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

Resources for Learning R

20 minutes

A brief overview of available resource to get started learning R.

Select pixels and compare spectral signatures in R

0.5 Hours

Plot and comapre the spectral signatures of multiple different land cover types using an interactive click-to-extract interface to select pixels.

Set up GitHub Working Directory - Quick Intro to Bash

1.0 - 1.5 Hours

This page reviews how to check that github is installed on your computer. It also provides a quick overview of Bash shell. Finally, we will setup a working GitHub directory.

Spatial Data Tutorial Series Capstone Challenges

0.5 - 1.0 Hours

This page contains capstone activities that complement several spatial data tutorial series.

Subsetting NEON HDF5 hyperspectral files to reduce file size

1.0 Hour

Take a large NEON hyperspectral HDF5 file and extract only the information that you need, then save as a new HDF5 file. For an example, we will take an existing hyperspectral dataset (~600Mb) and reduce it in size for subsequent tutorials.

The Basics of LiDAR - Light Detection and Ranging - Remote Sensing

0.25 Hours

Explore the basics of how a LiDAR system works and what a LiDAR system measures.

The Importance of Reproducible Science

1.0 Hour

This page outlines the tools and resources that you will need to complete the Data Institute activities.

The Relationship Between Raster Resolution, Spatial Extent & Number of Pixels

1 hour

Learn about the key attributes needed to work with raster data in non-GUI programs. Examples in R.

Time Series 00: Intro to Time Series Data in R - Managing Date/Time Formats

30 minutes

This tutorial will demonstrate how to import a time series dataset stored

Time Series 01: Why Metadata Are Important: How to Work with Metadata in Text & EML Format

30 minutes

This tutorial covers what metadata are, and why we need to work with metadata. It covers the 3 most common metadata formats: text file format, web page format and Ecological Metadata Language (EML).

Time Series 02: Dealing With Dates & Times in R - as.Date, POSIXct, POSIXlt

30 minutes

This tutorial explores working with date and time classes in R. We will

Time Series 03: Cleaning & Subsetting Time Series Data in R - NoData Values & Subset by Date

30 minutes

This tutorial explores how to deal with NoData values encountered in a time series dataset, in R. It also covers how to subset large data files by date and export the results to a csv (text format) file.

Time Series 04: Subset and Manipulate Time Series Data with dplyr

30 minutes

In this tutorial, we will use the group_by, summarize and mutate functions in the `dplyr` package to efficiently manipulate atmospheric data collected at the NEON Harvard Forest Field Site. We will use pipes to efficiently perform multiple tasks within a single chunk of code.

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 neonOS Package to Check for Duplicates and Join Tables

40 minutes

Use the functions in the neonOS package to process NEON observational data: check for duplicate data records and join data tables.

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.