Conditional heatmap

cond_heatmap shows the conditional distribution of the y of variables for each quantile bin of x. It is an alternative to cond_boxplot(), fine graining the distribution per qbin(). cond_barplot() highlights the median/mean of the quantile bins, while funq_plot() highlights the functional dependency of the median.

cond_heatmap(
  data,
  x = NULL,
  n = 100,
  min_bin_size = NULL,
  overlap = NULL,
  bins = c(n, 25),
  ncols = NULL,
  auto_fill = FALSE,
  show_bins = FALSE,
  fill = "#2f4f4f",
  low = "#eeeeee",
  high = "#2f4f4f",
  ...
)

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.

bins

integer vector with the number of bins to use for the x and y axis.

ncols

The number of column to be used in the layout.

auto_fill

If TRUE, use a different color for each category.

show_bins

If TRUE, show the bin boundaries on the x-axis.

fill

The color used for categorical variables.

low

The color used for low values in the heatmap.

high

The color used for high values in the heatmap.

...

Additional arguments to pass to the plot functions

Value

A list of ggplot objects.

See also

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

Examples

cond_heatmap(
  iris,
  x = "Petal.Length",
  n = 12
)


# \donttest{

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

  cond_heatmap(
    diamonds,
    x = "carat",
    bins <- c(100,100)
  )[6:8]

# }