I try to write a simple function to obtain the rate between columns in a dataframe on a aggregated level. I would like to obtain the same output as the output obtained by:
library(dplyr)
set.seed(1)
dat <- data.frame(x = rep(1:3, each = 5), a = runif(15, 0, 1), b = runif(15, 0, 2))
oper_fn <- function(df, oper){
oper <- enquo(oper)
df %>%
group_by(x) %>%
summarize(output = !! oper) %>%
ungroup()
}
oper_fn(dat, sum(a) / sum(b))
The following should also work:
oper_fn(dat, sum(a))
What is the way to do this in base R?
You can just split on x
and use sapply
to loop over the groups and apply your function, ie
sapply(split(dat, dat$x), function(i) sum(i$a) / sum(i$b))
# 1 2 3
#0.3448112 0.7289661 0.5581262
Another option using aggregate
tmp <- aggregate(.~x, dat, sum)
cbind(tmp[1], tmp['a']/tmp['b'])
# x a
#1 1 0.3448112
#2 2 0.7289661
#3 3 0.5581262
Or a one liner using transform
with aggregate
transform(aggregate(.~x, dat, sum), output = a/b)
# x a b output
#1 1 2.320376 6.729408 0.3448112
#2 2 3.194763 4.382595 0.7289661
#3 3 2.223499 3.983864 0.5581262
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