qbinplots

This package creates plots using quantile binning. Quantile binning is an exploratory data analysis tool that helps to see the distribution of the variables in a dataset as a function of the variable that is binned.

Installation

You can install the development version of qbinplots from GitHub with:

remotes::install_github("edwindj/qbinplots")

Example

library(qbinplots)
## basic example code

A quantile binning boxplot

qbin_boxplot(iris, "Sepal.Length", n = 12)

vs

A quantile binning barplot

qbin_barplot(iris, "Sepal.Length", n = 12)

table_plot(iris, "Sepal.Length", n=12)

vs

A quantile binning heatmap

qbin_heatmap(iris, "Sepal.Length", n=12, auto_fill = TRUE)

cond_boxplot(iris, "Sepal.Length", n=12, auto_fill = TRUE)

cond_barplot(iris, "Sepal.Length", n=12, auto_fill = TRUE)

funq_plot(iris, "Sepal.Length", 12, auto_fill = TRUE)

funq_plot(iris, "Sepal.Length", overlap = TRUE, min_bin_size = 20)

Choosing “Petal.Width”

funq_plot(iris, "Petal.Width", n=12)

library(palmerpenguins)
qbin_lineplot(penguins[1:7], x="body_mass_g", n = 19, ncols = 4)

Or the well-known diamonds dataset

data("diamonds", package = "ggplot2")
table_plot(diamonds[1:7], "carat")

data("diamonds", package = "ggplot2")
qbin_boxplot(diamonds[1:7], "carat")

funq_plot(diamonds[1:7], "carat")

We can zoom in on the carat variable, because the upper quantile bins are not very informative.

funq_plot(
  diamonds[1:7], 
  "carat", 
  auto_fill = TRUE,
  xlim = c(0, 2.5)
)

qbin_heatmap(
  iris,
  x = "Sepal.Length",
  n = 12,
  type="s"
)

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