Data Tutorials

Looking to improve your data skills using tools like R or Python? Want to learn more about working with a specific NEON data product? NEON develops online tutorials to help you improve your research. These self-paced tutorials are designed for you to used as standalone help on a single topic or as a series to learn new techniques.

Please note this section is currently under development, to explore additional tutorials in the interim, visit our older NEON Data Skills portal.

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

This tutorial covers how to modify (crop) a raster extent using the extent of a vector shapefile. It also covers extracting pixel values from defined locations stored in a spatial object.

Raster 02: When Rasters Don't Line Up - Reproject Raster Data in R

This tutorial explores issues associated with working with rasters in different Coordinate Reference Systems (CRS) & projections. When two rasters are in different CRS, they will not plot nicely together on a map. We will learn how to reproject a raster in R using the projectRaster function in the raster package.

Vector 03: When Vector Data Don't Line Up - Handling Spatial Projection & CRS in R

This tutorial will cover how to identify the CRS of a spatial vector object in R. It will also explore differences in units associated with different projections and how to reproject data using spTransform in R. Spatial data need to be in the same projection in order to successfully map and process them in non-gui tools such as R.

Raster 01: Plot Raster Data in R

This tutorial explains how to plot a raster in R using R's base plot function. It also covers how to layer a raster on top of a hillshade to produce an eloquent map.

Raster 00: Intro to Raster Data in R

This tutorial reviews the fundamental principles, packages and metadata/raster attributes that are needed to work with raster data in R. It covers the three core metadata elements that we need to understand to work with rasters in R: CRS, Extent and Resolution. It also explores missing and bad data values as stored in a raster and how R handles these elements. Finally, it introduces the GeoTiff file format.

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