Introduction to the EGRET package
Robert M. Hirsch and Laura DeCicco
2019-03-15
Introduction
EGRET includes statistics and graphics for streamflow history, water
quality trends, and the statistical modeling algorithm Weighted
Regressions on Time, Discharge, and Season (WRTDS). Please see the
official EGRET User Guide Robert M. Hirsch and De
Cicco (2015) for more information on the EGRET
package:
https://doi.org/10.3133/tm4A10
For information on getting started in R, downloading and installing
the package, see the New to R section.
This vignette assumes that you understand the concepts underlying
WRTDS, and reading the relevant sections of the User Guide at least the
first of these papers. Any use of trade, firm, or product names is for
descriptive purposes only and does not imply endorsement by the U.S.
Government.
EGRET Workflow
Subsequent sections of this vignette discuss the EGRET workflow steps
in greater detail. This section provides a handy cheat sheet for diving
into an EGRET analysis. The first example is for a flow history
analysis:
library(EGRET)
# Flow history analysis
############################
# Gather discharge data:
siteNumber <- "01491000" #Choptank River at Greensboro, MD
startDate <- "" # Get earliest date
endDate <- "" # Get latest date
Daily <- readNWISDaily(siteNumber,"00060",startDate,endDate)
# Gather site and parameter information:
# Here user must input some values for
# the default (interactive=TRUE)
INFO <- readNWISInfo(siteNumber,"00060")
INFO$shortName <- "Choptank River near Greensboro, MD"
############################
############################
# Check flow history data:
eList <- as.egret(INFO, Daily, NA, NA)
plotFlowSingle(eList, istat=7,qUnit="thousandCfs")
plotSDLogQ(eList)
plotQTimeDaily(eList, qLower=1,qUnit=3)
plotFour(eList, qUnit=3)
plotFourStats(eList, qUnit=3)
############################
# modify this for your own computer file structure:
savePath<-"/Users/rhirsch/Desktop/"
saveResults(savePath, eList)
The second workflow example is for a water quality analysis. It
includes data retrieval, merging of water quality and streamflow data,
running the WRTDS estimation, and various plotting functions available
in the EGRET package.
library(EGRET)
############################
# Gather discharge data:
siteNumber <- "01491000" #Choptank River near Greensboro, MD
startDate <- "" #Gets earliest date
endDate <- "2011-09-30"
# Gather sample data:
parameter_cd<-"00631" #5 digit USGS code
Sample <- readNWISSample(siteNumber,parameter_cd,startDate,endDate)
#Gets earliest date from Sample record:
#This is just one of many ways to assure the Daily record
#spans the Sample record
startDate <- min(as.character(Sample$Date))
# Gather discharge data:
Daily <- readNWISDaily(siteNumber,"00060",startDate,endDate)
# Gather site and parameter information:
# Here user must input some values:
INFO<- readNWISInfo(siteNumber,parameter_cd)
INFO$shortName <- "Choptank River at Greensboro, MD"
# Merge discharge with sample data:
eList <- mergeReport(INFO, Daily, Sample)
############################
############################
# Check sample data:
boxConcMonth(eList)
boxQTwice(eList)
plotConcTime(eList)
plotConcQ(eList)
multiPlotDataOverview(eList)
############################
############################
# Run WRTDS model:
eList <- modelEstimation(eList)
############################
############################
#Check model results:
#Require Sample + INFO:
plotConcTimeDaily(eList)
plotFluxTimeDaily(eList)
plotConcPred(eList)
plotFluxPred(eList)
plotResidPred(eList)
plotResidQ(eList)
plotResidTime(eList)
boxResidMonth(eList)
boxConcThree(eList)
#Require Daily + INFO:
plotConcHist(eList)
plotFluxHist(eList)
# Multi-line plots:
date1 <- "1985-09-01"
date2 <- "1997-09-0"
date3 <- "2010-09-01"
qBottom<-0.2
qTop<-10
plotConcQSmooth(eList, date1, date2, date3, qBottom, qTop,
concMax=2,legendTop = 0.8)
q1 <- 2
q2 <- 10
q3 <- 20
centerDate <- "07-01"
yearEnd <- 1980
yearStart <- 2010
plotConcTimeSmooth(eList, q1, q2, q3, centerDate, yearStart, yearEnd, legendTop = 0.7)
# Multi-plots:
fluxBiasMulti(eList)
#Contour plots:
clevel<-seq(0,2,0.5)
maxDiff<-0.8
yearStart <- 1980
yearEnd <- 2010
plotContours(eList, yearStart,yearEnd,qBottom,qTop,
contourLevels = clevel,qUnit=2)
plotDiffContours(eList, yearStart,yearEnd,
qBottom,qTop,maxDiff,qUnit=2)
# modify this for your own computer file structure:
savePath<-"/Users/rhirsch/Desktop/"
saveResults(savePath, eList)
EGRET Data Frames and
Retrieval Options
The EGRET package uses 3 default data frames throughout the
calculations, analysis, and graphing. These data frames are Daily, Sample, and INFO. The data frames are combined into a
named list for all EGRET functions using the as.egret
function (see Merge Report).
A package that EGRET depends on is called dataRetrieval. This package
provides the core functionality to import hydrologic data from USGS and
EPA web services. See the dataRetrieval vignette for more
information.
library(dataRetrieval)
vignette("dataRetrieval")
EGRET uses entirely SI units to store the data, but for purposes of
output, it can report results in a wide variety of units, which will be
discussed in the Units section. To start our
exploration, you must install the packages, then open EGRET with the
following command:
Daily
The Daily data frame can be imported into R either from USGS web
services (readNWISDaily) or user-generated files
(readUserDaily). After you run the WRTDS calculations by
using the function modelEstimation (as will be described in
the WRTDS section, additional columns are
inserted.
| Date |
Date |
Date |
date |
| Q |
number |
Discharge in m3/s |
m3/s |
| Julian |
number |
Number of days since January 1, 1850 |
days |
| Month |
integer |
Month of the year [1-12] |
months |
| Day |
integer |
Day of the year [1-366] |
days |
| DecYear |
number |
Decimal year |
years |
| MonthSeq |
integer |
Number of months since January 1, 1850 |
months |
| Qualifier |
character |
Qualifying code |
string |
| i |
integer |
Index of days, starting with 1 |
days |
| LogQ |
number |
Natural logarithm of Q |
numeric |
| Q7 |
number |
7 day running average of Q |
m3/s |
| Q30 |
number |
30 day running average of Q |
m3/s |
Columns added to Daily data frame after running
modelEstimation
| yHat |
number |
The WRTDS estimate of the log of concentration |
numeric |
| SE |
number |
The WRTDS estimate of the standard error of yHat |
numeric |
| ConcDay |
number |
The WRTDS estimate of concentration |
mg/L |
| FluxDay |
number |
The WRTDS estimate of flux |
kg/day |
| FNConc |
number |
Flow-normalized estimate of concentration |
mg/L |
| FNFlux |
number |
Flow-normalized estimate of flux |
kg/day |
Notice that the “Day of the year” column can span from 1 to 366. The
366 accounts for leap years. Every day has a consistent day of the year.
This means, February 28th is always the 59th day
of the year, Feb. 29th is always the 60th day of
the year, and March 1st is always the 61st day of
the year whether or not it is a leap year.
readNWISDaily
The readNWISDaily function retrieves the daily values
(discharge in this case) from a USGS web service. It requires the inputs
siteNumber, parameterCd, startDate, endDate, interactive, and
convert.
These arguments are described in detail in the
dataRetrieval vignette, however convert is a
new argument (which defaults to TRUE). The convert argument tells the
program to convert the values from cubic feet per second
(ft3/s) to cubic meters per second (m3/s) as shown
in the example Daily data frame. For EGRET applications with NWIS Web
retrieval, do not use this argument (the default is TRUE), EGRET assumes
that discharge is always stored in units of cubic meters per second. If
you don’t want this conversion and are not using EGRET, set
convert=FALSE in the function call.
siteNumber <- "01491000"
startDate <- "2000-01-01"
endDate <- "2013-01-01"
# This call will get NWIS (ft3/s) data , and convert it to m3/s:
Daily <- readNWISDaily(siteNumber, "00060", startDate, endDate)
If discharge values are negative or zero, the code will set all of
these values to zero and then add a small constant to all of the daily
discharge values. This constant is 0.001 times the mean discharge. The
code will also report on the number of zero and negative values and the
size of the constant. Use EGRET analysis only if the number of zero
values is a very small fraction of the total days in the record (say
less than 0.1% of the days), and there are no negative discharge values.
Columns Q7 and Q30 are the 7 and 30 day running averages for the 7 or 30
days ending on this specific date.
readUserDaily
The readUserDaily function will load a user-supplied
text file and convert it to the Daily data frame. The file should have
two columns, the first dates, the second values. The dates are formatted
either mm/dd/yyyy or yyyy-mm-dd. Using a 4-digit year is required. This
function has the following inputs: filePath, fileName, hasHeader
(TRUE/FALSE), separator, qUnit, and interactive (TRUE/FALSE). filePath
is a character that defines the path to your file, and the character can
either be a full path, or path relative to your R working directory. The
input fileName is a character that defines the file name (including the
extension).
