logical. Should this layer be included in the legends? See [ggplot2::layer()] for details.

inherit.aes

If `FALSE`, overrides the default aesthetics. See [ggplot2::layer()] for details.

...

Other arguments passed to [ggplot2::layer()], such as fixed aesthetics.

References

Helske, J., Helske, S., Cooper, M., Ynnerman, A., & Besancon, L. (2021). Can visualization alleviate dichotomous thinking? Effects of visual representations on the cliff effect. IEEE Transactions on Visualization and Computer Graphics, 27(8), 3397-3409 doi: 10.1109/TVCG.2021.3073466

Examples

library("dplyr")
library("ggplot2")
library("scales")

ci_levels <- c(0.999, 0.95, 0.9, 0.8, 0.5)
n <- length(ci_levels)
ci_levels <- factor(ci_levels, levels = ci_levels)
PlantGrowth %>% dplyr::group_by(group) %>%
  dplyr::summarise(
    mean = mean(weight),
    df = dplyr::n() - 1,
    se = sd(weight)/sqrt(df + 1)) %>%
 dplyr::full_join(
   data.frame(group =
     rep(levels(PlantGrowth$group), each = n),
     level = ci_levels), by = "group") -> d

p <- ggplot(data = d, aes(group)) +
 geom_student(aes(mean = mean, se = se, df = df,
   level = level, fill = level), draw_lines = c(0.95, 0.5))
p
g <- scales::seq_gradient_pal("#e5f5f9", "#2ca25f")
p + scale_fill_manual(values=g(seq(0,1,length = n))) + theme_bw()

p2 <- ggplot(data = d, aes(group)) +
 geom_student(aes(mean = mean, se = se, df = df,
   level = level, fill = level), type = "box", draw_lines = c(0.95, 0.5))
p2