Conditionally Count in dplyr

Question

I have some member order data that I would like to aggregate by week of order.

This is what the data looks like:

memberorders=data.frame(MemID=c('A','A','B','B','B','C','C','D'),              week = c(1,2,1,4,5,1,4,1),              value = c(10,20,10,10,2,5,30,3)) 

I'm using dplyr to group_by MemID and summarize "value" for week<=2 and week<=4 (to see how much each member ordered in weeks 1-2 and 1-4. The code I currently have is:

MemberLTV <- memberorders %>% group_by(MemID) %>% summarize( sum2 = sum(value[week<=2]), sum4 = sum(value[week<=4])) 

I'm now trying to add two more fields in summarize, count2 and count4, that would count the number of instances of each condition (week <=2 and week <=4).

The desired output is:

output  = data.frame(MemID = c('A','B','C','D'),                  sum2 = c(30,10,5,3),                  sum4 = c(30,20,35,3),                  count2 = c(2,1,1,1),                  count4 = c(2,2,2,1)) 

I'm guessing it's just a little tweak of the sum function but I'm having trouble figuring it out.

Answer 1

Try

 library(dplyr)  memberorders %>%          group_by(MemID) %>%          summarise(sum2= sum(value[week<=2]), sum4= sum(value[week <=4]),                    count2=sum(week<=2), count4= sum(week<=4))