Text files that contain this sort of data require some sort of a
separator, for example, a “csv” file (comma-separated value) file uses a
comma to separate the date and value column. A tab delimited file would
use a tab ("\t") rather than the comma (",").
Define the type of separator you choose to use in the function call in
the separator argument, the default is ",".
Another function input is a logical variable: hasHeader. The default is
TRUE. If your data does not have column names, set this variable to
FALSE.
Finally, qUnit is a numeric argument that defines the discharge units
used in the input file. The default is qUnit = 1 which assumes discharge
is in cubic feet per second. If the discharge in the file is already in
cubic meters per second then set qUnit = 2. If it is in some other units
(like liters per second or acre-feet per day), the user must pre-process
the data with a unit conversion that changes it to either cubic feet per
second or cubic meters per second.
So, if you have a file called “ChoptankRiverFlow.txt” located in a
folder called “RData” on the C drive (this example is for the Windows™
operating systems), and the file is structured as follows
(tab-separated):
date Qdaily
10/1/1999 107
10/2/1999 85
10/3/1999 76
10/4/1999 76
10/5/1999 113
10/6/1999 98
...
The call to open this file, convert the discharge to cubic meters per
second, and populate the Daily data frame would be:
fileName <- "ChoptankRiverFlow.txt"
filePath <- "C:/RData/"
Daily <- readUserDaily(filePath,fileName,
separator="\t")
Microsoft™ Excel files can be a bit tricky to import into R directly.
The simplest way to get Excel data into R is to open the Excel file in
Excel, then save it as a .csv file (comma-separated values).
Sample
The Sample data frame initially is populated with columns generated
by either the readNWISSample, readWQPSample,
or readUserSample functions. After you run the WRTDS
calculations using the modelEstimation function (as
described in the WRTDS section, additional columns
are inserted:
| Date |
Date |
Date |
date |
| ConcLow |
number |
Lower limit of concentration |
mg/L |
| ConcHigh |
number |
Upper limit of concentration |
mg/L |
| Uncen |
integer |
Uncensored data (1=true, 0=false) |
integer |
| ConcAve |
number |
Average concentration |
mg/L |
| Julian |
number |
Number of days since January 1, 1850 |
days |
| Month |
integer |
Month of the year [1-12] |
months |
| Day |
integer |
Day of the year [1-366] |
days |
| DecYear |
number |
Decimal year |
years |
| MonthSeq |
integer |
Number of months since January 1, 1850 |
months |
| SinDY |
number |
Sine of DecYear |
numeric |
| CosDY |
number |
Cosine of DecYear |
numeric |
| Q |
number |
Discharge |
cms |
| LogQ |
number |
Natural logarithm of discharge |
numeric |
Columns added to Sample data frame after running
modelEstimation
| yHat |
number |
estimate of the log of concentration |
numeric |
| SE |
number |
estimate of the standard error of yHat |
numeric |
| ConcHat |
number |
unbiased estimate of concentration |
mg/L |
As with the Daily data frame, the “Day of the year” column can span
from 1 to 366. The 366 accounts for leap years. Every day has a
consistent day of the year. This means, February 28th is
always the 59th day of the year, Feb. 29th is
always the 60th day of the year, and March 1st is
always the 61st day of the year whether or not it is a leap
year.
The censored value section is about summing
multiple constituents, including how interval censoring is used. Since
the Sample data frame is structured to only contain one constituent,
when more than one parameter codes are requested, the
readNWISSample function will sum the values of each
constituent as described below.
readNWISSample
The readNWISSample function retrieves USGS sample data
from NWIS. The arguments for this function are also siteNumber,
parameterCd, startDate, endDate, interactive. These are the same inputs
as readNWISDaily as described in the previous section.
siteNumber <- "01491000"
parameterCd <- "00618"
Sample <-readNWISSample(siteNumber,parameterCd,
startDate, endDate)
Information on USGS parameter codes can be found here:
https://help.waterdata.usgs.gov/codes-and-parameters/parameters
readWQPSample
The readWQPSample function retrieves Water Quality
Portal sample data (STORET, NWIS, STEWARDS). The arguments for this
function are siteNumber, characteristicName, startDate, endDate,
interactive.
site <- 'WIDNR_WQX-10032762'
characteristicName <- 'Specific conductance'
Sample <-readWQPSample(site,characteristicName,
startDate, endDate)
To request USGS data from the Water Quality Portal, the siteNumber
must have “USGS-” pasted before the identification number. For USGS
data, the characteristicName argument can be either a list
of 5-digit parameter codes, or the characteristic name. A table that
describes how USGS parameters relate with the defined characteristic
name can be found here:
https://www.waterqualitydata.us/public_srsnames
readUserSample
The readUserSample function will import a user-generated
file and populate the Sample data frame. The difference between sample
data and discharge data is that the code requires a third column that
contains a remark code, either blank or "<", which will
tell the program that the data were “left-censored” (or, below the
detection limit of the sensor). Therefore, the data must be in the form:
date, remark, value. An example of a comma-delimited file is:
cdate;remarkCode;Nitrate
10/7/1999,,1.4
11/4/1999,<,0.99
12/3/1999,,1.42
1/4/2000,,1.59
2/3/2000,,1.54
...
The call to open this file, and populate the Sample data frame
is:
fileName <- "ChoptankRiverNitrate.csv"
filePath <- "C:/RData/"
Sample <- readUserSample(filePath,fileName,
separator=",")
When multiple constituents are to be summed, the format can be date,
remark_A, value_A, remark_b, value_b, etc… A tab-separated example might
look like the file below, where the columns are date, remark dissolved
phosphate (rdp), dissolved phosphate (dp), remark particulate phosphorus
(rpp), particulate phosphorus (pp), remark total phosphate (rtp), and
total phosphate (tp):
date rdp dp rpp pp rtp tp
2003-02-15 0.020 0.500
2003-06-30 < 0.010 0.300
2004-09-15 < 0.005 < 0.200
2005-01-30 0.430
2005-05-30 < 0.050
2005-10-30 < 0.020
...
fileName <- "ChoptankPhosphorus.txt"
filePath <- "C:/RData/"
Sample <-readUserSample(filePath,fileName,
separator="\t")
Censored Values:
Summation Explanation
In the typical case where none of the data are censored (that is, no
values are reported as “less-than” values), the ConcLow = ConcHigh =
ConcAve which are all equal to the reported value, and Uncen = 1 for all
values. For the most common type of censoring, where a value is reported
as less than the reporting limit, then ConcLow = NA, ConcHigh =
reporting limit, ConcAve = 0.5 * reporting limit, and Uncen = 0.
To illustrate how the EGRET package handles a more complex censoring
problem, let us say that in 2004 and earlier, we computed total
phosphorus (tp) as the sum of dissolved phosphorus (dp) and particulate
phosphorus (pp). From 2005 and onward, we have direct measurements of
total phosphorus (tp). A small subset of this fictional data is
provided:
Example data
| 2003-02-15 |
|
0 |
|
0 |
|
NA |
| 2003-06-30 |
< |
0 |
|
0 |
|
NA |
| 2004-09-15 |
< |
0 |
< |
0 |
|
NA |
| 2005-01-30 |
|
NA |
|
NA |
|
0 |
| 2005-05-30 |
|
NA |
|
NA |
< |
0 |
| 2005-10-30 |
|
NA |
|
NA |
< |
0 |
EGRET will “add up” all the values in a given row to form the total
for that sample when using the Sample data frame. Thus, you only want to
enter data that should be added together. If you want a data frame with
multiple constituents that are not summed, do not use
readNWISSample, readWQPSample, or
readUserSample. The raw data functions:
getWQPdata, readNWISqw,
readWQPqw, readWQPdata from the EGRET package
will not sum constituents, but leave them in their individual
columns.
For example, we might know the value for dp on 5/30/2005, but we
don’t want to put it in the table because under the rules of this data
set, we are not supposed to add it in to the values in 2005.
For every sample, the EGRET package requires a pair of numbers to
define an interval in which the true value lies (ConcLow and ConcHigh).
In a simple uncensored case (the reported value is above the detection
limit), ConcLow equals ConcHigh and the interval collapses down to a
single point. In a simple censored case, the value might be reported as
<0.2, then ConcLow=NA and ConcHigh=0.2. We use NA instead of 0 as a
way to elegantly handle future logarithm calculations.
