Optimizing processing time in the nested for loops - R - r

Question

I have two datasets with 24k and 15k rows. I used nested for loops in order to rewrite some data... however it takes forever to compute the operation.
does anyone have a suggestion how to optimize the code to speed the process?
my code:
for(i in 1:length(data$kolicina)){
for(j in 1:length(df$kolicina)){
if(data$LIXcode[i] == df$LIXcode[j]){
data$kolicina[i] <- df$kolicina[j]
}
}
}
the full code with the imput looks like this:
df <- data[grepl("Trennscheiben", data$a_naziv) & data$SestavKolicina > 1,]
for(i in 1:length(df$kolicina)){
df$kolicina[i] <- df$kolicina[i] / 10
}
for(i in 1:length(data$kolicina)){
for(j in 1:length(df$kolicina)){
if(data$LIXcode[i] == df$LIXcode[j]){
data$kolicina[i] <- df$kolicina[j]
}
}
}
the data:
LIXcode a_naziv RacunCenaNaEM kolicina
LIX2017396957 MINI HVLP Spritzpistole 20,16 1
LIX2017396957 MINI HVLP Spritzpistole 20,16 1
LIX2017396963 Trennscheiben Ø115 Ø12 12,53 30
LIX2017396963 Trennscheiben Ø115 Ø12 12,53 1

Answer 1

I haven't tried this on my own machine, but this should work
fun <- function(x,y){
x[which(x$LIXcode %in% y$LIXcode)]$kolicina =
y[which(x$LIXcode %in% y$LIXcode)]$kolicina
}
}
fun(data,df)
R has the capability to do them all in parallel

Answer 2

As far as I understand, the question concerns table "dt1" with key column "a" and any number of value columns and any number of observations. And then we have a "dt2" that has some sort of mapping - which means that column "a" has unique values and some column "b" has values that need to be written into "dt1" where columns "a" match.
I would suggest joining tables:
require(data.table)
dt1 <- data.table(a = sample(1:10, 1000, replace = T),
b = sample(letters, 1000, replace = T))
dt2 <- data.table(a = 1:10,
b = letters[1:10])
output <- merge(dt1, dt2, by = "a", all.x = T)
Also you can try:
dt1[,new_value:=dt2$b[match(a, dt2$a)]
Both of these solutions are vectorized, therefore almost instant.
Base solution (no data.table syntax, although I'd highly recommend you to learn it):
dt1$new_value <- dt2$b[match(dt1$a, dt2$a)]
And that's if I understood the question correctly...
Here's a working solution to accommodate for expected output:
dt1[a %in% dt2$a, b:=dt2$b[match(a, dt2$a)]]