Package 'EGRET'

Description

Create a named list with the INFO, Daily, and Sample dataframes, and surface matrix. If any of these are not available, an NA should be

Usage

as.egret(INFO, Daily, Sample = NA, surfaces = NA)

Arguments

Value

eList named list with Daily, Sample, and INFO dataframes, along with the surfaces matrix. Any of these values can be NA, not all EGRET functions will work with missing parts of the named list eList.

See Also

readNWISDaily, readNWISSample

Examples

eList <- Choptank_eList
Daily <- getDaily(eList)
INFO <- getInfo(eList)
eList_flowHistory <- as.egret(INFO, Daily)
plotFlowSingle(eList_flowHistory, 1)
Sample <- getSample(eList)
surfaces <- getSurfaces(eList)
eList_full <- as.egret(INFO, Daily, Sample, surfaces)
plotFluxQ(eList_full)

Description

This function is used when the data analyst believes that a gap in the sample data record is so long that estimates during that period are not reliable. This is only used for periods of several years in duration. For this period, the values of Conc, Flux, FNConc and FNFlux are all converted to NA.

Usage

blankTime(eList, startBlank, endBlank)

Arguments

Details

The startBlank and endBlank arguments should generally coincide with the starting and ending date of the period of analysis that is being used. startBlank should be placed fairly close to the start of the period of no data and endBlank should be placed fairly close to the end of the period of no data. They do not eliminate any water quality data from the set of data being used to estimate the model, they only eliminate results computed for the specified blank period. If the data set has more than one large data gap the blankTime() function can be used multiple times to blank out multiple sets of results.

Value

eList named list with modified Daily data frame.

Examples

startBlank = "2004-10-01"
endBlank = "2006-09-30"
eList <- Choptank_eList
eList <- blankTime(eList, startBlank, endBlank)

Description

Data come from named list, which contains a Sample dataframe with the sample data, and an INFO dataframe with metadata.

Box widths are proportional to the square root of the number of samples in the month.

Although there are a lot of optional arguments to this function, most are set to a logical default.

Usage

boxConcMonth(eList, printTitle = TRUE, cex = 0.8, cex.axis = 1.1,
  cex.main = 1.1, las = 1, logScale = FALSE, tcl = 0.5,
  tinyPlot = FALSE, customPar = FALSE, showYLabels = TRUE, concLab = 1,
  showXLabels = TRUE, showXAxis = TRUE, showYAxis = TRUE, monthLab = 1,
  ...)

Arguments

See Also

boxplot

Examples

eList <- Choptank_eList
# Water year:
boxConcMonth(eList)
# Graphs consisting of Jun-Aug
eList <- setPA(eList, paStart=6,paLong=3)
boxConcMonth(eList)
spanish_month <- new("monthLabel",
                  monthAbbrev = c("enero",	"feb", 	"marzo", "abr",
                                  "mayo",	"jun",	"jul", "agosto", "set",
                                  "oct",	"nov", "dic"),
                  monthFull = c("enero",	"febrero", 	"marzo", "abril",
                                  "mayo",	"junio",	"julio", "agosto", "septiembre",
                                  "octubre",	"noviembre", "diciembre"),
                  monthSingle = c("E", "F", "M", "A", "M", "J", "J",
                                  "A", "S", "O", "N", "D"))
boxConcMonth(eList, monthLab = spanish_month, 
             showXLabels = FALSE, printTitle = FALSE)

Description

This function is used to compare the distribution of concentration in the sample and predicted data set. It shows three boxplots. One for the sample, one for the predictions on days with sample values, and one for all days (whether or not they had sample values). Box widths are proportional to the square root of the number of observations represented by the box.

Data come from named list, which contains a Sample dataframe with the sample data, a Daily dataframe with the daily flow data, and an INFO dataframe with metadata.

Although there are a lot of optional arguments to this function, most are set to a logical default.

Usage

boxConcThree(eList, tinyPlot = FALSE, printTitle = TRUE,
  moreTitle = "WRTDS", customPar = FALSE, font.main = 2, cex = 0.8,
  cex.main = 1.1, cex.axis = 1.1, concLab = 1, ...)

Arguments

See Also

boxplot

Examples

eList <- Choptank_eList
# Water year:
boxConcThree(eList)
# Graphs consisting of Jun-Aug
eList <- setPA(eList, paStart=6,paLong=3)
boxConcThree(eList)

Description

This function is used to compare the distribution of discharges in the sample data set and the discharges in the full daily data set. Note that discharge is plotted on a logarithmic axis. The boxplot is created using the log values but the scale is presented in the original units. An ideal situation would show the two boxes roughly similar to each other or the sample boxplot having median, upper quartile, and higher values being slightly greater than in the boxplot of all days.

Box widths are proportional to the square root of the number of observations (left box based on number of sampled days, right box based on total number of days in the record).

Data come from named list, which contains a Sample dataframe with the sample data, a Daily dataframe with the daily flow data, and an INFO dataframe with metadata.

Although there are a lot of optional arguments to this function, most are set to a logical default.

Usage

boxQTwice(eList, printTitle = TRUE, qUnit = 2, cex = 0.8,
  cex.main = 1.1, logScale = TRUE, cex.axis = 1.1, tcl = 0.5,
  las = 1, tinyPlot = FALSE, usgsStyle = FALSE, customPar = FALSE, ...)

