The matrixStats package provides highly optimized functions for
computing common summaries over rows and columns of matrices,
e.g. rowQuantiles(). There are also functions that operate on vectors,
e.g. logSumExp(). Their implementations strive to minimize both memory
usage and processing time. They are often remarkably faster compared
to good old apply() solutions. The calculations are mostly implemented
in C, which allow us to optimize beyond what is possible to do in
plain R. The package installs out-of-the-box on all common operating
systems, including Linux, macOS and Windows.
With a matrix
> x <- matrix(rnorm(20 * 500), nrow = 20, ncol = 500)it is many times faster to calculate medians column by column using
> mu <- matrixStats::colMedians(x)than using
> mu <- apply(x, MARGIN = 2, FUN = median)Moreover, if performing calculations on a subset of rows and/or columns, using
> mu <- colMedians(x, rows = 33:158, cols = 1001:3000)is much faster and more memory efficient than
> mu <- apply(x[33:158, 1001:3000], MARGIN = 2, FUN = median)For formal benchmarking of matrixStats functions relative to alternatives, see the Benchmark reports.