For the more complex example case, let us say dp is reported as
<0.01 and pp is reported as 0.3. We know that the total must be at
least 0.3 and could be as much as 0.31. Therefore, ConcLow=0.3 and
ConcHigh=0.31. Another case would be if dp is reported as <0.005 and
pp is reported <0.2. We know in this case that the true value could
be as low as zero, but could be as high as 0.205. Therefore, in this
case, ConcLow=NA and ConcHigh=0.205. The Sample data frame for the
example data would be:
## Date ConcLow ConcHigh Uncen ConcAve Julian Month Day DecYear MonthSeq
## 1 2003-02-15 0.52 0.520 1 0.5200 55927 2 46 2003.124 1838
## 2 2003-06-30 0.30 0.310 0 0.3050 56062 6 182 2003.494 1842
## 3 2004-09-15 NA 0.205 0 0.1025 56505 9 259 2004.706 1857
## 4 2005-01-30 0.43 0.430 1 0.4300 56642 1 30 2005.080 1861
## 5 2005-05-30 NA 0.050 0 0.0250 56762 5 151 2005.409 1865
## 6 2005-10-30 NA 0.020 0 0.0100 56915 10 304 2005.828 1870
## waterYear SinDY CosDY
## 1 2003 0.70252747 0.7116566
## 2 2003 0.03872228 -0.9992500
## 3 2004 -0.96134052 -0.2753623
## 4 2005 0.48250774 0.8758917
## 5 2005 0.54162782 -0.8406184
## 6 2006 -0.88204802 0.4711595
INFO
The INFO data frame stores information about the measurements, such
as station name, parameter name, drainage area, and so forth. There can
be many additional, optional columns, but the columns in the table below
are required to initiate the EGRET analysis. After you run the WRTDS
calculations (as described in the WRTDS section,
additional columns are automatically inserted into the INFO data frame
(see the EGRET User Guide for complete description of each term):
| shortName |
character |
Name of site, suitable for use in graphical
headings |
| staAbbrev |
character |
Abbreviation for station name, used in
saveResults |
| paramShortName |
character |
Name of constituent, suitable for use in graphical
headings |
| constitAbbrev |
character |
Abbreviation for constituent name, used in
saveResults |
| drainSqKm |
numeric |
Drainage area in km2 |
| paStart |
integer (1-12) |
Starting month of period of analysis |
| paLong |
integer (1-12) |
Length of period of analysis in months |
INFO data frame after running
modelEstimation
| bottomLogQ |
Lowest discharge in prediction surfaces |
dimensionless |
| stepLogQ |
Step size in log discharge in prediction surfaces |
dimensionless |
| nVectorLogQ |
Number of steps in discharge, prediction surfaces |
integer |
| bottomYear |
Starting year in prediction surfaces |
years |
| stepYear |
Step size in years in prediction surfaces |
years |
| nVectorYear |
Number of steps in years in prediction surfaces |
integer |
| windowY |
Half-window width in the time dimension |
year |
| windowQ |
Half-window width in the log discharge dimension |
dimensionless |
| windowS |
Half-window width in the seasonal dimension |
years |
| minNumObs |
Minimum number of observations for regression |
integer |
| minNumUncen |
Minimum number of uncensored observations |
integer |
readNWISInfo
The function readNWISInfo combines
readNWISsite and readNWISpCode from the
dataRetrieval package, producing one data frame called
INFO.
parameterCd <- "00618"
siteNumber <- "01491000"
INFO <- readNWISInfo(siteNumber,parameterCd, interactive=FALSE)
readWQPInfo
It is also possible to create the INFO data frame using information
from the Water Quality Portal. As with readWQPSample, if
the requested site is a USGS siteNumber, “USGS-” needs to be appended to
the siteNumber.
parameterCd <- "00618"
INFO_WQP <- readWQPInfo("USGS-01491000",parameterCd)
readUserInfo
The function readUserInfo can be used to convert comma
separated files into an INFO data frame. At a minimum, EGRET analysis
uses columns: param.units, shortName, paramShortName, constitAbbrev, and
drainSqKm. For example, if the following comma-separated file (csv) was
available as a file called “INFO.csv”, located in a folder called
“RData” on the C drive (this examples is for Windows™ operation system),
the function to convert it to an INFO data frame is as follows.
param.units, shortName, paramShortName, constitAbbrev, drainSqKm
mg/l, Choptank River, Inorganic nitrogen, N, 292.67
fileName <- "INFO.csv"
filePath <- "C:/RData/"
INFO <- readUserInfo(filePath, fileName)
Inserting
Additional Info
Any supplemental column that would be useful can be added to the INFO
data frame.
INFO$riverInfo <- "Major tributary of the Chesapeake Bay"
INFO$GreensboroPopulation <- 1931
Merge Report:
eList
Finally, there is a function called mergeReport that
will look at both the Daily and Sample data frame, and populate Q and
LogQ columns into the Sample data frame. Once mergeReport
has been run, the Sample data frame will be augmented with the daily
discharges for all the days with samples, and a named list with all of
the data frames will be created. In this vignette, we will refer to this
named list as eList: it is a list with potentially 3 data
frames: Daily, Sample, and INFO. For flow history analysis, the Sample
data frame can be NA.You can use the function as.egret to
create this “EGRET” object.
None of the water quality functions in EGRET will work without first
having run the mergeReport function.
siteNumber <- "01491000"
parameterCd <- "00631" # Nitrate
startDate <- "2000-01-01"
endDate <- "2013-01-01"
Daily <- readNWISDaily(siteNumber, "00060", startDate, endDate)
Sample <- readNWISSample(siteNumber,parameterCd, startDate, endDate)
INFO <- readNWISInfo(siteNumber, parameterCd)
eList <- mergeReport(INFO, Daily,Sample)
Perhaps you already have Daily, Sample, and INFO data frames, and
surfaces matrix (created after running the WRTDS
modelEstimation) that have gone though a deprecated version
of EGRET. You can create and edit an EGRET object as follows:
eListNew <- as.egret(INFO, Daily, Sample, surfaces)
#To pull out the INFO data frame:
INFO <- getInfo(eListNew)
#Edit the INFO data frame:
INFO$importantNews <- "New EGRET workflow started"
#Put new data frame in eListNew
eListNew$INFO <- INFO
#To pull out Daily:
Daily <- getDaily(eListNew)
#Edit for some reason:
DailyNew <- Daily[Daily$DecYear > 1985,]
#Put new Daily data frame back in eListNew:
eListNew$Daily <- DailyNew
#To create a whole new egret object:
eList_2 <- as.egret(INFO, DailyNew, getSample(eListNew), NA)
Units
EGRET uses entirely SI units to store the data, but for purposes of
output, it can report results in a wide variety of units. The defaults
are mg/L for concentration, cubic meters per second (m3/s)
for discharge, kg/day for flux, and km2 for drainage area.
When discharge values are imported from USGS Web services, they are
automatically converted from cubic feet per second (cfs) to cms unless
the argument convert in function readNWISDaily
is set to FALSE. This can cause confusion if you are not careful. For
all functions that provide output, you can define two arguments to set
the output units: qUnit and fluxUnit. qUnit and fluxUnit are defined by
either a numeric code or name. You can call two functions that can be
called to see the options: printqUnitCheatSheet and
printFluxUnitCheatSheet.
## The following codes apply to the qUnit list:
## 1 = cfs ( Cubic Feet per Second )
## 2 = cms ( Cubic Meters per Second )
## 3 = thousandCfs ( Thousand Cubic Feet per Second )
## 4 = thousandCms ( Thousand Cubic Meters per Second )
When a function has an input argument qUnit, you can define the
discharge units that will be used in the figure or table that is
generated by the function with the index (1-4) as shown above. Base your
choice on the units that are customary for your intended audience, but
also so that the discharge values don’t have too many digits to the
right or left of the decimal point.
printFluxUnitCheatSheet()
## The following codes apply to the fluxUnit list:
## 1 = poundsDay ( pounds/day )
## 2 = tonsDay ( tons/day )
## 3 = kgDay ( kg/day )
## 4 = thousandKgDay ( thousands of kg/day )
## 5 = tonsYear ( tons/year )
## 6 = thousandTonsYear ( thousands of tons/year )
## 7 = millionTonsYear ( millions of tons/year )
## 8 = thousandKgYear ( thousands of kg/year )
## 9 = millionKgYear ( millions of kg/year )
## 10 = billionKgYear ( billions of kg/year )
## 11 = thousandTonsDay ( thousands of tons/day )
## 12 = millionKgDay ( millions of kg/day )
## 13 = kgYear ( kg/year )
When a function has an input argument fluxUnit, you can define the
flux units with the index (1-12) as shown above. Base the choice on the
units that are customary for your intended audience, but also so that
the flux values don’t have too many digits to the right or left of the
decimal point. Tons are always “short tons” and not “metric tons”.
Note that when the analysis being done is for a single month or
season (see discussion of “Period of Analysis” in Flow History that the flux values represent a
rate (mass per unit time). Thus, if the period of analysis is the
combination of April and May, then a reported flux of 1000 kg/year
translates to a mass of 1000 * 61 / 365.25 = 167 kg. Thus, in general,
if the flux is reported as a mass per year, then to get a mass for the
season that value should be multiplied by the number of days in the
season and then divided by 365.25.
Flow History
This section describes functions included in the EGRET package that
provide a variety of table and graphical outputs for examining discharge
statistics based on time-series smoothing. These functions are designed
for studies of long-term change and work best for daily discharge data
sets of 50 years or longer. This type of analysis might be useful for
studying issues such as the influence of land use change, water
management change, or climate change on discharge conditions. This
includes potential impacts on average discharges, high discharges, and
low discharges, at annual time scales as well as seasonal or monthly
time scales.