Arguments

See Also

boxplot

Examples

eList <- Choptank_eList
# Water year:
boxQTwice(eList)
boxQTwice(eList, qUnit=1)
boxQTwice(eList, qUnit='cfs')
# Graphs consisting of Jun-Aug
eList <- setPA(eList, paStart=6,paLong=3)
boxQTwice(eList)

Description

This function produces a boxplot of the residuals from WRTDS, expressed in natural log concentration units. It provides an alternative for viewing the standardized residuals, where the each residual is divided by its estimated standard error. The monthly boxplot widths are proportional to the square root of the sample size. The residuals for a censored value are determined as the difference between the natural log of the average of the upper and lower. bounds on the sample value, minus the log space estimate of concentration.

Although there are a lot of optional arguments to this function, most are set to a logical default.

Data come from named list, which contains a Sample dataframe with the sample data, and an INFO dataframe with metadata

Usage

boxResidMonth(eList, stdResid = FALSE, las = 1, printTitle = TRUE,
  cex = 0.8, cex.axis = 1.1, cex.main = 1.1, font.main = 2,
  tinyPlot = FALSE, customPar = FALSE, monthLab = 1,
  randomCensored = FALSE, ...)

Arguments

See Also

boxplot

Examples

eList <- Choptank_eList
# Water year:
boxResidMonth(eList)
# Graphs consisting of Jun-Aug
eList <- setPA(eList, paStart = 6, paLong = 3)
boxResidMonth(eList)

Description

Computes the monthly mean values of discharge, concentration, flux, flow-normalized concentration and flow-normalized flux (Q, Conc, FNConc, Flux, and FNFlux) in SI units If WRTDSKalman has been run the outputs are averages for Q, Conc, GenConc, FNConc, Flux, GenFlux, and FNFlux. Note that the Flux, GenFlux, and FNFlux values are average flux values (not totals). For discharge the units are in m3/s, concentration is mg/L, and flux is kg/day. It returns a data frame containing month, year, decimal year, and mean values of DecYear, Q, Conc, GenConc, FNConc, Flux, GenFlux, and FNFlux.

Usage

calculateMonthlyResults(eList)

Arguments

Value

MonthlyResults data frame of numeric values describing the monthly average values

Examples

eList <- Choptank_eList
monthlyResults <- calculateMonthlyResults(eList)

Description

Basic plotting framework for EGRET dot plots. Graphical parameters default to values that work well with most plots, but all can be re-assigned. See ?par for complete definitions of most optional input variables.

Usage

censoredSegments(yBottom, yLow, yHigh, x, Uncen, col = "black", lwd = 1)

Arguments

See Also

segments

Examples

x <- c(1,2,3,4,5,6)
y <- c(1,3,4,3.3,4.4,7)
xlim <- c(min(x)*.75,max(x)*1.25)
ylim <- c(0,1.25*max(y))
xlab <- "Date"
ylab <- "Concentration"
xTicks <- pretty(xlim)
yTicks <- pretty(ylim)
genericEGRETDotPlot(x=x, y=y, 
                    xlim=xlim, ylim=ylim,
                    xlab=xlab, ylab=ylab,
                    xTicks=xTicks, yTicks=yTicks,
                    plotTitle="Test"
)
yBottom <- 0
yLow <- c(NA,3,4,3.3,4,7)
yHigh <- c(1,3,4,3.3,5,NA)
Uncen <- c(0,1,1,1,0,0)
censoredSegments(yBottom=yBottom,yLow=yLow,yHigh=yHigh,x=x,Uncen=Uncen)

Description

Checks that the start date is before the end date. If not, it will give the user the opportunity to correct, otherwise will create a warning.

Usage

checkStartEndDate(startDate, endDate, interactive = TRUE)

Arguments

Value

vector where first value is startDate, second is endDate

Examples

startDate <- '1985-01-01'		
endDate <- '1990-01-01'		
checkStartEndDate(startDate, endDate)

Description

checkSurfaceSpan

Usage

checkSurfaceSpan(eList)

Arguments

Examples

eList <- Choptank_eList
checkSurfaceSpan(eList)

Description

Example data representing data from the Choptank River at Greensboro, MD, USGS data Data is a named list of the Daily, Sample, INFO dataframes, and the surface matrix.

Examples

head(Choptank_eList$Daily)
head(Arkansas_eList$Daily)

Description

Takes an eList as the input. If there are duplicated dates in the Sample data frame, will randomly select one value for that date. If there are censored values in the data set they will be replaced by random censored values. If there are no days with duplicate samples and no censored valued then the eList returned by the function will be identical to the eList that is passed to it.

Usage

cleanUp(eList, seed = NA)

Arguments

Details

This function is run before each iteration of generating a random sequence in the WRTDSKalman function

Value

eList with duplicated dates in the Sample data frame randomly sampled and censored values are replaced by random values.

Examples

eList <- Choptank_eList

eList <- cleanUp(eList)

Description

Using raw data that has at least dateTime, value, code, populates the measured data portion of the Sample dataframe used in EGRET. ConcLow = Lower bound for an observed concentration ConcHigh = Upper bound for an observed concentration Uncen = 1 if uncensored, 0 if censored

Usage

compressData(data, verbose = TRUE)

Arguments

Value

data frame returnDataFrame data frame containing dateTime, ConcHigh, ConcLow, Uncen

Examples

dateTime <- c('1985-01-01', '1985-01-02', '1985-01-03')
comment1 <- c("","","")
value1 <- c(1,2,3)
comment2 <- c("","<","")
value2 <- c(2,3,4)
comment3 <- c("","","<")
value3 <- c(3,4,5)
dataInput <- data.frame(dateTime, comment1, value1, 
      comment2, value2, 
      comment3, value3, stringsAsFactors=FALSE)
compressData(dataInput)

Description

Some details about the concUnit class

Details

longPrefix

A character specifying the long name for concentration labels.

shortPrefix

A character specifying the short name for concentration labels.

