Is there a way to speed up a for loop? - r

Question

I am working with a for loop. The goal of the for loop is to simply test a condition and code the data appropriately. The loop has to iterate over 503,288 unique values and includes three if statements. Is there a way to speed up the for loop?
The code is as follows:
count<- 0
for(i in unique(Data$ID)){ #503288
#Subset Relevant Data
Loop_Before<- subset(Primary_Before, ID == i); Loop_After <- subset(Primary_After, ID == i)
if(nrow(Loop_Before) >= 1 & nrow(Loop_After) >= 1){
Data$Status[Data$ID == i] <- "Both Elections"
}
if(nrow(Loop_Before) >= 1 & nrow(Loop_After) == 0){
Data$Status[Data$ID == i] <- "Only Primary Election"
}
if(nrow(Loop_Before) >= 0 & nrow(Loop_After) == 1){
Data$Status[Data$ID == i] <- "Only General Election"
}
message(count<- count +1)
}
table(Data$Status)
Thank you for your help!

Answer 1

Avoid the for-loop entirely. I don't know your dataset but the following should be 10 or even 100 times faster:
library(tidyverse) # load some packages that will help
# let's create some sample data
Data <- data.frame(ID = c(1,1,1,1,2,2,2,3,3))
Primary_before <- data.frame(ID = c(0,1,2,2,3,3,3))
Primary_after <- data.frame(ID = c(1,3))
# now for every ID we count the nr of rows in both dataframes
summarised_before <- Primary_before %>%
group_by(ID) %>%
summarise(nrRows = n())
ID nrRows
<dbl> <int>
1 0 1
2 1 1
3 2 2
4 3 3
summarised_after <- Primary_after %>%
group_by(ID) %>%
summarise(nrRows = n())
ID nrRows
<dbl> <int>
1 1 1
2 3 1
# now we join them together
summarised_both <- summarised_after %>%
full_join(summarised_before, by = "ID", suffix = c("_after", "_before"))
# and now we do the final calculation
summarised_both %>%
mutate(nrRows_after = replace_na(nrRows_after, 0)) %>%
mutate(Status = case_when(nrRows_before >= 1 & nrRows_after >= 1 ~ "Both elections"
, nrRows_before >= 1 & nrRows_after == 0 ~ "Only primary election"
, nrRows_before >= 0 & nrRows_after == 1 ~ "Only general election")) %>%
filter(ID %in% Data$ID)
I saved the intermediate results, but you could also do it in one go, like this:
Primary_before %>%
group_by(ID) %>%
summarise(nrRows = n()) %>%
full_join(Primary_after %>%
group_by(ID) %>%
summarise(nrRows = n())
, by = "ID"
, suffix = c("_after", "_before")) %>%
mutate(nrRows_after = replace_na(nrRows_after, 0)) %>%
mutate(Status = case_when(nrRows_before >= 1 & nrRows_after >= 1 ~ "Both elections"
, nrRows_before >= 1 & nrRows_after == 0 ~ "Only primary election"
, nrRows_before >= 0 & nrRows_after == 1 ~ "Only general election")) %>%
filter(ID %in% Data$ID)