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Large Datasets in R - Plant Phenology & Temperature Data from NEON

By Megan A. Jones1, Lee F. Stanish2, Natalie Robinson2, Katherine D. Jones2, Cody Flagg2

1. National Ecological Observatory Network 2. National Ecological Observatory Network – Battelle

This module series covers how to import, manipulate, format and plot time series data stored in .csv format in R. Originally designed to teach researchers to use NEON plant phenology and air temperature data; has been used in undergraduate...

Listed in Teaching Materials | resource by group NEON - National Ecological Observatory Network

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Version 1.0 - published on 10 May 2018 doi:10.25334/Q4DQ3F - cite this

Licensed under CC Attribution 4.0 International according to these terms



This lesson series covers how to import, manipulate, format and plot time series data stored in .csv format in R. The lessons use temperature and plant phenology data to explore working with and visualizing data with different time scale intervals.  Originally designed to teach researchers to use NEON plant phenology (NEON.DP1.10055) and air temperature (NEON.DP1.00002) data, this series of three lessons provided detailed directions for working with large datasets in R.  Other instructors have adapted/implemented this lesson series in classrooms (see Forks). 

This series contains three lessons that can be used together or separately (although the third builds on the first two): 

  1. Work With NEON's Plant Phenology Data – Many organisms, including plants, show patterns of change across seasons - the different stages of this observable change are called phenophases. In this tutorial, we explore how to work with NEON plant phenophase data.
  2. Work with NEON's Single-Aspirated Air Temperature Data – In this tutorial, we explore the NEON single-aspirated air temperature data. Focus in on how to interpret the variables, how to work with date-time and date formats, and how to plot the data.
  3. Plot Continuous & Discrete Data Together – When working with data collected at different intervals, visualizing the data together can be a challenge. This tutorial discusses ways to plot plant phenology (discrete time series) and single-aspirated temperature (continuous time series) together.

Learning Objectives:  After completing the series, students will be able to:

  • work with data.frames in R (dplyr package),
  • convert timestamps stored as text strings to R date or datetime (e.g. POSIX) classes (lubridate package),
  • aggregate data across different time scales (day vs month) and
  • plot time series data (ggplot2 package).

Prerequisites: Students should have a basic understanding of R prior to starting this lesson.

Faculty Notes: As this series was designed for self-guided learning there are not a seperate set of faculty notes, however, the webpages provide context around the code being taught. Faculty may choose to implement this series in different ways depending on their desired goals, student familiarity with R, and classroom set up.

The series contains a downloadable teaching dataset that includes NEON plant phenology and air temperature data that have already been combined across months and field sites by data table.  These data are still "messy" (e.g., have gone through standard NEON QA/QC but have not been further manipulated as a teaching dataset) and are ready to use for the lessons.  

If a goal is to show how students can access public data from a large network, like NEON, faculty may want to start the lesson with a guided navigation through the portal where students will access and download their own phenology and/or temperature data set. If this option is selected, faculty should also include instruction on the neonUtilities package to stack (combine) the data. 

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