Description

Examples

fluxConst
fluxConst[['kgDay']]
fluxConst[['kgDay']]@unitName
qConst
qConst[['cfs']]
qConst[['cfs']]@qUnitName
concConst[['concentration']]
concConst[['concentration']]@shortPrefix

Description

This function computes the first day of the calendar year at which a specific fraction of the cumulative flow for that year has been exceeded. Typically one looks for the point where half the cumulative flow has happened (fract = 0.5). The portion of the year being considered is set by paStart and paLong. The matrix returned has 2 columns: the first is the year (integer when the period of analysis ends), the second is the day of the year when the fraction has been exceeded. None of the rows will have any NA values.

Usage

cumQdate(eList, paStart = 10, paLong = 12, fract = 0.5)

Arguments

Details

It is common to use this type of analysis on the snowmelt period of the year. If (for example) we assume that snowmelt starts with the month of March and ends in July then we would set paStart = 3 and paLong = 5

Value

annualSeries an integer matrix of two columns. The first column is the calendar year for the end of the period The second column is day of the year when the flow has exceeded the specified fraction of the entire period being considered

Examples

eList <- Choptank_eList
annualFlow <- cumQdate(eList)
head(annualFlow)
plot(annualFlow)
mod1 <- stats::lm(annualFlow[,2] ~ annualFlow[,1])
summary(mod1)

Description

Gives a summary of data to be used for WRTDS analysis

Usage

dataOverview(Daily, Sample)

Arguments

See Also

mergeReport

Examples

eList <- Choptank_eList
exDaily <- getDaily(eList)
exSample <- getSample(eList)
dataOverview(Daily = exDaily, Sample = exSample)

Description

Checks to see if format is YYYY-MM-DD. Also performs a few other date checks.

Usage

dateFormatCheck(date)

Arguments

Value

condition logical TRUE or FALSE if checks passed or failed

Examples

date <- '1985-01-01'
dateFormatCheck(date)
dateWrong <- '1999/1/7'
dateFormatCheck(dateWrong)

Description

Create a decimal date or date/time from a vector.

Usage

decimalDate(rawData)

Arguments

Examples

dateTime <- c('1984-02-28 13:56',
              '1984-03-01 00:00',
              '1986-03-01 00:00',
              '1986-10-15 00:00')
decimalDate(dateTime)

dateTime <- c('1984-02-28', 
              '1984-03-01',
              '1986-03-01',
              '1986-10-15')
decimalDate(dateTime)

Description

decimalHighLow figures out the highest and lowest decimal year based on water year. The input is a data frame with columns Month and DecYear.

Usage

decimalHighLow(df)

Arguments

Value

list with DecHigh and DecLow (water year high/low decimal values)

Examples

eList <- Choptank_eList
highLow <- decimalHighLow(eList$Sample)

DecHigh <- highLow[["DecHigh"]]
DecLow <- highLow[["DecLow"]]

Description

This function takes a fitted WRTDS model and computes error statistics the residuals used here are cross-validation residuals, which will be slightly larger than regular regression residuals in the case of censored data, the residuals are computed from random residuals computed from makeAugmentedSample(), the function returns a list of error statistics and also prints them to the console

Usage

errorStats(eList)

Arguments

Value

erStats a numeric vector consisting of the following statistics RsqLC the R squared value for predictions of ln(Concentration) RsqLF the R squared value for predictions of ln(Flux) rmse the root mean squared error for ln(Concentration), same value would apply for Flux sepPercent the standard error of prediction for Concentration, expressed in percent same value would apply for Flux

Examples

eList <- Choptank_eList
errorStats(eList)

Description

This function fits the WRTDS model n times (where n is the number of observations). For each fit, the data value being estimated is eliminated from the record. This gives predictions that do not depend on knowing the actual result for that day. Thus it provides for a more "honest" estimate of model performance than a traditional error analysis that uses all the data.

Usage

estCrossVal(DecLow, DecHigh, Sample, windowY = 7, windowQ = 2,
  windowS = 0.5, minNumObs = 100, minNumUncen = 50, edgeAdjust = TRUE,
  verbose = TRUE)

Arguments

Value

SampleCrossV data frame containing the sample data augmented by the results of the cross-validation exercise

Examples

eList <- Choptank_eList
Sample <- getSample(eList)
Daily <- getDaily(eList)
numDays <- length(Daily$DecYear)
DecLow <- Daily$DecYear[1]
DecHigh <- Daily$DecYear[numDays]

SampleCrossV <- estCrossVal(DecLow,DecHigh,Sample)

Description

Uses the surfaces matrix estimated in estSurfaces to estimate 6 daily time series and appends them to the Daily data frame. The time series are (in order): yHat, the estimated natural log of concentration, dimensionless SE, the standard error of the natural log of concentration ConcDay, the estimated concentration in mg/L FluxDay, the estimated flux in kg/day FNConc, the flow-normalized concentration in mg/L FNFlux, the flow-normalized flux in kg/day

Bin the LogQ values by day-of-year.

Usage

estDailyFromSurfaces(eList, localsurfaces = NA, localDaily = NA)

getConcFluxFromSurface(eList, allLogQsByDayOfYear, localDaily,
  localsurfaces = NA)

getSurfaceEstimates(eList, localsurfaces = NA, localDaily = NA)

bin_Qs(localDaily)

Arguments

Details

The results are stored in an augmented version of the Daily data frame, which is returned as part of an EGRET object.

