R Programming Language – Four Part Webinar Series

Introduction to RTuesday April 7, 20202:00 – 3:30 PM EST
TIDYVERSEThursday, April 9, 20202:00 – 3:30 PM EST
OPENAIRThursday, April 16, 20203:30 – 5:00 PM EST
GGPLOTThursday, April 23, 20203:30 – 5:00 PM EST

MARAMA will be hosting an R for Air Quality Applications Webinar Series over four weeks in April.

These webinars will offer students an introduction to the R Programming Language for air quality purposes. R is a free, open source language that enables users to automate workflows, handle much larger files than excel, and expand their capabilities when it comes to data analysis and visualizations. This training is geared towards state, local, and tribal air quality agency employees with little or no experience using R or programming.

There will be four separate webinars and participants are encouraged to attend whichever webinars they would find the most helpful. Participants will receive a certificate of participation for every webinar they attend. These webinars will be co-taught by Shane Cone (DE DNREC DAQ), Keith Hoffman (DE DNREC DAQ), and myself. Participants who are new to R, coding, or both should attend the first webinar as it provides necessary background knowledge for the following webinars. All software used in this webinar is free and must be installed prior to the webinar. Each webinar is described below:

Tuesday, April 7, 2020 | 2:00 – 3:30 EDT

This webinar will get you started coding in R in the RStudio environment. You’ll learn basic terminology and concepts that are essential for any future work in R, you’ll get some free, online resources to support future, independent learning, and you’ll get a quick glimpse at the topics in the following webinars to help you gauge your interest.

Thursday, April 9, 2020 | 2:00 – 3:30 EDT

The tidyverse is a set of tools in R that can be used to automate data preparation and analysis and streamline your workflow. It is based on the philosophy of “Tidy Data.”

Thursday, April 16, 2020 | 3:30 – 5:00 EDT

The OpenAir package in R enables the user to create a wide array of data visualizations and analyses for air quality monitoring data.

Thursday, April 23, 2020 | 3:30 – 5:00 EDT

The ggplot package is used to easily generate a wide variety of highly customizable data visualizations.

Instructors for the R webinar series:
Jenny St. Clair is an Environmental Data Specialist at MARAMA (Mid-Atlantic Regional Air Management Association, Inc.). She has a B.S. and an M.A. in Geography and Environmental Planning from Towson University. In her master’s thesis, she focused on using the R Programming Language and GIS tools such as ESRI’s ArcPy to better understand how spatial distributions of tornado-favorable environments might have changed in recent decades. She also enjoys hiking with her dog, Bilbo.

Shane Cone is an Environmental Scientist with the State of Delaware Division of Air Quality. has a bachelor’s degree in Earth Science, and spent a year in Graduate School in the niche field of geomicrobiology, where he first worked with big datasets of bioinformatics data. His Excel skills, honed by modeling groundwater flow and modeling fisheries in undergrad, were challenged by the size and complexity of big data. He has spent the last year and a half completing coursework for the Microsoft Professional Program in Data Science, which he will complete in 2019, with a goal of writing code 5 days a week (even if it’s only one line).

Keith Hoffman is also an Environmental Scientist with the State of Delaware Division of Air Quality. He has worked as a monitoring station operator, special projects coordinator, data analyst, auditor, and is currently training to take over as Quality Assurance Coordinator. Keith has a B.S. degree in Biology and a Minor in Music (Oboe) from Shenandoah University. He received his M.S. in Biology from Western Carolina University, where he used R and GIS to analyze and map the changing morphology of river cane for his thesis. He also enjoys music and visiting Sate/National Parks to practice nature photography.