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Given the sample data sampleDT below, I would appreciate any help to create a function that efficiently does the following:

For each variable whose name begins with dollar:

  • do 3-(5/j) in those rows where sampleDT$employer==1 ;

  • do 2*j in those rows where sampleDT$employer==0;

  • put the result of the operation in a new variable located in the column next to the one where it was based;

  • keep the values of dollar.wage_1 unchanged;

  • put the output of the operation in the new variable euro.wage_x whose name only replaces dollar by euro in the source variable dollar.wage_x. x is the number of dollar.wage variables.

  • create new variables named division.wage_x which contain for each pair dollar.wage_x and euro.wage_x the result of division of dollar.wage_x by euro.wage_x.

Where j stands for the values that the variables dollar.wage_1:dollar.wage_10 take.


Sample data

sampleDT<-structure(list(id = 1:10, N = c(10L, 10L, 10L, 10L, 10L, 10L, 
    10L, 10L, 10L, 10L), A = c(62L, 96L, 17L, 41L, 212L, 143L, 143L, 
    143L, 73L, 73L), B = c(3L, 1L, 0L, 2L, 170L, 21L, 0L, 33L, 62L, 
    17L), C = c(0.05, 0.01, 0, 0.05, 0.8, 0.15, 0, 0.23, 0.85, 0.23
    ), employer = c(1L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 0L, 0L), F = c(0L, 
    0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L), G = c(1.94, 1.19, 1.16, 
    1.16, 1.13, 1.13, 1.13, 1.13, 1.12, 1.12), H = c(0.14, 0.24, 
    0.28, 0.28, 0.21, 0.12, 0.17, 0.07, 0.14, 0.12), dollar.wage_1 = c(1.94, 
    1.19, 3.16, 3.16, 1.13, 1.13, 2.13, 1.13, 1.12, 1.12), dollar.wage_2 = c(1.93, 
    1.18, 3.15, 3.15, 1.12, 1.12, 2.12, 1.12, 1.11, 1.11), dollar.wage_3 = c(1.95, 
    1.19, 3.16, 3.16, 1.14, 1.13, 2.13, 1.13, 1.13, 1.13), dollar.wage_4 = c(1.94, 
    1.18, 3.16, 3.16, 1.13, 1.13, 2.13, 1.13, 1.12, 1.12), dollar.wage_5 = c(1.94, 
    1.19, 3.16, 3.16, 1.14, 1.13, 2.13, 1.13, 1.12, 1.12), dollar.wage_6 = c(1.94, 
    1.18, 3.16, 3.16, 1.13, 1.13, 2.13, 1.13, 1.12, 1.12), dollar.wage_7 = c(1.94, 
    1.19, 3.16, 3.16, 1.14, 1.13, 2.13, 1.13, 1.12, 1.12), dollar.wage_8 = c(1.94, 
    1.19, 3.16, 3.16, 1.13, 1.13, 2.13, 1.13, 1.12, 1.12), dollar.wage_9 = c(1.94, 
    1.19, 3.16, 3.16, 1.13, 1.13, 2.13, 1.13, 1.12, 1.12), dollar.wage_10 = c(1.94, 
    1.19, 3.16, 3.16, 1.13, 1.13, 2.13, 1.13, 1.12, 1.12)), row.names = c(NA, 
    -10L), class = "data.frame")

Head output

id N A  B  C   employer F G    H      dollar.wage_1 dollar.wage_2 dollar.wage_3 dollar.wage_4 dollar.wage_5 dollar.wage_6 dollar.wage_7 dollar.wage_8 dollar.wage_9 dollar.wage_10
1 10 62 3 0.05        1 0 1.94 0.14          1.94          1.93          1.95          1.94          1.94          1.94          1.94          1.94          1.94           1.94
2 10 96 1 0.01        1 0 1.19 0.24          1.19          1.18          1.19          1.18          1.19          1.18          1.19          1.19          1.19           1.19
3 10 17 0 0.00        0 0 1.16 0.28          3.16          3.15          3.16          3.16          3.16          3.16          3.16          3.16          3.16           3.16

I am looking for an efficient way to do this because my actual dataset has over 1000 variables dollar.wage_x, where x > 1000.

Thanks in advance for any help.

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