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Introduction to the Tidyverse

Introduction to the Tidyverse

Run the hidden code cell below to import the data used in this course.

# Load the Tidyverse
library(tidyverse)

# Read in the gapminder file
gapminder <- read.csv("datasets/gapminder.csv", sep = "\t")

Ch. 1: Data Wrangling

The filter verb

  • used to look at a subset of observations based on a particular condition.
  • used with pipe operator %>%
  • == is a logical equals.
# Filter for year 2007
gapminder %>%
	filter(year == 2007)
# Filtering for one country
gapminder %>%
	filter(country == "United States")
# Filtering for two variables
gapminder %>%
	filter(year == 2007, country == "United States")

The arrange verb

  • sorts a table based on a variable
# Sorting with `arrange`
gapminder %>%
	arrange(gdpPercap)
# Sorting in descending order
gapminder %>%
	arrange(desc(gdpPercap))
# Filtering then arranging
gapminder %>%
	filter(year == 2007) %>%
	arrange(desc(gdpPercap))

The mutate verb

  • mutate changes or adds variables
# Using mutate to change a variable
gapminder %>%
	mutate(pop = pop / 1000000)
# Using mutate to add a new variable
gapminder %>%
	mutate(gdp = gdpPercap * pop)
# Combining verbs
gapminder %>%
	mutate(gdp = gdpPercap * pop) %>%
	filter(year == 2007) %>%
	arrange(desc(gdp))

Chapter 2: Data Visualization

Visualizing with ggplot2

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