slam.R

library(purrr)
library(rap)

library(gapminder)
library(dplyr, warn.conflicts = FALSE)

oceania <- gapminder::gapminder %>% 
  filter(continent == "Oceania") %>% 
  mutate(yr1952 = year - 1952) %>% 
  select(-continent) %>% 
  group_nest(country)

# using slam() to promote the formula into something 
# pmap() can use
what <- ~ broom::tidy(stats::lm(lifeExp ~ yr1952, data))
fn <- slam(oceania, what)
fn
#> function (country, data) 
#> {
#>     value(broom::tidy(stats::lm(lifeExp ~ yr1952, data)))
#> }
#> <environment: 0x7fd025e54e70>
pmap(oceania, fn)
#> [[1]]
#> # A tibble: 2 x 5
#>   term        estimate std.error statistic  p.value
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)   68.4      0.337      203.  2.07e-19
#> 2 yr1952         0.228    0.0104      21.9 8.67e-10
#> 
#> [[2]]
#> # A tibble: 2 x 5
#>   term        estimate std.error statistic  p.value
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)   68.7      0.437      157.  2.66e-18
#> 2 yr1952         0.193    0.0135      14.3 5.41e- 8

# together
oceania %>% 
  pmap(slam(oceania, ~broom::tidy(stats::lm(lifeExp ~ yr1952, data))))
#> [[1]]
#> # A tibble: 2 x 5
#>   term        estimate std.error statistic  p.value
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)   68.4      0.337      203.  2.07e-19
#> 2 yr1952         0.228    0.0104      21.9 8.67e-10
#> 
#> [[2]]
#> # A tibble: 2 x 5
#>   term        estimate std.error statistic  p.value
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)   68.7      0.437      157.  2.66e-18
#> 2 yr1952         0.193    0.0135      14.3 5.41e- 8

# using pmap_dfr() to bind all results in one 
oceania %>% 
  pmap_dfr(slam(oceania, ~broom::tidy(stats::lm(lifeExp ~ yr1952, data))))
#> # A tibble: 4 x 5
#>   term        estimate std.error statistic  p.value
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)   68.4      0.337      203.  2.07e-19
#> 2 yr1952         0.228    0.0104      21.9 8.67e-10
#> 3 (Intercept)   68.7      0.437      157.  2.66e-18
#> 4 yr1952         0.193    0.0135      14.3 5.41e- 8

# wap() is less verbose to do the same thing
oceania %>% 
  wap(~broom::tidy(stats::lm(lifeExp ~ yr1952, data)))
#> [[1]]
#> # A tibble: 2 x 5
#>   term        estimate std.error statistic  p.value
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)   68.4      0.337      203.  2.07e-19
#> 2 yr1952         0.228    0.0104      21.9 8.67e-10
#> 
#> [[2]]
#> # A tibble: 2 x 5
#>   term        estimate std.error statistic  p.value
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>
#> 1 (Intercept)   68.7      0.437      157.  2.66e-18
#> 2 yr1952         0.193    0.0135      14.3 5.41e- 8

# rap() turns it into a column of the data 
oceania %>% 
  rap(model = ~broom::tidy(stats::lm(lifeExp ~ yr1952, data)))
#> # A tibble: 2 x 3
#>   country     data              model           
#>   <fct>       <list>            <list>          
#> 1 Australia   <tibble [12 × 5]> <tibble [2 × 5]>
#> 2 New Zealand <tibble [12 × 5]> <tibble [2 × 5]>

# which one can do with a mutate + pmap
oceania %>% 
  mutate(
    model = pmap(., slam(., ~broom::tidy(stats::lm(lifeExp ~ yr1952, data))))
  )
#> # A tibble: 2 x 3
#>   country     data              model           
#>   <fct>       <list>            <list>          
#> 1 Australia   <tibble [12 × 5]> <tibble [2 × 5]>
#> 2 New Zealand <tibble [12 × 5]> <tibble [2 × 5]>