Select i-th element if a condition occurs with for loop - r

Question

I have a dataframe (df) like this:
Rif dd A A A A A B B B B B C C C C C
a1 10 5 8 10 2 6 9 6 5 7 9 1 5 6 4 5
b1 20 12 7 1 5 9 10 5 3 8 7 3 6 1 9 8
c1 100 11 6 8 1 14 1 11 9 3 6 10 8 13 8 4
d1 70 4 3 7 8 11 19 2 6 7 1 20 18 7 10 7
I have a vector
rif <- c(0, 15, 50, 90, 110)
I would like to add to the df a column such that if dd(i) >= rif(i-1) & dd(i)
Rif dd A A A A A B B B B B C C C C C V1
a1 10 5 8 10 2 6 9 6 5 7 9 1 5 6 4 5 8
b1 20 12 7 1 5 9 10 5 3 8 7 3 6 1 9 8 1
c1 100 1 6 8 1 14 1 11 9 3 6 10 8 13 8 4 14
d1 70 4 3 7 8 11 19 2 6 7 1 20 18 7 10 7 8
The same should be done for V2 and V3 with respect to Bs and Cs columns.
ref <- c(0, 15, 50, 90, 110)
for (i in 2:length(ref)) {
for (j in 1:nrow(df)) {
if (df$dd >= ref[i-1] && df$dd< ref[i]) {
df[,"V1"] <- df[j,i]
}
}
}
I get the following error:
Error in if (..) :
missing value where TRUE/FALSE needed
Probably the if command is not the correct one.
could you help me?

Answer 1

I think you just need to better specify the rows and columns:
df <- data.frame(
c("a1","b1","c1","d1")
, c(10,20,100,70), c(5,12,11,4), c(8,7,6,3), c(10,1,8,7), c(2,5,1,8), c(6,9,14,11)
, c(9,10,1,19), c(6,5,11,2), c(5,3,9,6), c(7,8,3,7), c(9,7,6,1)
, c(1,3,10,20), c(5,6,8,18), c(6,1,13,7), c(4,9,8,10), c(5,8,4,7)
)
colnames(df) <- c("Rif", "dd", "A", "A", "A", "A", "A", "B", "B", "B", "B", "B", "C", "C", "C", "C", "C")
ref <- c(0, 15, 50, 90, 110)
for (i in 2:length(ref)) {
for (j in 1:nrow(df)) {
if (df$dd[j] >= ref[i-1] && df$dd[j] < ref[i]) {
df$V1[j] <- df[j,i+2]
df$V2[j] <- df[j,i+2+5]
df$V3[j] <- df[j,i+2+10]
}
}
}
which gives:
Rif dd A A A A A B B B B B C C C C C V1 V2 V3
1 a1 10 5 8 10 2 6 9 6 5 7 9 1 5 6 4 5 8 6 5
2 b1 20 12 7 1 5 9 10 5 3 8 7 3 6 1 9 8 1 3 1
3 c1 100 11 6 8 1 14 1 11 9 3 6 10 8 13 8 4 14 6 4
4 d1 70 4 3 7 8 11 19 2 6 7 1 20 18 7 10 7 8 7 10

Answer 2

Another option in base R:
lters <- c(A="A", B="B", C="C")
firstcol <- lapply(lters, function(x) match(x, colnames(DF)))
idx <- findInterval(DF$dd, rif)
for (l in lters)
DF[, paste0("V_", l)] <- as.integer(DF[cbind(seq_len(nrow(DF)), idx + firstcol[[l]])])
DF
output:
Rif dd A A.1 A.2 A.3 A.4 B B.1 B.2 B.3 B.4 C C.1 C.2 C.3 C.4 V_A V_B V_C
1 a1 10 5 8 10 2 6 9 6 5 7 9 1 5 6 4 5 8 6 5
2 b1 20 12 7 1 5 9 10 5 3 8 7 3 6 1 9 8 1 3 1
3 c1 100 11 6 8 1 14 1 11 9 3 6 10 8 13 8 4 14 6 4
4 d1 70 4 3 7 8 11 19 2 6 7 1 20 18 7 10 7 8 7 10
data:
DF <- structure(list(Rif = c("a1", "b1", "c1", "d1"), dd = c(10L, 20L,
100L, 70L), A = c(5L, 12L, 11L, 4L), A = c(8L, 7L, 6L, 3L), A = c(10L,
1L, 8L, 7L), A = c(2L, 5L, 1L, 8L), A = c(6L, 9L, 14L, 11L),
B = c(9L, 10L, 1L, 19L), B = c(6L, 5L, 11L, 2L), B = c(5L,
3L, 9L, 6L), B = c(7L, 8L, 3L, 7L), B = c(9L, 7L, 6L, 1L),
C = c(1L, 3L, 10L, 20L), C = c(5L, 6L, 8L, 18L), C = c(6L,
1L, 13L, 7L), C = c(4L, 9L, 8L, 10L), C = c(5L, 8L, 4L, 7L
)), class = "data.frame", row.names = c(NA, -4L))
rif <- c(0, 15, 50, 90, 110)
Another way is reorganize the data by separating the lookup values into another table and perform an update join using data.table:
library(data.table)
setDT(DF)
out <- DF[, .(rn=.I, Rif, dd)]
#reorganizing data
lc <- grepl("A|B|C", names(DF))
lutbl <- data.table(COL=names(DF)[lc], transpose(DF[, ..lc]))
lutbl <- melt(lutbl, measure.vars=patterns("V"), variable.name="rn")[,
c("rn", "rif") := .(as.integer(gsub("V", "", rn)), rep(rif, sum(lc)*nrow(DF)/length(rif)))]
#lookup and update
for (l in lters)
out[, paste0("NEW", l) := lutbl[COL==l][out, on=c("rn", "rif"="dd"), roll=-Inf, value]]
out:
rn Rif dd NEWA NEWB NEWC
1: 1 a1 10 8 6 5
2: 2 b1 20 1 3 1
3: 3 c1 100 14 6 4
4: 4 d1 70 8 7 10