Spatio-Temporal Workshop

April 12, 2016 - April 13, 2016
NEON & Data Carpentry

This two day workshop, taught at the USGS National Training Center at the Federal Center in Denver, CO on April 12-13, 2016, will cover how to work with spatio-temporal data in R.

Things You’ll Need To For The Workshop

Software

R & RStudio

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

If you already have these installed on your laptop, please be sure that you are running the most current version of RStudio, R and all packages that we'll be using in the workshop (listed below).

For details on installing RStudio in Mac, PC, or Linux operating systems, please see the Additional Set Up Instructions section at the bottom of this page.

Bash shell

You will need bash shell to complete this workshop. If you use a Mac, this is the default shell ("Terminal") on your computer. If you use a PC or Linux operating system you may need to install it. For complete installation instructions, please see the Additional Set Up Instructions section below.

Download Data

To be prepared for this workshop, please download & unzip the following files in advance:

Download NEON Teaching Data Subset: Site Layout Shapefiles

These vector data provide information on the site characterization and infrastructure at the National Ecological Observatory Network's Harvard Forest field site. The Harvard Forest shapefiles are from the Harvard Forest GIS & Map archives. US Country and State Boundary layers are from the US Census Bureau.

Download NEON Teaching Data Subset: Airborne Remote Sensing Data

The LiDAR and imagery data used to create this raster teaching data subset were collected over the National Ecological Observatory Network's Harvard Forest and San Joaquin Experimental Range field sites and processed at NEON headquarters. The entire dataset can be accessed by request from the NEON Data Portal.

Download NEON Teaching Data Subset: Landsat-derived NDVI raster files

The imagery data used to create this raster teaching data subset were collected over the National Ecological Observatory Network's Harvard Forest and San Joaquin Experimental Range field sites.
The imagery was created by the U.S. Geological Survey (USGS) using a multispectral scanner on a Landsat Satellite. The data files are Geographic Tagged Image-File Format (GeoTIFF).

Optional: you can download the global boundary files below if you wish to follow along with the data management demonstration on Coordinate Reference Systems.

So that your code will mirror that of the instructors, your R working directory should be set to a parent directory data that contains all of the above data files (unzipped). For complete directions, please see the Additional Set Up Instructions section below.

Install R Packages

You can chose to install each library individually if you already have some installed.

  • raster: install.packages("raster")
  • rasterVis: install.packages("rasterVis")
  • ggplot2: install.packages("ggplot2")
  • sp: install.packages("sp")
  • rgeos install.packages("rgeos")
  • rgdal (windows): install.packages("rgdal")
  • rgdal (mac): install.packages("rgdal",configure.args="--with-proj-include=/Library/Frameworks/PROJ.framework/unix/include --with-gdal-config=/Library/Frameworks/GDAL.framework/unix/bin/gdal-config --with-proj-lib=/Library/Frameworks/PROJ.framework/unix/lib")

Optional installation If you want to work through the metadata lesson which includes a section on the Ecological Metadata Language (EML), please install the following:

  • devtools: install.packages("devtools")
  • eml install_github("ropensci/EML", build=FALSE, dependencies=c("DEPENDS", "IMPORTS")) NOTE: You have to run the devtools library library(devtools) first, and then install_github will work the EML package is under development which is why the install occurs from GitHub and not can!

Update R Packages

In RStudio, you can go to Tools --> Check for package updates to update already installed libraries on your computer! Or, you can use update.packages() to update all packages that are installed in R automatically.

Workshop Instructors

  • Leah Wasser; @leahawasser; Supervising Scientist, NEON
  • Megan A. Jones; @meganahjones; Staff Scientists/Science Educator, NEON
  • Natalie Robinson; Staff Scientist, NEON

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

Twitter?

Please tweet @NEON_sci and with the #WorkWithData hashtag during the workshop.

Schedule

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

Day One

Time Topic
8:00 Please come early if you have any setup or installation problems
9:00 Geospatial Data Management - Intro to Geospatial Concepts
10:30 --------- BREAK ---------
10:45 Data Management: Coordinate Reference Systems
12:00 --------- Lunch ---------
1:00 Vector Data in R
2:30 --------- BREAK ---------
2:45 Vector Data in R, continued
4:15 Wrap-Up Day 1

Day Two

Time Topic
9:00 Questions From Day One
9:15 Getting Started with Raster Data in R
10:30 --------- BREAK ---------
10:45 Getting Started with Raster Data in R, continued
12:00 --------- Lunch ---------
1:00 Multi-band Raster & Raster Time Series Data in R
2:30 --------- BREAK ---------
2:45 Multi-band Raster & Raster Time Series Data in R, continued
4:15 Wrap-Up Day Two!

