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 dataset and function to apply to it
- 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
...when working interactive; use
callswhen calling from another function.
- Environment in which to evaluation expressions. In ordinary operation you should not need to set this parameter.
The functions work via simple substitution so that
x %.% f(y) is translated into
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
%.% is much more popular.
# If you're performing many operations you can either do step by step data("hflights", package = "hflights") a1 <- group_by(hflights, Year, Month, DayofMonth) a2 <- select(a1, Year:DayofMonth, ArrDelay, DepDelay) a3 <- summarise(a2, arr = mean(ArrDelay, na.rm = TRUE), dep = mean(DepDelay, 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: filter( summarise( select( group_by(hflights, Year, Month, DayofMonth), Year:DayofMonth, ArrDelay, DepDelay ), arr = mean(ArrDelay, na.rm = TRUE), dep = mean(DepDelay, 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: hflights %>% group_by(Year, Month, DayofMonth) %>% select(Year:DayofMonth, ArrDelay, DepDelay) %>% summarise( arr = mean(ArrDelay, na.rm = TRUE), dep = mean(DepDelay, na.rm = TRUE) ) %>% filter(arr > 30 | dep > 30)