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This tutorial explores how to deal with NoData values encountered in a time series dataset, in R. It also covers how to subset large files by date and export the results to a .csv (text) file.

R Skill Level: Intermediate - you’ve got the basics of R down and understand the general structure of tabular data.

Goals / Objectives

After completing this activity, you will:

  • Be able to subset data by date.
  • Know how to search for NA or missing data values.
  • Understand different possibilities on how to deal with missing data.

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

  • lubridate: install.packages("lubridate")
  • ggplot2: install.packages("ggplot2")

More on Packages in R - Adapted from Software Carpentry.

Download Data

Download NEON Teaching Data Subset: Meteorological Data for Harvard Forest

The data used in this lesson were collected at the National Ecological Observatory Network’s Harvard Forest field site. These data are proxy data for what will be available for 30 years on the NEON data portal for the Harvard Forest and other field sites located across the United States.

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.

Cleaning Time Series Data

It is common to encounter, large files containing more data than we need for our analysis. It is also common to encounter NoData values that we need to account for when analyzing our data.

In this tutorial, we’ll learn how to both manage NoData values and also subset and export a portion of an R object as a new .csv file.

In this tutorial, we will work with atmospheric data, containing air temperature, precipitation, and photosynthetically active radiation (PAR) data - metrics that are key drivers of phenology. Our study area is the NEON Harvard Forest Field Site.

Import Timeseries Data

We will use the lubridate and ggplot2 packages. Let’s load those first.

If you have not already done so, import the hf001-10-15min-m.csv file, which contains atmospheric data for Harvard Forest. Convert the datetime column to a POSIXct class as covered in the tutorial: Dealing With Dates & Times in R - as.Date, POSIXct, POSIXlt.

# Load packages required for entire script
library(lubridate)  # work with dates
library(ggplot2)  # plotting

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

# Load csv file containing 15 minute averaged atmospheric data 
# for the NEON Harvard Forest Field Site

# Factors=FALSE so data are imported as numbers and characters 
harMet_15Min <- read.csv(
  stringsAsFactors = FALSE)

# convert to POSIX date time class - US Eastern Time Zone
harMet_15Min$datetime <- as.POSIXct(harMet_15Min$datetime,
                                format = "%Y-%m-%dT%H:%M",
                                tz = "America/New_York")

Subset by Date

Our .csv file contains nearly a decade’s worth of data which makes for a large file. The time period we are interested in for our study is:

  • Start Time: 1 January 2009
  • End Time: 31 Dec 2011

Let’s subset the data to only contain these three years. We can use the subset() function, with the syntax: NewObject <- subset ( ObjectToBeSubset, CriteriaForSubsetting ) .

We will set our criteria to be any datetime that:

  1. Is greater than or equal to 1 Jan 2009 at 0:00 AND
  2. Is less than or equal to 31 Dec 2011 at 23:59.

We also need to specify the timezone so R can handle daylight savings and leap year.

# subset data - 2009-2011
harMet15.09.11 <- subset(harMet_15Min,
                         datetime >= as.POSIXct('2009-01-01 00:00',
                                                tz = "America/New_York") &
                         datetime <= as.POSIXct('2011-12-31 23:59',
                                               tz = "America/New_York"))

# View first and last records in the object 

##                   datetime
## 140255 2009-01-01 00:00:00
## 140256 2009-01-01 00:15:00
## 140257 2009-01-01 00:30:00
## 140258 2009-01-01 00:45:00
## 140259 2009-01-01 01:00:00
## 140260 2009-01-01 01:15:00


##                   datetime
## 245369 2011-12-31 22:30:00
## 245370 2011-12-31 22:45:00
## 245371 2011-12-31 23:00:00
## 245372 2011-12-31 23:15:00
## 245373 2011-12-31 23:30:00
## 245374 2011-12-31 23:45:00

It worked! The first entry is 1 January 2009 at 00:00 and the last entry is 31 December 2011 at 23:45.

Export data.frame to .CSV

We can export this subset in .csv format to use in other analyses or to share with colleagues using write.csv.

Data Tip: Remember, to give your output files concise, yet descriptive names so you can identify what it contains in the future. By default, the .csv file will be written to your working directory.

# write harMet15 subset data to .csv

Challenge: Subset & Plot Data

  1. Create a plot of daily precipitation for the month of July 2010 in Harvard Forest. Be sure to label x and y axes. Also be sure to give your plot a title.

  2. Create a plot of dew point (dewp) for the year, 2011 at Harvard Forest.

Managing Missing Data: NoData values

Find NoData Values

If we are lucky when working with external data, the NoData value is clearly specified in the metadata. No data values can be stored differently:

  • NA / nan: Sometimes this value is NA or nan (not a number).
  • A Designated Numeric Value (e.g. -9999): Character strings such as NA can not always be stored along side of numeric values in some file formats. Sometimes you’ll encounter numeric placeholders for noData values such as -9999 (a value often used in the GIS / Remote Sensing and Micrometerology domains.
  • Blank Values: sometimes noData values are left blank. Blanks are particularly problematic because we can’t be certain if a data value is purposefully missing (not measured that day or a bad measurement) or if someone unintentionally deleted it.

