Use the neonUtilities Package to Access NEON Data

Megan A. Jones, Claire K. Lunch
Table of Contents

This tutorial goes over how to use the neonUtilities R package (formerly the neonDataStackR package).

The package contains 5 functions:

  • stackByTable() Takes zip files downloaded from the Data Portal or downloaded by zipsByProduct(), unzips them, and joins the monthly files by data table to create a single file per table.
  • zipsByProduct() A wrapper for the NEON API; downloads data based on data product and site criteria. Stores downloaded data in a format that can then be joined by stackByTable().
  • getPackage() A wrapper for the NEON API; downloads one site-by-month zip file at a time.
  • byFileAOP() A wrapper for the NEON API; downloads remote sensing data based on data product, site, and year criteria. Preserves the file structure of the original data.
  • transformFileToGeoCSV() Converts any NEON data file in csv format into a new file with GeoCSV headers.

If you are only interested in joining data files downloaded from the NEON Data Portal, you will only need to use stackByTable(). Follow the instructions in the first two sections, to install neonUtilities and use stackByTable(), and you're done.

neonUtilities package

This package is intended to provide a toolbox of basic functionality for working with NEON data. It currently contains the functions listed above, but it is under development and more will be added in the future.

For more information on the package see the README in the associated GitHub repo NEONScience/NEON-utilities. To report bugs or request new features, post an issue in the GitHub repo issues page.

First, we must install the neonUtilities package from the GitHub repo. You must have the devtools package installed and loaded to do this.

# install devtools - can skip if already installed

## Installing package into '/Users/clunch/Library/R/3.4/library'
## (as 'lib' is unspecified)

## Warning in install.packages :
##   cannot open URL '': HTTP status was '404 Not Found'
## The downloaded binary packages are in
##  /var/folders/_k/gbjn452j1h3fk7880d5ppkx1_9xf6m/T//RtmpXvpkNh/downloaded_packages

# load devtools

# install neonUtilities from GitHub
install_github("NEONScience/NEON-utilities/neonUtilities", dependencies=TRUE)

## Downloading GitHub repo NEONScience/NEON-utilities@master
## from URL

## Installing neonUtilities

## '/Library/Frameworks/R.framework/Resources/bin/R' --no-site-file  \
##   --no-environ --no-save --no-restore --quiet CMD INSTALL  \
##   '/private/var/folders/_k/gbjn452j1h3fk7880d5ppkx1_9xf6m/T/RtmpXvpkNh/devtools33066acfe00f/NEONScience-NEON-utilities-a649bc8/neonUtilities'  \
##   --library='/Users/clunch/Library/R/3.4/library' --install-tests


# load neonUtilities
library (neonUtilities)

Join data files: stackByTable()

The function stackByTable() joins the month-by-site files from a data download. The output will yield data grouped into new files by table name. For example, the single aspirated air temperature data product contains 1 minute and 30 minute interval data. The output from this function is one .csv with 1 minute data and one .csv with 30 minute data.

Depending on your file size this function may run for a while. For example, in testing for this tutorial, 124 MB of temperature data took about 4 minutes to stack. A progress bar will display while the stacking is in progress.

Download the Data

To stack data from the Portal, first download the data of interest from the NEON Data Portal. To stack data downloaded from the API, see the zipsByProduct() section below.

Your data will download from the Portal in a single zipped file.

The stacking function will only work on zipped Comma Separated Value (.csv) files and not the NEON data stored in other formats (HDF5, etc).

Run stackByTable()

The example data below are single-aspirated air temperature.

To run the stackByTable() function, input the file path to the downloaded and zipped file.

# stack files - Mac OSX file path shown

Unpacking zip files
  |=========================================================================================| 100%
Stacking table SAAT_1min
  |=========================================================================================| 100%
Stacking table SAAT_30min
  |=========================================================================================| 100%
Finished: All of the data are stacked into  2  tables!
Copied the first available variable definition file to /stackedFiles and renamed as variables.csv
Stacked SAAT_1min which has 424800 out of the expected 424800 rows (100%).
Stacked SAAT_30min which has 14160 out of the expected 14160 rows (100%).
Stacking took 6.233922 secs
All unzipped monthly data folders have been removed.

From the single-aspirated air temperature data we are given two final tables. One with 1 minute intervals: SAAT_1min and one for 30 minute intervals: SAAT_30min.

In the same directory as the zipped file, you should now have an unzipped directory of the same name. When you open this you will see a new directory called stackedFiles. This directory contains one or more .csv files (depends on the data product you are working with) with all the data from the months & sites you downloaded. There will also be a single copy of the associated variables.csv and validation.csv files, if applicable (validation files are only available for observational data products).

These .csv files are now ready for use with the program of your choice.

Other options

Other input options in stackByTable() are: * savepath : allows you to specify the file path where you want the stacked files to go, overriding the default. * saveUnzippedFiles : allows you to keep the unzipped, unstacked files from an intermediate stage of the process; by default they are discarded.

