Conditional quantile boxplot

cond_boxplot() conditions all variables on x by quantile binning and shows the boxplots for the other variables for each value of qbinned x.

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

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))

color

The color to use for the line charts

fill

The fill color to use for the areas

auto_fill

If TRUE, use a different color for each category

ncols

The number of column to be used in the layout

xmarker

numeric, the x marker.

qmarker

numeric, the quantile marker to use that is translated in a x value.

show_bins

if TRUE a rug is added to the plot

xlim

numeric, the limits of the x-axis.

connect

if TRUE subsequent medians are connected.

...

Additional arguments to pass to the plot functions

Value

A list of ggplot objects.

Details

cond_boxplot is the same function as funq_plot() but with different defaults, namely connect = FALSE and auto_fill = FALSE. funq_plot highlights the functional relationship between x and the y-variables, by connecting the medians of the quantile bins.

qbin_boxplot() shows the boxplots of the quantile bins on a quantile scale.

See also

Other conditional quantile plotting functions: cond_barplot(), cond_heatmap(), funq_plot()

Examples

cond_boxplot(
  iris, x = "Petal.Length"
)
#> `overlap` not specified, using `overlap=FALSE`
#> `min_bin_size`=12, using `n=12`