Because the actual value used to designate missing data can vary depending upon what data we are working with, it is important to always check the metadata for the files associated NoData value. If the value is NA, we are in luck, R will recognize and flag this value as NoData. If the value is numeric (e.g., -9999), then we might need to assign this value to NA.

Data Tip: NA values will be ignored when performing calculations in R. However a NoData value of -9999 will be recognized as an integer and processed accordingly. If you encounter a numeric NoData value be sure to assign it to NA in R: objectName[objectName==-9999] <- NA

In the Why Metadata Are Important: How to Work with Metadata in Text & EML Format we viewed the metadata for these data and discovered that missing values are designated using NA - a common NoData value placeholder.

Excerpt from the metadata: airt: average air temperature. Average of daily averages. (unit: celsius / missing value: NA)

Check For NoData Values

We can quickly check for NoData values in our data using function. By asking for the sum() of we can see how many NA/ missing values we have.

# Check for NA values

## [1] 0


## [1] 2

# view rows where the air temperature is NA 

##                   datetime  jd airt f.airt rh f.rh dewp f.dewp prec f.prec
## 158360 2009-07-08 14:15:00 189   NA      M NA    M   NA      M    0       
## 203173 2010-10-18 09:30:00 291   NA      M NA    M   NA      M    0       
##        slrr f.slrr parr f.parr netr f.netr bar wspd f.wspd wres
## 158360  290         485         139         NA     M  2.1         1.8
## 203173   NA      M   NA      M   NA      M  NA     M   NA      M   NA
##        f.wres wdir f.wdir wdev f.wdev gspd f.gspd s10t f.s10t
## 158360          86          29         5.2        20.7       
## 203173      M   NA      M   NA      M   NA      M 10.9

The results above tell us there are NoData values in the datetime column. However, there are NoData values in other variables.

Challenge: NoData Values

How many NoData values are in the precipitation (prec) and PAR (parr) columns of our data?

Deal with NoData Values

When we encounter NoData values (blank, NaN, -9999, etc.) in our data we need to decide how to deal with them. By default R treats NoData values designated with a NA as a missing value rather than a zero. This is good, as a value of zero (no rain today) is not the same as missing data (e.g. we didn’t measure the amount of rainfall today).

How we deal with NoData values will depend on:

  • the data type we are working with
  • the analysis we are conducting
  • the significance of the gap or missing value

Many functions in R contains a na.rm= option which will allow you to tell R to ignore NA values in your data when performing calculations.

To Gap Fill? Or Not?

Sometimes we might need to “gap fill” our data. This means we will interpolate or estimate missing values often using statistical methods. Gap filling can be complex and is beyond the scope of this tutorial. The take away from this is simply that it is important to acknowledge missing values in your data and to carefully consider how you wish to account for them during analysis.

Other resources:

  1. R code for dealing with missing data: Quick-R: Missing Data

  2. The Institute for Digital Research and Education has an R FAQ on Missing Values.

Managing NoData Values in Our Data

For this tutorial, we are exploring the patterns of precipitation, and temperature as they relate to green-up and brown-down of vegetation at Harvard Forest. To view overall trends during these early exploration stages, it is okay for us to leave out the NoData values in our plots.

Data Tip: If we wanted to perform more advanced statistical analysis, we might consider gap-filling as our next step. Many data products, from towers such as FluxNet include a higher level, gap-filled product that we can download. More on Gap Filling

NoData Values Can Impact Calculations

It is important to consider NoData values when performing calculations on our data. For example, R will not properly calculate certain functions if there are NA values in the data, unless we explicitly tell it to ignore them.

# calculate mean of air temperature

## [1] NA

# are there NA values in our data?

## [1] 2

R will not return a value for the mean as there NA values in the air temperature column. Because there are only 2 missing values (out of 105,108) for air temperature, we aren’t that worried about a skewed 3 year mean. We can tell R to ignore noData values in the mean calculations using na.rm= (NA.remove).

# calculate mean of air temperature, ignore NA values

## [1] 8.467904

We now see that the 3-year average air temperature is 8.5°C.

Challenge: Import, Understand Metadata, and Clean a Data Set

We have been using the 15-minute data from the Harvard Forest. However, overall we are interested in larger scale patterns of greening-up and browning-down.
Thus a daily summary is sufficient for us to see overall trends.

  1. Import the Daily Meteorological data from the Harvard Forest (if you haven’t already done so in the Intro to Time Series Data in R - Managing Date / Time Format and Simple Plots using ggplot2 tutorial.)
  2. Check the metadata to see what the column names are for the variable of interest (precipitation, air temperature, PAR, day and time ).
  3. If needed, convert the data class of different columns.
  4. Check for missing data and decide what to do with any that exist.
  5. Subset out the data for the duration of our study: 2009-2011. Name the object “harMetDaily.09.11”.
  6. Export the subset to a .csv file.
  7. Create a plot of Daily Air Temperature for 2009-2011. Be sure to label, x- and y-axes. Also give the plot a title!

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