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Data Manipulation with dplyr

Data Manipulation with dplyr

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

# Load the Tidyverse
library(tidyverse)

# Load the course datasets
babynames <- read_rds("datasets/babynames.rds")
counties <- read_rds("datasets/counties.rds")

Chapter 1: Transforming data with dplyr

Exploring data with dplyr

  • Specializes in data manipulation
  • Chapter 1 verbs:
    • select()
    • filter()
    • arrange()
    • mutate()
glimpse(counties)
# select() verb
counties  %>% 
	select(state, county, population, unemployment)
# Creating a new table
counties_selected <- counties  %>% 
	select(state, county, population, unemployment)
counties_selected
# arrange() verb
counties_selected %>% 
	arrange(population)
# desc()
counties_selected %>% 
	arrange(desc(population))

filter()

  • extract observations based on conditions
counties_selected %>% 
	arrange(desc(population)) %>% 
	filter(state == "New York")
counties_selected %>% 
	arrange(desc(population)) %>% 
	filter(unemployment < 6)
# combining conditions
counties_selected %>% 
	arrange(desc(population)) %>% 
	filter(state == "New York",
		  unemployment < 6)
# mutate()
counties_selected %>% 
	mutate(unemployed_population = population * unemployment / 100) %>% 
	arrange(desc(unemployed_population))
# selecting and transforming with `mutate()`
counties %>% 
	mutate(state, county, population, unemployment,
		  unemployed_population = population * unemployment / 100, keep = "none") %>% 
	arrange(desc(unemployed_population))

Chapter 2: Aggregating Data

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