What's the higher-performance alternative to for-loops for subsetting data by group-id?

A recurring analysis paradigm I encounter in my research is the need to subset based on all different group id values, performing statistical analysis on each group in turn, and putting the results in an output matrix for further processing/summarizing.

How I typically do this in R is something like the following:

data.mat <- read.csv("...")  
groupids <- unique(data.mat$ID)  #Assume there are then 100 unique groups
  
results <- matrix(rep("NA",300),ncol=3,nrow=100)  

for(i in 1:100) {  
  tempmat <- subset(data.mat,ID==groupids[i])  

  # Run various stats on tempmat (correlations, regressions, etc), checking to  
  # make sure this specific group doesn't have NAs in the variables I'm using  
  # and assign results to x, y, and z, for example.  

  results[i,1] <- x  
  results[i,2] <- y  
  results[i,3] <- z  
}

This ends up working for me, but depending on the size of the data and the number of groups I'm working with, this can take up to three days.

Besides branching out into parallel processing, is there any "trick" for making something like this run faster? For instance, converting the loops into something else (something like an apply with a function containing the stats I want to run inside the loop), or eliminating the need to actually assign the subset of开发者_如何转开发 data to a variable?

Edit:

Maybe this is just common knowledge (or sampling error), but I tried subsetting with brackets in some of my code rather than using the subset command, and it seemed to provide a slight performance gain which surprised me. I have some code I used and output below using the same object names as above:

system.time(for(i in 1:1000){data.mat[data.mat$ID==groupids[i],]})  

   user  system elapsed  
 361.41   92.62  458.32

system.time(for(i in 1:1000){subset(data.mat,ID==groupids[i])})  

   user  system elapsed   
 378.44  102.03  485.94

Update:

In one of the answers, jorgusch suggested that I use the data.table package to speed up my subsetting. So, I applied it to a problem I ran earlier this week. In a dataset with a little over 1,500,000 rows, and 4 columns (ID,Var1,Var2,Var3), I wanted to calculate two correlations in each group (indexed by the "ID" variable). There are slightly more than 50,000 groups. Below is my initial code (which is very similar to the above):

data.mat <- read.csv("//home....")  
groupids <- unique(data.mat$ID)
  
results <- matrix(rep("NA",(length(groupids) * 3)),ncol=3,nrow=length(groupids))  

for(i in 1:length(groupids)) {  
  tempmat <- data.mat[data.mat$ID==groupids[i],] 

  results[i,1] <- groupids[i]  
  results[i,2] <- cor(tempmat$Var1,tempmat$Var2,use="pairwise.complete.obs")  
  results[i,3] <- cor(tempmat$Var1,tempmat$Var3,use="pairwise.complete.obs")    

}  

I'm re-running that right now for an exact measure of how long that took, but from what I remember, I started it running when I got into the office in the morning and it finished sometime in mid-afternoon. Figure 5-7 hours.

Restructuring my code to use data.table....

data.mat <- read.csv("//home....")  
data.mat <- data.table(data.mat)  
  
testfunc <- function(x,y,z) {  
  temp1 <- cor(x,y,use="pairwise.complete.obs")  
  temp2 <- cor(x,z,use="pairwise.complete.obs")  
  res <- list(temp1,temp2)  
  res  
}  

system.time(test <- data.mat[,testfunc(Var1,Var2,Var3),by="ID"])  

 user  system  elapsed  
16.41    0.05    17.44  

Comparing the results using data.table to the ones I got from using a for loop to subset all IDs and record results manually, they seem to have given me the same answers(though I'll have to check that a bit more thoroughly). That looks to be a pretty big speed increase.

Update 2:

Running the code using subsets finally finished up again:

   user     system   elapsed  
17575.79  4247.41   23477.00

Update 3:

I wanted to see if anything worked out differently using the plyr package that was also recommended. This is my first time using it, so I may have done things somewhat inefficiently, but it still helped substantially compared to the for loop with subsetting.

