.env pronouns make it explicit where to find
objects when programming with data-masked
<- 10 m %>% mutate(disp = .data$disp * .env$m)mtcars
.dataretrieves data-variables from the data frame.
.envretrieves env-variables from the environment.
Because the lookup is explicit, there is no ambiguity between both kinds of variables. Compare:
<- 10 disp %>% mutate(disp = .data$disp * .env$disp) mtcars %>% mutate(disp = disp * disp)mtcars
.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
.env is not an actual R
environment. For instance, it doesn't have a parent and the
subsetting operators behave differently.
In a magrittr pipeline,
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 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
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.