Access and Work with NEON Data – ESA 2019

NEON provides open ecological data from over 80 locations across the United States. NEON data cover a wide range of subject areas within ecology, including organismal observations, biogeochemistry, remote sensing, and micrometeorology. Some datasets already span several years of data collection.

This workshop will provide an introduction to discovering, accessing and preparing a variety of NEON data for your research, primarily using R. The workshop will be divided into two sections of roughly equal length.

1. The workshop will begin will be a code-along guide to NEON data, including
   a. discovering and accessing NEON data, via the data portal and the Application Programming Interface (API),
   b. understanding the contents and quality of various data packages, and
   c. performing common data merges, summarizations, and transformations.
   Instruction will include guidance in using R packages, including some that have been developed by NEON.
2. In the final portion, participants will work with NEON data of their choice, while instructors circulate to answer questions about data and coding. Participants may break into groups based on the data or analyses they wish to explore. For participants who attend the workshop without a specific dataset or analysis of interest, a guided project will be available, analyzing data accessed in the first half of the workshop.

Required Prior Knowledge

The workshop will assume that participants have a basic level of familiarity with working with data in R, including installing and loading packages, and data import.

This 5-hr workshop will be taught at the 2019 meeting of the Ecological Society of America (ESA) in Louisville, KY. You must be a registered attendee of the conference and register for this workshop with your conference registration to participate in this workshop. For more information, visit the Joint ESA/USSEE 201 annual meeting website.

Workshop Schedule

Location: Kentucky International Convention Center - L012 Please double check the conference schedule as rooms can change!

Please note that the schedule listed below may change depending upon the pace of the workshop!

Workshop Instructors & Helpers

• Claire Lunch; @dr_lunch, Research Scientist, Data Science; NEON program, Battelle
• Megan A. Jones; @meganahjones, Research Scientist, Science Education; NEON program, Battelle
• Christine Laney; Research Scientist, Data Science/Ecoinformatics; NEON program, Battelle
• Natalie Robinson; Research Scientist, Data Science/Ecology/GIS; NEON program, Battelle
• Cody Flagg; Research Scientist; NEON program, Battelle

Please get in touch with the instructors prior to the workshop with any questions.

Twitter?

Please tweet using the hashtag #NEONData & @NEON_Sci during this workshop!

Before the Workshop

To participant in this workshop, you will need a laptop with the most current version of R, and preferably RStudio, loaded on your computer.

Prior to the workshop you should have R and, preferably, RStudio installed on your computer.

Setting Up R & RStudio

Windows R/RStudio Setup

• Download R for Windows here
• Run the .exe file that was just downloaded
• Go to the RStudio Download page
• Under Installers select RStudio X.XX.XXX - Windows Vista/7/8/10
• Double click the file to install it

Once R and RStudio are installed, click to open RStudio. If you don't get any error messages you are set. If there is an error message, you will need to re-install the program.

Mac R/RStudio Setup

• Go to CRAN and click on Download R for (Mac) OS X
• Select the .pkg file for the version of OS X that you have and the file will download.
• Double click on the file that was downloaded and R will install
• Go to the RStudio Download page
• Under Installers select RStudio 0.98.1103 - Mac OS X XX.X (64-bit) to download it.
• Once it's downloaded, double click the file to install it

Once R and RStudio are installed, click to open RStudio. If you don't get any error messages you are set. If there is an error message, you will need to re-install the program.

Linux R/RStudio Setup

• R is available through most Linux package managers. You can download the binary files for your distribution from CRAN. Or you can use your package manager (e.g. for Debian/Ubuntu run sudo apt-get install r-base and for Fedora run sudo yum install R).
• To install RStudio, go to the RStudio Download page
• Under Installers select the version for your distribution.
• Once it's downloaded, double click the file to install it

Once R and RStudio are installed, click to open RStudio. If you don't get any error messages you are set. If there is an error message, you will need to re-install the program.

Install R Packages

Please have these packages installed and updated prior to the start of the workshop. The last two are optional if you want to do the Compare Canopy Height exercise or the NEON API tutorial.

• raster: install.packages("raster"); to work with raster files in R.
• rhdf5: install.packages("BiocManager"), BiocManager::install("rhdf5"); to work with HDF5 files in R
• neonUtilities: install.packages("neonUtilities"); neonUtiltities only works with R version 3.4 or higher. For further directions, see the start of the Use the neonUtilities Package to Access NEON Data tutorial.
• devtools: install.packages("devtools"); required to install using the install_github() function.
• geoNEON: install_github("NEONScience/NEON-geolocation/geoNEON”); to get specific location data for NEON data and samples. Optional for workshop.

If you want to install all of them, you can use the following code.

 install.packages("BiocManager")
 BiocManager::install("rhdf5")
 install.packages("raster")
 install.packages("neonUtilities")
 #optional, if wanting to do Compare Canopy Height exercise or NEON API tutorial
 install.packages("devtools")
 library(devtools)
 install_github("NEONScience/NEON-geolocation/geoNEON")

Update R Packages

In RStudio, you can go to Tools --> Check for package updates to update previously installed packages on your computer.

Or you can use update.packages() to update all packages that are installed in R automatically.

More on Packages in R