speeding up boolean logic loop in R - r

Question

I am very new to R but I am interested in learning more and improving.
I have a dataset with around 40,000+ rows containing the length of neuron segments. I want to compare the length trends of neurons of different groups. The first step in this analysis involves sorting the measurements into 1 of 6 different categories such as '<10' '10-15', '15-20', '20-25', '25-30', and '>30'.
I created these categories as appended columns using 'mutate' from the 'dplyr' package and now I am trying to write a boolean function to determine where the measurement fits by applying a value of '1' to the corresponding column if it fits, and a '0' if it doesn't.
Here is what I wrote:
for (i in 1:40019) {
{if (FinalData$Length[i] <=10)
{FinalData$`<10`[i]<-1
} else {FinalData$`<10`[i]<-0}} #Fills '<10'
if (FinalData$Length[i] >=10 & FinalData$Length[i]<15){
FinalData$`10-15`[i]<-1
} else{FinalData$`10-15`[i]<-0} #Fills'10-15'
if (FinalData$Length[i] >=15 & FinalData$Length[i]<20){
FinalData$`15-20`[i]<-1
} else{FinalData$`15-20`[i]<-0} #Fills '15-20'
if (FinalData$Length[i] >=20 & FinalData$Length[i]<25) {
FinalData$`20-25`[i]<-1
} else{FinalData$`20-25`[i]<-0} #Fills '20-25'
if(FinalData$Length[i] >=25 & FinalData$Length[i]<30){
FinalData$`25-30`[i]<-1
} else{FinalData$`25-30`[i]<-0} #Fills '25-30'
if(FinalData$Length[i] >=30){
FinalData$`>30`[i]<-1
} else{FinalData$`>30`[i]<-0} #Fills '>30'
}
This seems to work, but it takes a long time:
system.time(source('~/Desktop/Home/Programming/R/Boolean Loop R.R'))
user system elapsed
94.408 19.147 118.203
The way I coded this seems very clunky and inefficient. Is there a faster and more efficient way to code something like this or am I doing this appropriately for what I am asking for?
Here is an example of some of the values I am testing:
'Length': 14.362, 12.482337, 8.236, 16.752, 12.045
If I am not being clear about how the dataframe is structured, here is a screenshot:
How my data frame is organized

Answer 1

You can use the cut function in R. It is used to convert numeric values to factors:
x<-c(1,2,4,2,3,5,6,5,6,5,8,0,5,5,4,4,3,3,3,5,7,9,0,5,6,7,4,4)
cut(x = x,breaks = c(0,3,6,9,12),labels = c("grp1","grp2","grp3","grp4"),right=F)
set right = "T" or "F" as per your need.

Answer 2

You can vectorise that as follows (I made a sample of some data called DF)
DF <- data.frame(1:40000,sample(letters,1:40000,replace=T),"Length"=sample(1:40,40000,replace=T))
MyFunc <- function(x) {
x[x >= 10 & x < 15] <- "10-15"
x[x >= 15 & x < 20] <- "15-20"
x[x >= 20 & x < 25] <- "20-25"
x[x >= 25 & x < 30] <- "25-30"
x[x > 30] <- ">30"
x[x < 10] <- "<10"
return(x)
}
DF$Group <- MyFunc(DF[,3])
If it has to be 6 columns like that, you can modify the above to return a one or zero for the appropriate size and everything else, respectively, for each of the 6 columns.
Edit: I guess a series of ifelse might be best if it really has to be 6 columns like that.
e.g.
DF$'<10' <- sapply(DF$Length, function(x) ifelse(x < 10,1,0))