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.


Raster 03: Raster Calculations in R - Subtract One Raster from Another and Extract Pixel Values For Defined Locations

This tutorial covers how to subtract one raster from another using efficient methods - the overlay function compared to basic subtraction. We also cover how to extract pixel values from a set of locations - for example a buffer region around plot locations at a field site. Finally, it explains the basic principles of writing functions in R.

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.

Vector 01: Explore Shapefile Attributes & Plot Shapefile Objects by Attribute Value in R

This tutorial provides an overview of how to locate and query shapefile attributes as well as subset shapefiles by specific attribute values in R. It also covers plotting multiple shapefiles by attribute and building a custom plot legend.

Build & Work With Functions in R

This tutorial teaches the basics of creating a function in R.

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.


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