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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.

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

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Use the neonUtilities Package to Access NEON Data

20 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.
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Work with NEON OS & IS Data - Plant Phenology & Temperature

3 part series
Learn to work with NEON plant phenology (Observational System) and single aspirated air temperature (Instrumented System) data products.
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Document Your Code with R Markdown

3 part series
This series teaches you to use the R Markdown file format to document code and efficiently publish code results & outputs.
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Introduction to Working With Time Series Data in Text Formats in R

8 part series
The tutorials in this series cover how to open, work with and plot with phenology-related micrometeorological data in R. Additional topics include working with time and date classes (e.g., POSIXct, POSIXlt, and Date), subsetting time series data by date and time and created facetted or tiles sets of plots.
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Working With Raster Time Series Data in R

3 part series
Creation supported by NEON, Data Carpentry, SESYNC, and iPlant Collaborative
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.
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Intro to Vector Data in R

6 part series
Creation supported by NEON, Data Carpentry, SESYNC, and iPlant Collaborative
The data tutorials in this series cover how to open, work with and plot with vector-format spatial data (points, lines and polygons) in R. Additional, topics include working with spatial metadata (extent and coordinate reference system), working with spatial attributes and plotting data by attributes.
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