Module 5 - Data Transformations

Learning Objectives

• Working with boolean data, basic logical operations, conditionals, and logical subsetting
• Transformations and summaries of numeric data
• Using dplyr window functions

Readings

• RDS (R for Data Science): Chapters 12, 13, 17

Additional Resources:

• Feature Engineering: A-Z Absolutely comprehensive encyclopedia of feature engineering teachniques

• Feature Engineering and Selection, Chapter 1 Feature engineering is a natural extension of data transformation that is commonly used prior to the application of machine learning or statistical analyses.

• forcats package reference index . This is the reference for a very useful package for handling categorical data

• Wrangling Categorical Data in R. A. McNamara and N. Horton. The American Statistician (2018) Vol 72.

This paper discusses both tidyverse and base R approaches to categorical data.