dplyr, do(), extracting parameters from model without losing grouping variable

A slightly changed example from the R help for do(): ``` by_cyl <- group_by(mtcars, cyl) models <- by_cyl %>% do(mod = lm(mpg ~ disp, data = .)) coefficients<-models %>% do(data.frame(coef = coef(.$mod)[[1]])) ``` In the dataframe coefficients, there is the first coefficient of the linear model for each cyl group. My question is how can I produce a dataframe that contains not only a column with the coefficients, but also a column with the grouping variable. ===== Edit: I extend the example to try to make more clear my problem Let's suppose that I want to extract the coefficients of the model and some prediction. I can do this: ``` by_cyl <- group_by(mtcars, cyl) getpars <- function(df){ fit <- lm(mpg ~ disp, data = df) data.frame(intercept=coef(fit)[1],slope=coef(fit)[2]) } getprediction <- function(df){ fit <- lm(mpg ~ disp, data = df) x <- df$disp y <- predict(fit, data.frame(disp= x), type = "response") data.frame(x,y) } pars <- by_cyl %>% do(getpars(.)) prediction <- by_cyl %>% do(getprediction(.)) ``` The problem is that the code is redundant because I am fitting the model two times. My idea was to build a function that returns a list with all the information: ``` getAll <- function(df){ results<-list() fit <- lm(mpg ~ disp, data = df) x <- df$disp y <- predict(fit, data.frame(disp= x), type = "response") results$pars <- data.frame(intercept=coef(fit)[1],slope=coef(fit)[2]) results$prediction <- data.frame(x,y) results } ``` The problem is that I don't know how to use do() with the function getAll to obtain for example just a dataframe with the parameters (like the dataframe pars).