r - How to merge two data.table by different column names? -

i have 2 data.table x , y.

columns in x: area, id, value
columns in y: id, price, sales

create 2 data.tables:

x = data.table(area=c('us', 'uk', 'eu'),                id=c('c001', 'c002', 'c003'),                value=c(100, 200, 300)               )  y = data.table(id=c('c001', 'c002', 'c003'),                price=c(500, 200, 400),                sales=c(20, 30, 15)               ) 

and set keys x , y:

setkey(x, id) setkey(y, id) 

now try join x , y id in x , id in y:

merge(x, y) merge(x, y, by=c('id', 'id')) merge(x, y, by.x='id', by.y='id') 

all raised error saying column names in by argument invalid.

i referred manual of data.table , found merge function not supporting by.x , by.y arguments.

how join 2 data.tables different column names without changing column names?

append:
managed join 2 tables x[y], why merge function fails in data.table?

use operation:

x[y] #    area   id value price sales # 1:   c001   100   500    20 # 2:   uk c002   200   200    30 # 3:   eu c003   300   400    15 

or operation:

y[x] #      id price sales area value # 1: c001   500    20     100 # 2: c002   200    30   uk   200 # 3: c003   400    15   eu   300 

edit after edited question, read section 1.12 of faq: "what didifference between x[y] , merge(x,y)?", led me checkout ?merge , discovered there 2 different merge functions depending upon package using. default merge.data.frame data.table uses merge.data.table. compare

merge(x, y, by.x = "id", by.y = "id") # merge.data.table # error in merge.data.table(x, y, by.x = "id", by.y = "id") :  # non-empty vector of column names `by` required. 

with

merge.data.frame(x, y, by.x = "id", by.y = "id") #     id area value price sales # 1 c001     100   500    20 # 2 c002   uk   200   200    30 # 3 c003   eu   300   400    15 

edit completeness based upon comment @michael bernsteiner, looks data.table team planning on implementing by.x , by.y merge.data.table function, hasn't done yet.