save residuals with `dplyr`

I adapted an example from http://jimhester.github.io/plyrToDplyr/.

r <- iris %>%
  group_by(Species) %>%
  do(model = lm(Sepal.Length ~ Sepal.Width, data=.)) %>%
  do((function(mod) {
     data.frame(resid = residuals(mod$model))
  })(.))

corrected <- cbind(iris, r)

update Another method is to use the augment function in the broom package:

r <- iris %>%
  group_by(Species) %>%
  do(augment(lm(Sepal.Length ~ Sepal.Width, data=.))

Which returns:

Source: local data frame [150 x 10]
Groups: Species

   Species Sepal.Length Sepal.Width  .fitted    .se.fit      .resid       .hat
1   setosa          5.1         3.5 5.055715 0.03435031  0.04428474 0.02073628
2   setosa          4.9         3.0 4.710470 0.05117134  0.18952960 0.04601750
3   setosa          4.7         3.2 4.848568 0.03947370 -0.14856834 0.02738325
4   setosa          4.6         3.1 4.779519 0.04480537 -0.17951937 0.03528008
5   setosa          5.0         3.6 5.124764 0.03710984 -0.12476423 0.02420180
...

A solution that seems to be easier than the ones proposed so far and closer to the code of the original question is :

iris %>%
   group_by(Species) %>%
   do(data.frame(., resid = residuals(lm(Sepal.Length ~ Sepal.Width, data=.))))

Result :

# A tibble: 150 x 6
# Groups:   Species [3]
   Sepal.Length Sepal.Width Petal.Length Petal.Width Species   resid
          <dbl>       <dbl>        <dbl>       <dbl> <fct>     <dbl>
 1          5.1         3.5          1.4         0.2 setosa   0.0443
 2          4.9         3            1.4         0.2 setosa   0.190 
 3          4.7         3.2          1.3         0.2 setosa  -0.149 
 4          4.6         3.1          1.5         0.2 setosa  -0.180 
 5          5           3.6          1.4         0.2 setosa  -0.125 
 6          5.4         3.9          1.7         0.4 setosa   0.0681
 7          4.6         3.4          1.4         0.3 setosa  -0.387 
 8          5           3.4          1.5         0.2 setosa   0.0133
 9          4.4         2.9          1.4         0.2 setosa  -0.241 
10          4.9         3.1          1.5         0.1 setosa   0.120