Can I use a list as a hash in R? If so, why is it so slow?

Question

Before using R, I used quite a bit of Perl. In Perl, I would often use hashes, and lookups of hashes are generally regarded as fast in Perl.

For example, the following code will populate a hash with up to 10000 key/value pairs, where the keys are random letters and the values are random integers. Then, it does 10000 random lookups in that hash.

#!/usr/bin/perl -w use strict;  my @letters = ('a'..'z');  print @letters . "\n"; my %testHash;  for(my $i = 0; $i < 10000; $i++) {     my $r1 = int(rand(26));     my $r2 = int(rand(26));     my $r3 = int(rand(26));     my $key = $letters[$r1] . $letters[$r2] . $letters[$r3];     my $value = int(rand(1000));     $testHash{$key} = $value; }  my @keyArray = keys(%testHash); my $keyLen = scalar @keyArray;  for(my $j = 0; $j < 10000; $j++) {     my $key = $keyArray[int(rand($keyLen))];     my $lookupValue = $testHash{$key};     print "key " .  $key . " Lookup $lookupValue \n"; } 

Now that increasingly, I am wanting to have a hash-like data structure in R. The following is the equivalent R code:

testHash <- list()  for(i in 1:10000) {   key.tmp = paste(letters[floor(26*runif(3))], sep="")   key <- capture.output(cat(key.tmp, sep=""))   value <- floor(1000*runif(1))   testHash[[key]] <- value }  keyArray <- attributes(testHash)$names keyLen = length(keyArray);  for(j in 1:10000) {   key <- keyArray[floor(keyLen*runif(1))]   lookupValue = testHash[[key]]   print(paste("key", key, "Lookup", lookupValue)) } 

The code seem to be doing equivalent things. However, the Perl one is much faster:

>time ./perlHashTest.pl real    0m4.346s user    **0m0.110s** sys 0m0.100s 

Comparing to R:

time R CMD BATCH RHashTest.R  real    0m8.210s user    **0m7.630s** sys 0m0.200s 

What explains the discrepancy? Are lookups in R lists just not good?

Increasing to 100,000 list length and 100,000 lookups only exaggerates the discrepancy? Is there a better alternative for the hash data structure in R than the native list()?

Answer 1

The underlying reason is that R lists with named elements are not hashed. Hash lookups are O(1), because during insert the key is converted to an integer using a hash function, and then the value put in the space hash(key) % num_spots of an array num_spots long (this is a big simplification and avoids the complexity of dealing with collisions). Lookups of the key just require hashing the key to find the value's position (which is O(1), versus a O(n) array lookup). R lists use name lookups which are O(n).

As Dirk says, use the hash package. A huge limitation with this is that it uses environments (which are hashed) and overriding of [ methods to mimic hash tables. But an environment cannot contain another environment, so you cannot have nested hashes with the hash function.

A while back I worked on implementing a pure hash table data structure in C/R that could be nested, but it went on my project back burner while I worked on other things. It would be nice to have though :-)

Answer 2

You could try environments and/or the hash package by Christopher Brown (which happens to use environments under the hood).