Consider this example from Columbia River at The Dalles, OR.
siteNumber <- "14105700"
startDate <- ""
endDate <- ""
Daily <- readNWISDaily(siteNumber,"00060",startDate,endDate)
INFO <- readNWISInfo(siteNumber,"",interactive=FALSE)
INFO$shortName <- "Columbia River at The Dalles, OR"
eList <- as.egret(INFO, Daily, NA, NA)
You first must determine the period of analysis to use (PA). What is
the period of analysis? If you want to examine your data set as a time
series of water years, then the period of analysis is October through
September. If you want to examine the data set as calendar years then
the period of analysis is January through December. You might want to
examine the winter season, which you could define as December through
February, then those 3 months become the period of analysis. The only
constraints on the definition of a period of analysis are these: it must
be defined in terms of whole months; it must be a set of contiguous
months (like March-April-May), and have a length that is no less than 1
month and no more than 12 months. Define the PA by using two arguments:
paLong and paStart. paLong is the length of the PA, and paStart is the
first month of the PA.
Period of Analysis Information
| Calendar Year |
1 |
12 |
| Water Year |
10 |
12 |
| Winter |
12 |
3 |
| September |
9 |
1 |
To set a period running from December through February:
eList <- setPA(eList,paStart=12,paLong=3)
To set the default value (water year):
The next step can be to create the annual series of discharge
statistics. These are returned in a matrix that contain the statistics
described below. The statistics are based on the period of analysis set
with the setPA function.
Index of discharge statistics information
| 1 |
minimum 1-day daily mean discharg |
| 2 |
minimum 7-day mean of the daily mean discharges |
| 3 |
minimum 30-day mean of the daily mean discharges |
| 4 |
median of the daily mean discharges |
| 5 |
mean of the daily mean discharges |
| 6 |
maximum 30-day mean of the daily mean discharges |
| 7 |
maximum 7-day mean of the daily mean discharges |
| 8 |
maximum 1-day daily mean discharge |
Plotting Options
This section shows examples of the available plots appropriate for
studying discharge history. The plots here use the default variable
input options. For any function, you can get a complete list of input
variables (as described in the flow
history section) in a help file by typing a ? before the function
name in the R console. The EGRET user guide has more detailed
information for each plot type (https://pubs.usgs.gov/tm/04/a10/).
Finally, see the saving plots section for
information on saving plots.
The simplest way to look at these time series is with the function
plotFlowSingle. The statistic index (istat) must be defined
by the user, but for all other arguments there are default values so the
user isn’t required to specify anything else. To see a list of these
optional arguments and other information about the function, type
?plotFlowSingle in the R console. All of the graphs in
plotFlowSingle, plotFourStats, and all but one
of the graphs in plotFour, show both the individual annual values of the
selected discharge statistic (e.g. the annual mean or 7-day minimum),
but they also show a curve that is a smooth fit to those data. The curve
is a LOWESS (locally weighted scatterplot smooth). The algorithm for
computing it is provided in the User Guide (https://pubs.usgs.gov/tm/04/a10/),
in the section titled “The Smoothing Method Used in Flow History
Analyses.” The default is that the annual values of the selected
discharge statistics are smoothed with a “half-window width” of 20
years. The smoothing window is an optional user-defined option.
plotSDLogQ produces a graphic of the running standard
deviation of the log of daily discharge over time to visualize how
variability of daily discharge is changing over time. By using the
standard deviation of the log discharge the statistic becomes
dimensionless. The standard deviation plot is a way of looking at
variability quite aside from average values, so, in the case of a system
where discharge might be increasing over a period of years, this graphic
provides a way of looking at the variability relative to that changing
mean value. The standard deviation of the log discharge is much like a
coefficient of variation, but it has sample properties that make it a
smoother measure of variability. People often comment about how things
like urbanization or enhanced greenhouse gases in the atmosphere are
bringing about an increase in variability, and this analysis is one way
to explore that idea. plotFour, plotFourStats,
and plot15 are all designed to plot several graphs from the
other functions in a single figure.
plotFlowSingle(eList, istat=5,qUnit="thousandCfs")
plotSDLogQ(eList)
Here is an example of looking at daily mean discharge for the full
water year and then looking at mean daily discharge for the winter
season only for the Merced River at Happy Isles Bridge in Yosemite
National Park in California. First, we look at the mean daily discharge
for the full year (after having read in the data and metadata):
# Merced River at Happy Isles Bridge, CA:
siteNumber<-"11264500"
Daily <-readNWISDaily(siteNumber,"00060",startDate="",endDate="")
INFO <- readNWISInfo(siteNumber,"",interactive=FALSE)
INFO$shortName <- "Merced River at Happy Isles Bridge, CA"
eList <- as.egret(INFO, Daily, NA, NA)
plotFlowSingle(eList, istat=5)
# Then, we can run the same function, but first set
# the pa to start in December and only run for 3 months.
eListMerced <- setPA(eList,paStart=12,paLong=3)
plotFlowSingle(eListMerced,istat=5,qMax=200)
What these figures show us is that on an annual basis there is very
little indication of a long-term trend in mean discharge, but for the
winter months there is a pretty strong indication of an upward trend.
This could well be related to the climate warming in the Sierra Nevada,
resulting in a general increase in the ratio of rain to snow in the
winter and more thawing events.
plotFourStats(eList, qUnit=3)
plotQTimeDaily is simply a time series plot of
discharge. But, it is most suited for showing events above some
discharge threshold. In the simplest case, it can plot the entire
record, but given the line weight and use of an arithmetic scale it
primarily provides a visual focus on the higher values.
The Mississippi example illustrates a very long record with a long
gap of more than 60 years with no discharges above 300,000 cfs, followed
by the last followed by the 49 years from 1965 through 2013 with 6
events above that threshold. plotQTimeDaily requires
startYear and endYear, along with some other optional arguements (see
?plotQTimeDaily for more details).
#Mississippi River at Keokuk Iowa:
siteNumber<-"05474500"
Daily <-readNWISDaily(siteNumber,"00060",startDate="",endDate="")
INFO <- readNWISInfo(siteNumber,"",interactive=FALSE)
INFO$shortName <- "Mississippi River at Keokuk Iowa"
eList <- as.egret(INFO, Daily, NA, NA)
plotQTimeDaily(eList, qUnit=3,qLower=300)
Table Options
Sometimes it is easier to consider results in table formats rather
than graphically. Similar to the function plotFlowSingle,
the printSeries will print the requested discharge
statistics (Table \(\ref{table:istat}\)), as well as return the
results in a data frame. A small sample of the output is printed
below.
seriesResult <- printSeries(eList, istat=3, qUnit=3)
Mississippi River at Keokuk Iowa
Water Year
30-day minimum
Thousand Cubic Feet per Second
year annual smoothed
value value
1879 22.6 30.1
1880 31.7 28.7
1881 23.0 27.5
...
2011 51.0 32.4
2012 34.3 32.1
2013 16.2 31.8
Another way to look at the results is to consider how much the
smoothed values change between various pairs of years. These changes can
be represented in four different ways.
- As a change between the first and last year of the pair, expressed
in the discharge units selected.
- As a change between the first and last year of the pair, expressed
as a percentage of the value in the first year
- As a slope between the first and last year of the pair, expressed in
terms of the discharge units per year.
- As a slope between the first and last year of the pair, expressed as
a percentage change per year (a percentage based on the value in the
first year).
Another argument can be very useful in this function: yearPoints. In
the default case, the set of years that are compared are at 5 year
intervals along the whole data set. If the data set was quite long this
can be a daunting number of comparisons. For example, in an 80 year
record, there would be 136 such pairs. Instead, we could look at changes
between only 3 year points: 1890, 1950, and 2010:
tableFlowChange(eList, istat=3, qUnit=3,yearPoints=c(1890,1950,2010))
##
## Mississippi River at Keokuk Iowa
## Water Year
## 30-day minimum
##
## Streamflow Trends
## time span change slope change slope
## 10^3 cfs 10^3cfs/yr % %/yr
## 1890 to 1950 0.84 0.014 3.7 0.061
## 1890 to 2010 9.5 0.08 41 0.35
## 1950 to 2010 8.7 0.15 36 0.61
See here for instructions on
converting an R data frame to a table in Microsoft™ software. Excel,
Microsoft, PowerPoint, Windows, and Word are registered trademarks of
Microsoft Corporation in the United States and other countries.
Summary of Water
Quality Data (without using WRTDS)
Before you run the WRTDS model, it is helpful to examine the measured
water quality data graphically to better understand its behavior,
identify possible data errors, and visualize the temporal distribution
of the data (identify gaps). It is always best to clear up these issues
before moving forward.
The examples below use the Choptank River at Greensboro, MD. The
Choptank River is a small tributary of the Chesapeake Bay. Inorganic
nitrogen (nitrate and nitrite) has been measured from 1979 onward.
First, we need to load the discharge and nitrate data into R. Before we
can graph or use it for WRTDS analysis, we must bring the discharge data
into the Sample data frame. We do this by using the
mergeReport function which merges the discharge information
and also provides a compact report about some major features of the data
set.
