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Wise advice. Use both because the real tool that you are using for data analysis is the computer, not the programming language. R and Python are both just part

Wise advice. Use both because the real tool that you are using for data analysis is the computer, not the programming language. R and Python are both just parts of the toolset. Nowadays there are lots of ways to combine the two from Rpy2 to Orange3 to Jupyter and the Beaker Notebook. Notably the last two let you use Groovy,Java,Scala and a host of other languages as well. Apache Taverna also plays in this space of integrating multiple tools with different strengths to do a job. R will likely never be eclipsed by anything because it has such a broad and deep collection of statistical libraries. But Python won't go away because it is a great tool for general purpose computing and even hardcore stats heads have a lot of general purpose computing problems to deal with. It is sad to read R code that copies files, gets data from S3 buckets, runs SQL queries, and so on. So much of it is crudely hacked together and even the libraries that support this are shoddily built. The best of both worlds is to use Python for pre and post processing, but R for the stats libraries (CRAN, BioConductor). For lots of S3 wrangling the best tool is a Java library called Je tS3t, and using a language like Groovy or Scala makes it easy to tame. And Groovy is integrated deeply into Jenkins which has evolved beyond a CI tool into a general purpose dashboard for managing and running "jobs". Works great for big data stuff that is not purely Map/Reduce. Beaker Notebook is leading the charge by integrating seamless conversion of data frames between languages so that you can write a script in two or three languages at the same time, building on the strengths of each one. If you stick with just one language then expect the next generation of data scientists to leap far beyond you in a few years. A sea change is coming.

vorg 10y ago
> the last two let you use Groovy,Java,Scala and a host of other languages as well Neither Python nor R run on the JVM, so if you end up using Java,Scala,Kotlin,etc then you've decided to open that JVM can of worms which is another huge pile of tradeoffs. > Je tS3t, and using a language like Groovy or Scala makes it easy to tame. And Groovy is integrated deeply into Jenkins which has evolved beyond a CI tool into a general purpose dashboard If you end up there, know that only a subset of Apache Groovy is used by Jenkins, e.g. Groovy collections methods aren't supported. Each step along the "native Python or R" -> "Java on JVM" -> "Scala or Groovy" -> "Jenkins as dashboard" decision process entails some cost-benefit tradeoffs which need to be assessed.

vorg 10y ago

> the last two let you use Groovy,Java,Scala and a host of other languages as well Neither Python nor R run on the JVM, so if you end up using Java,Scala,Kotlin,etc then you've decided to open that JVM can of worms which is another huge pile of tradeoffs. > Je tS3t, and using a language like Groovy or Scala makes it easy to tame. And Groovy is integrated deeply into Jenkins which has evolved beyond a CI tool into a general purpose dashboard If you end up there, know that only a subset of Apache Groovy is used by Jenkins, e.g. Groovy collections methods aren't supported. Each step along the "native Python or R" -> "Java on JVM" -> "Scala or Groovy" -> "Jenkins as dashboard" decision process entails some cost-benefit tradeoffs which need to be assessed.