Functional quantile plot

funq_plot() conditions on variable x with quantile binning and plots the median and interquartile range of numerical variables and level frequency of the other variables as a function the x variable.

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

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.

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 ggplot object with the plots

Details

By highlighting and connecting the median values it creates a functional view of the data. What is the (expected) median given a certain value of x?

It qbins the x variable and plots the medians of the qbins vs the other variables, thereby creating a functional view of x to the rest of the data, calculating the statistics for each bin, hence the name funq_plot.

See also

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

Examples

funq_plot(iris, "Sepal.Length", xmarker=5.5)
#> `overlap` not specified, using `overlap=FALSE`
#> `min_bin_size`=12, using `n=12`


# \donttest{

  funq_plot(
    iris,
    x = "Sepal.Length",
    xmarker=5.5,
    overlap = TRUE
  )



  data("diamonds", package="ggplot2")
  funq_plot(diamonds[c(1:4,7)], "carat", xlim=c(0,2))


  if (require(palmerpenguins)){
    funq_plot(
      penguins[1:7],
      x = "body_mass_g",
      xmarker=4650,
      ncol = 3
    )
  }
#> `overlap` not specified, using `overlap=FALSE`
#> `min_bin_size`=18, using `n=19`



# }