Use the neonUtilities Package to Access NEON Data

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

The package contains several 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().
• loadByProduct(): Combines the functionality of zipsByProduct() and stackByTable(): Downloads the specified data, stacks the files, and loads the files to the R environment.
• 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.
• byTileAOP(): Downloads remote sensing data for the specified data product, subset to tiles that intersect a list of coordinates.
• readTableNEON(): Reads NEON data tables into R, using the variables file to assign R classes to each column.
• transformFileToGeoCSV(): Converts any NEON data file in csv format into a new file with GeoCSV headers.

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. To report bugs or request new features, post an issue in the GitHub repo issues page.

First, we must install and load the neonUtilities package.

# install neonUtilities - can skip if already installed
install.packages("neonUtilities")
# load neonUtilities
library(neonUtilities)

Download files and load directly to R: loadByProduct()

The most popular function in neonUtilities is loadByProduct(). This function downloads data from the NEON API, merges the site-by-month files, and loads the resulting data tables into the R environment, assigning each data type to the appropriate R class. It combines the actions of the zipsByProduct(), stackByTable(), and readTableNEON() functions, described below.

This is a popular choice because it ensures you're always working with the latest data, and it ends with ready-to-use tables in R. However, if you use it in a workflow you run repeatedly, keep in mind it will re-download the data every time.

loadByProduct() works on most observational (OS) and sensor (IS) data, but not on surface-atmosphere exchange (SAE) data, remote sensing (AOP) data, and some of the data tables in the microbial data products. For functions that download AOP data, see the byFileAOP() and byTileAOP() sections in this tutorial. For functions that work with SAE data, see the NEON eddy flux data tutorial.

The inputs to loadByProduct() control which data to download and how to manage the processing:

• dpID: the data product ID, e.g. DP1.00002.001
• site: defaults to "all", meaning all sites with available data; can be a vector of 4-letter NEON site codes, e.g. c("HARV","CPER","ABBY").
• startdate and enddate: defaults to NA, meaning all dates with available data; or a date in the form YYYY-MM, e.g. 2017-06. Since NEON data are provided in month packages, finer scale querying is not available. Both start and end date are inclusive.
• package: either basic or expanded data package. Expanded data packages generally include additional information about data quality, such as chemical standards and quality flags. Not every data product has an expanded package; if the expanded package is requested but there isn't one, the basic package will be downloaded.
• avg: defaults to "all", to download all data; 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.
• nCores: Number of cores to use for parallel processing. Defaults to 1, i.e. no parallelization.

The dpID is the data product identifier of the data you want to download. The DPID can be found on the Explore Data Products page. It will be in the form DP#.#####.###

Let's get triple-aspirated air temperature data (DP1.00003.001) from Moab and Onaqui (MOAB and ONAQ), from May--August 2018, and name the data object trip.temp:

trip.temp <- loadByProduct(dpID="DP1.00003.001", 
                           site=c("MOAB","ONAQ"),
                           startdate="2018-05", 
                           enddate="2018-08")


Continuing will download files totaling approximately 7.994569 MB. Do you want to proceed y/n: y
Downloading 8 files
  |========================================================================================================| 100%

Unpacking zip files using 1 cores.
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s  
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s  
Stacking operation across a single core.
Stacking table TAAT_1min
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s  
Stacking table TAAT_30min
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=00s  
Merged the most recent publication of sensor position files for each site and saved to /stackedFiles
Copied the most recent publication of variable definition file to /stackedFiles
Finished: Stacked 2 data tables and 2 metadata tables!
Stacking took 0.8517601 secs

The object returned by loadByProduct() is a named list of data frames. To work with each of them, select them from the list using the $ operator.

names(trip.temp)
View(trip.temp$TAAT_30min)

If you prefer to extract each table from the list and work with it as an independent object, you can use the list2env() function:

list2env(trip.temp, .GlobalEnv)

For more details about the contents of the data tables and metadata tables, check out the Download and Explore tutorial.

If you want to be able to close R and come back to these data without re-downloading, you'll want to save the tables locally. We recommend also saving the variables file, both so you'll have it to refer to, and so you can use it with readTableNEON() (see below).

write.csv(TAAT_30min, 
          "~/Downloads/TAAT_30min.csv", 
          row.names=F)
write.csv(variables_00003, 
          "~/Downloads/variables_00003.csv", 
          row.names=F)

But, if you want to save files locally and load them into R (or another platform) each time you run a script, instead of downloading from the API every time, you may prefer to use zipsByProduct() and stackByTable() instead of loadByProduct().

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
stackByTable(filepath="~neon/data/NEON_temp-air-single.zip")


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, validation, and sensor_positions files, if applicable (validation files are only available for observational data products, and sensor position files are only available for instrument data products).

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

To read the data tables into R, we recommend using readTableNEON(), which will assign each column to the relevant R class, based on the metadata in the variables file. This ensures time stamps and missing data are interpreted correctly in R.

