Le 26/05/2021 à 16:36, Vincent Carey a écrit :
On this theme, the following proved sufficient to ingest and
convert sparse csv without column headers or row names:
Nice to share your final solution which could be further shorten to smth
like:
#include "RcppArmadillo.h"
using namespace Rcpp;
//
On this theme, the following proved sufficient to ingest and
convert sparse csv without column headers or row names:
#include "RcppArmadillo.h"
using namespace Rcpp;
// [[Rcpp::depends(RcppArmadillo)]]
// [[Rcpp::export]]
List parse_sparse_csv_impl(SEXP fname) {
using namespace Rcpp;
std::strin
Hi Vince,
Aaron Lun (CC-ed) has written scuttle::readSparseCounts() for this purpose.
You may have already seen it since it's in BioC.
It's written in R rather than C++ but I imagine it's pretty efficient
since, well, it's written by Aaron
(https://github.com/LTLA/scuttle/blob/master/R/readSparseC
Thanks Dirk, lots of useful information there. I wonder whether the sparse
ingestion
problem would best be solved with multiple passes -- it seems one would want
to learn the dimensions and the number of nonzero elements per row to
allocate the index vectors, and then populate them and the data ve
Vincent,
In the broad terms of the question the best answer may be a simple "sure".
More seriously, there have been many approaches. Consider for example the
recent Rcpp Gallery post lead by Zach (with some edits by me):
https://gallery.rcpp.org/articles/sparse-matrix-class/
It's focus on not
This problem has been discussed in various places but I don't
see a clear solution. Certain applications are generating
large comma-delimited files with mostly zero entries. The aim
is to ingest efficiently, converting to sparse representation
a record at a time. Presumably a triplet format woul
