Work with NEON's Single-Aspirated Air Temperature Data

Lee Stanish, Megan A. Jones, Natalie Robinson
Katie Jones, Cody Flagg, Josh Roberti
Table of Contents

In this tutorial, we explore the NEON single-aspirated air temperature data. We start using data that has already been "stacked" using the neonDataStackR package. We then discuss how to interpret the variables, how to work with date-time and date formats, and finally how to plot the data.

This lesson is part of a series on how to work with both discrete and continuous time series data.


After completing this activity, you will be able to:

  • work with "stacked" NEON Single-Aspirated Air Temperature data.
  • correctly format date-time data.
  • use dplyr functions to filter data.
  • plot time series data in scatter plots using ggplot function.

Things You’ll Need To Complete This Tutorial

You will need the most current version of R and, preferably, RStudio loaded on your computer to complete this tutorial.

Install R Packages

  • ggplot2: install.packages("ggplot2")
  • dplyr: install.packages("dplyr")
  • scales: install.packages("scales")
  • tidyr: install.packages("tidyr")
  • lubridate: install.packages("lubridate")

More on Packages in R – Adapted from Software Carpentry.

Download Data

NEON Teaching Data Subset: Plant Phenology & Single Aspirated Air Temperature

The data used in this lesson were collected at the National Ecological Observatory Network's Domain 02 field sites. This teaching data subset represent a small subset of the data NEON will collect over 30 years and at 20 domains across the United States. NEON data are available on the NEON data portal.

Download Dataset

Set Working Directory: This lesson assumes that you have set your working directory to the location of the downloaded and unzipped data subsets.

An overview of setting the working directory in R can be found here.

R Script & Challenge Code: NEON data lessons often contain challenges that reinforce learned skills. If available, the code for challenge solutions is found in the downloadable R script of the entire lesson, available in the footer of each lesson page.

Additional Resources

Explore Temperature Data

The following sections provide a brief overview of the NEON single aspirated air temperature data. When designing a research project using this data, you need to consult the documents associated with this data product and not rely solely on this summary.

NEON Air Temperature Data

Temperature is continuously monitored by NEON a by two methods. At terrestrial sites temperature for the top of the tower will be derived from the triple redundant aspirated air temperature sensor. This is provided as NEON data product NEON.DP1.00003. Single Aspirated Air Temperature Sensors (SAATS) are deployed to develop temperature profiles at the tower at NEON terrestrial sites and on the micromet station at NEON aquatic sites. This is provided as NEON data product NEON.DP1.00002.

Single-aspirated Air Temperature

Temperature profiles will be ascertained by deploying SAATS at various heights on the core tower infrastructure and mobile platforms. Air temperature at aquatic sites will be measured using a single SAAT at a standard height of 3m above ground level. Air temperature for this data product is provided as one- and thirty-minute averages of 1 Hz observations. Temperature observations are made using platinum resistance thermometers, which are housed in a fan aspirated shield to reduce radiative bias. The temperature is measured in Ohms and subsequently converted to degrees Celsius. Details on the conversion can be found in the associated Algorithm Theoretic Basis Document (ATBD).

Available Data Tables

When downloaded, data comes with several .csv file for each site and month-year selected. There is a 1-minute average and a 30-minute average for each level at which there is a sensor. It is important to understand the file names to know which file is which.

The readme associated with the data provides the following information: The file naming convention for sensor data files is NEON.DOM.SITE.DPL.PRNUM.REV.TERMS.HOR.VER.TMI.DESC


  • DOM refers to the domain of data acquisition (D01 or D20)
  • SITE refers to the standardized four-character alphabetic code of the site of data acquisition.
  • DPL refers to the data product processing level
  • PRNUM refers to the data product number (see the Data Product Catalog.)
  • REV refers to the revision number of the data product. (001 = initial REV, Engineering-Grade or Provisional; 101 = initial REV, Science-Grade)
  • TERMS is used in data product numbering to identify a sub-product or discrete vector of metadata. Since each download file typically contains several sub-products, this field is set to 00000 in the file name to maintain consistency with the data product numbering scheme.
  • HOR refers to measurement locations within one horizontal plane.
  • VER refers to measurement locations within one vertical plane. For example, if eight temperature measurements are collected, one at each tower level, the number in the VER field would range from 010-080.
  • TMI is the Temporal Index; refers to the temporal representation, averaging period, or coverage of the data product (e.g., minute, hour, month, year, sub-hourly, day, lunar month, single instance, seasonal, annual, multi-annual)
  • DESC is an abbreviated description of the data product

