qbin_boxplot: Quantile binned boxplot

Description

qbin_boxplot creates quantile binned boxplots from data using x as the binning variable. It focuses on the change of median between qbins. It is a complement to qbin_heatmap() which focuses on the distribution within the qbins.

Usage

qbin_boxplot(
  data,
  x = NULL,
  n = 100,
  min_bin_size = NULL,
  ncols = NULL,
  overlap = NULL,
  connect = FALSE,
  color = "#002f2f",
  fill = "#2f4f4f",
  auto_fill = FALSE,
  qmarker = NULL,
  xmarker = NULL,
  ...
)

Value

A list of ggplot objects.

Arguments

data: a data.frame to be binned
x: character variable name used for the quantile binning
n: integer number of quantile bins.
min_bin_size: integer minimum number of rows/data points that should be in a quantile bin. If NULL it is initially sqrt(nrow(data))
ncols: The number of column to be used in the layout
overlap: logical if TRUE the quantile bins will overlap. Default value will be FALSE.
connect: if TRUE subsequent boxplots are connected
color: The color to use for the lines
fill: The color to use for the bars
auto_fill: If TRUE, use a different color for each category
qmarker: numeric, the quantile marker to use.
xmarker: numeric the x marker, i.e. the value for x that is translated into a q value.
...: Additional arguments to pass to the plot functions

Details

The data is binned by the x and a boxplot is created for each bin. The median of the subsequent boxplots are connected to highlight jumps in the data. It hints at the dependecy of the variable on the binning variable.

Examples

Run this code

qbin_boxplot(
  iris,
  x = "Sepal.Length",
)

# \donttest{
  qbin_boxplot(
    iris,
    x = "Sepal.Length",
    connect = TRUE,
    overlap = TRUE
  )

  qbin_boxplot(
    iris,
    x = "Sepal.Length",
    connect = TRUE,
    xmarker = 5.5,
    auto_fill = TRUE
  )

  data("diamonds", package="ggplot2")

  qbin_boxplot(
    diamonds[1:7],
    "carat",
    auto_fill = TRUE
  )

  qbin_boxplot(
    diamonds[1:7],
    "price",
    auto_fill = TRUE,
  )
# }