In mgimond/tukeyedar: Tukey Inspired Exploratory Data Analysis Functions

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tukeyedar

The tukeyedar package houses data exploration tools. Many functions are inspired by work published by @eda1977, @understanding_eda1983, @applied_eda1981, and @visdata1993.

This package was initially developed for an introductory course in Exploratory Data Analysis (EDA). As such, many of its functions have not undergone the rigorous testing and validation typically required for research or production use. Users are advised to exercise caution and verify results independently before applying the package in critical or high-stakes contexts.

Installation

The package is available from GitHub. It can be installed using the following command:

install.packages("remotes")
remotes::install_github("mgimond/tukeyedar")

Note that the vignettes may not be automatically generated with the above command, however, the vignettes are available on this website (see next section). If you want a local version of the vignettes, add the build_vignettes = TRUE parameter.

remotes::install_github("mgimond/tukeyedar", build_vignettes = TRUE)

If, for some reason the vignettes are not created, you might want to re-install the package with the force=TRUE parameter.

remotes::install_github("mgimond/tukeyedar", build_vignettes = TRUE, force=TRUE)

Vignettes

It’s strongly recommended that you read the vignettes. These can be accessed from this website:

• A detailed rundown of the resistant line function
• The median polish
• The empirical QQ plot
• The symmetry QQ plot
• The residual-fit spread plot

If you chose to have the vignettes locally created when you installed the package, then you can view them locally via vignette("RLine", package = "tukeyedar"). If you use a dark themed IDE, the vignettes may not render very well so you might opt to view them in a web browser via the functions RShowDoc("RLine", package = "tukeyedar").

Using the tukeyedar functions

All functions start with eda_. For example, to generate a three point summary plot of the mpg vs. disp from the mtcars dataset, type:

library(tukeyedar)
eda_3pt(mtcars, disp, mpg)

Note that most functions are pipe friendly. For example:

# Using R >= 4.1
mtcars |>  eda_3pt(disp, mpg)

# Using magrittr (or any of the tidyverse packages)
library(magrittr)
mtcars %>% eda_3pt(disp, mpg)

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mgimond/tukeyedar documentation built on Feb. 1, 2025, 4:02 a.m.