The tukeyedar package houses data exploration tools. Many functions are inspired by work published by Tukey (1977), Hoaglin (1983), Velleman and Hoaglin (1981), and Cleveland (1993). Note that this package is in beta mode, so use at your own discretion.

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

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

Note that the vignettes will 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)

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").

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 examples:

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

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