Additional Set Up Resources

Setting Up R & RStudio

Windows R/RStudio Setup

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

  • If your Mac is set up for UiO use, you can install R from Managed Software Center

  • 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 10.6+ (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 RStudi, 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.

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h2 id="set-wd">Set Working Directory to Downloaded Data

1) Download Data

After clicking on the Download Data button, the data will automatically download to the computer.

2) Locate .zip file

Second, find the downloaded .zip file. Many browsers save downloaded files to your computer’s Downloads directory. If you have previously specified a specific directory (folder) for downloaded files, the .zip file will download there.

3) Move to **data** directory

Third, move the downloaded file to a directory called data within the Documents directory on your computer. You can choose to place the data in other locations, however, you will need to remember to set your R Working Directory to that location and not as we demonstrate in the workshop.

4) Unzip/uncompress

Fourth, we need to unzip/uncompress the file so that the data files can be accessed. Use your favorite tool that can unpackage/open .zip files (e.g., winzip, Archive Utility, etc). The files will now be accessible in three directories:

These directories contain all of the subdirectories and files that we will use in this workshop.

5) Set working directory

Fifth, we need to set the working directory in R to this data directory that is parent to the directories containing the data we want. For complete directions, on how to do that check out the Set A Working Directory in R tutorial.

GDAL installation for MAC

You may need to install GDAL in order for rgdal to work properly. Click here to watch a video on installing gdal using homebrew on your Mac. Or, you can visit this link to install GDAL 1.11 complete.

Day One

Time Topic
8:00 Please come early if you have any setup or installation problems
9:00 Geospatial Data Management - Intro to Geospatial Concepts
Answer a Spatio-temporal Research Question with Data - Where to Start?
Data Management: Spatial Data Formats
Data about Data -- Intro to Metadata Formats & Structure
10:30 --------- BREAK ---------
10:45 Data Management: Coordinate Reference Systems
12:00 - 1:00 --------- Lunch ---------
1:00 Vector Data in R
Open and Plot Shapefiles in R - Getting Started with Point, Line and Polygon Vector Data
Explore Shapefile Attributes & Plot Shapefile Objects by Attribute Value in R
Plot Multiple Shapefiles and Create Custom Legends in Base Plot in R
2:30 --------- BREAK ---------
2:45 Vector Data in R, continued
When Vector Data Don't Line Up - Handling Spatial Projection & CRS in R
Convert from .csv to a Shapefile in R
4:15 Wrap-Up Day 1

Series: Intro to Vector Data in R

You can choose to do this optional data activity (opens in a new window), or continue to the next lesson plan.

Day Two

Time Topic
9:00 Questions From Day One
9:15 Getting Started with Raster Data in R
Intro to Raster Data in R
Plot Raster Data in R
10:30 --------- BREAK ---------
10:45 Getting Started with Raster Data in R, continued
When Rasters Don't Line Up - Reproject Raster Data in R
Raster Calculations in R - Subtract One Raster from Another and Extract Pixel Values For Defined Locations
Crop Raster Data and Extract Summary Pixels Values From Rasters in R
12:00 - 1:00 --------- Lunch ---------
1:00 Multi-band Raster & Raster Time Series Data in R
Work With Multi-Band Rasters - Image Data in R
Raster Time Series Data in R
Plot Raster Time Series Data in R Using RasterVis and Levelplot
Extract NDVI Summary Values from a Raster Time Series
2:30 --------- BREAK ---------
2:45 Multi-band Raster & Raster Time Series Data in R, continued
4:15 Wrap-Up Day Two!

Series: Introduction to Working with Raster Data in R

You can choose to do this optional data activity (opens in a new window), or continue to the next lesson plan.

Tutorial: Vector 05: Crop Raster Data and Extract Summary Pixels Values From Rasters in R

You can choose to do this optional data activity (opens in a new window), or continue to the next lesson plan.

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