Example usage:

             savepath="~data/allTemperature", saveUnzippedFiles=T)

Download files to be stacked: zipsByProduct()

The function zipsByProduct() is a wrapper for the NEON API, it downloads zip files for the data product specified and stores them in a format that can then be passed on to stackByTable().

One of the inputs to zipsByProduct() is the data product ID, or DPID, of the data you want to download. The DPID can be found in the data product box on the new data browse page, or in the data product catalog. It will be in the form DP#.#####.###; the DPID of single aspirated air temperature is DP1.00002.001.

Input options for zipsByProduct() are:

  • dpID: the data product ID, e.g. DP1.00002.001
  • site: either the 4-letter code of a single site, e.g. HARV, or "all", indicating all sites with data available
  • package: either basic or expanded data package
  • avg: either "all", to download all data (the default), or the number of minutes in the averaging interval. See example below; only applicable to IS data
  • savepath: the file path you want to download to; defaults to the working directory
  • check.size: T or F: should the function pause before downloading data and warn you about the size of your download? Defaults to T; if you are using this function within a script or batch process you will want to set it to F.

Here, we'll download single-aspirated air temperature data from Harvard Forest (HARV).

zipsByProduct(dpID="DP1.00002.001", site="HARV", 
              package="basic", check.size=T)

Continuing will download files totaling approximately 121.470836 MB. Do you want to proceed y/n: y
trying URL ''
Content type 'application/zip' length 1593260 bytes (1.5 MB)
downloaded 1.5 MB

(Further URLs omitted for space. Function returns a message 
  for each URL it attempts to download from)

36 zip files downloaded to /Users/neon/filesToStack00002

Downloaded files can now be passed to stackByTable() to be stacked. Another input is required in this case, folder=T.


For many sensor data products, download sizes can get very large, and stackByTable() takes a long time. The 1-minute or 2-minute files are much larger than the longer averaging intervals, so if you don't need high- frequency data, the avg input option lets you choose which averaging interval to download.

This option is only applicable to sensor (IS) data, since OS data are not averaged.

Download only the 30-minute data for triple-aspirated air temperature at HARV:

zipsByProduct(dpID="DP1.00003.001", site="HARV", 
              package="basic", avg=30, check.size=T)

Continuing will download files totaling approximately 5.56142 MB. Do you want to proceed y/n: y
trying URL ''
Content type 'application/octet-stream' length 56534 bytes (55 KB)
downloaded 55 KB

(Further URLs omitted for space. Function returns a message 
  for each URL it attempts to download from)

76 files downloaded to /Users/neon/filesToStack00003

And the 30-minute files can be stacked as usual:


Download a single zip file: getPackage()

If you only need a single site-month (e.g., to test code you're writing), the getPackage() function can be used to download a single zip file. Here we'll download the November 2017 temperature data from HARV.

getPackage("DP1.00002.001", site_code="HARV", 
           year_month="2017-11", package="basic")

The file should now be saved to your working directory.

Download remote sensing files: byFileAOP()

Remote sensing data files can be very large, and NEON remote sensing (AOP) data are stored in a directory structure that makes them easier to navigate. byFileAOP() downloads AOP files from the API while preserving their directory structure. This provides a convenient way to access AOP data programmatically.

Be aware that downloads from byFileAOP() can take a VERY long time, depending on the data you request and your connection speed. You may need to run the function and then leave your machine on and downloading for an extended period of time.

Here the example download is the Ecosystem Structure data product at Hop Brook (HOPB) in 2017; we use this as the example because it's a relatively small year-site-product combination.

byFileAOP("DP3.30015.001", site="HOPB", 
          year="2017", check.size=T)

Continuing will download 36 files totaling approximately 140.3 MB . Do you want to proceed y/n: y
trying URL ''
Content type 'application/octet-stream' length 4009489 bytes (3.8 MB)
downloaded 3.8 MB

(Further URLs omitted for space. Function returns a message 
  for each URL it attempts to download from)

Successfully downloaded  36  files.
NEON_D01_HOPB_DP3_716000_4704000_CHM.tif downloaded to /Users/neon/DP3.30015.001/2017/FullSite/D01/2017_HOPB_2/L3/DiscreteLidar/CanopyHeightModelGtif
NEON_D01_HOPB_DP3_716000_4705000_CHM.tif downloaded to /Users/neon/DP3.30015.001/2017/FullSite/D01/2017_HOPB_2/L3/DiscreteLidar/CanopyHeightModelGtif

(Further messages omitted for space.)

The files should now be downloaded to a new folder in your working directory.

Convert files to GeoCSV: transformFileToGeoCSV()

transformFileToGeoCSV() takes a NEON csv file, plus its corresponding variables file, and writes out a new version of the file with GeoCSV headers. This allows for compatibility with data provided by UNAVCO and other facilities.

Inputs to transformFileToGeoCSV() are the file path to the data file, the file path to the variables file, and the file path where you want to write out the new version. It works on both single site-month files and on stacked files.

In this example, we'll convert the November 2017 temperature data
from HARV that we downloaded with getPackage() earlier. First, you'll need to unzip the file so you can get to the data files. Then we'll select the file for the tower top, which we can identify by the 050 in the VER field (see the file naming conventions page for more information).


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