Nested loop in R: columns then rows

Question

I am trying to write a nested for loop in R, but am running into problems. I have researched as much as possible but can't find (or understand) the help I need. I am fairly new to R, so any advice on this looping would be appreciated, or if there is a simpler, more elegant way!

I have generated a file of daily temperatures for many many locations (I'll call them sites), and the file columns are set up like this:

year month day unix_time site_a site_b site_c site_d ... on and on

For each site (within each column), I want to run through the temperature values and create new columns (or a new data frame) with a number (a physiological rate) that corresponds with a range of those temperatures. (for example, temperatures less than 6.25 degrees have a rate of -1.33, temperatures between 6.25 and 8.75 have a rate of 0.99, etc). I have created a loop that does this for a single column of data. For example:

for(i in 1:dim(data)[1]){
if (data$point_a[i]<6.25) data$rate_point_a[i]<--1.33 else
if (data$point_a[i]>=6.25 && data$point_a[i]<8.75) data$rate_point_a[i]<-0.99 else
if (data$point_a[i]>=8.75 && data$point_a[i]<11.25) data$rate_point_a[i]<-3.31 else
if (data$point_a[i]>=11.25 && data$point_a[i]<13.75) data$rate_point_a[i]<-2.56 else
if (data$point_a[i]>=13.75 && data$point_a[i]<16.25) data$rate_point_a[i]<-1.81 else
if (data$point_a[i]>=16.25 && data$point_a[i]<18.75) data$rate_point_a[i]<-2.78 else
if (data$point_a[i]>=18.75 && data$point_a[i]<21.25) data$rate_point_a[i]<-3.75 else
if (data$point_a[i]>=21.25 && data$point_a[i]<23.75) data$rate_point_a[i]<-1.98 else
if (data$point_a[i]>=23.75 && data$point_a[i]<26.25) data$rate_point_a[i]<-0.21
}

The above code gives me a new column called "rate_site_a" that has my physiological rates. What I am having trouble doing is nesting this loop into another loop that runs through all of the columns. I have tried things such as:

for (i in 1:ncol(data)){

#for each row in that column
for (s in 1:length(data)){

    if ([i]<6.25) rate1[s]<--1.33 else  ...

I guess I don't know how to make the "if else" statement refer to the correct places. I know that I can't add the "rate" columns onto the existing data frame, as this would increase my ncol as I go through the loop, so need to put them into another data frame (though don't think this is my main issue). I am going to have many many many points to work through and would rather not have to do them one at a time, hence my attempt at a nested loop.

Any help would be much appreciated. Here is a link to some sample data if that is helpful. http://dl.dropbox.com/u/17903768/AVHRR_output.txt Thanks in advance!

Answer 1

Use ifelse which is vectorized:

ifelse(data$point<= 6.25,-1.33,ifelse(data$point<= 8.25,-0.99,ifelse(data$point<= 11.25,-3.31,.... .Until finished.

For instance:

 datap=read.table('http://dl.dropbox.com/u/17903768/AVHRR_output.txt',header=T)


apply(datap[,5:9],2,function(x){
datap$x =   
ifelse(x<=6.25,1.33,
 ifelse(x<=8.75,-0.99, 
  ifelse(x<=11.25,-3.31, 
   ifelse(x<=13.75,-2.56, 
    ifelse(x<=16.25,-1.81,
     ifelse(x<=18.75,-2.78, 
      ifelse(x<=21.25,-3.75, 
       ifelse(x<=23.75,-1.98,-0.21))))))))})

Answer 2

Andres answer is great for the apply part to get you thru all the "temperature" columns. I'm stuck here without a copy of R (at work) to experiment with, but I suspect if you create a vector of your cutoff values xcut <- c(0,6.25,8.75,.11.25,...
and just do
x <- xcut[(which(x>xcut))]
you'll have a much simpler bit of code, and easier to edit as well. (note: I added the 0 value to avoid problems with small x values :-) )

Answer 3

here's another way using just logicals:

    DAT <- read.table("http://dl.dropbox.com/u/17903768/AVHRR_output.txt",header=TRUE,as.is=TRUE)

    recodecolumn <- function(x){
        out <- vector(length=length(x))
        out[x < 6.25] <- 1.33
        out[x >= 6.25 & x < 8.75] <- .99
        out[x >= 8.75 & x < 11.25] <- 3.31
        out[x >= 11.25 & x < 13.25] <- 2.56
        out[x >= 13.25 & x < 16.25] <- 1.81
        out[x >= 16.25 & x < 18.75] <- 2.78
        out[x >= 18.75 & x < 21.25] <- 3.75
        out[x >= 21.25 & x < 23.75] <- 1.98
        out[x >= 23.75 & x < 26.25] <- 0.21
            out
    }

    NewCols <- apply(DAT[,5:9],2,recodecolumn)
    colnames(NewCols) <- paste("rate",1928:1932,sep="_")
    DAT <- cbind(DAT,NewCols)

Answer 4

I find that findInterval is useful in situations like this instead of nested if else statements as it is already vectorized and returns the position within a vector of cutoff points.

 DAT <- read.table("http://dl.dropbox.com/u/17903768/AVHRR_output.txt",header=TRUE,as.is=TRUE)

 recode.fn <- function(x){
    cut.vec <- c(0, seq(6.25,26.25,by = 2.5),Inf)
    recode.val <- c(-1.33, 0.99, 3.31, 2.56,1.81,2.78,3.75,1.98, 0.21)
    cut.interval <- findInterval(x, cut.vec, FALSE)
    return(recode.val[cut.interval])
 }

# Add on recoded data to existing data frame
DAT[,10:14] <- sapply(DAT[,5:9],FUN=recode.fn)