Value

egret object with altered Daily dataframe

Daily dataframe with yHat, SE, ConcDay and FluxDay calulated

Examples

eList <- Choptank_eList
#################################################
# This is usually done in modelEstimation:
Daily <- getDaily(eList)
surfaceIndexParameters<-surfaceIndex(Daily)
INFO <- eList$INFO
INFO$bottomLogQ<-surfaceIndexParameters[['bottomLogQ']]
INFO$stepLogQ<-surfaceIndexParameters[['stepLogQ']]
INFO$nVectorLogQ<-surfaceIndexParameters[['nVectorLogQ']]
INFO$bottomYear<-surfaceIndexParameters[['bottomYear']]
INFO$stepYear<-surfaceIndexParameters[['stepYear']]
INFO$nVectorYear<-surfaceIndexParameters[['nVectorYear']]
eList$INFO <- INFO
#################################################

Daily <- estDailyFromSurfaces(eList)

Description

This function uses weighted survival regression to estimate three surfaces that cover the complete range of DecYear and log(Q) values in the Daily data set. These surfaces are: (1) is the estimated log concentration (yHat), (2) is the estimated standard error (SE), (3) is the estimated concentration (ConcHat). They are mapped as an array that covers the complete space of daily discharge and time. The first index is discharge, layed out in 14 equally spaced levels of log(Q). The second index is time, layed out as 16 increments of the calendar year, starting January 1. It returns the 3 dimensional array called surfaces. This array will be used to estimate these 3 quantities for any given day in the daily values record.

Usage

estSurfaces(eList, surfaceStart = NA, surfaceEnd = NA, localSample = NA,
  windowY = 7, windowQ = 2, windowS = 0.5, minNumObs = 100,
  minNumUncen = 50, edgeAdjust = TRUE, verbose = TRUE,
  interactive = NULL, run.parallel = FALSE)

Arguments

Value

surfaces array containing the three surfaces estimated, array is 3 dimensional

Examples

eList <- Choptank_eList

surfaces <- estSurfaces(eList)

surfaceStart <- "1984-10-01"
surfaceEnd <- "1986-09-30"
surfaces_1 <- estSurfaces(eList, surfaceStart, surfaceEnd)

wall_sample <- head(eList$Sample, n=500)

surface_wall <- estSurfaces(eList, localSample = wall_sample)

Description

Used for updating the Sample dataframe if ConcLow or ConcHigh is manually adjusted. Adjusts ConcAve and Uncen columns.

Usage

fixSampleFrame(eList)

Arguments

Value

localSample data frame

Examples

eList <- Choptank_eList
Sample <- eList$Sample
Sample[1,c("ConcLow","ConcHigh")] <- c(NA, 0.01) # Adjusted to left-censored
Sample[2,c("ConcLow","ConcHigh")] <- c(1.1, 1.3) # Adjusted to interval-censored
Sample[3,c("ConcLow","ConcHigh")] <- c(1.3, 1.3) # Simple adjustment
eList$Sample <- Sample
eList <- fixSampleFrame(eList)
eList$Sample[1:3,]

Description

This function implements generalized flow normalization. This means that for determining the flow normalized concentration and flow normalized flux for any given year, there is a specified list of years from which to create the discharge record used in the flow-normalization process. That set of years is defined by the dateInfo object.

Usage

flexFN(eList, dateInfo, localsurfaces = NA, oldSurface = FALSE,
  flowNormStartCol = "flowNormStart", flowNormEndCol = "flowNormEnd",
  flowStartCol = "flowStart", flowEndCol = "flowEnd")

Arguments

Value

named list, eList, containing INFO, Daily, Sample, and surfaces objects

Examples

eList <- Choptank_eList
eList <- setUpEstimation(eList)
flowNormStart <- c("1979-10-01","1990-01-01","1992-10-10")
flowNormEnd <- c("1995-06-06","2004-03-03","2011-09-29")
flowStart <- c("1979-10-01","1995-06-07","2004-03-04")
flowEnd <- c("1995-06-06","2004-03-03","2011-09-29") 
dateInfo <- data.frame(flowNormStart,
                       flowNormEnd,
                       flowStart, 
                       flowEnd, 
                       stringsAsFactors = FALSE)

newEList <- flexFN(eList, dateInfo)
plotFluxHist(newEList)
flexPlotAddOn(newEList)

wallSurface <- estSurfaces(eList, localSample = eList$Sample[1:500,])
wallEList <- flexFN(eList, dateInfo, localsurface = wallSurface)
plotFluxHist(wallEList)

Description

Flexible Flow Normalization Plot Add On

Usage

flexPlotAddOn(eList, showArrows = TRUE, showRect = TRUE,
  customPalette = NULL)

Arguments

Examples

eList <- Choptank_eList
eList <- setUpEstimation(eList)
flowNormStart <- c("1979-10-01","1990-01-01","1992-10-10")
flowNormEnd <- c("1995-06-06","2004-03-03","2011-09-29")
flowStart <- c("1979-10-01","1995-06-07","2004-03-04")
flowEnd <- c("1995-06-06","2004-03-03","2011-09-29") 
dateInfo <- data.frame(flowNormStart,
                       flowNormEnd,
                       flowStart, 
                       flowEnd, 
                       stringsAsFactors = FALSE)

newEList <- flexFN(eList, dateInfo)
plotFluxHist(newEList)
flexPlotAddOn(newEList)

plotFluxHist(newEList)
flexPlotAddOn(newEList, customPalette=c("#d5ce48", "#fd300f", "#3e0289"))

Description

This function is useful for helping the analyst determine the empirical probability distribution of streamflow for a particular part of the year or for the whole year. This is particularly useful in setting up discharge scales for various other plots in this package.

