regression output in dplyr - r

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'

Answer 1

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, .)))

Answer 2

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