working with lists of models using the pipe syntax - r

Question

I often like to fit and examine multiple models that relate two variables in an R dataframe.
I can do that using syntax like this:
require(tidyverse)
require(broom)
models <- list(hp ~ exp(cyl), hp ~ cyl)
map_df(models, ~tidy(lm(data=mtcars, formula=.x)))
But I'm used to the pipe syntax and was hoping to be able to something like this:
mtcars %>% map_df(models, ~tidy(lm(data=., formula=.x)))
That makes it clear that I'm "starting" with mtcars and then doing stuff to it to generate my output. But that syntax doesn't work, giving an error Error: Index 1 must have length 1.
Is there a way to write my purrr:map() function in a way that I can pipe mtcars into it to get the same output as the working code above? I.e.
mtcars %>% <<<something>>>

Answer 1

tl/dr: mtcars %>% {map_df(models, function(.x) tidy(lm(data=., formula=.x)))}
Or mtcars %>% map_df(models, ~tidy(lm(..1,..2)), ..2 = .)
There are 2 problems with the solution you've tried.
The first is that you need to use curly braces if you want to place the dot in an unusual place.
library(magrittr)
1 %>% divide_by(2) # 0.5 -> this works
1 %>% divide_by(2,.) # 2 -> this works as well
1 %>% divide_by(2,mean(.,3)) # this doesn't
1 %>% divide_by(.,2,mean(.,3)) # as it's equivalent to this one
1 %>% {divide_by(2,mean(.,3))} # but this one works as it forces all dots to be explicit.
The second is that you can't use the dot with the ~ formulation in the way you intended, try map(c(1,2), ~ 3+.) and map(c(1,2), ~ 3+.x) (or even map(c(1,2), ~ 3+..1)) and you'll see you get the same result. By the time you use the dot in a ~ formula it's not linked to the pipe function anymore.
To make sure the dot is interpreted as mtcars you need to use the good old function(x) ... definition.
This works:
mtcars %>% {map_df(models, function(.x) tidy(lm(data=., formula=.x)))}
Finally, as a bonus, here's what I came up with, trying to find a solution without curly braces :
mtcars %>% map(models,lm,.) %>% map_df(tidy)
mtcars %>% map_df(models, ~tidy(lm(..1,..2)), ..2 = .)

Answer 2

This should work and does not involve the complexity of functions or {}. A purely purrr solution.
library(tidyverse)
library(broom)
models <- list(hp ~ exp(cyl), hp ~ cyl)
mtcars %>%
list %>% # make it a list
cross2(models) %>% # get combinations
transpose %>% # put into a nice format
set_names("data", "formula") %>% # set names to lm arg names
pmap(lm) %>% # fit models
map_df(tidy) # tidy it up

Answer 3

This is a little at odds with how purrr::map works. You are mapping over the list of models (one item of the list at a time), not the dataframe (which would be one column of the dataframe at a time). Because the dataframe stays constant even with other model expressions, I don't think mapping will work for this situation.
However, you could get the syntax you want from defining a custom function based on the one you have above.
library(tidyverse)
library(broom)
models <- list(hp ~ exp(cyl), hp ~ cyl)
models_to_rows <- function(data, models_list) {
models_list %>%
map_df(~tidy(lm(data=data, formula=.x)))
}
mtcars %>%
models_to_rows(models)
#> term estimate std.error statistic p.value
#> 1 (Intercept) 89.60052274 9.702303069 9.234975 2.823542e-10
#> 2 exp(cyl) 0.04045315 0.004897717 8.259594 3.212750e-09
#> 3 (Intercept) -51.05436157 24.981944312 -2.043650 4.985522e-02
#> 4 cyl 31.95828066 3.883803355 8.228604 3.477861e-09