Usage

flowDuration(eList, centerDate = "09-30", qUnit = 2, span = 365,
  monthLab = 1)

Arguments

Value

qDuration A named vector with flow duration information.

Examples

eList <- Choptank_eList
# for a window of 30 days either side of June 25 expressed in units
# of cfs:
flowDuration(eList, "06-25", qUnit = 1, span = 30) 
# for a flow-duration curve covering the whole year, 
# expressed in units of cms, and returning a data frame of results: 
qDuration <- flowDuration(eList, qUnit = 2)

Description

These plots use the jack-knife estimates from WRTDS to investigate the potential flux bias problem. It can also be used for estimates constructed by other methods (such as LOADEST) if the results are stored in a data frame organized like the Sample data frame. It allows additional label information to indicate what method is used. The use of this plot is described in Hirsch, Robert M., 2014. Large Biases in Regression-Based Constituent Flux Estimates: Causes and Diagnostic Tools. Journal of the American Water Resources Association (JAWRA) 1-24. DOI: 10.1111/jawr.12195

Although there are a lot of optional arguments to this function, most are set to a logical default.

Data come from named list, which contains a Sample dataframe with the sample data, a Daily dataframe with the daily flow data, and an INFO dataframe with metadata.

Usage

fluxBiasMulti(eList, qUnit = 2, fluxUnit = 3, moreTitle = "WRTDS",
  cex = 0.7, cex.axis = 1.1, cex.main = 1.1, randomCensored = FALSE,
  col = "black", lwd = 1, concLab = 1, monthLab = 1, ...)

Arguments

Examples

eList <- Choptank_eList
# Water year:
fluxBiasMulti(eList)
fluxBiasMulti(eList, fluxUnit = 2)
# Graphs consisting of Jun-Aug
eList <- setPA(eList,paStart=6,paLong=3)
fluxBiasMulti(eList)

Description

Computes three versions of the flux bias: The first where all censored values are set to their miniumum. The second where all censored values are set to their maximum. The third which is the average of the other two. In practice there is rarely a noticable difference among them.

Usage

fluxBiasStat(localSample)

Arguments

Value

fluxBias a vector of three numerical values, a lower bound, upper bound and an average estimate of the ratio of (mean estimated flux - mean observed flux) / mean estimated flux. Typically one should use fluxBias[3]

Examples

eList <- Choptank_eList
Sample <- getSample(eList)
fluxBias <- fluxBiasStat(Sample)

Description

Some details about the fluxUnit class

Details

shortName

A character specifying the short name.

unitFactor

A numeric representing the conversion factor

unitName

A character specifying the full name.

unitExpress

An expression specifying the full name starting with Observed.

unitExpressTiny

An expression specifying the abbreviated name starting with Observed.

unitEstimate

An expression specifying the full name starting with Estimated.

unitEstimateTiny

An expression specifying the abbreviated name starting with Estimated.

unitUSGS

A character specifying flux with full text.

shortCode

A number for quick lookup

Description

Discharge axis tick generation

Usage

generalAxis(x, maxVal, minVal, units = NA, logScale = FALSE,
  tinyPlot = FALSE, padPercent = 5, concentration = TRUE, concLab = 1,
  usgsStyle = FALSE, prettyDate = TRUE)

Arguments

Examples

eList <- Choptank_eList
Daily <- getDaily(eList)
INFO <- getInfo(eList)
x <- Daily$Q
max <- max(x)
min <- 0
units <- INFO$param.units
generalAxis(x, max, min, units)
min <- min(x)
generalAxis(x, max, min, units, log=TRUE)
generalAxis(Daily$ConcDay, 100, 0, concLab = "concentration")

Description

Basic plotting framework for EGRET dot plots. Graphical parameters default to values that work well with most plots, but all can be re-assigned. See ?par for complete definitions of most optional input variables.

Usage

genericEGRETDotPlot(x, y, xlim, ylim, xTicks = pretty(xlim),
  yTicks = pretty(ylim), printTitle = TRUE, xaxs = "i", xlab = "",
  yaxs = "i", ylab = "", plotTitle = "", pch = 20, cex = 0.7,
  cex.main = 1.3, font.main = 2, cex.lab = 1.2, tcl = 0.5,
  cex.axis = 1, las = 1, xDate = FALSE, tinyPlot = FALSE,
  hLine = FALSE, oneToOneLine = FALSE, rmSciX = FALSE, rmSciY = FALSE,
  customPar = FALSE, col = "black", lwd = 1, showXLabels = TRUE,
  showYLabels = TRUE, showXAxis = TRUE, showYAxis = TRUE,
  removeFirstX = FALSE, removeLastX = FALSE, removeFirstY = FALSE,
  removeLastY = FALSE, ...)

Arguments

Examples

eList <- Choptank_eList
Daily <- getDaily(eList)
x <- Daily$Date
y <- Daily$Q
xlim <- c(min(x),max(x))
ylim <- c(min(y),1.05*max(y))
xlab <- "Date"
ylab <- "Flow"
genericEGRETDotPlot(x=x, y=y, 
                    xlim=xlim, ylim=ylim,
                    xlab=xlab, ylab=ylab,
                    plotTitle="Test"
)

Description

From a named list or EGRET object, extract the Daily dataframe

Usage

getDaily(x, ...)

## S3 method for class 'egret'
getDaily(x, ...)

## Default S3 method:
getDaily(x, ...)

Arguments

Value

Daily dataframe

See Also

readNWISDaily, readNWISSample

Examples

eList <- Choptank_eList
Daily <- getDaily(eList)

Description

From a named list or EGRET object, extract the INFO dataframe

Usage

getInfo(x, ...)

