
Symbols are a kind of defused expression that represent objects in environments.
sym()
and syms()
take strings as input and turn them into
symbols.
data_sym()
and data_syms()
create calls of the form
.data$foo
instead of symbols. Subsetting the .data
pronoun
is more robust when you expect a data-variable. See
The data mask ambiguity.
Only tidy eval APIs support the .data
pronoun. With base R
functions, use simple symbols created with sym()
or syms()
.
sym(x)syms(x)
data_sym(x)
data_syms(x)
For sym()
and syms()
, a symbol or list of symbols. For
data_sym()
and data_syms()
, calls of the form .data$foo
.
For sym()
and data_sym()
, a string. For syms()
and
data_syms()
, a list of strings.
Defusing R expressions
Metaprogramming patterns
# Create a symbol
sym("cyl")
# Create a list of symbols
syms(c("cyl", "am"))
# Symbolised names refer to variables
eval(sym("cyl"), mtcars)
# Beware of scoping issues
Cyl <- "wrong"
eval(sym("Cyl"), mtcars)
# Data symbols are explicitly scoped in the data mask
try(eval_tidy(data_sym("Cyl"), mtcars))
# These can only be used with tidy eval functions
try(eval(data_sym("Cyl"), mtcars))
# The empty string returns the missing argument:
sym("")
# This way sym() and as_string() are inverse of each other:
as_string(missing_arg())
sym(as_string(missing_arg()))
Run the code above in your browser using DataLab