#Choptank River at Greensboro, MD:
siteNumber <- "01491000"
startDate <- "1979-10-01"
endDate <- "2011-09-30"
param<-"00631"
Daily <- readNWISDaily(siteNumber,"00060",startDate,endDate)
INFO<- readNWISInfo(siteNumber,param,interactive=FALSE)
INFO$shortName <- "Choptank River"
Sample <- readNWISSample(siteNumber,param,startDate,endDate)
eList <- mergeReport(INFO, Daily, Sample)
Plotting Options
This section shows examples of the available plots appropriate for
analyzing data prior to performing a WRTDS analysis. The plots here use
the default variable input options. For any function, you can get a
complete list of input variables in a help file by typing a ? before the
function name in the R console. See Water Quality
Plotting Input for information on the available input variables for
these plotting functions.
Note that for any of the plotting functions that show the sample
data, if a value in the data set is a non-detect (censored), it is
displayed on the graph as a vertical line. The top of the line is the
reporting limit and the bottom is either zero, or if the graph is
plotting log concentration values the minimum value on the y-axis. This
line is an “honest” representation of what we know about about that
observation and doesn’t attempt to use a statistical model to make an
estimate below the reporting limit.
boxConcMonth(eList)
boxQTwice(eList,qUnit=1)
Note that the statistics to create the boxplot in
boxQTwice are performed after the data are
log-transformed.
plotConcTime(eList)
plotConcQ(eList, qUnit=1)
It is interesting to note the change in the convention for rounding
of data values that occurred around 1995.
plotFluxQ(eList, fluxUnit=4)
multiPlotDataOverview(eList, qUnit=1)
The multiPlotDataOverview function uses a log scale as
default. To change the concentration axes to an arithmetic scale, use
logScaleConc=FALSE in the
multiPlotDataOverview function call.
Table Options
Another useful tool for checking the data before running the WRTDS
estimations is flowDuration. This is a utility function
that can help you define the discharge ranges that we want to explore.
It prints out key points on the discharge duration curve. Define the
points for a particular part of the year using the
centerDate and span arguments, although the
points can be defined for the entire year (default).
flowDuration(eList, qUnit=1)
##
## Flow Duration for Choptank River
##
## Flow duration is based on full year
##
## Discharge units are Cubic Feet per Second
## min 5% 10% 25% 50% 75% 90% 95% max
## 0.35 12.00 16.00 33.00 85.00 163.00 290.00 462.00 8700.00
flowDuration(eList, qUnit=1, centerDate="09-30", span=30)
##
## Flow Duration for Choptank River
##
## Flow duration period is centered on September 30
## And spans the period from August 31 To October 30
##
## Discharge units are Cubic Feet per Second
## min 5% 10% 25% 50% 75% 90% 95% max
## 2.5 8.8 11.0 15.0 27.0 67.0 138.0 223.0 5600.0
Weighted Regressions on
Time, Discharge and Season (WRTDS)
WRTDS creates a model of the behavior of concentration as a function
of three components: time trend, discharge, and season. You can use
WRTDS to estimate annual or seasonal mean concentrations and fluxes as
well as describe long-term trends in the behavior of the system. In this
section, we will step though the process required for a WRTDS analysis.
WRTDS Results provides details about the
available methods for viewing and evaluating the model results.
Once you have looked at your data using the tools described in Summary of Water Quality Data, and have determined there
are sufficient representative data, it is time to run the WRTDS model.
Assuming you are using the defaults, with data frames called Daily,
Sample, and INFO, the modelEstimation function runs the
WRTDS modeling algorithm:
eList <- modelEstimation(eList)
Details of the options available when running
modelEstimation can be found in WRTDS Estimation Input. This function is
slow, and shows the progress in percent complete. See the references and
manual for more information. It’s important to understand that this is
the one function that will globally change your Daily, Sample, and INFO
data frames. It also creates a new matrix surfaces, and a
new data frame AnnualResults.
Finally, it is a good idea to save your results because of the
computational time that has been invested in producing these results.
The workspace is saved to a directory that you designate savePath and
the file name is determined by the abbreviations for station and
constituent that were required entries when the
readNWISInfo function was used. The command for saving the
workspace is:
#An example directory name
savePath <- "C:/Users/egretUser/WRTDS_Output/"
saveResults(savePath, eList)
This saves all of the objects in your workspace. If you have saved
workspaces from R versions earlier than 3.0, a warning will appear when
you open them in R 3.0 (or later). Re-saving the workspace using R 3.0
(or later) should get rid of the warning.
Using saveResults, the workspace is saved with
INFO$staAbbrev and INFO$constitAbbrev as the
filename (separated by a period), and the extension .RData. So, if
staAbbrev was “Chop” and the constitAbbrev was “NO3” the file name would
be “Chop.NO3.RData”. To load the data in some future session the
commands could be:
loadPath <- "C:/Users/egretUser/WRTDS_Output/"
staAbbrev <- "Chop"
constitAbbrev <- "NO3"
pathToFile <- paste0(loadPath,staAbbrev,".",
constitAbbrev,".RData")
load(pathToFile)
WRTDS Results
At this point (after having run modelEstimation) we can
start considering how to view the annual averages for the variables that
have been calculated. See Post-WRTDS
Plotting Input for common input variables for these functions.
Additionally, check the help files (in the R console, type ? followed by
the function name).
Plotting Options
Check the help files or manual for more details on the following
functions. See Saving Plots for information
on saving plots. In these examples, we will return to looking at the
data in the water year by using the setPA function. Most
plotting functions will use the period of analysis information in the
INFO data frame to determine what data are plotted. There are only four
graph or table functions that don’t allow the user to specify a Period
of Analysis (PA). These are: plotContour,
plotDiffContour, plotConcTimeSmooth,
plotConcQSmooth.
# Return to water year:
eList <- setPA(eList)
yearStart <- 2008
yearEnd <- 2010
plotConcTimeDaily(eList, yearStart, yearEnd)
plotFluxTimeDaily(eList, yearStart, yearEnd)
plotConcPred(eList)
plotFluxPred(eList)
plotResidPred(eList)
plotResidQ(eList, qUnit=1)
plotResidTime(eList)
boxResidMonth(eList)
plotConcHist(eList)
plotFluxHist(eList)
The placement of the legend is controlled by legendLeft and
legendTop. If both are set to 0 (the default values), the legend is
placed near the lower left corner of the graphic. Otherwise, the value
specified for legendLeft places the left edge of the legend, and
legendTop specifies the top edge of the legend. The units for legendLeft
and legendTop are discharge (in units specified by qUnit) and
concentration, respectively. The legend can also be turned off with
printLegend=FALSE. These are also functions that do not recognize the
period of analysis in the INFO data frame. However, by choosing
centering dates and appropriate half-windows, seasonal behavior can
easily be observed in these plots.
# Multi-line plots:
date1 <- "1985-09-01"
date2 <- "1997-09-01"
date3 <- "2010-09-01"
qBottom <- 0.2
qTop <- 10
plotConcQSmooth(eList, date1, date2, date3, qBottom, qTop,
concMax=2,legendTop = 0.5)
plotConcQSmooth(eList, date1, date2, date3,
qBottom, qTop, legendTop = 0.5,logScale=TRUE)
q1 <- 2
q2 <- 10
q3 <- 20
centerDate <- "07-01"
yearEnd <- 1980
yearStart <- 2010
plotConcTimeSmooth(eList, q1, q2, q3, centerDate,
yearStart, yearEnd, legendTop = 0.4)
plotConcTimeSmooth(eList, q1, q2, q3, centerDate,
yearStart, yearEnd,
legendTop = 0.4,logScale=TRUE)
fluxBiasMulti(eList, qUnit=1)
The contour plot functions also do not recognize the PA from the INFO
data frame. They represent the overall results of the WRTDS analysis. To
specify contourLevels in the contour plots use the
seq function (type ?seq for details). In
general, use of the seq function would look like this:
contourLevels = seq(from,to,by). In the example shown above
we are requesting contour levels that run from 0 to 2 in steps of
0.2.
clevel<-seq(0,2,0.2)
plotContours(eList, yearStart=1980,yearEnd=2010,qBottom=0.5,qTop=20,
contourLevels = clevel)
The function plotDiffContours plots the difference
between two selected years (year0 and year1). It can help clarify what
combinations of seasons and flow conditions have been showing increases
and decreases over the period covered.
plotDiffContours(eList, year0=1990,year1=2010,
qBottom=0.5,qTop=20,maxDiff=0.6)
Table Options
Sometimes it is easier to consider the results in table form rather
than graphically. The function `tableResults} produces a simple text
table that contains the annual values for the results. Each row of the
output represents a year and includes: year, average discharge, average
concentration, flow-normalized concentration, average flux, and
flow-normalized flux. If paLong != 12 make sure you take note of the
explanation of the seasonal flux values discussed at the end of Units. A small sample of the output is printed
below.
tableResults(eList)
returnDF <- tableResults(eList)
Choptank River
Inorganic nitrogen (nitrate and nitrite)
Water Year
Year Discharge Conc FN_Conc Flux FN_Flux
cms mg/L 10^6 kg/yr
1980 4.25 0.949 1.003 0.1154 0.106
1981 2.22 1.035 0.999 0.0675 0.108
...
