The magrittr package offers a set of operators which make your code more readable 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 straightforward to add to the sequence of operations wherever it may be needed.
If you are new to magrittr, the best place to start is the pipes chapter in R for data science.
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)))
Here, “equivalent” is not technically exact: evaluation is non-standard, and the left-hand side is evaluated before passed on to the right-hand side expression. However, in most cases this has no practical implication.
x %>% f(y, .)
is equivalent to f(y, x)
x %>% f(y, z = .)
is equivalent to f(y, z = x)
It is straightforward 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 = ncol(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 = ncol(x))
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.
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)
#> [1] 0.3361992
data.frame(z = rnorm(100)) %$%
ts.plot(z)