Lifecycle: experimental

The tukeyedar package houses a subset of functions used in Exploratory Data Analysis (EDA). Most functions are inspired by work published by Tukey (1977), D. C. Hoaglin and Tukey (1983) and Velleman and Hoaglin (1981). Note that this package is in beta mode, so use at your own discretion. Many of the plots generated from these functions are not necessarily geared for publication but are designed to focus the viewer’s attention on the patterns generated by the plots (hence the reason for light colored axes and missing axes labels for some of the plots ).

The functions available in this package include:

Function Description
eda_boxls Parallel boxplots with level and spread equalization
eda_ltrim Trim lower values of a vector
eda_rtrim Trim upper values of a vector
eda_ltrim_df Trim lower records of a dataframe
eda_rtrim_df Trim upper records of a dataframe
eda_re Re-express using Tukey powers or Box-Cox transformation
eda_lsum Letter value summaries
eda_sl Spread-level funcion
eda_lm Generate scatter plot along with regression line and LOESS curve
eda_3pt Generate 3-point summary of data and plot half-slopes
eda_unipow Generate matrix of re-expressed univariate values based on ladder of powers
eda_bipow Generate matrix of re-expressed bivariate values and plot 3-point summary half-slopes
eda_rline Fit a three-group resistant line to bivariate data


This package can be installed from github (the installation process makes use of the devtools package).


Note that the vignettes will not be automatically generated with the above command; note too that 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.

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

The vignette will require that dplyr be installed since the eda_sl function relies on it. If dplyr is not already installed, the aforementioned syntax will automatically install it for you.

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

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

Read the vignettes!

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

If you chose to have the vignettes locally created when you installed the package, then you can view them locally via vignette("Introduction", package = "tukeyedar") and 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("Introduction", package = "tukeyedar") and RShowDoc("RLine", package = "tukeyedar").

Using the 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:

eda_3pt(mtcars, disp, mpg)

#> $slope1
#> [1] -0.1117241
#> $slope2
#> [1] -0.0220894
#> $hsrtio
#> [1] 0.1977137
#> $xmed
#> [1]  95.1 167.6 360.0
#> $ymed
#> [1] 27.30 19.20 14.95

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)
mtcars %>% eda_3pt(disp, mpg)

D. C. Hoaglin, F. Mosteller, and J. W. Tukey. 1983. Understanding Robust and Exploratory Data Analysis. Wiley.
Tukey, John W. 1977. Exploratory Data Analysis. Addison-Wesley.
Velleman, P. F., and D. C. Hoaglin. 1981. Applications, Basics and Computing of Exploratory Data Analysis. Boston: Duxbury Press.