r - Linear Regression fit (augment) on a different dataset

Question

I'm simply trying to calculate the prediction (fitted values) on a different dataset the regression model was built on using dplyr and the augment function. However I keep getting errors. Even without using dplyr, the augment function seems to only accept the dataset the model was built on. Any solution to resolve that? Below is one of my attempt. Thank you.

data1 <- head(mtcars,20)

model <- mtcars %>%
  group_by(cyl) %>%
  do(fit = lm(wt ~ mpg, .),
     data = (.)) %>%
  augment(fit, data1)

Answer 1

Use augment in mutate and use map to pass one model at a time in augment.

library(broom)
library(dplyr)
library(purrr)

mtcars %>%
  group_by(cyl) %>%
  do(fit = lm(wt ~ mpg, .),
     data = (.)) %>%
  ungroup() %>%
  mutate(col   = map(fit, augment, newdata = data1))

Also since do has been superseded you can fit the model in summarise.

mtcars %>%
  group_by(cyl) %>%
  summarise(fit = list(lm(wt ~ mpg)), 
            data = list(cur_data())) %>%
  mutate(col   = map(fit, augment, newdata = data1))

#    cyl fit    data               col               
#  <dbl> <list> <list>             <list>            
#1     4 <lm>   <tibble [11 × 11]> <tibble [20 × 14]>
#2     6 <lm>   <tibble [7 × 11]>  <tibble [20 × 14]>
#3     8 <lm>   <tibble [14 × 11]> <tibble [20 × 14]>