Web Frameworks for R – A Brief Overview

Having recently announced the beakr web framework for R, we have received several questions about context and why we choose beakr over other options for some of our web services. This post will attempt to answer some of those questions by providing a few opinions on beakr and other web frameworks for R.

The comparison will by no means be exhaustive but will attempt to briefly summarize some of the key features each web framework has to offer. While there are some differences in the approach each package takes to developing web services, they all share similar basic functionality. In the end, the choice of a particular framework will come down largely to personal preference.

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When is a number not a number?

Have you ever asked yourself whether your telephone number is really a number?  It’s got numbers in it but does it measure anything?

How about your credit card number?  PO Box?  Social Security Number?  Zip code? What would happen if you subtracted one of these from another?

As it turns out, many of the “numbers” we deal with every day are actually identifiers and not a measure of something.  Sadly, too many data managers do not distinguish between the two even though making this distinction is quite simple.

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beakr – A small web framework for R

What is beakr?

beakr is an unopinionated and minimalist web framework for developing and deploying web services with R. It is designed to make it as simple as possible for data scientists and engineerings to quickly write web applications, services, and APIs without worrying about lower-level details or high-level side-effects. In other words, beakr is made to be explicit, robust, and scalable – and the batteries are not included.

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Standard Latitudes and Longitudes

What?  Where?  When?

These are key questions that every scientist or other collector of environmental data must answer.

  • What is the value of the thing we are measuring?
  • Where are we taking the measurement?
  • When are we taking the measurement?

In a previous post we discussed how to standardize “when”.  But what about “where”?

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Easier Error Handling in R with try()

In a previous post, we looked at error handling in R with the tryCatch() function and how this could be used to write Java style try-catch-finally blocks. This time we’ll look at what can be done with the try() function and how we can easily process warning and error messages to take appropriate action when something goes wrong.

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Basic Error Handing in R with tryCatch()

The R language definition section on Exception Handling describes a very few basics about exceptions in R but is of little use to anyone trying to write robust code that can recover gracefully in the face of errors. In fact, if you do a little searching you will find that quite a few people have read through the ?tryCatch documentation but come away just as confused as when they started. In this post we’ll try to clarify a few things and describe how R’s error handling functions can be used to write code that functions similarly to Java’s try-catch-finally construct.

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