Therefore, we can interpret the following .csv file name


as NEON data from Smithsonian Environmental Research Center (SERC) located in Domain 02 (D02). The specific data product is level 1 data product (DP1) and is single aspirated temperature data (00002). There have not been revisions, there are no associated terms, and there is no differentiation in horizontal plane. This data comes from the first (010) vertical level of the tower. The temporal interval is 30-minute averaged data (030; the other option in our data is 1 minute averaging. Finally there is the abbreviated description that is more human readable and tells us again that it is single-aspirated air temperature at 30 minute averages.

Stack NEON Data

All the above data are delivered in a site and year-month format. When you download data, you will get a single zipped file containing a directory for each month and site that you've requested data for. Dealing with these separate files from even one or two sites over a 12 month period can be a bit overwhelming. Luckily NEON provides an R package neonUtilities that takes the unzipped downloaded file and joining the data files.

For more on this function check out the Use the neonDataStackR package to access NEON data tutorial.

When we do this for our temperature data we get two files, one for 1 minute SAAT and 30 minute SAAT, with all the data from your site and date range of interest.

Let's start by loading our data of interest.

Import Data

This tutorial uses 30 minute temperature data from the single aspirated temperature sensors mounted on level 03 of the NEON tower.

# Load required libraries

# set working directory to ensure R can find the file we wish to import
# setwd("working-dir-path-here")

# Read in data
temp30_sites <- read.csv('NEON-pheno-temp-timeseries/temp/SAAT_30min.csv', stringsAsFactors = FALSE)

Explore Temp. Data

Now that you have the data, let's take a look at the readme and understand what's in the data.

# Get a general feel for the data: View structure of data frame

## 'data.frame':    219160 obs. of  14 variables:
##  $ domainID           : chr  "D02" "D02" "D02" "D02" ...
##  $ siteID             : chr  "SCBI" "SCBI" "SCBI" "SCBI" ...
##  $ horizontalPosition : int  0 0 0 0 0 0 0 0 0 0 ...
##  $ verticalPosition   : int  10 10 10 10 10 10 10 10 10 10 ...
##  $ startDateTime      : chr  "2015-04-01T00:00:00Z" "2015-04-01T00:30:00Z" "2015-04-01T01:00:00Z" "2015-04-01T01:30:00Z" ...
##  $ endDateTime        : chr  "2015-04-01T00:30:00Z" "2015-04-01T01:00:00Z" "2015-04-01T01:30:00Z" "2015-04-01T02:00:00Z" ...
##  $ tempSingleMean     : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ tempSingleMinimum  : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ tempSingleMaximum  : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ tempSingleVariance : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ tempSingleNumPts   : int  NA NA NA NA NA NA NA NA NA NA ...
##  $ tempSingleExpUncert: num  NA NA NA NA NA NA NA NA NA NA ...
##  $ tempSingleStdErMean: num  NA NA NA NA NA NA NA NA NA NA ...
##  $ finalQF            : int  1 1 1 1 1 1 1 1 1 1 ...

View readme and variables file. This will guide you on what the data are.

Select Site(s) of Interest

Currently, we have data from several sites in our dataset. Let's start by limiting the data to our site of interest.

The following format allows us to easily change sites or select data from multiple sites.

# set site of interest
siteOfInterest <- c("SCBI")

# use filter to select only the site of Interest 
# using %in% allows one to add a vector if you want more than one site. 
temp30 <- filter(temp30_sites, siteID%in%siteOfInterest)

Quality Flags

The sensor data undergo a variety of quality assurance and quality control checks. Data can pass or fail these many checks. The expanded data package includes all of these quality flags, which can allow you to decide if not passing one of the checks will significantly hamper your research and if you should therefore remove the data from your analysis. The data that we are using is the basic data package and only includes the finalQF flag.

