Data are stored in tabular forms and are often organized differently depending on its use. In this module, you will learn how to reorganize data to produce a "tidy" data set, where every variable is stored in its own column, every observation is stored in its own row, and each value is stored in a table cell.

Data analysis rarely involves a single data table and you will be required to combine multiple related tables to answer questions you are interested in. In this module, you will learn and practice mutating variables and filtering observations from relational data.

This module will introduce string manipulation in R. You will learn the basics of strings, including string creation, merging, and subsetting. Then, you will use regular expressions to describe and view patterns in strings.

In the last module of the course, you will use the forcats package in the tidyverse to work with categorical variables, variables that have discrete values. The forcats package introduces factors - data objects used to categorize the data in levels. You will practice creating and modifying factors.