example-list-fun-col.R

library(tidyverse)

by_species <- iris %>%
  group_by(Species) %>% 
  nest()

specific_models <- tibble(
  Species = unique(iris$Species),
  model = list(function(d) lm(Sepal.Length ~ Petal.Width + Petal.Length, d),
               function(d) lm(Sepal.Length ~ Petal.Width, d),
               function(d) lm(Sepal.Length ~ Sepal.Width + Petal.Length, d))
)

left_join(by_species, specific_models) %>% 
  mutate(fit = map2(model, data, ~.x(.y)),
         fit = map(fit, broom::tidy)) %>% 
  select(Species, fit) %>% 
  unnest(fit)
  
# # A tibble: 8 x 6
# # Groups:   Species [3]
#   Species    term          estimate  std.error statistic      p.value
#   <fct>      <chr>            <dbl>      <dbl>     <dbl>        <dbl>
# 1 setosa     (Intercept)  4.247508  0.4114336  10.32368  1.134052e-13
# 2 setosa     Petal.Width  0.7121346 0.4874038   1.461077 1.506479e- 1
# 3 setosa     Petal.Length 0.3989790 0.2957742   1.348931 1.838227e- 1
# 4 versicolor (Intercept)  4.044640  0.4229151   9.563717 1.068715e-12
# 5 versicolor Petal.Width  1.426365  0.3155204   4.520673 4.035422e- 5
# 6 virginica  (Intercept)  0.6247824 0.5248675   1.190362 2.398819e- 1
# 7 virginica  Sepal.Width  0.2599540 0.1533376   1.695305 9.663372e- 2
# 8 virginica  Petal.Length 0.9348189 0.08960197 10.43302  8.009442e-14 
  

# Or for the same output with dplyr 1.0 syntax
  
left_join(by_species, specific_models) %>% 
  rowwise() %>%
  summarize(fit = list(model(data)),
            fit = list(broom::tidy(fit))) %>%
  unnest(fit)
  
# or even more directly
left_join(by_species, specific_models) %>% 
  rowwise() %>%
  summarize(model(data) %>% broom::tidy()) 
  
# it's also poosible to model for subgroup combinations, which isn't 
# particularly helpful in this case, but could be in others (e.g., effect size calcs)

left_join(by_species, specific_models, by = character()) %>% 
  rowwise() %>%
  summarize(model(data) %>% broom::tidy())