## S3 method for class 'egret'
getInfo(x, ...)

## Default S3 method:
getInfo(x, ...)

Arguments

Value

INFO dataframe

See Also

readNWISDaily, readNWISSample

Examples

eList <- Choptank_eList
INFO <- getInfo(eList)

Description

From a named list or EGRET object, extract the Sample dataframe

Usage

getSample(x, ...)

getSample(x, ...)

getSample.default(x, ...)

Arguments

Value

Sample dataframe

See Also

readNWISDaily, readNWISSample

Examples

eList <- Choptank_eList
Sample <- getSample(eList)

Description

From a named list or EGRET object, extract the surfaces matrix

Usage

getSurfaces(x, ...)

## S3 method for class 'egret'
getSurfaces(x, ...)

## Default S3 method:
getSurfaces(x, ...)

Arguments

Value

Sample dataframe

See Also

readNWISDaily, readNWISSample

Examples

eList <- Choptank_eList
surfaces <- getSurfaces(eList)

Description

Populates INFO data frame from either NWIS (readNWISInfo), Water Quality Portal (readWQPInfo), or user-supplied files (readUserInfo).

Usage

readNWISInfo(siteNumber, parameterCd, interactive = TRUE)

readWQPInfo(siteNumber, parameterCd, interactive = TRUE)

readUserInfo(filePath, fileName, hasHeader = TRUE, separator = ",",
  interactive = TRUE)

Arguments

Value

INFO data frame. Any metadata can be stored in INFO. However, there are 8 columns that EGRET uses by name in some functions:

*** Additionally, EGRET assumes that all concentrations are saved in mg/l. If some variation of 'mg/l' is not found in INFO$param.units, functions that calculate flux will issue a warning. This is because the conversion from mg/l to the user-specified flux unit (e.g., kg/day) uses hard-coded conversion factors.

See Also

readNWISsite, readNWISpCode

whatWQPsites

Examples

# These examples require an internet connection to run
# Automatically gets information about site 05114000 and temperature

INFO <- readNWISInfo('05114000','00010',interactive = FALSE)

# These examples require an internet connection to run
# Automatically gets information about site 01594440 and temperature, no interaction with user
nameToUse <- 'Specific conductance'
pcodeToUse <- '00095'

# INFO <- readWQPInfo('USGS-04024315',pcodeToUse, interactive = FALSE)

# INFO2 <- readWQPInfo('WIDNR_WQX-10032762',nameToUse, interactive = FALSE)
# To adjust the label names:
# INFO$shortName <- "Little"
# INFO$paramShortName <- "SC"

filePath <- system.file("extdata", package="EGRET")
fileName <- 'infoTest.csv'
INFO <- readUserInfo(filePath,fileName, separator=",",interactive=FALSE)

Description

Checks object to see if it is an EGRET object

Usage

is.egret(x)

Arguments

Value

logical

Examples

eList <- Choptank_eList
is.egret(eList)

Description

This function is used in cases where there are numerical problems with the estimation of the WRTDS model. This mostly happens during bootstrap estimation or when the data sets are very large. In order to reduce the collinearity in the explanatory variables, some random noise is added to the time and log discharge variables in the Sample data frame.

Usage

jitterSam(Sam, V = 0.2)

Arguments

Value

SamR a data frame structured like the Sam data frame but with the time and discharge variables modified by adding random jitter

Examples

eList <- Choptank_eList
Sample_jitter <- jitterSam(eList$Sample)

Description

Axis tick marks for a log scale for cases where the data cover many orders of magnitude and the graph is small. These tick marks are designed to progress by factors of 10.

Usage

logPretty1(xMin, xMax)

Arguments

Value

xTicks A vector representing the values for each of the tick marks

Examples

xMin <- 0.7
xMax <- 990000
logPretty1(xMin, xMax)
xMin <- 3
xMax <- 15
logPretty1(xMin, xMax)

Description

Axis tick marks for a log scale. These tick marks are designed to progress with 3 tick marks for every factor of 10. For example: 2,5,10,20,50,100,200,500.

Usage

logPretty3(xMin, xMax)

Arguments

Value

xTicks A vector representing the values for each of the tick marks

Examples

logPretty3(0.7, 990000)
logPretty3(3, 15)

Description

Part of the flowHistory system. The data come from Daily and INFO data frames. Note that the function setPA must be run before this to establish the period of analysis (e.g. water year).

Usage

makeAnnualSeries(eList, edgeAdjust = TRUE)

Arguments

Details

The smooth is a loess smooth computed on the log flow values and then transformed back to real space Smoothing window is a fixed number of years, the window width has a default value of 20 years but can be modified by changing its value in INFO data frame (using setPA function)

Value

annualSeries matrix that contains the annual series of streamflow statistics annualSeries is a matrix 3 * 8 * numYears, where numYears is the number of years in the data set in the first dimension 1 is the year, 2 is the actual value, 3 is the smoothed value in the second dimension, the index is the istat value (identifying the flow statistic) the third dimension is year

Examples

eList <- Choptank_eList
annualSeries <- makeAnnualSeries(eList)

Description

This function is used to add two columns to the Sample data frame: rResid and rObserved. rResid is the randomized residual value computed in log concentration units, and rObserved is the randomized 'observed' value of concentration in concentration units. Both of these are computed for all censored samples ("less than values"). They are created for purposes of plotting and are not used in any computations in EGRET.

Usage

makeAugmentedSample(eList)

Arguments

Details

The WRTDS model must be estimated before this function can be run. The random value that is generated lies between the reporting limit and zero and is distributed as a truncated log-normal distribution, with parameters derived from the fitted WRTDS model. These random values are never used in any computations in EGRET but are used for purposes of plotting the data set or residuals. When plotted in other functions they are shown as open circles.

