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?

r data.table
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