regression output in dplyr

Question

I would like to define similar functions as in the 'broom' package

library(dplyr)
library(broom)

mtcars %>% 
  group_by(am) %>% 
  do(model = lm(mpg ~ wt, .)) %>% 
  glance(model)

works fine. But how do I defne custom functions like

myglance <- function(x, ...) {
  s <- summary(x)
  ret <- with(s, data.frame(r2=adj.r.squared, a=coefficients[1], b=coefficients[2]))
  ret
}


mtcars %>% 
  group_by(am) %>% 
  do(model = lm(mpg ~ wt, .)) %>% 
  myglance(model)

Error in eval(substitute(expr), data, enclos = parent.frame()) : invalid 'envir' argument of type 'character'

score 3 · Accepted Answer · answered Nov 26 '15 at 03:05

glance works this way because the broom package defines a method for rowwise data frames here. If you were willing to bring in that whole .R file (along with the col_name utility from here), you could use my code to do the same thing:

myglance_df <- wrap_rowwise_df(wrap_rowwise_df_(myglance))

mtcars %>% 
  group_by(am) %>% 
  do(model = lm(mpg ~ wt, .)) %>% 
  myglance_df(model)

There's also a workaround that doesn't require adding so much code from broom: change the class of each of your models, and define your own glance function on that class.

glance.mylm <- function(x, ...) {
  s <- summary(x)
  ret <- with(s, data.frame(r2=adj.r.squared, a=coefficients[1], b=coefficients[2]))
  ret
}

mtcars %>% 
  group_by(am) %>% 
  do(model = lm(mpg ~ wt, .)) %>% 
  mutate(model = list(structure(model, class = c("mylm", class(model))))) %>%
  glance(model)

Finally, you also have the option of performing myglance on the model right away.

mtcars %>% 
  group_by(am) %>% 
  do(myglance(lm(mpg ~ wt, .)))

thank you very much for the package and your detailed answer! — ckluss, Nov 26 '15 at 07:49

score 1 · Answer 2 · edited May 23 '17 at 12:23

Here is my take on how it would work, basically the approach would be:

Extract the appropriate column from the dataframe (My solution is based on this answer, there must be a better way, and I hope someone will correct me!
run lapply on the result and construct the variables that you wanted in the myglance function you have above.
run do.call with rbind to return a data.frame.

myglance <- function(df, ...) {
  # step 1
  s <- collect(select(df, ...))[[1]] # based on this answer: https://stackoverflow.com/a/21629102/1992167

  # step 2
  lapply(s, function(x) {
    data.frame(r2 = summary(x)$adj.r.squared,
               a = summary(x)$coefficients[1],
               b = summary(x)$coefficients[2])
  }) %>% do.call(rbind, .) # step 3
}

Output:

> mtcars %>% 
+   group_by(am) %>% 
+   do(model = lm(mpg ~ wt, .)) %>%
+   myglance(model)
         r2        a         b
1 0.5651357 31.41606 -3.785908
2 0.8103194 46.29448 -9.084268

regression output in dplyr

2 Answers2