Quantile binned heatmap

qbin_heatmap shows the distribution of the y of variables for each quantile bin of x. It is an alternative to qbin_boxplot(), fine graining the distribution per qbin(). qbin_barplot() highlights the median/mean of the quantile bins, while

qbin_heatmap(
  data,
  x = NULL,
  n = 25,
  min_bin_size = NULL,
  overlap = NULL,
  bins = c(n),
  type = c("gradient", "size"),
  ncols = NULL,
  auto_fill = 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.

type

The type of heatmap to use. Either "gradient" or "size".

ncols

The number of column to be used in the layout.

auto_fill

If TRUE, use a different color for each category.

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 qbin plotting functions: qbin_barplot(), qbin_boxplot(), qbin_lineplot()

Examples

# \donttest{
  qbin_heatmap(
    iris,
    "Sepal.Length",
    auto_fill = TRUE
  )
#> `overlap` not specified, using `overlap=FALSE`
#> `min_bin_size`=12, using `n=12`


  qbin_heatmap(
    iris,
    "Sepal.Length",
    auto_fill = TRUE,
    type = "size"
  )
#> `overlap` not specified, using `overlap=FALSE`
#> `min_bin_size`=12, using `n=12`


  qbin_heatmap(
    iris,
    "Sepal.Length",
    overlap = TRUE,
    auto_fill = TRUE
  )


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

  qbin_heatmap(
    diamonds[c(1,7:9)],
    x = "price",
    n = 150
  )




# }