Iterating though slightly different models in purrr

I have the following code comparing the rmse of models that differ only in the polynomial term.

library(tidyverse)
data(mtcars)
cv_mtcars = mtcars %>%
  crossv_kfold(k = 10)
cv_mtcars %>%
  mutate(model1 = map(train, ~lm(disp ~ wt, data = .)),
         model2 = map(train, ~lm(disp ~I(wt^2), data = .)),
         model3 = map(train, ~lm(disp ~I(wt^3), data = .)),         
         model4 = map(train, ~lm(disp ~I(wt^4), data = .)),
         model5 = map(train, ~lm(disp ~I(wt^5), data = .)),
         model6 = map(train, ~lm(disp ~I(wt^6), data = .)),
        order1 = map2_dbl(model1, test, ~rmse(.x, .y)),
        order2 = map2_dbl(model2, test, ~rmse(.x, .y)),
        order3 = map2_dbl(model3, test, ~rmse(.x, .y)),
        order4 = map2_dbl(model4, test, ~rmse(.x, .y)),
        order5 = map2_dbl(model5, test, ~rmse(.x, .y)),
        order6 = map2_dbl(model6, test, ~rmse(.x, .y))) %>% 
  select(order1,order2,order3,order4,order5,order6) %>% gather(1:6,key=model,value=value) %>% 
  ggplot()+
  geom_point(aes(x=factor(model),y=value))+
  labs(y="rmse",x="polynomial",title="Model Assesment",subtitle="disp~I(wt^x)")

Is there a more efficient way to iterate through my models? I feel like I am writing more code than I need to.

You can iterate through the models with an outer call to map to iterate over the polynomial orders and an inner call to map to iterate over the 10 folds. In the code below, I've used poly(wt, i) instead of I(wt^i), because I(wt^i) generates a polynomial with only the highest-order term, while poly(wt, i) generates a polynomial with terms of all orders up to the highest order. I've saved the rmse for each fold in the model_cv object, but you can, of course, pipe it directly into ggplot instead.

set.seed(50)
model_cv = setNames(1:6, 1:6) %>% 
  map_df(function(i) { 
    map2_dbl(cv_mtcars[["train"]], cv_mtcars[["test"]], function(train, test) {
      model = lm(disp ~ poly(wt,i), data=train)
      rmse(model, test)
    })
  }) %>% 
  gather(`Polynomial Order`, rmse)

ggplot(model_cv, aes(`Polynomial Order`, rmse)) +
  geom_point() +
  stat_summary(fun.y=mean, geom="point", pch="_", colour="red", size=7) +
  labs(title="Model Assesment",subtitle="disp ~ poly(wt, order)")