I come across this problem on a regular basis as well, and always end up fiddling for a while. Because the LHS of `:=` is also dynamic, I'm not sure there's any more elegant approach. One alternative might be to create several temporary data.tables holding the rowSums and then cbind()?

for (i in age_brackets) {
tmp <- dt[, rowSums(.SD, na.rm=T), by=.(origin, race, sex,year, total_pop), .SDcols=i]
 dt <- cbind(dt, tmp)
}

--Mel.

On 12/9/2015 7:19 AM, Santosh Srinivas wrote:
Hello All,

I am sure there is a much more efficient way to do this. Please advise any suggestions.
For now, I have boot fixed this the crude way :-(

age_brackets <- c("pop_0:pop_3","pop_4:pop_6","pop_7:pop_9")

for (i in age_brackets) {
cmdText <- paste('dt[, paste("",i,sep=""):= rowSums(.SD, na.rm=TRUE), by=list(origin, race, sex,year, total_pop), .SDcols=',i,']', sep="")
print(cmdText)
eval(parse(text=cmdText))
}


On Tue, Dec 8, 2015 at 11:13 PM, Santosh Srinivas <[email protected] <mailto:[email protected]>> wrote:

    Hello All,

    I have a dataset as below with a reproducible example after that.
    My actual data has about 100 columns.

    I want columns that represent the rowSums for sets .. eg. pop_0_3,
    pop_4_6, pop_7_9  .. this is sum of population in age group of 0-3
    for example.

    How can I do that using indexes of the columns?

    
---------------------------------------------------------------------------------------------------------------------------------------------------------

        origin race sex year total_pop   pop_0   pop_1   pop_2   pop_3
      pop_4   pop_5   pop_6   pop_7 pop_8   pop_9
     1:      0    0   0 2014 318748017 3971847 3957864 3972081 4003272
    4001929 4002977 4132455 4152653 4118628 4105776
     2:      0    0   0 2015 321368864 4000831 3988161 3974109 3986357
    4015656 4013264 4013790 4142998 4163270 4129322
     3:      0    0   0 2016 323995528 4029356 4017346 4004585 3988434
    3998839 4026967 4024121 4024481 4153686 4174008
     4:      0    0   0 2017 326625791 4057231 4046063 4033932 4019069
    4000955 4010232 4037777 4034839 4035311 4164487
     5:      0    0   0 2018 329256465 4083375 4074132 4062816 4048550
    4031712 4012371 4021117 4048454 4045696 4046249
     6:      0    0   0 2019 331883986 4107606 4100469 4091055 4077589
    4061316 4043229 4023269 4031853 4059256 4056646
     7:      0    0   0 2020 334503458 4128810 4124893 4117546 4105953
    4090466 4072931 4054223 4034013 4042721 4070166
     8:      0    0   0 2021 337108968 4145903 4146269 4142090 4132527
    4118898 4102128 4083950 4065004 4044832 4053623
     9:      0    0   0 2022 339698079 4159190 4163587 4163657 4157230
    4145600 4130675 4113256 4094835 4075940 4055771
    10:      0    0   0 2023 342267302 4169856 4177093 4181156 4178958
    4170441 4157505 4141921 4124243 4105873 4086972


    
---------------------------------------------------------------------------------------------------------------------------------------------------------


    #
    
https://www.census.gov/population/projections/files/downloadables/NP2014_D1.csv

    require("data.table")

    dt <- structure(list(origin = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
    0L), race = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), sex = c(0L,
    0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), year = 2014:2023, total_pop =
    c(318748017L,
    321368864L, 323995528L, 326625791L, 329256465L, 331883986L,
    334503458L,
    337108968L, 339698079L, 342267302L), pop_0 = c(3971847L, 4000831L,
    4029356L, 4057231L, 4083375L, 4107606L, 4128810L, 4145903L, 4159190L,
    4169856L), pop_1 = c(3957864L, 3988161L, 4017346L, 4046063L,
    4074132L, 4100469L, 4124893L, 4146269L, 4163587L, 4177093L),
        pop_2 = c(3972081L, 3974109L, 4004585L, 4033932L, 4062816L,
        4091055L, 4117546L, 4142090L, 4163657L, 4181156L), pop_3 =
    c(4003272L,
        3986357L, 3988434L, 4019069L, 4048550L, 4077589L, 4105953L,
        4132527L, 4157230L, 4178958L), pop_4 = c(4001929L, 4015656L,
        3998839L, 4000955L, 4031712L, 4061316L, 4090466L, 4118898L,
        4145600L, 4170441L), pop_5 = c(4002977L, 4013264L, 4026967L,
        4010232L, 4012371L, 4043229L, 4072931L, 4102128L, 4130675L,
        4157505L), pop_6 = c(4132455L, 4013790L, 4024121L, 4037777L,
        4021117L, 4023269L, 4054223L, 4083950L, 4113256L, 4141921L
        ), pop_7 = c(4152653L, 4142998L, 4024481L, 4034839L, 4048454L,
        4031853L, 4034013L, 4065004L, 4094835L, 4124243L), pop_8 =
    c(4118628L,
        4163270L, 4153686L, 4035311L, 4045696L, 4059256L, 4042721L,
        4044832L, 4075940L, 4105873L), pop_9 = c(4105776L, 4129322L,
        4174008L, 4164487L, 4046249L, 4056646L, 4070166L, 4053623L,
        4055771L, 4086972L)), .Names = c("origin", "race", "sex",
    "year", "total_pop", "pop_0", "pop_1", "pop_2", "pop_3", "pop_4",
    "pop_5", "pop_6", "pop_7", "pop_8", "pop_9"), class = c("data.table",
    "data.frame"), row.names = c(NA, -10L))


    
---------------------------------------------------------------------------------------------------------------------------------------------------------

    Thank you.
    Santosh




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