Cleaning data

When working with data, you can generally expect to find human errors, missing entries, and numerical outliers. These types of errors usually need to be corrected, handled, or removed to prepare a dataset for analysis.

In Chapter 5, Manipulating Text Data - An Introduction to Regular Expressions, I will demonstrate how to use regular expressions, a tool to identify, extract, and modify patterns in text data. Chapter 5, Manipulating Text Data - An Introduction to Regular Expressions, includes a project to use regular expressions to extract street names.

In Chapter 6, Cleaning Numerical Data - An Introduction to R and Rstudio, I will demonstrate how to use RStudio to conduct two common tasks for cleaning numerical data: outlier detection and NA handling.