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Using the data.table package in R, I am trying to create a cartesian product of two data.tables using the merge method as one would do in base R. In base the following works: #assume this order data orders <- data.frame(date = as.POSIXct(c('2012-08-28','2012-08-29','2012-09-01')), first.name = as.character(c('John','George','Henry')), last.name = as.character(c('Doe','Smith','Smith')), qty = c(10,50,6)) #and these dates dates <- data.frame(date = seq(from = as.POSIXct('2012-08-28'), to = as.POSIXct('2012-09-07'), by = 'day')) #get the unique customers cust<-unique(orders[,c('first.name','last.name')]) #using merge from base R, get the cartesian product merge(dates, cust, by = integer(0)) However, the same technique does not work using data.table and this error is thrown: "Error in merge.data.table(dates.dt, cust.dt, by = integer(0)) : A non-empty vector of column names for `by` is required." #data.table approach library(data.table) orders.dt <- data.table(orders) dates.dt <- data.table(dates) cust.dt <- unique(orders.dt[, list(first.name, last.name)]) #try to use merge (data.table) in the same manner as base merge(dates.dt, cust.dt, by = integer(0)) Error in merge.data.table(dates.dt, cust.dt, by = integer(0)) : A non-empty vector of column names for `by` is required. I want the result to reflect all customer names for all dates, just like in base, but do it in a data.table-centric way. Is this possible?

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