R profvis - code profiling in R Usually, when you run R code interactively, it is easy to spot which parts of the script are the most time-consuming. Nevertheless, for functions or nested loops, it may be not so obvious.
R multidplyr - dplyr meets parallel processing Note: I assume that you are familiar with dplyr. If not, I suggest using first the following tutorial: https://cran.rstudio.com/web/packages/dplyr/vignettes/introduction.html. Intro dplyr is one of
R R - Heat maps with ggplot2 Heat maps are a very useful graphical tool to better understand or present data stored in matrix in more accessible form. E.g. they are very helpful during seeking/comparing missing values in
R R - parallel computing in 5 minutes (with foreach and doParallel) Parallel computing is easy to use in R thanks to packages like doParallel. However, before we decide to parallelize our code, still we should remember that there is a trade-off between simplicity and
R R - devtools and RCurl If you see during devtools (or any other R package) installation on Ubuntu these sort of errors: * installing *source* package ‘RCurl’ ... ** package ‘RCurl’ successfully unpacked and MD5 sums checked checking for curl-config... no
R Object serialization in R Saving and restoring objects in R is simple and sometimes it might be very helpful. Especially if you want to keep results from a very time-consuming analysis which obviously you don't want to