cattonum

Special note

I am currently looking for someone who would be interested in becoming the official maintainer of the package. Advanced skill in R or previous package development experience is not necessary. I will be able to help out with code review, etc. Please comment on this issue if you are interested.

Summary

cattonum (“cat to num”) provides different ways to encode categorical features as numerics. Its goal is to be a one-stop shop for all categorical encoding needs. It includes the following:

• aggregate function encoding: catto_aggregate()
• dummy encoding: catto_dummy()
• frequency encoding: catto_freq()
• label encoding: catto_label()
• leave-one-out encoding: catto_loo()
• mean encoding: catto_mean()
• median encoding: catto_median()
• one-hot encoding: catto_onehot()

There are many existing packages with which to encode categorical features, including (among others):

• CatEncoders
• dummies
• embed
• fastDummies
• FeatureHashing
• h2o
• makedummies
• recipes
• vtreat

Installation

The development version can be installed from the develop branch on GitHub, which is the default branch.

remotes::install_github("bfgray3/cattonum", ref = "develop")

The latest official release can be installed from CRAN.

install.packages("cattonum")

Usage

library(cattonum)
data(iris)
head(catto_loo(iris, response = Sepal.Length))
#> # A cattonum_df with the following data:
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width  Species
#> 1          5.1         3.5          1.4         0.2 5.004082
#> 2          4.9         3.0          1.4         0.2 5.008163
#> 3          4.7         3.2          1.3         0.2 5.012245
#> 4          4.6         3.1          1.5         0.2 5.014286
#> 5          5.0         3.6          1.4         0.2 5.006122
#> 6          5.4         3.9          1.7         0.4 4.997959

Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By contributing, you agree to abide by its terms.