Transformation

Chapter 4 Data Transformation

The 5 verbs of data wrangling

• Pick observations by their values (filter()).
• Reorder the rows (arrange()).
• Pick variables by their names (select()).
• Create new variables with functions of existing variables (mutate()).
• Collapse many values down to a single summary (summarise()).
• (group_by())

library(nycflights13)
library(tidyverse)

We will continue to work with the flights dataset that is in the ggplot2 package.

flights

Change the code from the Transformation presentation to using the pipe %>%.

filter()

filter(flights, month == 1, day == 1)

arrange()

arrange(flights, year, month, day)

arrange()

arrange(flights, desc(dep_delay))

select()

select(flights, year, month, day)

select()

select(flights, time_hour, air_time, everything())

mutate()

flights_sml <- select(flights, 
  year:day, 
  ends_with("delay"), 
  distance, 
  air_time
)
mutate(flights_sml,
  gain = dep_delay - arr_delay,
  speed = distance / air_time * 60
)

summarize()

summarise(flights, delay = mean(dep_delay, na.rm = TRUE))

by_day <- group_by(flights, year, month, day)
summarise(by_day, delay = mean(dep_delay, na.rm = TRUE))

Combining multiple operations using functions and assignment <-

by_dest <- group_by(flights, dest)
delay <- summarise(by_dest,
  count = n(),
  dist = mean(distance, na.rm = TRUE),
  delay = mean(arr_delay, na.rm = TRUE)
)
delay <- filter(delay, count > 20, dest != "HNL")

Combining multiple operations using functions and assignment <-, note the ggplot “piping” using the +

ggplot(data = delay, mapping = aes(x = dist, y = delay)) +
  geom_point(aes(size = count), alpha = 1/3) +
  geom_smooth(se = FALSE)

It looks like delays increase with distance up to ~750 miles and then decrease. Maybe as flights get longer there’s more ability to make up delays in the air?

geom_smooth() using method = ‘loess’ and formula ‘y ~ x’

Combining multiple operations with the pipe %>%

Does this code read better?

delays <- flights %>% 
  group_by(dest) %>% 
  summarise(
    count = n(),
    dist = mean(distance, na.rm = TRUE),
    delay = mean(arr_delay, na.rm = TRUE)
  ) %>% 
  filter(count > 20, dest != "HNL")
head(delays)