A pass of the check is 0, while a fail is 1. Let's see if we have data with a quality flag.

# Are there quality flags in your data? Count 'em up


## [1] 99389

How do we want to deal with this quality flagged data. This may depend on why it is flagged and what questions you are asking. The expanded data package will be useful for determining this.

For our demonstration purposes here we will keep the flagged data.

What about null (NA) data?

# Are there NA's in your data? Count 'em up
sum($tempSingleMean) )

## [1] 29129


## [1] NA

Why was there no output?

We had previously seen that there are NA values in the temperature data. Given there are NA values, R, by default, won't calculate a mean (and many other summary statistics) as the NA values could skew the data.


tells R to ignore them for calculation,etc

# create new dataframe without NAs
temp30_noNA <- temp30 %>%
    drop_na(tempSingleMean)  # tidyr function

# alternate base R
# temp30_noNA <- temp30[!$tempSingleMean),]

# did it work?

## [1] 0

What is the range of dates in our dataset?

# View the date range

## [1] "2015-04-01T00:00:00Z" "2016-12-31T23:30:00Z"

# what format are they in? 

##  chr [1:120791] "2015-04-26T12:00:00Z" "2015-04-26T12:30:00Z" ...

Ah, here we have a date and time format and there are non standard characters in it. Currently our data are in character format. We will need to convert them into a date-time format.

R - Date-Time - The POSIX classes

If we have a column containing both date and time we need to use a class that stores both date AND time. Base R offers two closely related classes for date and time: POSIXct and POSIXlt.


The as.POSIXct method converts a date-time string into a POSIXct class.

# Convert character data to date and time.
timeDate <- as.POSIXct("2015-10-19 10:15")   

##  POSIXct[1:1], format: "2015-10-19 10:15:00"


## [1] "2015-10-19 10:15:00 MDT"

as.POSIXct stores both a date and time with an associated time zone. The default time zone selected, is the time zone that your computer is set to which is most often your local time zone (Mountain Daylight Time in this example).

POSIXct stores date and time in seconds with the number of seconds beginning at 1 January 1970. Negative numbers are used to store dates prior to 1970. Thus, the POSIXct format stores each date and time a single value in units of seconds. Storing the data this way, optimizes use in data.frames and speeds up computation, processing and conversion to other formats.

# to see the data in this 'raw' format, i.e., not formatted according to the 
# class type to show us a date we recognize, use the `unclass()` function.

## [1] 1445271300
## attr(,"tzone")
## [1] ""

Here we see the number of seconds from 1 January 1970 to 9 October 2015 (1445271300). Also, we see that a time zone (tzone) is associated with the value in seconds.

Data Tip: The unclass method in R allows you to view how a particular R object is stored.


The POSIXct format is optimized for storage and computation. However, humans aren't quite as computationally efficient as computers! Also, we often want to quickly extract some portion of the data (e.g., months). The POSIXlt class stores date and time information in a format that we are used to seeing (e.g., second, min, hour, day of month, month, year, numeric day of year, etc).

# Convert character data to POSIXlt date and time
timeDatelt<- as.POSIXlt("2015-10-19 10:15")  

##  POSIXlt[1:1], format: "2015-10-19 10:15:00"


## [1] "2015-10-19 10:15:00 MDT"


## $sec
## [1] 0
## $min
## [1] 15
## $hour
## [1] 10
## $mday
## [1] 19
## $mon
## [1] 9
## $year
## [1] 115
## $wday
## [1] 1
## $yday
## [1] 291
## $isdst
## [1] 1
## $zone
## [1] "MDT"
## $gmtoff
## [1] NA

When we convert a string to POSIXlt, and view it in R, it still looks similar to the POSIXct format. However, unclass() shows us that the data are stored differently. The POSIXlt class stores the hour, minute, second, day, month, and year separately.

Months in POSIXlt

POSIXlt has a few quirks. First, the month values stored in the POSIXlt object use zero-based indexing. This means that month #1 (January) is stored as 0, and month #2 (February) is stored as 1. Notice in the output above, October is stored as the 9th month ($mon = 9).

