[英]using `rlang::exec` with functions that use `rlang::ensym`
I am trying to write a custom function which is a bit more complicated so for the sake of simplicity I have created toy examples. 我正在尝试编写一个更复杂的自定义函数,因此为了简单起见,我创建了玩具示例。
Let's say I want to write a function that- 假设我想写一个函数 -
"quoted"
and unquoted
arguments "quoted"
和不unquoted
参数 So I write a function to run a t-test (works as expected): 所以我编写了一个运行t检验的函数(按预期工作):
set.seed(123)
library(rlang)
library(tidyverse)
# t-test function
fun_t <- function(data, x, y) {
# make sure both quoted and unquoted arguments work
x <- rlang::ensym(x)
y <- rlang::ensym(y)
# t-test
broom::tidy(stats::t.test(
formula = rlang::new_formula({{ y }}, {{ x }}),
data = data
))
}
# works fine
fun_t(mtcars, am, wt)
#> # A tibble: 1 x 10
#> estimate estimate1 estimate2 statistic p.value parameter conf.low
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1.36 3.77 2.41 5.49 6.27e-6 29.2 0.853
#> # ... with 3 more variables: conf.high <dbl>, method <chr>,
#> # alternative <chr>
fun_t(mtcars, "am", "wt")
#> # A tibble: 1 x 10
#> estimate estimate1 estimate2 statistic p.value parameter conf.low
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1.36 3.77 2.41 5.49 6.27e-6 29.2 0.853
#> # ... with 3 more variables: conf.high <dbl>, method <chr>,
#> # alternative <chr>
Then I write a function to run an anova (works as expected): 然后我编写一个函数来运行anova(按预期工作):
# anova function
fun_anova <- function(data, x, y) {
# make sure both quoted and unquoted arguments work
x <- rlang::ensym(x)
y <- rlang::ensym(y)
# t-test
broom::tidy(stats::aov(
formula = rlang::new_formula({{ y }}, {{ x }}),
data = data
))
}
# works fine
fun_anova(mtcars, cyl, wt)
#> # A tibble: 2 x 6
#> term df sumsq meansq statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 cyl 1 18.2 18.2 47.4 0.000000122
#> 2 Residuals 30 11.5 0.384 NA NA
fun_anova(mtcars, "cyl", "wt")
#> # A tibble: 2 x 6
#> term df sumsq meansq statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 cyl 1 18.2 18.2 47.4 0.000000122
#> 2 Residuals 30 11.5 0.384 NA NA
Then I write a meta-function to choose the appropriate function from above- 然后我写一个元函数从上面选择适当的函数 -
fun_meta <- function(data, x, y) {
# make sure both quoted and unquoted arguments work
x <- rlang::ensym(x)
y <- rlang::ensym(y)
# which test to run?
if (nlevels(data %>% dplyr::pull({{ x }})) == 2L) {
.f <- fun_t
} else {
.f <- fun_anova
}
# executing the appropriate function
rlang::exec(
.fn = .f,
data = data,
x = x,
y = y
)
}
# using the meta-function
fun_meta(mtcars, am, wt)
#> Only strings can be converted to symbols
fun_meta(mtcars, "am", "wt")
#> Only strings can be converted to symbols
But this doesn't seem to work. 但这似乎不起作用。 Any ideas on what I am doing wrong here and how to get this to work?
关于我在这里做错了什么以及如何让它发挥作用的任何想法?
It seems like the problem is stemming from passing what amounted to, eg, x = rlang::ensym(am)
to your individual functions via rlang::exec()
in your meta function. 似乎问题源于通过元函数中的
rlang::exec()
将所得到的内容(例如x = rlang::ensym(am)
传递给您的各个函数。
The ensym()
function takes only strings or symbols, so doing this led to the error message. ensym()
函数只接受字符串或符号,因此这样做会导致错误消息。 Given this, converting your x
and y
arguments to strings should help. 鉴于此,将
x
和y
参数转换为字符串应该会有所帮助。
So the meta function could be: 因此元函数可以是:
fun_meta <- function(data, x, y) {
# make sure both quoted and unquoted arguments work
x <- rlang::ensym(x)
y <- rlang::ensym(y)
# which test to run?
if (dplyr::n_distinct(data %>% dplyr::pull({{ x }})) == 2L) {
.f <- fun_t
} else {
.f <- fun_anova
}
# executing the appropriate function
rlang::exec(
.fn = .f,
data = data,
x = rlang::as_string(x),
y = rlang::as_string(y)
)
}
(I switched to n_distinct()
from nlevels
because am
and cyl
aren't factors and so I wasn't getting the right results to compare to your original results.) (我切换到
n_distinct()
从nlevels
因为am
和cyl
并不因素,所以我没有得到正确的结果进行比较原始结果。)
Now using both bare symbols and strings work: 现在使用裸符号和字符串工作:
fun_meta(mtcars, am, wt)
# A tibble: 1 x 10
estimate estimate1 estimate2 statistic p.value parameter conf.low conf.high
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1.36 3.77 2.41 5.49 6.27e-6 29.2 0.853 1.86
# ... with 2 more variables: method <chr>, alternative <chr>
> fun_meta(mtcars, "am", "wt")
fun_meta(mtcars, "am", "wt")
# A tibble: 1 x 10
estimate estimate1 estimate2 statistic p.value parameter conf.low conf.high
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1.36 3.77 2.41 5.49 6.27e-6 29.2 0.853 1.86
# ... with 2 more variables: method <chr>, alternative <chr>
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