2010 7.19 1.323 1.438 0.2236 0.149
2011 5.24 1.438 1.457 0.1554 0.148
Table created from head(returnDF)
| 1980 |
4 |
0.95 |
1.003 |
0.115 |
0.106 |
| 1981 |
2 |
1.03 |
0.999 |
0.068 |
0.108 |
| 1982 |
3 |
1.04 |
0.993 |
0.098 |
0.110 |
| 1983 |
5 |
1.01 |
0.993 |
0.133 |
0.112 |
| 1984 |
6 |
0.99 |
1.002 |
0.160 |
0.114 |
| 1985 |
2 |
1.06 |
1.017 |
0.049 |
0.116 |
The other table option is tableChange. This is a
function that provides for the computation of changes or slopes between
any selected pairs of time points. These computations are made only on
the flow-normalized results. A detailed explaination of
“flow-normalized” result is in the official EGRET user guide.
tableChange(eList, yearPoints=c(2000,2005,2010))
##
## Choptank River
## Inorganic nitrogen (nitrate and nitrite)
## Water Year
##
## Concentration trends
## time span change slope change slope
## mg/L mg/L/yr % %/yr
##
## 2000 to 2005 0.088 0.018 7 1.4
## 2000 to 2010 0.19 0.019 15 1.5
## 2005 to 2010 0.098 0.02 7.3 1.5
##
##
## Flux Trends
## time span change slope change slope
## 10^6 kg/yr 10^6 kg/yr /yr % %/yr
## 2000 to 2005 0.0065 0.0013 4.7 0.93
## 2000 to 2010 0.0097 0.00097 6.9 0.69
## 2005 to 2010 0.0032 0.00063 2.2 0.43
Finally, tableChangeSingle operates exactly the same as
tableChange except for the addition argument flux. This
function provides either concentration results or flux results, but not
both. This can be useful when you are producing many output tables for a
report that is entirely focused on concentration or one that is entirely
focused on flux. The arguments are identical to those for tableChange,
except for the final two arguments. The argument flux
defaults to TRUE. When flux=TRUE the output is only for flux, and when
flux=FALSE the output is only for concentration. See Creating tables for instructions on
converting an R data frame to a table in Microsoft™ software.
returnDF <- tableChangeSingle(eList, yearPoints=c(2000,2005,2010))
Table created from tableChangeSingle
function
| 2000 |
2005 |
0 |
0.018 |
7.0 |
1.4 |
| 2000 |
2010 |
0 |
0.019 |
15.0 |
1.5 |
| 2005 |
2010 |
0 |
0.020 |
7.3 |
1.5 |
Extending Plots Past
Defaults
The basic plotting options were shown in the section WRTDS Results. This section demonstrates some
ways to extend the capabilities of the EGRET plots. EGRET plots use R’s
basic plotting options. You set many of the formatting details of
plotting routines in R by using “Graphical Parameters”. To read about
all of these graphical parameters see ?par. When the
graphical functions in EGRET are coded, a set of default values for many
of these parameters are chosen, but you can override all of these
default values. Additionally, you can add features to a plot after
calling the plot function. To change the plot margins (mar), font, or
other graphical parameters initially assigned, set the argument
customPar to TRUE.
A few of R’s base graphical parameters are especially useful within
the plot functions.
Useful plotting parameters to adjust in EGRET plotting
functions. For details of any of these see ?par.
| cex |
Size of data point symbols, relative to default |
decimal number |
| cex.main |
Size of font for plot title, relative to default |
decimal number |
| cex.lab |
Size of font for axis label text, relative to
default |
decimal number |
| cex.axis |
Size of font for axis annotation (numbers), relative
to default |
decimal number |
| col |
Color of data point symbols or lines |
color name in "}" |
| lwd |
Width of lines, relative to default |
decimal number |
| pch |
Type of symbol to use for data points |
integer values |
| lty |
Line type number (such as dash or dot) |
integer values |
After the plot is made, many other functions that might be useful to
call, such as to add text, legend, lines, etc.
Useful functions to add on to default plots. Type ? then the
function name to get help on the individual function.
| mtext |
add text based on specified side of plot |
| text |
add text to a specific point on plot |
| legend |
add a legend |
| grid |
add grid |
| abline |
add line |
| arrows |
add arrow |
Some basic examples are shown below.
plotConcQ(eList, cex.axis=2,cex.main=1.5,logScale=TRUE)
plotConcQ(eList, cex.lab=2,cex=2,logScale=TRUE)
First, the margin is adjusted to c(8,8,8,8), requiring customPar set
to TRUE. The margin vector represents the margin spacing of the 4
sides of a plot in the order: bottom, left, top, right.
Next, the text labels were adjusted, color set to "blue",
point and line size increased, and the point type changed form a solid
circle(pch=20) to solid diamond (pch=18). A grid, legend, arrow, and
text are added after the plot is produced.
plotConcQ(eList, logScale=TRUE)
par(mar=c(8,8,8,8))
plotConcQ(eList, customPar=TRUE,col="blue",cex=1.1,
cex.axis=1.4,cex.main=1.5,cex.lab=1.2,
pch=18,lwd=2,logScale=TRUE)
grid(lwd=2)
legend(4.5,.09,"Choptank Nitrogen", pch=18, col="blue",bg="white")
arrows(3, 0.14, 1, .05,lwd=2)
text(12,.14,"Censored Value")
Only a few fonts are consistent on all operating systems. The
following figure shows how to change to the Serif font, as well as how
to use the mtext function. To see the available fonts for pdf output on
your computer, type names(pdfFonts()).The available fonts
are quite limited in base R. To expand the font choices, a useful R
library, “extrafont” can help.
# Switching to serif font:
par(family="serif")
plotFluxPred(eList, customPar=TRUE)
mtext(side=3,line=-3,"Serif font example",cex=3)
You can also extend the contour plots. The default y-axis is
determined from qTop and qBottom. Occasionally, you may need to use a
custom axis by specifying yTicks. It is also nice to be able to adjust
the color scheme of the contour plots. There are some color schemes
built into base R such as heat.colors, topo.colors, terrain.colors, and
cm.colors. Alternatively, you can set colors by using the
colorRampPalette function. For example, a black and white
color scheme might be required. In another example, the
plotDiffContours might make more sense to go from yellow to
white for the negative values, and white to blue for the positive
values. Examples are shown below for modifying a contour plot and
modifying a difference contour plot.
colors <- colorRampPalette(c("white","black"))
yTicksModified <- c(.5,1,10,25)
plotContours(eList, 2001,2010,0.5,50,
contourLevels = seq(0,2.5,0.5),qUnit=2,
yTicks=yTicksModified,
color.palette=colors,
flowDuration=FALSE,
tcl=0.2,tick.lwd=2.5)
colors <- colorRampPalette(c("yellow","white","blue"))
maxDiff<-0.6
par(oma=c(1,1,1,1))
plotDiffContours(eList, year0=2001,year1=2010,qBottom=0.5,qTop=50,
maxDiff,lwd=2,qUnit=2,
color.palette=colors,
flowDuration=FALSE, customPar=TRUE)
It is also possible to create custom multi-panel plots. In the
simplest example, you can use the tinyPlot=TRUE option.
par(mfcol = c(2, 2), oma = c(0, 1.7, 6, 1.7))
plotFluxQ(eList, tinyPlot=TRUE,printTitle=FALSE,
fluxUnit=9,logScale=TRUE,fluxMax=1)
plotConcQ(eList, tinyPlot=TRUE,printTitle=FALSE)
plotFluxHist(eList, tinyPlot=TRUE,printTitle=FALSE,fluxMax=1)
plotConcHist(eList, tinyPlot=TRUE,printTitle=FALSE,concMax=3)
mtext("Custom multi-pane graph using tinyPlot=TRUE", outer=TRUE, font=2)
Finally, the following figure shows a method to create a panel of
plots with a finer control.
par(mar=c(3.5,3.5,0.2,0.2), # whitespace around the plots
oma=c(1,1,3,1), # outer margin
mgp=c(2,0.5,0), # spacing between the label numbers and plots
mfcol = c(2,2)) # rows/columns
plotFluxQ(eList, tinyPlot=TRUE,printTitle=FALSE,
fluxUnit=9,logScale=TRUE,fluxMax=1,
showXLabels=FALSE,showXAxis=FALSE,
showYLabels=TRUE,customPar=TRUE)
plotConcQ(eList, tinyPlot=TRUE,printTitle=FALSE, customPar=TRUE,
removeLastY=TRUE,removeLastX=TRUE,
showYLabels=TRUE)
plotFluxHist(eList, tinyPlot=TRUE,printTitle=FALSE,fluxMax=1,
showYLabels=FALSE,showYAxis=FALSE,
showXLabels=FALSE,showXAxis=FALSE, customPar=TRUE)
plotConcHist(eList, tinyPlot=TRUE,printTitle=FALSE,concMax=3,
showYLabels=FALSE, showYAxis=FALSE, customPar=TRUE)
mtext("Custom multi-pane graph using customPar", outer=TRUE, font=2)
Getting Started in
R
This section describes the options for installing the EGRET
package.
New to R?
If you are new to R, you will need to first install the latest
version of R, which can be found here: https://www.r-project.org/.
At any time, you can get information about any function in R by
typing a question mark before the function’s name. This opens a file
that describes the function, the required arguments, and provides
working examples.
