A one-day course in using Stata and R together

This article is originally published at https://robertgrantstats.wordpress.com

I am going to be giving this course on 17 August this year. It is organised by my company BayesCamp and will take place online from 1300 local British Summer Time.

I am excited to offer this new course because I think there is a real gap in the market here. Lots of people want to tap into both Stata as a commercial package with a lot of well-constructed and thoroughly tested tools for analysis and graphics, as well as R as a flexible, analysis-oriented programming language. Obviously you could run a bit of code here and a bit there and keep notes as to what you did (and heaven knows we’ve all been there), but it is safer, faster and more reproducible to integrate your workflow across both Stata and R. I’ll show you some ways of doing that and you’ll leave with some useful Stata commands and R functions that you can start using straight away.

And if you’re wondering why anyone would want to use both, here’s some pros and cons. Stata has an imperative scripting language (with macro substitution) that does a lot more than you probably think it does. R is a functional, highly vectorised programming language. What is hard in one is often quite easy in the other.

Some R advantages:

• faster graphics
• sometimes more flexible graphics
• have more than one data file open, plus various arrays
• lists of diverse object types
• useR communities + a lot of Stack Overflow vel sim
• functional programming can be handy…
• magrittr piping
• Rcpp integration with C++
• packages for hip stuff like Spark, H2O.ai, Keras etc etc…
• rmarkdown, knitr etc for outputs
• bespoke parallelisation

Stata advantages:

• simpler to bootstrap most estimation and modelling methods
• simpler multiple imputation
• imperative programming with macro substitution can be handy
• most models are achieved with less typing
• customer support + Statalist
• Mata, and Java/C/C++ plugins
• lots of economics/econometrics functionality
• neat structural equation model building
• webdoc, dyndoc etc for outputs
• built-in parallelisation (if you have MP flavor)
• super-clean SVG output
• margins and marginsplot are really good for communicating findings

You can read more and book here.

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This article is originally published at https://robertgrantstats.wordpress.com
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