This is a bit of a philosophical question about data.table join syntax. I am finding more and more uses for data.tables, but still learning...

The join format X[Y] for data.tables is very concise, handy and efficient, but as far as I can tell, it only supports inner joins and right outer joins. To get a left or full outer join, I need to use merge:

  • X[Y, nomatch = NA] -- all rows in Y -- right outer join (default)
  • X[Y, nomatch = 0] -- only rows with matches in both X and Y -- inner join
  • merge(X, Y, all = TRUE) -- all rows from both X and Y -- full outer join
  • merge(X, Y, all.x = TRUE) -- all rows in X -- left outer join

It seems to me that it would be handy if the X[Y] join format supported all 4 types of joins. Is there a reason only two types of joins are supported?

For me, the nomatch = 0 and nomatch = NA parameter values are not very intuitive for the actions being performed. It is easier for me to understand and remember the merge syntax: all = TRUE, all.x = TRUE and all.y = TRUE. Since the X[Y] operation resembles merge much more than match, why not use the merge syntax for joins rather than the match function's nomatch parameter?

Here are code examples of the 4 join types:

# sample X and Y data.tables
library(data.table)
X <- data.table(t = 1:4, a = (1:4)^2)
setkey(X, t)
X
#    t  a
# 1: 1  1
# 2: 2  4
# 3: 3  9
# 4: 4 16

Y <- data.table(t = 3:6, b = (3:6)^2)
setkey(Y, t)
Y
#    t  b
# 1: 3  9
# 2: 4 16
# 3: 5 25
# 4: 6 36

# all rows from Y - right outer join
X[Y]  # default
#  t  a  b
# 1: 3  9  9
# 2: 4 16 16
# 3: 5 NA 25
# 4: 6 NA 36

X[Y, nomatch = NA]  # same as above
#    t  a  b
# 1: 3  9  9
# 2: 4 16 16
#
r join data.table
Edit
Report