根据键计算多行的标志

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, ie 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