The dplyr::summarize() function can apply arbitrary functions over the data, but it seems that function must return a scalar value. I'm curious if there is a reasonable way to handle functions that return a vector value without making multiple calls to the function.
Here's a somewhat silly minimal example. Consider a function that gives multiple values, such as:
f <- function(x,y){
coef(lm(x ~ y, data.frame(x=x,y=y)))
}
and data that looks like:
df <- data.frame(group=c('A','A','A','A','B','B','B','B','C','C','C','C'), x=rnorm(12,1,1), y=rnorm(12,1,1))
I'd like to do something like:
df %>%
group_by(group) %>%
summarise(f(x,y))
and get back a table that has 2 columns added for each of the returned values instead of the usual 1 column. Instead, this errors with: Expecting single value
Of course we can get multiple values from dlpyr::summarise() by giving the function argument multiple times:
f1 <- function(x,y) coef(lm(x ~ y, data.frame(x=x,y=y)))[[1]]
f2 <- function(x,y) coef(lm(x ~ y, data.frame(x=x,y=y)))[[2]]
df %>%
group_by(group) %>%
summarise(a = f1(x,y), b = f2(x,y))
This gives the desired output:
group a b
1 A 1.7957245 -0.339992915
2 B 0.5283379 -0.004325209
3 C 1.0797647 -0.074393457
but coding in this way is ridiculously crude and ugly.
data.table handles this case more succinctly:
dt <- as.data.table(df)
dt[, f(x,y), by="group"]
but creates an output that extend the table using additional rows instead of additional columns, resulting in an output that is both confusing and harder to work with:
group V1
1: A 1.795724536
2: A -0.339992915
3: B 0.528337890
4: B -0.004325209
5: C 1.079764710
6: C -0.074393457
Of course there are more classic apply strategies we could use here,
sapply(levels(df$group), function(x) coef(lm(x~y, df[df$group == x, ])))
A B C
(Intercept) 1.7957245 0.528337890 1.07976471
y -0.3399929 -0.004325209 -0.07439346
but this sacrifices both the elegance and I suspect the speed of the grouping. In particular, note that we cannot use our pre-defined function f in this case, but have to hard code the grouping into the function definition.
Is there a dplyr function for handling this case? If not, is there a more elegant way to handle this process of evaluating vector-valued functions over a data.frame by group?