Value

eList named list with modified Sample data frame.

Examples

choptankAugmented <- makeAugmentedSample(Choptank_eList)

Description

Create a data frame that organizes date segmentations for runSeries.

Usage

makeDateInfo(windowSide, surfaceStart, surfaceEnd, firstQDate0, lastQDate0)

Arguments

Examples

windowSide <- 7
surfaceStart <- "1984-01-01"
surfaceEnd <- "2012-12-31"
firstQDate0 <- "1970-01-01"
lastQDate0 <- "2014-06-01"
dateInfo <- makeDateInfo(windowSide, 
                         surfaceStart, surfaceEnd, 
                         firstQDate0, lastQDate0)

Description

This function does three things. 1) It transfers the daily discharge value from the Daily data frame to to Sample data frame for those days with samples. 2) It merges the INFO, Daily and Sample data frames to form an eList object, 3) and it prints out a "report" of basic information about the Daily and Sample data frames.

Usage

mergeReport(INFO, Daily, Sample = NA, surfaces = NA, verbose = TRUE,
  interactive = NULL)

Arguments

Details

There must be an INFO and a Daily data frame for this function to work. That would be the case for a study of flow only, with no consideration of water quality. If water quality is being considered then INFO, Daily, and Sample all need to be provided in the call to this function.

Note that the Sample dataframe in the global environment does not update with the flow information.

Value

eList named list with at least INFO, and Daily data frames. It can also include a Sample data frame.

See Also

readNWISDaily, readNWISSample

Examples

siteNumber <- '01491000'
pCode <- '00631'

Daily <- readNWISDaily(siteNumber,'00060', '1984-10-01', '')
Sample <- readNWISSample(siteNumber,pCode, '1984-10-01', '')
INFO <- readNWISInfo(siteNumber,pCode,interactive=FALSE)
eList <- mergeReport(INFO, Daily, Sample)
Sample <- eList$Sample
plot(eList)

# Create eList with no water quality data:

eList <- mergeReport(INFO, Daily, Sample = NA)
plotFour(eList)

Description

This one function does three things. 1) a jack-knife cross-validation of a WRTDS model in which it augments the Sample data frame in the eList, 2) fits the WRTDS model creating the surfaces matrix and places it in the eList (the surfaces matrix expresses the estimated concentration as a function of discharge and time), and 3) estimates the daily values of concentration and flux, and flow normalized concentration and flux and places these in the Daily data frame in the eList. It returns a named list with the following dataframes: Daily, INFO, Sample, and the matrix: surfaces.

Usage

modelEstimation(eList, windowY = 7, windowQ = 2, windowS = 0.5,
  minNumObs = 100, minNumUncen = 50, edgeAdjust = TRUE, verbose = TRUE,
  run.parallel = FALSE)

Arguments

Value

eList named list with INFO, Daily, and Sample dataframes, along with the surfaces matrix.

Examples

eList <- Choptank_eList

eList <- modelEstimation(eList)

Description

Some details about the monthLabel class

Details

monthAbbrev

A character specifying the abbreviated month name.

monthFull

A character specifying the full month name

monthSingle

A character specifying the single letter of the month.

Description

This function produces the 4 plots based only on the data stored in the eList. The four plots are 1) log concentration versus log discharge, 2) log concentration versus time 3) a boxplot of log concentration by month, and 4) a side-by-side boxplot of the sampled discharges and all daily discharges. To save space, the graphic is labeled only at the top of the 4 graph display.

Although there are a lot of optional arguments to this function, most are set to a logical default.

Usage

multiPlotDataOverview(eList, qUnit = 2, cex.main = 1.2,
  randomCensored = FALSE, logScaleConc = TRUE, logScaleQ = TRUE,
  concLab = 1)

Arguments

See Also

plotConcQ, boxConcMonth, plotConcTime, boxQTwice

Examples

eList <- Choptank_eList
# Water year:
multiPlotDataOverview(eList, qUnit=1)
# Graphs consisting of Jun-Aug
eList <- setPA(eList, paStart=6,paLong=3)
multiPlotDataOverview(eList, qUnit=1) 

# Custom axes:
eList$INFO$param.units <- "ng"
qConst_precip <- new("qUnit",
                     qShortName = "   mm  ",
                     qUnitFactor = 1,
                     qUnitName = "Millimeter",
                     qUnitExpress = expression(paste("Precipitation in ",mm)),
                     qUnitTiny = expression(paste("Precipitation ", "(", mm, ")")),
                     shortCode = 1,
                     unitUSGS = "Precipitation, in mm",
                     prefix = "Precipitation")

deposition <- new("concUnit",
                  longPrefix = "Deposition",
                  shortPrefix = "Dep")

multiPlotDataOverview(eList, 
                      qUnit = qConst_precip, 
                      concLab = deposition)

Description

Part of flowHistory system. All results are expressed as runoff (mm/day). The individual plots are constructed by the same method as used in plotFlowSingle. The annual results are based on the Water Year. The seasons are defined as the following groups of months: SON, DJF, MAM, JJA.

