Having problems updating do() function - r

Question

I am trying to update my function using the new version of dplyr.
First, I had this function (old version):
slope.k <- function(data, Treatment, Replicate, Day, Ln.AFDMrem){
fitted_models <- data %>% group_by(Treatment, Replicate) %>%
do(model = lm(Ln.AFDMrem ~ Day, data = .))
broom::tidy(fitted_models,model) %>% print(n = Inf)
}
However, the do() function was superseded. Now, I am trying to update with this (new) version:
slope.k <- function(data, Treatment, Replicate, Day, Ln.AFDMrem){
mod_t <- data %>% nest_by(Treatment, Replicate) %>%
mutate(model = list(lm(Ln.AFDMrem ~ Day, data = data))) %>%
summarise(tidy_out = list(tidy(model)))
unnest(select(mod_t, Treatment, tidy_out)) %>% print(n = Inf)
}
However, it doesn't work properly, because I have the following warnings:
Warning messages:
1: `cols` is now required when using unnest().
Please use `cols = c(tidy_out)`
2: `...` is not empty.
We detected these problematic arguments:
* `needs_dots`
These dots only exist to allow future extensions and should be empty.
Did you misspecify an argument?
Thanks in advance!!!

Answer 1

The issue would be the use of select with unnest. It can be reproduced by changing the select to c
libary(dplyr)
library(broom)
library(tidyr)
mtcars %>%
nest_by(carb, gear) %>%
mutate(model = list(lm(mpg ~ disp + drat, data = data))) %>%
summarise(tidy_out = list(tidy(model)), .groups = 'drop') %>%
unnest(c(tidy_out))
-output
# A tibble: 33 x 7
# carb gear term estimate std.error statistic p.value
# <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
# 1 1 3 (Intercept) -8.50 NaN NaN NaN
# 2 1 3 disp 0.0312 NaN NaN NaN
# 3 1 3 drat 7.10 NaN NaN NaN
# 4 1 4 (Intercept) -70.5 302. -0.234 0.854
# 5 1 4 disp -0.0445 0.587 -0.0757 0.952
# 6 1 4 drat 25.5 62.4 0.408 0.753
# 7 2 3 (Intercept) -3.72 8.57 -0.434 0.739
# 8 2 3 disp 0.0437 0.0123 3.54 0.175
# 9 2 3 drat 1.90 2.88 0.661 0.628
#10 2 4 (Intercept) -10.0 226. -0.0443 0.972
# … with 23 more rows
Also, after the mutate, step, we can directly use the unnest on the 'tidy_out' column
If we use as a function, assuming that unquoted arguments are passed as column names
slope.k <- function(data, Treatment, Replicate, Day, Ln.AFDMrem){
ln_col <- rlang::as_string(ensym(Ln.AFDMrem))
day_col <- rlang::as_string(ensym(Day))
data %>%
nest_by({{Treatment}}, {{Replicate}}) %>%
mutate(model = list(lm(reformulate(day_col, ln_col), data = data))) %>%
summarise(tidy_out = list(tidy(model)), .groups = 'drop') %>%
unnest(tidy_out)
}
slope.k(mtcars, carb, gear, disp, mpg)
# A tibble: 22 x 7
carb gear term estimate std.error statistic p.value
<dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
1 1 3 (Intercept) 22.0 5.35 4.12 0.152
2 1 3 disp -0.00841 0.0255 -0.329 0.797
3 1 4 (Intercept) 52.6 8.32 6.32 0.0242
4 1 4 disp -0.279 0.0975 -2.86 0.104
5 2 3 (Intercept) 1.25 3.49 0.357 0.755
6 2 3 disp 0.0460 0.0100 4.59 0.0443
7 2 4 (Intercept) 36.6 6.57 5.57 0.0308
8 2 4 disp -0.0978 0.0529 -1.85 0.206
9 2 5 (Intercept) 47.0 NaN NaN NaN
10 2 5 disp -0.175 NaN NaN NaN
# … with 12 more rows