Last updated: 2020-02-25

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Knit directory: RcppComputingClub/

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Avoiding R bottlenecks with Rcpp

I will introduce the Rcpp package for streamlining R code with C++ functions to avoid some common computational bottlenecks when writing R functions in a manner that’s easy to use with minimal C++ knowledge. During the presentation, I will provide an example of developing a package with compiled code and demonstrate the “workflowr” package for organizing your projects into a version controlled website for documenting your results.

This document is broken into three main sections:

  1. Brief introduction to Rcpp
  2. Building a package
  3. Case study: Skew Normal Mixture distributions

Using workflowr

This document was created using workflowr. WorkflowR is an R package that allows for an effective and reproducible way to share organize your projects and share your results with collaborators and advisors. The benefit of workflowr is that it allows you to build an organized project template with analyses in Rmarkdown, use git to version control and push to github or gitlab, and publish your results to a website with only a handful of function calls within R.

The main functions you need are:

  1. wflow_start() – Start a new project
  2. wflow_build() – Render Rmarkdown and display html
  3. wflow_status() – show status of project/files needed to be committed
  4. wflow_publish() – deploy website, push to github/gitlab

Other useful functions are:

When deploying your website to the internet, note that github pages doesn’t allow for private sites. If privacy is a large concern in your research, consider using gitlab instead.

Learn more here: https://jdblischak.github.io/workflowr/