To see the raw code for a particular function, type the name of the
function, without parentheses:
R User: Installing
EGRET
To install the EGRET packages and its dependencies:
install.packages("EGRET")
After installing the package, you need to open the library each time
you re-start R. This is done with the simple command:
Common Function
Variables
This section describes variables that are common for a variety of
function types.
Flow History
Plotting Input
Variables used in flow history plots (plot15,
plotFour, plotFourStats,
plotQTimeDaily, plotSDLogQ)
| istat |
The discharge statistic to be plotted: 1-8. Must be
specified, see Table \(\ref{table:istat}\). |
|
| yearStart |
The decimal year (decYear) value where you want the
graph to start |
NA |
| yearEnd |
The decimal year (decYear) value where you want the
graph to end |
NA |
| qMax |
User specified upper limit on y axis (can be used when
we want several graphs to all share the same scale). Value is specified
in the discharge units that the user selects. |
NA |
| printTitle |
can be TRUE or FALSE, you may want FALSE if it is
going to be a figure with a caption or if it is a part of a multipanel
plot. |
TRUE |
| tinyPlot |
Can be TRUE or FALSE, the TRUE option assures that
there will be a small number of tick marks, consistent with printing in
a small space |
FALSE |
| runoff |
Can be TRUE or FALSE. If true then discharge values
are reported as runoff in mm/day. This can be very useful in multi-site
analyses. |
FALSE |
| qUnit |
An index indicating what discharge units to use.
Options run from 1 to 6 (see section \(\ref{#units}\)). The choice should be based
on the units that are customary for the audience but also, the choice
should be made so that the discharge values don’t have too many digits
to the right or left of the decimal point. |
1 |
| printStaName |
Can be TRUE or FALSE, if TRUE the name of the
streamgage is stated in the plot title. |
TRUE |
| printPA |
Can be TRUE or FALSE, if TRUE the period of analysis
is stated in the plot title. |
TRUE |
| printIstat |
Can be TRUE or FALSE, if TRUE the name of the
statistic (e.g. 7-day minimum discharge) is stated in the plot
title. |
TRUE |
Water Quality
Plotting Input
Selected variables used in water quality analysis
plots
| qUnit |
Determines what units will be used for discharge, see
Units |
2 |
| printTitle |
If TRUE the plot has a title. If FALSE no title
(useful for publications where there will be a caption) |
TRUE |
| qLower |
The lower bound on the discharge on the day of
sampling that will be used in forming a subset of the sample data set
that will be displayed in the graph. It is expressed in the units
specified in qUnit. If qLower = NA, then the lower bound is set to
zero. |
|
| qUpper |
The upper bound on the discharge on the day of
sampling that will be used in forming a subset of the sample data set
that will be displayed in the graph. It is expressed in the units
specified in qUnit. If qUpper = NA, then the upper bound is set to
infinity. |
|
| paLong |
The length of the time period that will be used in
forming a subset of the sample data set that will be displayed in the
graph, expressed in months. |
12 |
| paStart |
The starting month for the time period that will be
used in forming a subset of the sample data set that will be displayed
in the graph. It is expressed in months (calendar months). |
10 |
| concMax |
The upper limit on the vertical axis of graphs showing
concentration values in mg/L (NA sets value to just above maximum). |
NA |
| concMin |
The lower limit on the vertical axis of graphs showing
concentration values in mg/L (NA sets value to just below minimum for
log scales, zero for linear). |
NA |
| fluxUnit |
Determines what units will be used for flux (see
Section \(\ref{#units}\)). |
9 |
| fluxMax |
The upper limit on the vertical axis of graphs showing
flux values. |
|
Post-WRTDS Plotting
Input
Selected variables used in plots for analysis of WRTDS model
results
| qUnit |
Determines what units will be used for discharge, see
section Units |
2 |
| fluxUnit |
An index indicating what flux units will be used , see
section Units |
3 |
| stdResid |
This is an option. If FALSE, it prints the regular
residuals (they are in ln concentration units). If TRUE, it is the
standardized residuals. These are the residuals divided by their
estimated standard error (each residual has its own unique standard
error). In theory, the standardized residuals should have mean zero and
standard deviation of 1 |
FALSE |
| printTitle |
If TRUE the plot has a title. If FALSE no title
(useful for publications where there will be a caption) |
TRUE |
| startYear |
The starting date for the graph, expressed as decimal
years, for example, 1989 |
NA |
| endYear |
The ending date for the graph, expressed as decimal
years, for example, 1996 |
NA |
| moreTitle |
A character variable that adds additional information
to the graphic title. Typically used to indicate the estimation
method. |
|
| fluxMax |
The upper limit on the vertical axis of graphs showing
flux values. |
NA |
| concMax |
The upper limit on the vertical axis of graphs showing
concentration values. |
NA |
| plotFlowNorm |
If TRUE the graph shows the annual values as circles
and the flow-normalized values as a green curve. If false, it only shows
the annual values. |
TRUE |
Variables used in EGRET contour plots:
plotContours and plotDiffContours
| qUnit |
Determines what units will be used for discharge, see
section Units |
2 |
| qBottom |
The lower limit of the discharge value for the graphs
in the units specified by qUnit |
|
| qTop |
The upper limit of the discharge value for the graphs
in the units specified by qUnit |
|
| printTitle |
If TRUE the plot has a title. If FALSE no title
(useful for publications where there will be a caption) |
TRUE |
| yearStart |
The starting date for the graph, expressed as decimal
years, for example, 1989 |
|
| yearEnd |
The ending date for the graph, expressed as decimal
years, for example, 1996 |
|
| whatSurface |
This should generally be at its default value. At
whatSurface = 3, the plotted surface shows the expected value of
concentration. For whatSurface = 1, it shows the yHat surface (natural
log of concentration). For whatSurface = 2, it shows the SE surface (the
standard error in log concentration). |
3 |
| contourLevels |
With the default value the contour intervals are set
automatically, which generally will NOT be a very good choice, but they
may provide a starting point. If you want to specify contourLevels, use
the seq function. In general it would look like:
contourLevels = seq(from,to,by). |
NA |
| maxDiff |
In the plotDiffCountours function instead
of using contourLevels, the contours are set by maxDiff which is the
absolute value of the maximum difference to be plotted. Contour
intervals are set to run from -maxDiff to maxDiff. |
|
| span |
Specifies the smoothness of the discharge duration
information that goes on this graph. A larger value will make it
smoother. The default should work well in most cases. |
60 |
| pval |
The probability value for the discharge frequency
information shown on the plot. When flowDuration=TRUE, the plot has two
black curves on it. In the default value case these are at the 5 and 95
percent levels on the seasonal discharge duration curve. pval = 0.01
would place these at the 1 and 99 percent points. pval = 0.1 would place
them at 10 and 90. |
0.05 |
| vert1 |
This simply plots a vertical black line on the graph
at a particular time (defined in decimal years). It is used to
illustrate the idea of a “vertical slice” through the contour plot,
which might then be shown in a subsequent use of
plotConcQSmooth. |
NA |
| vert2 |
This gives the location of a second vertical black
line on the graph at a particular time (defined in decimal years). |
NA |
| horiz |
This simply plots a horizontal black line on the graph
at a particular discharge value (defined in the units specified by
qUnit). It is used to illustrate the idea of the seasonal cycle in
concentrations for a given discharge and the long-term change in this
cycle. |
NA |
| flowDuration |
If TRUE it draws the discharge duration lines at the
specified probabilities. If FALSE, the discharge duration lines are left
off. |
TRUE |
Variables used in EGRET plotConcQSmooth and/or
plotConcTimeSmooth functions
| date1 |
This is the date for the first curve to be shown on
the plotConcQSmooth graph. It must be in the form
"yyyy-mm-dd" (it must be in quotes) |
|
| date2 |
This is the date for the second curve to be shown on
the plot ("yyyy-mm-dd"), If you don’t want a second curve
then the argument must be date2=NA |
|
| date3 |
This is the date for the third curve to be shown on
the plot ("yyyy-mm-dd"), If you don’t want a third curve
then the argument must be date3=NA |
|
| q1 |
This is the discharge for the first curve on the
plotConcTime smooth graph. It is in units specified by
qUnit |
|
| q2 |
This is the discharge for the second curve. If you
don’t want a second curve then the argument must be q2=NA |
|
| q3 |
This is the discharge for the third curve. If you
don’t want a third curve then the argument must be q3=NA |
|
| qUnit |
Determines what units will be used for discharge, see
printqUnitCheatSheet |
2 |
| qLow |
The discharge value that should form the left edge of
the plotConcQSmooth graph in the user-selected discharge units. |
|
| qHigh |
The discharge value that should form the right edge of
the plotConcQSmooth graph in the user-selected discharge
units. |
|
| centerDate |
This is the month and day at the center of the time
window for the plotConcTimeSmooth graph. It must be in the
form "mm-dd" in quotes |
|
| yearStart |
The starting year for the
plotConcTimeSmooth graph |
|
| yearEnd |
The ending year for the
plotConcTimeSmooth graph |
|
| legendLeft |
This determines the placement of the legend on the
graph. It establishes the left edge of the legend and is expressed in
the discharge units being used. The default (which is NA) will let it be
placed automatically. The legend can end up conflicting with one or more
of the curves. Once the location of the curves is established then this
can be set in a way that avoids conflict. |
0 |
| legendTop |
This determines the placement of the legend on the
graph. It establishes the top edge of the legend and is expressed
according to the concentration values on the y-axis. The default (which
is NA) will let it be placed automatically. The legend can end up
conflicting with one or more of the curves. Once the location of the
curves is established then this can be set in a way that avoids
conflict. |
0 |
| concMax |
Maximum value for the vertical axis of the graph. The
reason to set concMax is if you want to make several plots that have the
same vertical axis. |
NA |
| concMin |
[This one is only used when logScale=TRUE]. Minimum
value for the vertical axis of the graph. The reason to set concMin is
if you want to make several plots that have the same vertical axis. |
NA |
| bw |
Default is FALSE, which means we want a color plot. If
bw=TRUE that means it should be black and white. |
|
| printTitle |
If TRUE the plot has a title. If FALSE no title
(useful for publications where there will be a caption). |
FALSE |
| printValues |
If TRUE the estimated values that make up the plotted
lines are printed on the console. If FALSE they are not printed. This
could be useful if you wanted to compute various comparisons across time
periods. |
FALSE |
| windowY |
This is the half-window width for time in WRTDS. It
has units of years. |
7 |
| windowQ |
This is the half-window width for discharge in WRTDS.
