Announcing another slopegraph plotting function – June 14, 2018

[This article was first published on Chuck Powell, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

A couple of weeks ago I wrote a blog post about slopegraphs. There was some polite interest and it was a good chance to practice my functional programming skills so I decided to see if I could make a decent R function from what I had learned. It’s in pretty good shape so I just pushed an update to CRAN (it will take awhile to process). You can also get the latest version from GitHub.

The documentation for it is here. Longer term I hope to move it here.



The package also includes other functions that I find useful for teaching statistics as well as actually practicing the art. They typically are not “new” methods but rather wrappers around either base R or other packages and concepts I’m trying to master.

  • Plot2WayANOVA which as the name implies conducts a 2 way ANOVA and plots the results using ggplot2
  • PlotXTabs which as the name implies plots cross tabulated variables using ggplot2
  • neweta which is a helper function that appends the results of a Type II eta squared calculation onto a classic ANOVA table
  • Mode which finds the modal value in a vector of data
  • SeeDist which wraps around ggplot2 to provide visualizations of univariate data.
  • OurConf is a simulation function that helps you learn about confidence intervals


# Install from CRAN

# Or the development version from GitHub
# install.packages("devtools")


Many thanks to Dani Navarro and the book > (Learning Statistics with R) whose etaSquared function was the genesis of neweta.

“He who gives up safety for speed deserves neither.” (via)

A shoutout to some other packages I find essential.

  • stringr, for strings.
  • lubridate, for date/times.
  • forcats, for factors.
  • haven, for SPSS, SAS and Stata files.
  • readxl, for .xls and .xlsx files.
  • modelr, for modelling within a pipeline
  • broom, for turning models into tidy data
  • ggplot2, for data visualisation.
  • dplyr, for data manipulation.
  • tidyr, for data tidying.
  • readr, for data import.
  • purrr, for functional programming.
  • tibble, for tibbles, a modern re-imagining of data frames.

Leaving Feedback

If you like CGPfunctions, please consider leaving feedback here.


Contributions in the form of feedback, comments, code, and bug reports are most welcome. How to contribute:

  • Issues, bug reports, and wish lists: File a GitHub issue.
  • Contact the maintainer ibecav at by email.

To leave a comment for the author, please follow the link and comment on their blog: Chuck Powell. offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)