Using R – Calling C code ‘Hello World!’

One of the reasons that R has so much functionality is that people have incorporated a lot of academic code written in C, C++, Fortran and Java into various packages.  Libraries written in these languages are often both robust and fast.  If you are using R to support people in a particular field, you may be called upon to incorporate some outside code into your R environment.  Unfortunately, much of the documentation on how to do this is written at a very high level.  In this post we will distil some of the available information on calling C code from R into three “Hello World” examples.

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Ten UNIX commands every data manager should know

Working with data from varied sources can be frustrating — some data will be in CSV format; some in XML; some available as HTML pages; other data as relational databases or MS Excel spreadsheets.

This post will cover the UNIX tools that every data manager needs to be familiar with in order to work with varied data sources.

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Logging and error handling in operational systems

Operational systems, by definition, need to work without human input. Systems are considered “operational” after they have ben thoroughly tested and shown to work properly with a variety of input.

However, no software is perfect and no real-world system operates with 100% availability or 100% consistent input. Things occasionally go wrong – perhaps intermittently. In a situation with occasional failures it is vitally important to have good logging and error handling. The MazamaCoreUtils R package helps with these tasks.

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Best Best Practices Ever!

Every once in a while I read something that is so insightful, so clearly written and so well documented that it enters my own personal pantheon of “Best Ever” documents. I recently added a new, simply divine article titled Best Practices for Scientific Computing and hope that everyone reading this post also takes the time to read that article. I’m including the outline here only to encourage you to read the article in it’s entirety.  It is extremely well written.

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When k-means clustering fails

Letting the computer automatically find groupings in data is incredibly powerful and is at the heart of “data mining” and “machine learning”. One of the most widely used methods for clustering data is k-means clustering. Unfortunately, k-means clustering can fail spectacularly as in the example below.

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