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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.

## Overview

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

## Installation

# Install from CRAN
install.packages("CGPfunctions")

# Or the development version from GitHub
# install.packages("devtools")
devtools::install_github("ibecav/CGPfunctions")


## Credits

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.
• 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.

## Contributing

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 gmail.com by email.