Finding percentage in a sub-group using group_by and summarise

Question

I am new to dplyr and trying to do the following transformation without any luck. I've searched across the internet and I have found examples to do the same in ddply but I'd like to use dplyr.

I have the following data:

   month   type  count
1  Feb-14  bbb   341
2  Feb-14  ccc   527
3  Feb-14  aaa  2674
4  Mar-14  bbb   811
5  Mar-14  ccc  1045
6  Mar-14  aaa  4417
7  Apr-14  bbb  1178
8  Apr-14  ccc  1192
9  Apr-14  aaa  4793
10 May-14  bbb   916
..    ...  ...   ...

I want to use dplyr to calculate the percentage of each type (aaa, bbb, ccc) at a month level i.e.

   month   type  count  per
1  Feb-14  bbb   341    9.6%
2  Feb-14  ccc   527    14.87%
3  Feb-14  aaa  2674    ..
..    ...  ...   ...

I've tried

data %>%
  group_by(month, type) %>%
  summarise(count / sum(count))

This gives a 1 as each value. How do I make the sum(count) sum across all the types in the month?

Answer 1

Try

library(dplyr)
data %>%
    group_by(month) %>%
    mutate(countT= sum(count)) %>%
    group_by(type, add=TRUE) %>%
    mutate(per=paste0(round(100*count/countT,2),'%'))

We could also use left_join after summarising the sum(count) by 'month'

Or an option using data.table.

 library(data.table)
 setkey(setDT(data), month)[data[, list(count=sum(count)), month], 
               per:= paste0(round(100*count/i.count,2), '%')][]