Chain together multiple operations.

The downside of the functional nature of dplyr is that when you combine multiple data manipulation operations, you have to read from the inside out and the arguments may be very distant to the function call. These functions providing an alternative way of calling dplyr (and other data manipulation) functions that you read can from left to right.

chain(..., env = parent.frame())

chain_q(calls, env = parent.frame())

lhs %.% rhs

lhs %>% rhs

A sequence of data transformations, starting with a dataset. The first argument of each call should be omitted - the value of the previous step will be substituted in automatically. Use chain and ... when working interactive; use
Environment in which to evaluation expressions. In ordinary operation you should not need to set this parameter.
A dataset and function to apply to it

The functions work via simple substitution so that x %.% f(y) is translated into f(x, y).


chain was deprecated in version 0.2, and will be removed in 0.3. It was removed in the interest of making dplyr code more standardised and %.% is much more popular.

  • %.%
  • %>%
  • chain
  • chain_q
# If you're performing many operations you can either do step by step
if (require("nycflights13")) {
a1 <- group_by(flights, year, month, day)
a2 <- select(a1, arr_delay, dep_delay)
a3 <- summarise(a2,
  arr = mean(arr_delay, na.rm = TRUE),
  dep = mean(dep_delay, na.rm = TRUE))
a4 <- filter(a3, arr > 30 | dep > 30)

# If you don't want to save the intermediate results, you need to
# wrap the functions:
      group_by(flights, year, month, day),
      arr_delay, dep_delay
    arr = mean(arr_delay, na.rm = TRUE),
    dep = mean(dep_delay, na.rm = TRUE)
  arr > 30 | dep > 30

# This is difficult to read because the order of the operations is from
# inside to out, and the arguments are a long way away from the function.
# Alternatively you can use chain or \%>\% to sequence the operations
# linearly:

flights %>%
  group_by(year, month, day) %>%
  select(arr_delay, dep_delay) %>%
    arr = mean(arr_delay, na.rm = TRUE),
    dep = mean(dep_delay, na.rm = TRUE)
  ) %>%
  filter(arr > 30 | dep > 30)
Documentation reproduced from package dplyr, version, License: MIT + file LICENSE

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