These functions parse and transform text into R expressions. This is the first step to interpret or evaluate a piece of R code written by a programmer.

parse_expr(x)

parse_exprs(x)

parse_quo(x, env)

parse_quos(x, env)

Arguments

x

Text containing expressions to parse_expr for parse_expr() and parse_exprs(). Can also be an R connection, for instance to a file. If the supplied connection is not open, it will be automatically closed and destroyed.

env

The environment for the quosures. Depending on the use case, a good default might be the global environment but you might also want to evaluate the R code in an isolated context (perhaps a child of the global environment or of the base environment).

Value

parse_expr() returns an expression, parse_exprs() returns a list of expressions. Note that for the plural variants the length of the output may be greater than the length of the input. This would happen is one of the strings contain several expressions (such as "foo; bar").

Details

parse_expr() returns one expression. If the text contains more than one expression (separated by semicolons or new lines), an error is issued. On the other hand parse_exprs() can handle multiple expressions. It always returns a list of expressions (compare to base::parse() which returns a base::expression vector). All functions also support R connections.

The versions suffixed with _quo and _quos return quosures rather than raw expressions.

Life cycle

  • parse_quosure() and parse_quosures() were soft-deprecated in rlang 0.2.0 and renamed to parse_quo() and parse_quos(). This is consistent with the rule that abbreviated suffixes indicate the return type of a function.

See also

Examples

# parse_expr() can parse any R expression: parse_expr("mtcars %>% dplyr::mutate(cyl_prime = cyl / sd(cyl))")
#> mtcars %>% dplyr::mutate(cyl_prime = cyl/sd(cyl))
# A string can contain several expressions separated by ; or \n parse_exprs("NULL; list()\n foo(bar)")
#> [[1]] #> NULL #> #> [[2]] #> list() #> #> [[3]] #> foo(bar) #>
# You can also parse source files by passing a R connection. Let's # create a file containing R code: path <- tempfile("my-file.R") cat("1; 2; mtcars", file = path) # We can now parse it by supplying a connection: parse_exprs(file(path))
#> [[1]] #> [1] 1 #> #> [[2]] #> [1] 2 #> #> [[3]] #> mtcars #>