Suppose if I have a dataset dt
dt <- as.data.table(mtcars)
For the first row by 'cyl' variable, I want to update a new column test to the value of 'qsec' variable. Also I don't want to drop other observations but instead the value pf test to zero for them.
The below code gives the first row. But I am confused on how to update a column and retain everything
dt[order(cyl), .SD[c(1)], by=cyl]
Example of needed output
mpg cyl disp hp drat wt qsec vs am gear carb test
1: 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 18.61
2: 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 16.46
3: 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 0.00
4: 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1 0.00
5: 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1 0.00
6: 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2 17.02
NOTE : It is for a big data so would really appreciate if there is an efficient code that works faster.
We can do this with rowid
without grouping
library(data.table)
dt[, test := (rowid(cyl) == 1) * qsec]
-output
head(dt, 8)
# mpg cyl disp hp drat wt qsec vs am gear carb test
#1: 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 16.46
#2: 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 0.00
#3: 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 18.61
#4: 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 0.00
#5: 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 17.02
#6: 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 0.00
#7: 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 0.00
#8: 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 0.00
Or another option is .I
which is very fast
dt[, test := 0] # // create a column of 0's
i1 <- dt[, .I[1], cyl]$V1 # // get the index of the first element for each cyl
dt[i1, test := qsec] # // specify it in i and update the test
You can use replace
:
library(data.table)
dt <- as.data.table(mtcars)
dt[, test := replace(qsec, -1, 0), cyl]
dt
# mpg cyl disp hp drat wt qsec vs am gear carb test
# 1: 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 16.46
# 2: 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 0.00
# 3: 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 18.61
# 4: 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 0.00
# 5: 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 17.02
# 6: 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 0.00
# 7: 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 0.00
# 8: 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 0.00
#...
#...
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