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

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

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

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

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 00: Intro to Time Series Data in R - Managing Date/Time Formats & Simple Plots using ggplot2

This tutorial will demonstrate how to import a time series dataset stored in .csv format into R. It will explore data classes and will walk through how to convert date data, stored as a character string, into a date class that R can recognize and plot efficiently.

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

This tutorial explores working with date and time classes in R. We will overview the differences between As.Date, POSIXct and POSIXlt as used to convert a date/time field in character (string) format to a date-time format that is recognized by R. This conversion supports efficient plotting, subsetting and analysis of time series data.

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