sptidy

An R package that produces a tidy output for tidymodels model evaluation!

Introduction

Sptidy implements a tidy and augment function for Tidymodel’s linear regression and kmeans clustering to ease model selection and assessment tasks. This package is a simplified reimplementation of the existing tidy and augment functions in the Broom package. Sptidy’s family of tidy functions returns a dataframe that summarizes important model information, while the augment function expands the original dataframe to include additional model specific information by observation. This package is meant to complement Sktidy, a Python package that was created to tidy up the scikit-learn package.

Features

The functions that this package currently support include:

• tidy_kmeans(): Returns inertia, cluster location, and number of associated points at the level of clusters in a tidy format.

• tidy_lr(): Returns coefficients and corresponding feature names in a tidy format.

• augment_lr() : Returns predictions and residuals for each point in the training data set in a tidy format.

• augment_kmeans() : Returns assigned cluster and distance from cluster center for the data the kmeans algorithm was fitted with in a tidy format.

How sptidy fits into the R ecosystem

Tidymodels is a “meta-package” for modeling and statistical analysis that share the underlying design philosophy, grammar, and data structures of the tidyverse. One of the packages it includes is broom which takes the messy output of built-in functions in R, such as lm, nls, or t.test, and turns them into tidy data frames. The tidy data refers to outputting the results in a data.frame where each variable has its own column, each observation has its own row, and each value has its own cell. In sptidy, we implement the functions tidy() and augment() for the linear regression model from the parsnip package which is included in the tidymodels package using the function linear_reg() and the KMeans model from R stats package using the function kmeans().

Installation

You can install the released version of sptidy from CRAN with:

install.packages("sptidy")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("JacobMcFarlane/sptidy")

Example

This is a basic example which shows you how to solve a common problem:

#library(sptidy)
## basic example code

What is special about using README.Rmd instead of just README.md? You can include R chunks like so:

summary(cars)
#>      speed           dist       
#>  Min.   : 4.0   Min.   :  2.00  
#>  1st Qu.:12.0   1st Qu.: 26.00  
#>  Median :15.0   Median : 36.00  
#>  Mean   :15.4   Mean   : 42.98  
#>  3rd Qu.:19.0   3rd Qu.: 56.00  
#>  Max.   :25.0   Max.   :120.00

You’ll still need to render README.Rmd regularly, to keep README.md up-to-date. devtools::build_readme() is handy for this. You could also use GitHub Actions to re-render README.Rmd every time you push. An example workflow can be found here: https://github.com/r-lib/actions/tree/master/examples.

You can also embed plots, for example:

In that case, don’t forget to commit and push the resulting figure files, so they display on GitHub and CRAN.