How to rescale value in a loop based

[Marta95]

Dear all,

I have these columns in an excel file:
N_GEN CAPTURE CUM_ADULTI_SV CUM_ADULTI_G1 CUM_ADULTI_G2 A1 A2 A3
1 0 0,1 0,01 0,001 1 0,1 0,3
1 1 0,5 0,1 0,05 2 0,5 0,8
1 0 1 0,5 0,1 2,5 1 1
1 5 1,2 0,8 0,5 3 1,5 1,2
2 1 1,5 1 0,9 4 2 1,8
2 2 1,8 1,2 1 5 3 2
2 7 2 1,5 1,1 5,5 3,2 2,1
2 9 2,1 1,6 1,2 5,6 3,5 2,9
3 1 2,2 1,7 1,5 6 4 3
3 2 2,5 2 1,8 7 5 4
3 7 3 3 3 9 7 5
3 10 3,5 4 3,5 9,3 8 6

I have to create a new column in this dataframe through a loop of functions like this:
A1[1](sum(capture[group_by N_GEN 1])/(sum(A1[group_by N_GEN1])
A1[2](sum(capture[group_by N_GEN 1])/(sum(A1[group_by N_GEN1])
A1[3]*(sum(capture[group_by N_GEN 1])/(sum(A1[group_by N_GEN1])
ecc... based on generation number

I tried with this function:
DB1 <- for(x in DB$N_GEN){
if(x == 1) {
mutate(AR1 = DB$CUM_ADULTI_SV*(sum(DB$CAPTURE)/sum(DB$A1)))
} else if (x == 2) {
mutate(AR2 = DB$CUM_ADULTI_G1*(sum(DB$CAPTURE)/sum(DB$A2)))
} else {
mutate(AR3 = DB$CUM_ADULTI_G2*(sum(DB$CAPTURE)/sum(DB$A3)))
}
}
DB1

But it doesnt give me any good results
Thanks

[HanOostdijk]

In general it works best if you

• follow the advice in the FAQ
• specify the exact error you get

Do you mean something like this:

library(dplyr)
library(tibble)

db1 <- tibble::tribble(
~N_GEN, ~CAPTURE, ~CUM_ADULTI_SV, ~CUM_ADULTI_G1, ~CUM_ADULTI_G2, ~A1, ~A2, ~A3,
1, 0, 0.1, 0.01, 0.001, 1, 0.1, 0.3,
1, 1, 0.5, 0.1, 0.05, 2, 0.5, 0.8,
1, 0, 1, 0.5, 0.1, 2.5, 1, 1,
1, 5, 1.2, 0.8, 0.5, 3, 1.5, 1.2,
2, 1, 1.5, 1, 0.9, 4, 2, 1.8,
2, 2, 1.8, 1.2, 1, 5, 3, 2,
2, 7, 2, 1.5, 1.1, 5.5, 3.2, 2.1,
2, 9, 2.1, 1.6, 1.2, 5.6, 3.5, 2.9,
3, 1, 2.2, 1.7, 1.5, 6, 4, 3,
3, 2, 2.5, 2, 1.8, 7, 5, 4,
3, 7, 3, 3, 3, 9, 7, 5,
3, 10, 3.5, 4, 3.5, 9.3, 8, 6
)

db2 <- db1 |>
  group_by(N_GEN) |>
  mutate(AR1 = CUM_ADULTI_SV*(sum(CAPTURE)/sum(A1)),
         AR2 = CUM_ADULTI_G1*(sum(CAPTURE)/sum(A2)),
         AR3 = CUM_ADULTI_G2*(sum(CAPTURE)/sum(A3))
         )

[technocrat]

See the FAQ for how to put you question in a form that makes it easier to attract answers, beginning with avoiding having to create the data frame.