创建一个调用列表并将它们拼接到:
library(dplyr)
library(gapminder)
cols <- list("country", "year")
ops <- list("%in%", ">=")
vals <- list(c("Albania", "France"), 2007)
# Assumes LHS is the name of a variable and OP is
# the name of a function
op_call <- function(op, lhs, rhs) {
call(op, sym(lhs), rhs)
}
my_filter <- function(data, cols, ops, vals) {
exprs <- purrr::pmap(list(ops, cols, vals), op_call)
data %>% dplyr::filter(!!!exprs)
}
gapminder %>% my_filter(cols, ops, vals)
#> # A tibble: 2 × 6
#> country continent year lifeExp pop gdpPercap
#> <fct> <fct> <int> <dbl> <int> <dbl>
#> 1 Albania Europe 2007 76.4 3600523 5937.
#> 2 France Europe 2007 80.7 61083916 30470.
在这里,我们不必担心范围问题,因为 (a) 假定列名在数据掩码中定义,(b) 值按值传递并内联到创建的调用中,(c) 函数假定为二元运算符,并且很少重新定义它们。
为了允许自定义用户功能,我们可以采用两种方法。首先,我们可以使用一个环境并手动创建配额new_quosure()
:
op_call <- function(op, lhs, rhs, env = caller_env()) {
new_quosure(call(op, sym(lhs), rhs), env)
}
my_filter <- function(data, cols, ops, vals, env = caller_env()) {
exprs <- purrr::pmap(list(ops, cols, vals), op_call, env)
data %>% dplyr::filter(!!!exprs)
}
gapminder %>% my_filter(cols, ops, vals)
local({
my_op <- `%in%`
gapminder %>% my_filter(cols, list("my_op", ">="), vals)
})
#> # A tibble: 2 × 6
#> country continent year lifeExp pop gdpPercap
#> <fct> <fct> <int> <dbl> <int> <dbl>
#> 1 Albania Europe 2007 76.4 3600523 5937.
#> 2 France Europe 2007 80.7 61083916 30470.
另一种可能更简单的方法是允许调用包含内联函数。为此,请使用rlang::call2()
代替base::call()
:
op_call <- function(op, lhs, rhs) {
call2(op, sym(lhs), rhs)
}
my_filter <- function(data, cols, ops, vals) {
exprs <- purrr::pmap(list(ops, cols, vals), op_call)
data %>% dplyr::filter(!!!exprs)
}
local({
my_op <- `%in%`
gapminder %>% my_filter(cols, list(my_op, ">="), vals)
})
#> # A tibble: 2 × 6
#> country continent year lifeExp pop gdpPercap
#> <fct> <fct> <int> <dbl> <int> <dbl>
#> 1 Albania Europe 2007 76.4 3600523 5937.
#> 2 France Europe 2007 80.7 61083916 30470.
内联函数的缺点是,这将阻止优化和到其他 dplyr 后端的可移植性。