Count flags across multiple rows depending on key - r

Question

I have a dataset that consists of customers and accounts where a customer can have multiple accounts. The dataset has several 'flags' on each account.
I'm trying to get a count of 'unique' hits on these flags per customer, i.e. if 3 accounts have flag1 I want this to count as 1 hit, but if just one of the accounts have flag2 too I want this to count as 2. Essentially, I want to see how many flags each customer hits across all of their accounts.
Example Input data frame:
cust acct flag1 flag2 flag3
a 123 0 1 0
a 456 1 1 0
b 789 1 1 1
c 428 0 1 0
c 247 0 1 0
c 483 0 1 1
Example Output dataframe:
cust acct flag1 flag2 flag3 UniqueSum
a 123 0 1 0 2
a 456 1 1 0 2
b 789 1 1 1 3
c 428 0 1 0 2
c 247 0 1 0 2
c 483 0 1 1 2
I've tried to use the following:
fSumData <- ddply(fData, "cust", numcolwise(sum, c(flag1,flag2,flag3))
but this sums the acct column too giving me one row per customer where I'd like to have the same amount of rows as the customer has accounts.

Answer 1

Using data.table:
require(data.table) # v1.9.6
dt[, un := sum(sapply(.SD, max)), by = cust, .SDcols = flag1:flag3]
We group by cust, and on the subdata for each group for columns flag1, flag2, flag3 (achieved using .SD and .SDcols), we extract each column's max, and summing it up would give the total number of 1's.
We update the original table with these values by reference using the LHS := RHS notation (see Reference Semantics vignette).
where dt is:
dt = fread('cust acct flag1 flag2 flag3
a 123 0 1 0
a 456 1 1 0
b 789 1 1 1
c 428 0 1 0
c 247 0 1 0
c 483 0 1 1')

Answer 2

One way that comes to my mind, is to colSum for each cust and check which are greater than 0. For example,
> tab
cust acct flag1 flag2 flag3
1 a 123 0 1 0
2 a 456 1 1 0
3 b 789 1 1 1
4 c 428 0 1 0
5 c 247 0 1 0
6 c 483 0 1 1
> uniqueSums <- sapply(tab$cust, function(cust) length(which(colSums(tab[tab$cust == cust,3:5]) > 0)))
> cbind(tab, uniqueSums = uniqueSums)
cust acct flag1 flag2 flag3 uniqueSums
1 a 123 0 1 0 2
2 a 456 1 1 0 2
3 b 789 1 1 1 3
4 c 428 0 1 0 2
5 c 247 0 1 0 2
6 c 483 0 1 1 2
For each value of cust, the function in sapply finds the rows, does a vectorized sum and checks for values that are greater than 0.

Answer 3

Here's an approach using library(dplyr):
df %>%
group_by(cust) %>%
summarise_each(funs(max), -acct) %>%
mutate(UniqueSum = rowSums(.[-1])) %>%
select(-starts_with("flag")) %>%
right_join(df, "cust")
#Source: local data frame [6 x 6]
#
# cust UniqueSum acct flag1 flag2 flag3
# (fctr) (dbl) (int) (int) (int) (int)
#1 a 2 123 0 1 0
#2 a 2 456 1 1 0
#3 b 3 789 1 1 1
#4 c 2 428 0 1 0
#5 c 2 247 0 1 0
#6 c 2 483 0 1 1

Answer 4

I was able to answer my own question after reading Roman's post, I did something like this where f data is my dataset.
fSumData <- ddply(fData, "cust", numcolwise(sum))
fSumData$UniqueHits <- ifelse(fSumData$flag1 >= 1;1,0) + ifelse(fSumData$flag2 >= 1;1;0) + ifelse(fSumData$flag3 >= 1;1;0)
I found this to be a bit faster than Roman's solution when running against my dataset, but am unsure if it's the optimal solution. Thank you all for your input this helped a ton!

Answer 5

The underused rowsum could be, also, of use:
rowSums(rowsum(DF[-(1:2)], DF$cust) > 0)[DF$cust]
#a a b c c c
#2 2 3 2 2 2