Years in POSIXlt

Years are also stored differently in the POSIXlt class. Year values are stored using a base index value of 1900. Thus, 2015 is stored as 115 ($year = 115 - 115 years since 1900).

Data Tip: To learn more about how R stores information within date-time and other objects check out the R documentation by using ? (e.g., ?POSIXlt). NOTE: you can use this same syntax to learn about particular functions (e.g., ?ggplot).

Having dates classified as separate components makes POSIXlt computationally more resource intensive to use in data.frames. This slows things down! We will thus use POSIXct for this tutorial.

Data Tip: There are other R packages that support date-time data classes, including readr, zoo and chron.

Convert to Date-time Class

When we convert from a character to a date-time class we need to tell R how the date and time information are stored in each string. To do this, we can use format=. Let's have a look at one of our date-time strings to determine it's format.

# view one date-time field

## [1] "2015-04-26T12:00:00Z"

Looking at the results above, we see that our data are stored in the format: Year-Month-Day "T" Hour:Minute (2005-04-26T12:00:00Z). We can use this information to populate our format string using the following designations for the components of the date-time data:

  • %Y - year
  • %m - month
  • %d - day
  • %H:%M:%S - hours:minutes:seconds

Data Tip: A list of options for date-time format is available in the strptime function help: help(strptime). Check it out!

The "T" inserted into the middle of datetime isn't a standard character for date and time, nor is the "Z" at the end, however we can tell R where the characters are so R can ignore them and interpret the correct date and time as follows: format="%Y-%m-%dT%H:%M:%".

All NEON data are reported in UTC which is the same as GMT.

# convert to Date Time 
temp30_noNA$startDateTime <- as.POSIXct(temp30_noNA$startDateTime,
                                                                                format = "%Y-%m-%dT%H:%M:%SZ", tz = "GMT")
# check that conversion worked

##  POSIXct[1:120791], format: "2015-04-26 12:00:00" "2015-04-26 12:30:00" ...

Looks good! Except that all the times are in UTC (or GMT), but our phenology are daily data. If we want to match the two up precisely, we'd need our date-time date on a local time zone to correctly aggregate on a date.

Convert to Local Time Zone

Our site of interest SCBI is in the eastern US time zone. We want to convert to that local time zone so that we can correctly aggreggate date on a daily scale. Depending on your research question, this may not be an imperative step.

We can find out the correct code for our time zone by looking it up: Wikipedia: List of tz database time zones.

## Convert to Local Time Zone 

## Conver to local TZ in new column
temp30_noNA$dtLocal <- format(temp30_noNA$startDateTime, 
                                                            tz="America/New_York", usetz=TRUE)

## check it
head(select(temp30_noNA, startDateTime, dtLocal))

##            startDateTime                 dtLocal
## 2665 2015-04-26 12:00:00 2015-04-26 08:00:00 EDT
## 2666 2015-04-26 12:30:00 2015-04-26 08:30:00 EDT
## 2667 2015-04-26 13:00:00 2015-04-26 09:00:00 EDT
## 2668 2015-04-26 13:30:00 2015-04-26 09:30:00 EDT
## 2669 2015-04-26 14:00:00 2015-04-26 10:00:00 EDT
## 2670 2015-04-26 14:30:00 2015-04-26 10:30:00 EDT

Now we have the startDateTime correctly formatted and can now use any function that needs a date or date-time class of data.

Subset by Date

Now that the date is correctly formatted we can easily choose a desired date range using the filter() function.

Let's select only the 2016 data.

# Limit dataset to dates of interest (2016-01-01 to 2016-12-31)
# alternatively could use ">=" and start with 2016-01-01 00:00
temp30_TOI <- filter(temp30_noNA, dtLocal>"2015-12-31 23:59")

# View the date range

## [1] "2016-01-01 00:00:00 EST" "2016-12-31 18:30:00 EST"

Challenge: Methods Work with Appropriate Classes

What happens if you try to subset by date using this method if the data aren't in a date-time class? Hint: Try it out with our previous temp30 object.