It has units of ln(discharge). |
2 |
| windowS |
This is the half-window width for seasons in WRTDS. It
has units of years. |
0.5 |
| edgeAdjust |
Specify whether to use the modified method for
calculating the windows at the edge of the record. The modified method
tends to reduce curvature near the start and end of record |
TRUE |
Creating tables in
Microsoft™ software from an R data frame
A few steps that are required to create a table in Microsoft™
software (Excel, Word, PowerPoint, etc.) from an R data frame. There are
a variety of good methods, one of which is detailed here. The example we
will step through is creation of a table in Microsoft™ Excel based on
the data frame tableData:
tableData <- tableResults(eList)
##
## Choptank River
## Inorganic nitrogen (nitrate and nitrite)
## Water Year
##
## Year Discharge Conc FN_Conc Flux FN_Flux
## cms mg/L 10^6 kg/yr
##
## 1980 4.25 0.949 1.003 0.1154 0.106
## 1981 2.22 1.035 0.999 0.0675 0.108
## 1982 3.05 1.036 0.993 0.0985 0.110
## 1983 4.99 1.007 0.993 0.1329 0.112
## 1984 5.72 0.990 1.002 0.1597 0.114
## 1985 1.52 1.057 1.017 0.0489 0.116
## 1986 2.63 1.062 1.038 0.0903 0.119
## 1987 3.37 1.079 1.062 0.1142 0.122
## 1988 1.87 1.120 1.085 0.0660 0.125
## 1989 5.61 1.055 1.105 0.1638 0.127
## 1990 4.01 1.115 1.125 0.1349 0.129
## 1991 2.75 1.172 1.143 0.0980 0.130
## 1992 2.19 1.203 1.159 0.0810 0.132
## 1993 3.73 1.215 1.173 0.1306 0.132
## 1994 5.48 1.144 1.187 0.1634 0.133
## 1995 2.41 1.266 1.201 0.0928 0.134
## 1996 6.24 1.134 1.213 0.1980 0.135
## 1997 5.83 1.180 1.221 0.1884 0.136
## 1998 4.88 1.236 1.229 0.1593 0.137
## 1999 2.90 1.277 1.238 0.0919 0.138
## 2000 4.72 1.213 1.253 0.1627 0.139
## 2001 4.88 1.251 1.268 0.1655 0.140
## 2002 1.24 1.321 1.285 0.0483 0.141
## 2003 8.64 1.140 1.303 0.2664 0.143
## 2004 5.28 1.274 1.321 0.1832 0.144
## 2005 3.81 1.360 1.341 0.1444 0.146
## 2006 3.59 1.382 1.362 0.1409 0.147
## 2007 4.28 1.408 1.382 0.1593 0.149
## 2008 2.56 1.477 1.401 0.1008 0.149
## 2009 3.68 1.409 1.419 0.1328 0.149
## 2010 7.19 1.323 1.438 0.2236 0.149
## 2011 5.24 1.438 1.457 0.1554 0.148
First, save the data frame as a tab delimited file (you don’t want to
use comma delimited because there are commas in some of the data
elements):
write.table(tableData, file="tableData.tsv",sep="\t",
row.names = FALSE,quote=FALSE)
This will save a file in your working directory called tableData.tsv.
You can see your working directory by typing getwd() in the
R console. Opening the file in a general-purpose text editor, you should
see the following:
Year Discharge [cms] Conc [mg/L] FN_Conc [mg/L] Flux [10^6kg/yr] FN_Flux [10^6kg/yr]
1980 4.25 0.949 1.003 0.1154 0.106
1981 2.22 1.035 0.999 0.0675 0.108
1982 3.05 1.036 0.993 0.0985 0.110
...
Next, follow the steps below to open this file in Excel:
- Open Excel
- Click on the File tab
- Click on the Open option
- Navigate to the working directory (as shown in the results of
getwd())
- Next to the File name text box, change the dropdown type to All
Files (.)
- Double click tableData.tsv
- A text import wizard will open up, in the first window, choose the
Delimited radio button if it is not automatically picked, then click on
Next.
- In the second window, click on the Tab delimiter if it is not
automatically checked, then click Finished.
- Use the many formatting tools within Excel to customize the
table
From Excel, it is simple to copy and paste the tables in other word
processing or presentation software products. An example using one of
the default Excel table formats is here.
A simple table produced in Microsoft™ Excel
Saving Plots
Plots can be saved from R as JPG, PNG, PDF, and Postscript files. JPG
and PNG are easy to use in any number of programs (Microsoft™ Word or
PowerPoint, for example), but the images cannot be resized later. PDF
and Postscript images are easily re-sizable.
There are three steps to saving plots. The first is to open the
“device” (and declare the output type and file name). The second step is
to execute the function just as you would when plotting to the screen,
but no output will appear. The third step is to turn off the device. It
is also possible to put many plots within the same pdf. Some simple
examples should demonstrate this easily:
jpeg("plotFlowSingle.jpg")
plotFlowSingle(eList, 1)
dev.off()
png("plotFlowSingle.png")
plotFlowSingle(eList,1)
dev.off()
pdf("plotFlowSingle.pdf")
plotFlowSingle(eList,1)
dev.off()
postscript("plotFlowSingle.ps")
plotFlowSingle(eList,1)
dev.off()
#Many plots saved to one pdf:
pdf("manyPlots.pdf")
plotFlowSingle(eList,1)
plotFlowSingle(eList,2)
plotFlowSingle(eList,3)
plotFlowSingle(eList,4)
dev.off()
There are many additional options for each of these devices. See the
R help files for more information. One useful option for the larger
fluxBiasMulti graph is to adjust the height and width of
the output. The output of fluxBiasMulti is larger than the
default pdf or postscript devices. Therefore, specifying the height and
width eliminates R having to re-size the graphic:
postscript("fluxBiasMulti.ps", height=10,width=8)
fluxBiasMulti(eList)
dev.off()
Disclaimer
This software has been approved for release by the U.S. Geological
Survey (USGS). Although the software has been subjected to rigorous
review, the USGS reserves the right to update the software as needed
pursuant to further analysis and review. No warranty, expressed or
implied, is made by the USGS or the U.S. Government as to the
functionality of the software and related material nor shall the fact of
release constitute any such warranty. Furthermore, the software is
released on condition that neither the USGS nor the U.S. Government
shall be held liable for any damages resulting from its authorized or
unauthorized use.
Bibliography
Choquette, Anne F, Robert M Hirsch, Jennifer C Murphy, LT Johnson, and
RB Confesor Jr. 2019. “Tracking Changes in Nutrient Delivery to
Western Lake Erie: Approaches to
Compensate for Variability and Trends in Streamflow.” Journal
of Great Lakes Research 45 (1): 21–39.
Hirsch, Robert M, Stacey A Archfield, and Laura A De Cicco. 2015.
“A Bootstrap Method for Estimating Uncertainty of Water Quality
Trends.” Environmental Modelling & Software 73:
148–66.
Hirsch, Robert M, and Laura A De Cicco. 2015. “User Guide to
Exploration and Graphics for
RivEr Trends (EGRET) and dataRetrieval: R Packages for
Hydrologic Data.” 2328-7055. US Geological Survey.
Hirsch, Robert M., Douglas L. Moyer, and Stacey A. Archfield. 2010.
“Weighted Regressions on Time,
Discharge, and Season (WRTDS),
with an Application to Chesapeake
Bay River Inputs1.”
JAWRA Journal of the American Water Resources Association 46
(5): 857–80.
https://doi.org/10.1111/j.1752-1688.2010.00482.x.
Sprague, Lori A., Robert M. Hirsch, and Brent T. Aulenbach. 2011.
“Nitrate in the Mississippi River and
Its Tributaries, 1980 to 2008:
Are We Making
Progress?” Environmental Science &
Technology 45 (17): 7209–16.
https://doi.org/10.1021/es201221s.
