The pipe

The real power of the pipe is to combine multiple verbs. To find the fast flights to Houston’s IAH airport: you need to combine filter(), mutate(), select(), and arrange():

flights |> 
  filter(dest == "IAH") |> 
  mutate(speed = distance / air_time * 60) |> 
  select(year:day, dep_time, carrier, flight, speed) |> 
  arrange(desc(speed))

## # A tibble: 7,198 × 7
##     year month   day dep_time carrier flight speed
##    <int> <int> <int>    <int> <chr>    <int> <dbl>
##  1  2013     7     9      707 UA         226  522.
##  2  2013     8    27     1850 UA        1128  521.
##  3  2013     8    28      902 UA        1711  519.
##  4  2013     8    28     2122 UA        1022  519.
##  5  2013     6    11     1628 UA        1178  515.
##  6  2013     8    27     1017 UA         333  515.
##  7  2013     8    27     1205 UA        1421  515.
##  8  2013     8    27     1758 UA         302  515.
##  9  2013     9    27      521 UA         252  515.
## 10  2013     8    28      625 UA         559  515.
## # ℹ 7,188 more rows

Using the pipe operator makes complex operations easier to read and follow. Each step is clear as the verbs start at the beginning of the line. Without the pipe, functions would be nested inside each other, making the code harder to understand.

arrange(
  select(
    mutate(
      filter(
        flights, 
        dest == "IAH"
      ),
      speed = distance / air_time * 60
    ),
    year:day, dep_time, carrier, flight, speed
  ),
  desc(speed)
)

Another way but using intermediate objects:

flights1 <- filter(flights, dest == "IAH")
flights2 <- mutate(flights1, speed = distance / air_time * 60)
flights3 <- select(flights2, year:day, dep_time, carrier, flight, speed)
arrange(flights3, desc(speed))

Both forms have their uses, but the pipe operator usually makes data analysis code easier to write and read.

Tip: To add the pipe to your code, use the built-in keyboard shortcut Ctrl/Cmd + Shift + M.