Publications by Jonathan Callahan
Using R — Package Installation Problems
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 does not. Error messages give a hint as to what went wrong but do not necessarily tell you how to resolve the pr...
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Population Databrowser
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 international population trends. We have done our best to make this release Open, Transparent and Reproducible by prov...
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R Class for Wildfire Scientists
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 scientists works on monitoring and modeling wildfire emissions, smoke and air quality. The AirFire team has granted permissio...
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MazamaSpatialUtils Package
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 scripts use these datasets to return values such as country, state, timezone, watershed, e...
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Function Argument Lists and missing()
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 function and use it to call another function, taking care of optional arguments with or without default values. Authors of R functions ofte...
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Visualizing Bikeshare Data
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 the bait. Below is a brief description of our Pronto Databrowser submission. At Mazama Science we focus ...
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When k-means Clustering Fails
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 for clustering data is k-means clustering. Unfortunately, k-means clustering can fail spectacularly as in the exa...
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Python style logging in R
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 by wrapping the futile.logger package. Making Messy Data Pretty Our real world scenario involves R sc...
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Introducing the PWFSLSmoke Package
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 downloading available data from the United States Environmental Pr...
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Improved Python-style Logging in R
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 script. Today, we highlight an improved, documented version that can be sourced by your R scripts or dropped into your...
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