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.
Let's say I want to write a function that-
"quoted" and unquoted argumentsSo I write a function to run a t-test (works as expected):
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):
# 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-
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. Any ideas on what I am doing wrong here and how to get this to work?
It seems like the problem is stemming from passing what amounted to, e.g., x = rlang::ensym(am) to your individual functions via rlang::exec() in your meta function.
The ensym() function takes only strings or symbols, so doing this led to the error message. Given this, converting your x and y arguments to strings should help.
So the meta function could be:
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.)
Now using both bare symbols and strings work:
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>