1.4 — Data Wrangling

(Answer Key)

Required Packages

First, install the following two packages with the command install.packages("tidyverse") and install.packages("gapminder") in the console below.¹ Alternatively, you will probably already get a yellow banner at the top of this file indicating you need to install the packages, and can install them by clicking Install. Don’t install any package in an R chunk in this document, since it needs to be installed into R Studio.

Then, load the package by running (clicking the green play button) the chunk below:

library("tidyverse") # my friend and yours

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

✔ ggplot2 3.3.6     ✔ purrr   0.3.4
✔ tibble  3.1.8     ✔ dplyr   1.0.9
✔ tidyr   1.2.0     ✔ stringr 1.4.0
✔ readr   2.1.2     ✔ forcats 0.5.1

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library("gapminder") # for dataset

gapminder <- gapminder # explicitly save data as a dataframe

Warm Up to dplyr with gapminder Again

Question 1

Let’s look at the data again by running the following chunk. glimpse() is a suped-up tidyverse version of str(). You can also start to see how to use the pipe operator %>%.

gapminder %>% 
  glimpse()

Rows: 1,704
Columns: 6
$ country   <fct> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", …
$ continent <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, …
$ year      <int> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997, …
$ lifeExp   <dbl> 28.801, 30.332, 31.997, 34.020, 36.088, 38.438, 39.854, 40.8…
$ pop       <int> 8425333, 9240934, 10267083, 11537966, 13079460, 14880372, 12…
$ gdpPercap <dbl> 779.4453, 820.8530, 853.1007, 836.1971, 739.9811, 786.1134, …

Question 2

Now select() only the variables year, lifeExp, and country.

gapminder %>% 
  select(year, lifeExp, country)

Question 3

Now select() all variables except pop.

gapminder %>%
  select(-pop)

Question 4

rename() the variable continent to cont.

gapminder %>%
  rename(cont = continent)

Question 5

arrange() the data by year.

gapminder %>%
  arrange(year)

Question 6

Now arrange() by year, but in descending order.

gapminder %>%
  arrange(desc(year))

Question 7

Now arrange() by year, then by lifeExp

gapminder %>%
  arrange(year, lifeExp)

Question 8

Let’s try subsetting some rows. filter() observations with pop greater than 1 billion (9 zeros).

gapminder %>%
  filter(pop > 1000000000)

Question 9

Redo the same command from question 8, but of that subset of data, only look at India.

gapminder %>%
  filter(pop > 1000000000,
         country == "India")

Question 10

Let’s pipe a bunch of commands together. select() your data to look only at year, gdpPercap, and country in the year 1997, for countries that have a gdpPercap greater than 20,000, and arrange() them alphabetically.

gapminder %>%
  select(year, gdpPercap, country) %>%
  filter(year == 1997,
         gdpPercap > 20000) %>%
  arrange(country)

Question 11

Make a new variable with mutate() called GDP, which is equal to gdpPercap * pop.

gapminder %>%
  mutate(GDP = gdpPercap * pop)

Question 12

Make a new variable that is pop in millions.

gapminder %>%
  mutate(pop_mil = pop / 1000000)