Conditional quantile barplot

Source: R/cond_barplot.R

cond_barplot() conditions all variables on x by quantile binning and shows the median or mean of the other variables for each x.

cond_barplot(
  data,
  x = NULL,
  n = 100,
  min_bin_size = NULL,
  overlap = NULL,
  ncols = NULL,
  fill = "#2f4f4f",
  auto_fill = FALSE,
  show_bins = FALSE,
  type = c("median", "mean"),
  ...
)

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))

overlap

logical if TRUE the quantile bins will overlap. Default value will be FALSE.

ncols

The number of column to be used in the layout.

fill

The color to use for the bars.

auto_fill

If TRUE, use a different color for each category

show_bins

If TRUE, show the bins on the x-axis.

type

The type of statistic to use for the bars.

...

Additional arguments to pass to the plot functions

Value

A list of ggplot objects.

See also

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

Examples

# plots the expected median conditional on Sepal.Width
cond_barplot(iris, "Sepal.Width", n = 12)


# \donttest{

  # plots the expected median
  cond_barplot(iris, "Sepal.Width", n = 12, show_bins = TRUE)


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

  cond_barplot(diamonds[c(1:4, 7)], "carat", auto_fill = TRUE)


  if (require(palmerpenguins)) {
    p <- cond_barplot(penguins[1:7], "body_mass_g", auto_fill = TRUE)
    print(p)

    # compare with qbin_boxplot
    p <- cond_boxplot(penguins[1:7], "body_mass_g", auto_fill = TRUE)
    print(p)
  }
#> Loading required package: palmerpenguins
#> `overlap` not specified, using `overlap=FALSE`
#> `min_bin_size`=18, using `n=19`

#> `overlap` not specified, using `overlap=FALSE`
#> `min_bin_size`=18, using `n=19`

# }