efficient use of R data.table and unique()

Question

Is there a more efficient query than the following

DT[, list(length(unique(OrderNo)) ),customerID]

to refine a LONG format table with customer id's, order number and product line items, meaning that there will be duplicate rows with the same order id if a customer has purchased more than 1 item in that transaction.

Trying to work out unique purchases. length() gives a count of all order id's by customer ID including duplicates, looking for just the unique number.

Edit from here:

Here is some dummy code. Ideally what i am looking for is the output from the first query using the unique().

df <- data.frame(
             customerID=as.factor(c(rep("A",3),rep("B",4))),
             product=as.factor(c(rep("widget",2),rep("otherstuff",5))),
             orderID=as.factor(c("xyz","xyz","abd","qwe","rty","yui","poi")),
             OrderDate=as.Date(c("2013-07-01","2013-07-01","2013-07-03","2013-06-01","2013-06-02","2013-06-03","2013-07-01"))
             )

DT.eg <- as.data.table(df)
#Gives unique order counts
DT.eg[, list(orderlength = length(unique(orderID)) ),customerID]
#Gives counts of all orders by customer
DT.eg[,.SD, keyby=list(orderID, customerID)][, .N, by=customerID]

         ^
         |
  This should be .N, not .SD  ~ R.S.

Answer 1

if you are trying to count the number of unique purchases per customer, use

 DT[, .N, keyby=list(customerId, OrderNo)][, .N, by=customerId]

Answer 2

As of version 1.9.6 (on CRAN 19 Sep 2015), data.table has gained the helper function uniqueN() which is equivalent to length(unique(x)) but much faster (according to data.table NEWS).

With this,

DT.eg[, list(orderlength = length(unique(orderID)) ),customerID]

and

DT.eg[,.N, keyby=list(orderID, customerID)][, .N, by=customerID]

can be rewritten as

DT.eg[, .(orderlength = uniqueN(orderID)), customerID]

   customerID orderlength
1:          A           2
2:          B           4