dot-data | R Documentation |
.data
and .env
pronounsThe .data
and .env
pronouns make it explicit where to find
objects when programming with data-masked
functions.
m <- 10 mtcars %>% mutate(disp = .data$disp * .env$m)
.data
retrieves data-variables from the data frame.
.env
retrieves env-variables from the environment.
Because the lookup is explicit, there is no ambiguity between both kinds of variables. Compare:
disp <- 10 mtcars %>% mutate(disp = .data$disp * .env$disp) mtcars %>% mutate(disp = disp * disp)
Note that .data
is only a pronoun, it is not a real data
frame. This means that you can't take its names or map a function
over the contents of .data
. Similarly, .env
is not an actual R
environment. For instance, it doesn't have a parent and the
subsetting operators behave differently.
.data
versus the magrittr pronoun .
In a magrittr pipeline, .data
is not necessarily interchangeable with the magrittr pronoun .
.
With grouped data frames in particular, .data
represents the
current group slice whereas the pronoun .
represents the whole
data frame. Always prefer using .data
in data-masked context.
.data
live?The .data
pronoun is automatically created for you by
data-masking functions using the tidy eval framework.
You don't need to import rlang::.data
or use library(rlang)
to
work with this pronoun.
However, the .data
object exported from rlang is useful to import
in your package namespace to avoid a R CMD check
note when
referring to objects from the data mask. R does not have any way of
knowing about the presence or absence of .data
in a particular
scope so you need to import it explicitly or equivalently declare
it with utils::globalVariables(".data")
.
Note that rlang::.data
is a "fake" pronoun. Do not refer to
rlang::.data
with the rlang::
qualifier in data masking
code. Use the unqualified .data
symbol that is automatically put
in scope by data-masking functions.
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