Day 1: Tidying data

In-class worksheet

June 1st, 2022

In this worksheet, we will use the library tidyverse:

library(tidyverse)

Tidying data with pivot_longer()and pivot_wider()

1.1 Making tables longer

Consider the following two data sets, male_haireyecolor and female_haireyecolor. The data sets record the occurrence of hair and eye color phenotype combinations in a class of statistics students. Use head() to preview these data sets; are they tidy?

# download male data set
male_haireyecolor <- read_csv("https://rachaelcox.github.io/classes/datasets/male_haireyecolor.csv")

# download female data set
female_haireyecolor <- read_csv("https://rachaelcox.github.io/classes/datasets/female_haireyecolor.csv")

Use the function pivot_longer() to rearrange both data sets such that there is one observation per row for each combination of hair and eye color. Remember: You can run ?pivot_longer to pull up argument details and example usage.

# your R code here

1.2 Making tables wider

Consider the following data set persons, which contains information about the sex, weight, and height of 200 individuals. Use head() to preview the data set; is it tidy?

# download persons data set
persons <- read_csv("https://rachaelcox.github.io/classes/datasets/persons.csv")

Rearrange the persons data frame so that you have one column for subject, one for sex, one for weight, and one for height using the function pivot_wider. Remember: You can run ?pivot_wider to pull up argument details and example usage.

# your R code here