add rows in a data.table but not when certain colu

Question

I have a data.table dat with 4 columns, say (col1, col2, col3, col4).

Input data:

structure(list(col1 = c(5.1, 5.1, 4.7, 4.6, 5, 5.1, 5.1, 4.7, 
4.6, 5), col2 = c(3.5, 3.5, 3.2, 3.1, 3.6, 3.5, 3.5, 3.2, 3.1, 
3.6), col3 = c(1.4, 1.4, 1.3, 1.5, 1.4, 3.4, 3.4, 1.3, 1.5, 1.4
), col4 = structure(c(1L, 1L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 4L), .Label = c("setosa", 
"versicolor", "virginica", "eer"), class = "factor")), .Names = c("col1", 
"col2", "col3", "col4"), row.names = c(NA, -10L), class = c("data.table", 
"data.frame"))

r
    col1 col2 col3   col4
 1:  5.1  3.5  1.4 setosa
 2:  5.1  3.5  1.4 setosa
 3:  4.7  3.2  1.3 setosa
 4:  4.6  3.1  1.5 setosa
 5:  5.0  3.6  1.4 setosa
 6:  5.1  3.5  3.4    eer
 7:  5.1  3.5  3.4    eer
 8:  4.7  3.2  1.3    eer
 9:  4.6  3.1  1.5    eer
10:  5.0  3.6  1.4    eer

I am performing a following operation on col3 for each unique value of col4

dat[ , r_new:= sum(col3, na.rm = T), .(col4)]    #syntax 1

So, above sytnax is creating a new column r_new with values got by adding those values of col3 where col4 is same. So, each unique value of col4 will have a unuique value in column r_new.

What I want to do now, is do the same as above but not include those rows where col1 and col2 are taking same values (something like below)

dat[col1 is different OR col2 is different , r_new:= sum(col3, na.rm = T), .(col4)]

What this will do, while performing sum function over rows, it will not include those rows where both col1 and col2 are taking same values.

How can I include this condition in the same syntax as 1?

Expected Output:

    col1 col2 col3   col4 r_new
 1:  5.1  3.5  1.4 setosa   5.6
 2:  5.1  3.5  1.4 setosa   5.6
 3:  4.7  3.2  1.3 setosa   5.6
 4:  4.6  3.1  1.5 setosa   5.6
 5:  5.0  3.6  1.4 setosa   5.6
 6:  5.1  3.5  3.4    eer   7.6
 7:  5.1  3.5  3.4    eer   7.6
 8:  4.7  3.2  1.3    eer   7.6
 9:  4.6  3.1  1.5    eer   7.6
10:  5.0  3.6  1.4    eer   7.6

As you can see in the expected output, for setosa row 1 and 2 took same value for col1 and col2 and for err rows 6 and 7 took same values for col1 and col2, so we did not add those rows (we just considered them once). Dont worry about col3 (it will take same value if col1 and col2 are taking same values.

EDIT: Second dput:

structure(list(col1 = c(5.1, 5.1, 4.7, 4.6, 5, 5.1, 5.1, 4.7, 
4.6, 5.1), col2 = c(3.5, 3.5, 3.2, 3.1, 3.6, 3.5, 3.5, 3.2, 3.1, 
3.4), col3 = c(1.4, 1.4, 1.3, 1.5, 1.4, 3.4, 3.4, 1.3, 1.5, 3.4
), col4 = c("A", "A", "A", "A", "A", "B", "B", "B", "B", "B"), 
    count = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1), r_new = c(5.6, 5.6, 
    5.6, 5.6, 5.6, 9.6, 9.6, 9.6, 9.6, 9.6)), .Names = c("col1", 
"col2", "col3", "col4", "count", "r_new"), row.names = c(NA, 
-10L), class = c("data.table", "data.frame"))

    col1 col2 col3 col4 count r_new
 1:  5.1  3.5  1.4    A     1   5.6
 2:  5.1  3.5  1.4    A     1   5.6
 3:  4.7  3.2  1.3    A     1   5.6
 4:  4.6  3.1  1.5    A     1   5.6
 5:  5.0  3.6  1.4    A     1   5.6
 6:  5.1  3.5  3.4    B     1   9.6
 7:  5.1  3.5  3.4    B     1   9.6
 8:  4.7  3.2  1.3    B     1   9.6
 9:  4.6  3.1  1.5    B     1   9.6
10:  5.1  3.4  3.4    B     1   9.6

EDIT 2: Third dput

   col1 col2 col3 col4 count r_new
 1:  5.1  3.5  1.4    A     1   5.6
 2:  5.1  3.5  1.4    A     1   5.6
 3:  4.7  3.2  1.3    A     1   5.6
 4:  4.6  3.1  1.5    A     1   5.6
 5:  5.0  3.6  1.4    A     1   5.6
 6:  5.1  3.5  3.4    B     1   6.2
 7:  5.1  3.5  3.4    B     1   6.2
 8:  4.7  3.2  1.3    B     1   6.2
 9:  4.6  3.1  1.5    B     1   6.2
10:  5.1  3.5  3.4    B     1   6.2


structure(list(col1 = c(5.1, 5.1, 4.7, 4.6, 5, 5.1, 5.1, 4.7, 
4.6, 5.1), col2 = c(3.5, 3.5, 3.2, 3.1, 3.6, 3.5, 3.5, 3.2, 3.1, 
3.5), col3 = c(1.4, 1.4, 1.3, 1.5, 1.4, 3.4, 3.4, 1.3, 1.5, 3.4
), col4 = c("A", "A", "A", "A", "A", "B", "B", "B", "B", "B"), 
    count = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1), r_new = c(5.6, 5.6, 
    5.6, 5.6, 5.6, 6.2, 6.2, 6.2, 6.2, 6.2)), .Names = c("col1", 
"col2", "col3", "col4", "count", "r_new"), row.names = c(NA, 
-10L), class = c("data.table", "data.frame"))

Answer 1

We can subset col3 inside j using ?data.table::duplicated.

dat[, r_new := sum(col3[!duplicated(.SD, by = c("col1","col2"))], na.rm = T), by = col4]  

> dat
#      col1 col2 col3 col4 count r_new
# 1:  5.1  3.5  1.4    A     1   5.6
# 2:  5.1  3.5  1.4    A     1   5.6
# 3:  4.7  3.2  1.3    A     1   5.6
# 4:  4.6  3.1  1.5    A     1   5.6
# 5:  5.0  3.6  1.4    A     1   5.6
# 6:  5.1  3.5  3.4    B     1   6.2
# 7:  5.1  3.5  3.4    B     1   6.2
# 8:  4.7  3.2  1.3    B     1   6.2
# 9:  4.6  3.1  1.5    B     1   6.2
#10:  5.1  3.5  3.4    B     1   6.2

Answer 2

Accept mtoto's answer as that's easier to read, but here's an alternative.

DT[, r_new:=unique(.SD,by=c("col1","col2"))[,sum(col3, na.rm=TRUE)], by=col4]