SAAT30 <- readTableNEON(
  dataFile='~/stackedFiles/SAAT_30min.csv',
  varFile='~/stackedFiles/variables_00002.csv'
)

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:

stackByTable(filepath="~neon/data/NEON_temp-air-single.zip", 
             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().

Input options for zipsByProduct() are the same as those for loadByProduct() described above, except for nCores.

Here, we'll download single-aspirated air temperature (DP1.00002.001) data from Wind River Experimental Forest (WREF) for April and May of 2019.

zipsByProduct(dpID="DP1.00002.001", site="WREF", 
              startdate="2019-04", enddate="2019-05",
              package="basic", check.size=T)


Continuing will download files totaling approximately 12.750557 MB. Do you want to proceed y/n: y
Downloading 2 files
  |========================================================================================================| 100%
2 files downloaded to /Users/neon/filesToStack00002

Downloaded files can now be passed to stackByTable() to be stacked.

stackByTable(filepath="/Users/neon/filesToStack00002")

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 single-aspirated air temperature at WREF:

zipsByProduct(dpID="DP1.00002.001", site="WREF", 
              startdate="2019-04", enddate="2019-05",
              package="basic", avg=30, check.size=T)


Continuing will download files totaling approximately 2.101936 MB. Do you want to proceed y/n: y
Downloading 17 files
  |========================================================================================================| 100%
17 files downloaded to /Users/neon/filesToStack00002

The 30-minute files can be stacked as usual, and can be read into R using readTableNEON():

stackByTable(filepath="/Users/neon/filesToStack00002")
SAAT30 <- readTableNEON(
  dataFile='/Users/neon/filesToStack00002/stackedFiles/SAAT_30min.csv',
  varFile='/Users/neon/filesToStack00002/stackedFiles/variables_00002.csv'
)

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 'https://neon-aop-product.s3.data.neonscience.org:443/2017/FullSite/D01/2017_HOPB_2/L3/DiscreteLidar/CanopyHeightModelGtif/NEON_D01_HOPB_DP3_716000_4704000_CHM.tif?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20180410T233031Z&X-Amz-SignedHeaders=host&X-Amz-Expires=3599&X-Amz-Credential=pub-internal-read%2F20180410%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Signature=92833ebd10218f4e2440cb5ea78d1c8beac4ee4c10be5c6aeefb72d18cf6bd78'
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.

Download remote sensing files for specific coordinates: byTileAOP()

Often when using remote sensing data, we only want data covering a certain area - usually the area where we have coordinated ground sampling. byTileAOP() queries for data tiles containing a specified list of coordinates. It only works for the tiled, AKA mosaicked, versions of the remote sensing data, i.e. the ones with data product IDs beginning with "DP3".

Here, we'll download tiles of vegetation indices data (DP3.30026.001) corresponding to select observational sampling plots. For more information about accessing NEON spatial data, see the API tutorial and the in-development geoNEON package.

For now, assume we've used the API to look up the plot centroids of plots SOAP_009 and SOAP_011 at the Soaproot Saddle site. You can also look these up in the Spatial Data folder of the document library. The coordinates of the two plots in UTMs are 298755,4101405 and 299296,4101461. These are 40x40m plots, so in looking for tiles that contain the plots, we want to include a 20m buffer. The "buffer" is actually a square, it's a delta applied equally to both the easting and northing coordinates.

byTileAOP(dpID="DP3.30026.001", site="SOAP", 
          year="2018", easting=c(298755,299296),
          northing=c(4101405,4101461),
          buffer=20)


trying URL 'https://neon-aop-product.s3.data.neonscience.org:443/2018/FullSite/D17/2018_SOAP_3/L3/Spectrometer/VegIndices/NEON_D17_SOAP_DP3_299000_4101000_VegIndices.zip?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20190313T225238Z&X-Amz-SignedHeaders=host&X-Amz-Expires=3600&X-Amz-Credential=pub-internal-read%2F20190313%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Signature=e9ae6858242b48df0677457e31ea3d86b2f20ac2cf43d5fc02847bbaf2e1da47'
Content type 'application/octet-stream' length 47798759 bytes (45.6 MB)
==================================================
downloaded 45.6 MB

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

Successfully downloaded  2  files.
NEON_D17_SOAP_DP3_298000_4101000_VegIndices.zip downloaded to /Users/neon/DP3.30026.001/2018/FullSite/D17/2018_SOAP_3/L3/Spectrometer/VegIndices
NEON_D17_SOAP_DP3_299000_4101000_VegIndices.zip downloaded to /Users/neon/DP3.30026.001/2018/FullSite/D17/2018_SOAP_3/L3/Spectrometer/VegIndices

The 2 tiles covering the SOAP_009 and SOAP_011 plots have
been downloaded.

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).

transformFileToGeoCSV("~/NEON.D01.HARV.DP1.00002.001.000.050.030.SAAT_30min.2017-11.basic.20171207T181046Z.csv", 
                      "~/NEON.D01.HARV.DP1.00002.001.variables.20171207T181046Z.csv",
                      "~/SAAT_30min_geo.csv")