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.

Code for all script based tutorials can be downloaded at the end of the tutorial. Original files can also be found on GitHub.

All materials are freely available for you to use and reuse. We suggest the following citation for tutorials:  [AUTHOR(S), NEON (National Ecological Observatory Network)]. Data Tutorial: [TUTORIAL NAME]. [URL] (accessed [DATE OF ACCESS]). See Citation Guidelines for examples, and for guidance in citing data and code.

Tutorials

Time Series 05: Plot Time Series with ggplot2 in R

30 minutes

This tutorial uses ggplot2 to create customized plots of time series data. We will learn how to adjust x- and y-axis ticks using the scales package, how to add trend lines to a scatter plot and how to customize plot labels, colors and overall theme.

Time Series 06: Create Plots with Multiple Panels, Grouped by Time Using ggplot Facets

30 minutes

This tutorial covers how to plot subsetted time series data (e.g., plot by season) using facets() and ggplot2. It also covers how to plot multiple metrics in one display panel.

Time Series Culmination Activity: Plot using Facets & Plot NDVI with Time Series Data

30 minutes

This tutorial is a data integration wrap-up culmination activity for the spatio-temporal time series tutorials.

Unsupervised Spectral Classification in Python: Endmember Extraction

1 hour

Learn to classify spectral data using Endmember Extraction, Spectral Information Divergence, and Spectral Angle Mapping.

Unsupervised Spectral Classification in Python: KMeans & PCA

1 hour

Learn to classify spectral data using KMeans and Principal Components Analysis (PCA).

Use the neonUtilities Package to Access NEON Data

40 minutes

Use the neonUtilities R package to download data, and to convert downloaded data from zipped month-by-site files into a table with all data of interest. Temperature data are used as an example.

Using an API Token when Accessing NEON Data with neonUtilities

20 minutes

Get an API token tied to your NEON user account, and use it for faster download speeds when accessing NEON data via the neonUtilities package.

Using neonUtilities in Python

0.5 hour

Use the neonUtilities R package in Python, via the rpy2 library.

Using the NEON API in R

1 - 1.5 hours

Tutorial for getting data from the NEON API, using R and the R package httr

Using the neonstore Package to Download and Store NEON Data

40 minutes

Download data using the neonstore package to maintain a reproducible local archive for your analyses, and stack from the local data using the stackFromStore() function in neonUtilities.

Vector 00: Open and Plot Shapefiles in R - Getting Started with Point, Line and Polygon Vector Data

30 minutes

This spatial data tutorial explains the how to open and plot shapefiles containing point, line and polygon vector data in R.

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

30 minutes

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.

Vector 02: Plot Multiple Shapefiles and Create Custom Legends in Base Plot in R

30 minutes

This tutorial provides an overview of how to create a a plot of multiple shapefiles using base R plot. It also explores adding a legend with custom symbols that match your plot colors and symbols.

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

30 minutes

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.

Vector 04: Convert from .csv to a Shapefile in R

45 minutes

This tutorial covers how to convert a .csv file that contains spatial coordinate information into a spatial object in R. We will then export the spatial object as a Shapefile for efficient import into R and other GUI GIS applications including QGIS and ArcGIS

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

1 hour

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.

Version Control with GitHub

This series teaches why version control is important and how to use a common version control tool, GitHub. GitHub also allows for collaboration within the environment.

Series

7 part series

What is a CHM, DSM and DTM? About Gridded, Raster LiDAR Data

0.25 - 0.5 Hours

Understand LiDAR data product formats and learn the basics of how a LiDAR data are processed.

Work with NEON OS & IS Data - Plant Phenology & Temperature

Learn to work with NEON plant phenology (Observational System) and single aspirated air temperature (Instrumented System) data products.

Series

3 part series

Work With NEON's Plant Phenology Data

1 hour

Learn to work with NEON plant phenology observation data (NEON.DP1.10055).

Work with NEON's Single-Aspirated Air Temperature Data

1 hour

This tutorial demonstrates how to work with NEON single-aspirated air temperature data. Specific tasks include conversion to POSIX date/time class, subsetting by date, and plotting the data.

Working With Raster Time Series Data in R

This series covers how to open, work with and plot with multi-band raster data and raster time series data in R using both plot and rasterVis levelPlot.

Series

3 part series

Working With Time Series Data Within a Nested HDF5 File in R

1.0 - 1.5 Hours

Explore, extract and visualize temporal temperature data collected from a NEON flux tower from multiple sites and sensors in R. Learn how to extract metadata and how to use nested loops and dplyr to perform more advanced queries and data manipulation.