Hadley Wickham talks about R performance:

http://adv-r.had.co.nz/Performance.html

dplyr, tidyr & ggplot2

https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html

Set of functions with fast implementation to replace the base ones.

Flexibility over performance:

\(+\) minimal upfront planning

\(-\) hard to optimize

Less flexibility => faster

Win speed by being 'sparse':

• General functions are slow.

• \(lm.fit()\) instead of \(lm()\)

• \(vapply()\) instead of \(sapply()\)

• look on CRAN for existing solutions

Win speed by being precise:

• R has to guess if no argument is given.

• \(unlist(x, use.names = F)\) instead of \(unlist(x)\)

• use option \(colClasses\) in \(read.csv()\)

• Byte code compilation.

• Parallelisation.

'Parallel R' by Q. Ethan McCallum and Stephen Weston

• Object is copied each time it's expanded.

• The apply functions are not the solution!

• Vectorisation when possible (because C loops).

• Package on CRAN that makes it very simple to connect C++ to R.

• More flexible and often faster than functional optimisation !

• Good free tutorial for C++:

http://www.learncpp.com/

• Further reference:

'Seamless R and C++ integration with Rcpp' by Dirk Eddelbuettel

1. Need a working C++ compiler.

2. Install and load Rcpp.

3. Open a C++ file in RStudio.

4. '#include <Rcpp.h>' and 'using namespace Rcpp;' are needed at the top.

5. '// [[Rcpp::export]]' before a function.

6. Write your Rcpp functions.

7. 'sourceCpp()' the file where needed.

• Some objects are not created through assignment.

• = instead of <- to assign values.

• Declare type of all variables.

• Add semicolons at the end of each line.

• for(init; check; increment)

• Vectors start at 0 !

• Use ( instead of [ to index the matrix.

• Explicit 'return' statement.

• Scalar: double, int, String, bool.

• Vector: NumericVector, IntegerVector, CharacterVector, LogicalVector.

• Matrix: NumericMatrix, IntegerMatrix, CharacterMatrix, LogicalMatrix.

• Also exists equivalent of list and data-frame.

• Etc.

When stuck with a slow piece of code:

• Look for alternative implementations in well-known packages.

• Choose a function specifically tailored for the task.

• Be explicit with the options.

When considering writing a loop in R:

• Think about a vectorized alternative.

• Code it in Rcpp!