As a quick follow-up to my previous posts about parsing fasta files in perl, python, and ruby I wanted to make a quick note about a efficient way to get the data into R.
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| #include <fstream> | |
| #include <Rcpp.h> | |
| using namespace Rcpp; | |
| // [[Rcpp::export]] | |
| CharacterVector read_fasta(std::string file) { | |
| CharacterVector records; | |
| std::ifstream in(file.c_str()); | |
| in.get(); // remove first '>' | |
| std::string rec; | |
| while(getline(in,rec,'>')){ | |
| int newLineLoc = rec.find('\n'); | |
| std::string header = rec.substr(0,newLineLoc); | |
| std::string sequence = rec.substr(newLineLoc+1, rec.length()-newLineLoc-2); | |
| sequence.erase(std::remove(sequence.begin(),sequence.end(),'\n'),sequence.end()); | |
| records[header]=sequence; | |
| } | |
| return(records); | |
| } |
library(Rcpp)
sourceCpp("read_fasta.cpp")
library(microbenchmark)
fasta_lengths <- function(file) {
records = read_fasta(file)
sapply(records, nchar)
}
microbenchmark(fasta_lengths("Hg19.fa"), times = 1)And the results
## Unit: seconds
## expr min lq median uq max
## 1 fasta_lengths("Hg19.fa") 33.99 33.99 33.99 33.99 33.99So this is actually faster than the python implementation, an impressive feat, Rcpp is a very nice package!
