Lesson 13: Factors in R
Readings
Announcements
From next week onwards (from Thursday 10/16 and through the rest of the course), we will unfortunately have to shift classrooms (due to a scheduling error in the course roster) * Lectures will be in Morrison Hall 348 * Thursday labs will be in Warren Hall B73 * Friday labs will stay in the same room (Warren 101)
Today’s learning objectives
By the end of today’s class, you should be able to:
- Describe key features of factor variables in R
- Manipulate factor levels to improve plots of categorical data
Introduction to factors
First we’ll review how factors work by going over the beginning of Chapter 16 in R4DS
Getting set up for exploring the forcats package
Today, we will be working with the gapminder dataset
that many of you have started exploring in the lab session a few weeks
ago.
The data in the gapminder package is a subset of the Gapminder
dataset, which contains data on the health and wealth of nations
over the past decades. It was pioneered by Hans Rosling, who
is famous for describing the prosperity of nations over time through
famines, wars and other historic events with this beautiful data
visualization in his 2006
TED Talk: The best stats you’ve ever seen:
We will primarily use a subset of the gapminder data included in the
R package gapminder. So first we need to install that
package and load it, along with the tidyverse. Then have a look at the
data in gapminder
library(tidyverse)
library(gapminder) #install.packages("gapminder")
# For being able to compare plots side by side, I'm also going to use the gridExtra package today
library(gridExtra) #install.packages("gridExtra")
Better plots with factor level manipulation
R uses factors to handle categorical variables, variables that have a fixed and known set of possible values. As such, this data type looks like character data type from the outset, but it can contain additional information to manage the levels and the order (or sequence) of the categorical values. Factors are important for modeling, but are also helpful for reordering character vectors to improve display in graphics.
We’ll go over Jenny
Bryan’s illustration of how a few powerful functions from the
forcats package can significantly improve our handling of
factor variables and visualization of data with categorical variables.
The code used in-class can be found here