Using the same variables and setup as before...

data.mat <- read.csv("//home....")  
system.time(hmm <- ddply(data.mat,"ID",function(df)c(cor(df$Var1,df$Var2,  use="pairwise.complete.obs"),cor(df$Var1,df$Var3,use="pairwise.complete.obs"))))  

  user  system elapsed  
250.25    7.35  272.09  

This is pretty much exactly what the plyr package is designed to make easier. However it's unlikely that it will make things much faster - most of the time is probably spent doing the statistics.

Besides plyr, you can try to use foreach package to exclude explicit loop counter, but I don't know if it will give you any performance benefits.

Foreach, neverless, gives you a quite simple interface to parallel chunk processing if you have multicore workstation (with doMC/multicore packages) (check Getting Started with doMC and foreach for details), if you exclude parallel processing only because it is not very easy to understand for students. If it is not the only reason, plyr is very good solution IMHO.

Personally, I find plyr not very easy to understand. I prefer data.table which is also faster. For instance you want to do the standard deviation of colum my_column for each ID.

dt <- datab.table[df] # one time operation...changing format of df to table
result.sd <- dt[,sd(my_column),by="ID"] # result with each ID and SD in second column 

Three statements of this kind and a cbind at the end - that is all you need. You can also use dt do some action for only one ID without a subset command in an new syntax:

result.sd.oneiD<- dt[ID="oneID",sd(my_column)]  

The first statment refers to rows (i), the second to columns (j).

If find it easier to read then player and it is more flexible, as you can also do sub domains within a "subset"... The documentation describes that it uses SQL-like methods. For instance, the by is pretty much "group by" in SQL. Well, if you know SQL, you can probably do much more, but it is not necessary to make use of the package. Finally, it is extremely fast, as each operation is not only parallel, but also data.table grabs the data needed for calculation. Subset, however, maintain the levels of the whole matrix and drag it trough the memory.

You have already suggested vectorizing and avoiding making unnecessary copies of intermediate results, so you are certainly on the right track. Let me caution you not to do what i did and just assume that vectorizing will always give you a performance boost (like it does in other languages, e.g., Python + NumPy, MATLAB).

An example:

# small function to time the results:
time_this = function(...) {
  start.time = Sys.time(); eval(..., sys.frame(sys.parent(sys.parent()))); 
  end.time = Sys.time(); print(end.time - start.time)
}

# data for testing: a 10000 x 1000 matrix of random doubles
a = matrix(rnorm(1e7, mean=5, sd=2), nrow=10000)

# two versions doing the same thing: calculating the mean for each row
# in the matrix
x = time_this( for (i in 1:nrow(a)){ mean( a[i,] ) } )
y = time_this( apply(X=a, MARGIN=1, FUN=mean) )

print(x)    # returns => 0.5312099
print(y)    # returns => 0.661242

The 'apply' version is actually slower than the 'for' version. (According to the Inferno author, if you are doing this you are not vectorizing, you are 'loop hiding'.)

But where you can get a performance boost is by using built-ins. Below, i've timed the same operation as the two above, just using the built-in function, 'rowMeans':

z = time_this(rowMeans(a))
print(z)    # returns => 0.03679609

An order of magnitude improvement versus the 'for' loop (and the vectorized version).

The other members of the apply family are not just wrappers over a native 'for' loop.

a = abs(floor(10*rnorm(1e6)))

time_this(sapply(a, sqrt))
# returns => 6.64 secs

time_this(for (i in 1:length(a)){ sqrt(a[i])})
# returns => 1.33 secs

'sapply' is about 5x slower compared with a 'for' loop.

Finally, w/r/t vectorized versus 'for' loops, i don't think i ever use a loop if i can use a vectorized function--the latter is usually less keystrokes and and it's a more natural way (for me) to code, which is a different kind of performance boost, i suppose.