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qbinplots

CRAN status R-CMD-check Lifecycle: experimental

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
)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.