# 1.4 — Data Wrangling in the tidyverse — R Practice

## Getting Set Up

Before we begin, start a new file with File $$\rightarrow$$ New File $$\rightarrow$$ R Script. As you work through this sheet in the console in R, also add (copy/paste) your commands that work into this new file. At the end, save it, and run to execute all of your commands at once.

First things first, load tidyverse.

library("tidyverse")

## Warm Up to dplyr With gapminder

### 1.

Load gapminder. First, select() the variables year, lifeExp, country.

### 2.

select() all variables except pop.

### 3.

rename() continent to cont.

### 4.

arrange() by year.

### 5.

arrange() by year, but in descending order.

### 6.

arrange() by year, then by life expectancy.

### 7.

filter() observations with pop greater than 1 billion.

### 8.

Of those, look only at India.

### 9.

Try out the pipe (%>%) if you haven’t already, by chaining commands: 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.

### 10.

mutate() a new variable called GDP that is equal to gdpPercap * pop.

### 11.

mutate() a new population variable that is the pop in millions.

### 12.

summarize() to get the average GDP per capita.

### 13.

Get the number of observations, average, minimum, maximum, and standard deviation for GDP per capita.

### 14.

Get the average GDP per capita over time. Hint, first group_by() year.

### 15.

Get the average GDP per capita by continent.

### 16.

Get the average GDP per capita by year and by continent.Hint: do year first, if you do continent first, there are no years to group by!

Then save this as another tibble called gdp. Create a ggplot of a line graph of average continent GDP over time using the gdp data.

### 17.

Try it again all in one command with the pipe %>%. Instead of saving the data as gdp, pipe it right into ggplot!Hint: You can use . as a placeholder.

## Example: the Economics of College Majors

Now let’s step it up to work with some data “in the wild” to answer some research questions. This will have you combine your dplyr skills and add some new things such as importing with readr.

Let’s look at fivethirtyeight’s article " The Economic Guide To Picking A College Major ". fivethirtyeight is great about making the data behind their articles public, we can download all of their data here. Search for college majors and click download (the blue arrow button).This will download a .zip file that contains many spreadsheets. Unzip it with a program that unzips files (such as WinZip, 7-zip, the Unarchiver, etc).

We will look at the recent-grads.csv file.

The description in the readme file for the data is as follows:

Rank Rank by median earnings
Major_code Major code, FO1DP in ACS PUMS
Major Major description
Major_category Category of major from Carnevale et al
Total Total number of people with major
Sample_size Sample size (unweighted) of full-time, year-round ONLY (used for earnings)
Men Male graduates
Women Female graduates
ShareWomen Women as share of total
Employed Number employed (ESR == 1 or 2)
Full_time Employed 35 hours or more
Part_time Employed less than 35 hours
Full_time_year_round Employed at least 50 weeks (WKW == 1) and at least 35 hours (WKHP >= 35)
Unemployed Number unemployed (ESR == 3)
Unemployment_rate Unemployed / (Unemployed + Employed)
Median Median earnings of full-time, year-round workers
P25th 25th percentile of earnigns
P75th 75th percentile of earnings
College_jobs Number with job requiring a college degree
Non_college_jobs Number with job not requiring a college degree
Low_wage_jobs Number in low-wage service jobs

### 18.

Import the data with read_csv() and assign it to an object called majors.One way to avoid error messages is to move (on your computer) recent_grads.csv to the same folder as R’s working directory, which again you can check with getwd().

The first argument of this command is the name of the original file, in quotes.If the file is in a different folder, the argument is the full path in quotes.

### 19.

Look at the data with glimpse(). This is a suped-up version of str() in tidyverse.

### 20.

What are all of the unique values of Major? How many are there?

### 21.

Which major has the lowest unemployment rate?

### 22.

What are the top 3 majors that have the highest percentage of women?

#### 23.

Make a boxplot of Median wage by Major_Category.You won’t be able to read the labels easily, so add theme(axis.text.x=element_text(angle=45, hjust=1) to angle x-axis labels (and move them down by 1)

#### 24.

Which major category is the least popular in this sample?Hint: use group_by first.

#### 25.

Is there a systematic difference in median earnings between STEM majors and non-STEM majors? First define:

stem_categories <- c("Biology & Life Science",
"Computers & Mathematics",
"Engineering",
"Physical Sciences")

Next, make a variable called stem, for whether or not a Major_category is "stem" or "not_stem".^[Hint: try out the ifelse() function which has three inputs: condition(s) for a variable(s), what to do if TRUE (the if), and what to if FALSE (the else), i.e.

stem = ifelse(my_conditions,
yes = do_this_if_TRUE,
no = do_this_if_FALSE)

You’ll of course need to change the do_this into something! ]

Then summarize() median for stem and not stem groups.