How to use if-statement in apply function? - r

Question

Since I have to read over 3 go of data, I would like to improve mycode by changing two for-loop and if-statement to the applyfunction.
Here under is a reproducible example of my code. The overall purpose (in this example) is to count the number of positive and negative values in "c" column for each value of x and y. In real case I have over 150 files to read.
# Example of initial data set
df1 <- data.frame(a=rep(c(1:5),times=3),b=rep(c(1:3),each=5),c=rnorm(15))
# Another dataframe to keep track of "c" counts
dfOcc <- data.frame(a=rep(c(1:5),times=3),"positive"=c(0),"negative"=c(0))
So far I did this code, which works but is really slow:
for (i in 1:nrow(df)) {
x = df[i,"a"]
y = df[i,"b"]
if (df[i,"c"]>=0) {
dfOcc[which(dfOcc$a==x && dfOcc$b==y),"positive"] <- dfOcc[which(dfOcc$a==x && dfOcc$b==y),"positive"] +1
}else{
dfOcc[which(dfOcc$a==x && dfOcc$b==y),"negative"] <- dfOcc[which(dfOcc$a==x && dfOcc$b==y),"negative"] +1
}
}
I am unsure whether the code is slow due to the size of the files (260k rows each) or due to the for-loop?
So far I managed to improve it in this way:
dfOcc[which(dfOcc$a==df$a & dfOcc$b==df$b),"positive"] <- apply(df,1,function(x){ifelse(x["c"]>0,1,0)})
This works fine in this example but not in my real case:
It only keeps count of the positive c and running this code twice might be counter productive
My original datasets are 260k rows while my "tracer" is 10k rows (the initial dataset repeats the a and b values with other c values
Any tip on how to improve those two points would be greatly appreciated!

Answer 1

I think you can simply count and spread the data. This will be easier and will work on any group and dataset. You can change group_by(a) to group_by(a, b) if you want to count grouping both a and b column.
library(dplyr)
library(tidyr)
df1 %>%
group_by(a) %>%
mutate(sign = ifelse(c > 0, "Positive", "Negative")) %>%
count(sign) %>%
spread(sign, n)

Answer 2

package data.table might help you do this in one line.
df1 <- data.table(data.frame(a=rep(c(1:5),times=3),b=rep(c(1:3),each=5),c=rnorm(15)))
posneg <- c("positive" , "negative") # list of columns needed
df1[,(posneg) := list(ifelse(c>0, 1,0), ifelse(c<0, 1,0))] # use list to combine the 2 ifelse conditions
for more information , try
?data.table
if you really want the positive negative counts to be in a separate dataframe,
dfOcc <- df1[,c("a", "positive","negative")]