PWFSLSmoke 1.0: Visualizing Wildfire Smoke Data

Posted on January 17, 2018 by Helen Miller

Mazama Science has released the first official version (1.0) of the PWFSLSmoke R package for working with PM2.5 monitoring data. A beta version was released last year, along with an accompanying blog post. In this post, we discuss the purpose and uses of the PWFSLSmoke package and demonstrate some of the core functionality.   read more …

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Improved Python-style Logging in R

Posted on March 15, 2017 by Jonathan Callahan

Last 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 package’s R/ directory to provide easy file and console logging.

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Introducing the PWFSLSmoke Package

Posted on March 12, 2017 by Jonathan Callahan

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 Protection Agency (US EPA) and it’s AirNow air quality site. Additional sources of PM2.5 data made accessible by the package include: AIRSIS (password protected), the Western Regional Climate Center (WRCC) and the open source site OpenAQ.

In this post we discuss the reasons for creating this package and provide examples of its use.   read more …

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Python-style Logging in R

Posted on August 24, 2016 by Jonathan Callahan

We 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.

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When k-means Clustering Fails

Posted on February 3, 2016 by Jonathan Callahan

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.   read more …

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