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 …   read more ... [Read more...]

This entry is part 21 of 21 in the series Using RLast August, in Python-style Logging in R, we described using an R script as a wrapper around the futile.logger package to generate log files for an operational R data processing …   read more ... [Read more...]

Mazama Science has just released the PWFSLSmoke package. Source code is available on GitHub. Here is the package description: Utilities for working with air quality monitoring data with a focus on small particulates (PM2.5) generated by wildfire smoke. Functions are provided for …   read more ... [Read more...]

This entry is part 20 of 20 in the series Using RWe are increasingly using R in “operational” settings that require robust error handling and logging. In this post we describe a quick-and-dirty way to get python style multi-level log files …   read more ... [Read more...]

This entry is part 19 of 19 in the series Using RLetting 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 …   read more ... [Read more...]

This entry is part 18 of 18 in the series Using RSeattle’s Pronto bikeshare system recently announced a Data Challenge for data visualization using their first year of trip data. As avid cyclists and data analysis junkies, we of course took …   read more ... [Read more...]

This entry is part 17 of 17 in the series Using RSometimes it is useful to write a wrapper function for an existing function. In this short example we demonstrate how to grab the list of arguments passed to a …   read more ... [Read more...]

This entry is part 15 of 15 in the series Using RMazama Science has just released its first package on CRAN — MazamaSpatialUtils. Here is the description: A suite of conversion scripts to create internally standardized spatial polygons dataframes. Utility …   read more ... [Read more...]

This entry is part 14 of 14 in the series Using RMazama Science has just finished creating class materials on using R for the AirFire team at the USFS Pacific Wildland Fire Sciences Lab in Seattle, Washington. This team of …   read more ... [Read more...]

This entry is part 13 of 13 in the series Using RAt Mazama Science we produce web based tools for interrogating important datasets. We are proud to announce the release of a new Population databrowser that allows users to review …   read more ... [Read more...]

This entry is part 3 of 12 in the series Using RThe post titled Installing Packages described the basics of package installation with R.  The process is wonderfully simple when everything goes well.  But it can be maddening when it …   read more ... [Read more...]

This entry is part 14 of 12 in the series Using RYes, this post condenses 50+ hours of learning into a 15 minute tutorial.  Read ‘em and weep.  (That is, you read while I weep.) OK.  For the last week …   read more ... [Read more...]

This entry is part 12 of 12 in the series Using RIn two previous posts we described how R can call C code with .C() and the more complex yet more robust option of calling C code with .Call().  Here …   read more ... [Read more...]

This entry is part 10 of 12 in the series Using RIn an introductory post on R APIs to C code, Calling C Code ‘Hello World!’, we explored the .C() function with some ‘Hello World!’ baby steps.  In this post …   read more ... [Read more...]

This entry is part 6 of 11 in the series Using ROne 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 …   read more ... [Read more...]

To many people, R is like the Everglades. They’ve heard of it, they know it’s big and has amazing treasures deep inside. Articles in the media can make it look irresistible. But after a personal or even second hand experience …  &#16... [Read more...]

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 …   read mo... [Read more...]

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, …   read mo... [Read more...]

Open-source R is an amazing tool for statistical analysis and data visualization. Serious R gurus have found ways to do just about anything entirely within the R environment. Nevertheless, there are many of us who wish to plug R into …  &#16... [Read more...]

One of the reasons to use R for analysis and visualization is the rich ecosystem of ‘packages’ contributed by others. In most cases, just as with smartphones, “There’s a package for that.” If you want to be efficient you n... [Read more...]