––– For a discussion of date formats is including Date, POSIXct, & POSIXlt see the NEON Data Skills tutorial Time Series 02: Dealing With Dates & Times in R - as.Date, POSIXct, POSIXlt .*

Scatterplots with ggplot

We can use ggplot to create scatter plots. To create a bar plot, we change the geom element from geom_bar() to geom_point().

Now that we have data subsetted, let's plot the data. But which data to select? We have several options:

  • tempSingleMean: the mean temperature for the interval
  • tempSingleMinimum: the minimum temperature during the interval
  • tempSingleMaximum: the maximum temperature for the interval

Depending on exactly what question you are asking you may prefer to use one over the other. For many applications, the mean temperature of the one or 30 minute interval will provide the best representation of the data.

Let's plot it.

# plot temp data
tempPlot <- ggplot(temp30_TOI, aes(dtLocal, tempSingleMean)) +
    geom_point() +
    ggtitle("Single Asperated Air Temperature") +
    xlab("Date") + ylab("Temp (C)") +
    theme(plot.title = element_text(lineheight=.8, face="bold", size = 20)) +
    theme(text = element_text(size=18))


Given all the data -- 68,000+ observations -- it took a little while for that to plot.

What patterns can you see in the data?

Right now we are looking at all the data points in the dataset. However, we may want to view or aggregate the data differently:

  • aggregated data: min, mean, or max over a some duration
  • the number of days since a freezing temperatures
  • or some other segregation of the data.

Given that in the previous tutorial, Work With NEON's Plant Phenology Data, we were working with phenology data collected on a daily scale let's aggregate to that level.

Aggregate Data by Day

We can use the dplyr package functions to aggregate the data. However, we have to choose what product we want from the aggregation. Again, you might want daily minimum temps, mean temperature or maximum temps depending on your question.

In the context of phenology, minimum temperatures might be very important if you are interested in a species that is very frost susceptible. Any days with a minimum temperature below 0C could dramatically change the phenophase. For other species or climates, maximum thresholds may be very import. Or you might be most interested in the daily mean.

For this tutorial, let's stick with maximum daily temperature (of the interval means).

# convert to date, easier to work with
temp30_TOI$sDate <- as.Date(temp30_TOI$dtLocal)

# did it work

##  Date[1:68422], format: "2016-01-01" "2016-01-01" "2016-01-01" "2016-01-01" "2016-01-01" ...

# max of mean temp each day
temp_day <- temp30_TOI %>%
    group_by(sDate) %>%
    distinct(sDate, .keep_all=T) %>%

Now we can plot the daily temperature.

# plot Air Temperature Data across 2016 using daily data
tempPlot_dayMax <- ggplot(temp_day, aes(sDate, dayMax)) +
    geom_point() +
    ggtitle("Daily Max Air Temperature") +
    xlab("") + ylab("Temp (C)") +
    theme(plot.title = element_text(lineheight=.8, face="bold", size = 20)) +
    theme(text = element_text(size=18))


What do we gain by this visualization? What do we loose over the 30 minute intervals?

ggplot - Subset by Time

Sometimes we want to scale the x- or y-axis to a particular time subset without subsetting the entire data_frame. To do this, we can define start and end times. We can then define these limits in the scale_x_date object as follows:

scale_x_date(limits=start.end) +

Let's plot just the first three months of the year.

# Define Start and end times for the subset as R objects that are the time class
startTime <- as.Date("2016-01-01")
endTime <- as.Date("2016-03-31")

# create a start and end time R object
start.end <- c(startTime,endTime)

##  Date[1:2], format: "2016-01-01" "2016-03-31"

# View data for first 3 months only
# And we'll add some color for a change. 
tempPlot_dayMax3m <- ggplot(temp_day, aes(sDate, dayMax)) +
           geom_point(color="blue", size=1) +  # defines what points look like
           ggtitle("Air Temperature\n Jan - March") +
           xlab("Date") + ylab("Air Temperature (C)")+ 
                date_breaks="1 week",
                date_labels="%b %d"))


## Warning: Removed 267 rows containing missing values (geom_point).

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