The magrittr package offers a set of operators which promote semantics that will improve your code by
The operators pipe their left-hand side values forward into expressions that appear on the right-hand side, i.e. one can replace f(x)
with x %>% f
, where %>%
is the (main) pipe-operator. When coupling several function calls with the pipe-operator, the benefit will become more apparent. Consider this pseudo example
the_data <-
read.csv('/path/to/data/file.csv') %>%
subset(variable_a > x) %>%
transform(variable_c = variable_a/variable_b) %>%
head(100)
Four operations are performed to arrive at the desired data set, and they are written in a natural order: the same as the order of execution. Also, no temporary variables are needed. If yet another operation is required, it is straight-forward to add to the sequence of operations wherever it may be needed.
To install the current development version use devtools:
devtools::install_github("smbache/magrittr")
To install the CRAN version:
install.packages("magrittr")
x %>% f
is equivalent to f(x)
x %>% f(y)
is equivalent to f(x, y)
x %>% f %>% g %>% h
is equivalent to h(g(f(x)))
x %>% f(y, .)
is equivalent to f(y, x)
x %>% f(y, z = .)
is equivalent to f(y, z = x)
It is straight-forward to use the placeholder several times in a right-hand side expression. However, when the placeholder only appears in a nested expressions magrittr will still apply the first-argument rule. The reason is that in most cases this results more clean code.
x %>% f(y = nrow(.), z = ncol(.))
is equivalent to f(x, y = nrow(x), z = nrow(x))
The behavior can be overruled by enclosing the right-hand side in braces:
x %>% {f(y = nrow(.), z = ncol(.))}
is equivalent to f(y = nrow(x), z = nrow(x))
To define a unary function on the fly in the pipeline, enclose the body of such function in braces, and refer to the argument as .
, e.g.
iris %>%
{
n <- sample(1:10, size = 1)
H <- head(., n)
T <- tail(., n)
rbind(H, T)
} %>%
summary
Any pipeline starting with the .
will return a function which can later be used to apply the pipeline to values. Building functions in magrittr is therefore similar to building other values.
f <- . %>% cos %>% sin
# is equivalent to
f <- function(.) sin(cos(.))
Some right-hand sides are used for their side effect (e.g. plotting, printing to a file, etc) and it may be convenient to be able to subsequently continue the pipeline. The "tee" operator, %T>%
can be used for this purpose and works exactly like %>%
, except it returns the left-hand side value, rather than the potential result of the right-hand side operation:
rnorm(200) %>%
matrix(ncol = 2) %T>%
plot %>% # plot usually does not return anything.
colSums
Many functions accept a data argument, e.g. lm
and aggregate
, which is very useful in a pipeline where data is first processed and then passed into such a function. There are also functions that do not have a data argument, for which it is useful to expose the variables in the data. This is done with the %$%
operator:
iris %>%
subset(Sepal.Length > mean(Sepal.Length)) %$%
cor(Sepal.Length, Sepal.Width)
data.frame(z = rnorm(100)) %$%
ts.plot(z)
There is also a pipe operator which can be used as shorthand notation in situations where the left-hand side is being "overwritten":
iris$Sepal.Length <-
iris$Sepal.Length %>%
sqrt
To avoid the repetition of the left-hand side immediately after the assignment operator, use the %<>%
operator:
iris$Sepal.Length %<>% sqrt
This operator works exactly like %>%
, except the pipeline assigns the result rather than returning it. It must be the first pipe operator in a longer chain.
For more detail, see the package vignette
vignette("magrittr")