Ceci n'est pas une pipe

Now that we've covered tidy data, there is one more concept that is very common in the tidyverse that we should discuss. This is the pipe (%>%) from the magrittr package. This is similar to the Unix pipe, and it takes the left-hand side of the pipe and applies the right-hand side function to it. Take the following code:

mpg %>% summary()

The preceding code is equivalent to the following code:

summary(mpg)

As another example, look at the following code:

gapminder %>% filter(year > 1960)

The preceding code is equivalent to the following code:

filter(gapminder, year > 1960)

Piping greatly enhances the readability of code that requires several steps to execute. Take the following code:

x %>% f %>% g %>% h

The preceding code is equivalent to the following code:

h(g(f(x)))

To demonstrate with a real example, take the following code:

groupedData = gapminder %>%
   filter(year > 1960) %>%
   group_by(continent, year) %>%
   summarise(meanLife = mean(lifeExp))

The preceding code is equivalent to the following code:

summarise(
  group_by(
    filter(gapminder, year > 1960),
  continent, year),
meanLife = mean(lifeExp))

Hopefully, it should be obvious which is the easier to read of the two.