Week 3

Data transformation techniques

In this week of the course, you’ll learn about the most important types of variables that you’ll encounter inside a data frame and learn the tools you can use to manipulate them.

We will go through the chapters from the R for Data Science (2nd edition) book as you will need these techniques for your project; they’re designed to be largely standalone so that they can be read in advance.

Data transformation techniques - logical vectors and numbers (WC)

  • Chapter 12 teaches you about logical vectors. These are the simplest types of vectors, but are extremely powerful. You’ll learn how to create them with numeric comparisons, how to combine them with Boolean algebra, how to use them in summaries, and how to use them for condition transformations.

  • Chapter 13 dives into tools for vectors of numbers, the powerhouse of data science. You’ll learn more about counting and a bunch of important transformation and summary functions.

Data transformation techniques - Strings and regular expressions (WC)

  • Chapter 14 will give you the tools to work with strings: you’ll slice them, you’ll dice them, and you’ll stick them back together again. This chapter mostly focuses on the stringr package, but you’ll also learn some more tidyr functions devoted to extracting data from character strings.

  • Chapter 15 introduces you to regular expressions, a powerful tool for manipulating strings. This chapter will take you from thinking that a cat walked over your keyboard to reading and writing complex string patterns.

Data transformation techniques - Factors, datatimes and missing values (WC)

  • Chapter 16 introduces factors: the data type that R uses to store categorical data. You use a factor when variable has a fixed set of possible values, or when you want to use a non-alphabetical ordering of a string.

  • Chapter 17 will give you the key tools for working with dates and date-times. Unfortunately, the more you learn about date-times, the more complicated they seem to get, but with the help of the lubridate package, you’ll learn to how to overcome the most common challenges.

Data transformation techniques - join and aggregation methods (WC)

  • Chapter 18 discusses missing values in depth. We’ve discussed them a couple of times in isolation, but now it’s time to discuss them holistically, helping you come to grips with the difference between implicit and explicit missing values, and how and why you might convert between them.

  • Chapter 19 finishes up this part of the book by giving you tools to join two (or more) data frames together. Learning about joins will force you to grapple with the idea of keys, and think about how you identify each row in a dataset.