Usage

plot15(eList, yearStart, yearEnd)

Arguments

Details

For formatting purposes it is best to use the following commands before calling the plot15 function (savePath is the pathname for directory to store the output) # plotName <- paste(savePath, "plot15.", eList$INFO$shortName, ".ps", sep = "") # postscript(file = plotName, width = 8, height = 10, horizontal = FALSE, family = "Helvetica") Then after running plot15, the user needs to give the command dev.off()

See Also

plot1of15

Examples

eList <- Choptank_eList

plot15(eList, yearStart = 1980, yearEnd = 2010)
dev.off()

Description

Part of the flowHistory system. It is designed to create each of the component graphs for the function plot15. The 15 graphs include annual and four seasonal graphs for each of 3 flow statistics: 1-day maximum, mean, and 7-day minimum. The computations involved are the same as the ones used in plotFlowSingle or in makeAnnualSeries

Usage

plot1of15(eList, yearStart, yearEnd, qf, istat, isBottom = FALSE)

Arguments

Examples

eList <- Choptank_eList
plot1of15(eList, 1980, 2010, 0.2938476, 5)

Description

Data come from named list (eList), which contains a Daily dataframe with the daily flow data, and an INFO dataframe with metadata.

The annual concentrations are "time-weighted" mean concentrations (as opposed to "flow-weighted"). The annual results reported are for a specified "period of analysis" which can be an entire water year, a calendar, a season or even an individual month. User specifies this period of analysis in the call to setupYears.

User can specify plotting of three possible series. All are in units of mg/L. Annual mean concentration WRTDS_K version of annual mean concentration (requires that WRTDSKalman has been run) Flow normalized mean concentration

Although there are a lot of optional arguments to this function, most are set to a logical default.

Usage

plotConcHist(eList, yearStart = NA, yearEnd = NA, concMax = NA,
  printTitle = TRUE, tinyPlot = FALSE, usgsStyle = FALSE,
  plotFlowNorm = TRUE, plotAnnual = TRUE, plotGenConc = FALSE,
  cex = 0.8, cex.axis = 1.1, cex.main = 1.1, lwd = 2, col = "black",
  col.pred = "green", concLab = 1, col.gen = "red", customPar = FALSE,
  ...)

Arguments

See Also

setupYears, genericEGRETDotPlot

Examples

yearStart <- 2001
yearEnd <- 2010
eList <- Choptank_eList

plotConcHist(eList, yearStart, yearEnd)

Description

Data come from named list, which contains a Sample dataframe with the sample data, and an INFO dataframe with metadata.

Although there are a lot of optional arguments to this function, most are set to a logical default.

Usage

plotConcPred(eList, concMax = NA, logScale = FALSE, printTitle = TRUE,
  tinyPlot = FALSE, cex = 0.8, cex.axis = 1.1, cex.main = 1.1,
  customPar = FALSE, col = "black", lwd = 1, randomCensored = FALSE,
  concLab = 1, usgsStyle = FALSE, ...)

Arguments

See Also

selectDays, genericEGRETDotPlot

Examples

eList <- Choptank_eList
# Water year:
plotConcPred(eList)

# Graphs consisting of Jun-Aug
eList <- setPA(eList, paStart=6,paLong=3)
plotConcPred(eList)

Description

Data come from named list, which contains a Sample dataframe with the sample data, and an INFO dataframe with metadata. Discharge is plotted on a log scale.

Although there are a lot of optional arguments to this function, most are set to a logical default.

Usage

plotConcQ(eList, qUnit = 2, tinyPlot = FALSE, logScale = FALSE,
  randomCensored = FALSE, concMax = NA, concMin = NA,
  printTitle = TRUE, cex = 0.8, cex.axis = 1.1, cex.main = 1.1,
  usgsStyle = FALSE, rmSciX = FALSE, rmSciY = FALSE, customPar = FALSE,
  col = "black", lwd = 1, concLab = 1, ...)

Arguments

Details

The function has two possible ways to plot censored values (e.g. "less-than values").

The default is to plot them as a vertical line that goes from the reporting limit down to the bottom of the graph.

The alternative is to set randomCensored = TRUE. In this case a random value is used for plotting the individual sample value. This random value lies between the reporting limit and zero and it is distributed as a truncated log normal based on the fitted WRTDS model.

The function makeAugmentedSample must be run first if randomCensored = TRUE. Running makeAugmentedSample requires that modelEstimation has already been run.

These random censored values are used to create more readable plots and are not used in any computations about the data set. The random censored values are shown as open circles and the non-censored data are shown as filled dots.

See Also

selectDays, genericEGRETDotPlot

Examples

eList <- Choptank_eList
# Water year:
plotConcQ(eList)
plotConcQ(eList, logScale=TRUE)
# Graphs consisting of Jun-Aug
eList <- setPA(eList, paStart=6,paLong=3)
plotConcQ(eList)

Description

These plots are like a vertical slice of the estimated concentration surface that is seen in the plotContours function. These plots show how the concentration-discharge relationship is changing over time. Typically the time points selected would be in three years at the same time of year spaced out over the period of record. But that is not necessary. Another possibility is to use this to explore seasonal differences. In this case the three dates would be in the same year but different times during the year.

This plot can also help identify situations where the windowQ may be too small. If there are substantial oscillations of some of the curves, then the windowQ should be increased. Alternatively, windowQ may be too large. This can be seen when the windowQ is reduced (say to 1.0). A good choice of windowQ would be a value just great enough to damp out oscillations in the curves.

Although there are a lot of optional arguments to this function, most are set to a logical default.

Data come from named list, which contains a Sample dataframe with the sample data, and an INFO dataframe with metadata.

Usage

plotConcQSmooth(eList, date1, date2, date3, qLow, qHigh, qUnit = 2,
  legendLeft = 0, legendTop = 0, concMax = NA, concMin = NA,
  bw = FALSE, printTitle = TRUE, printValues = FALSE, minNumObs = 100,
  minNumUncen = 50, colors = c("black", "red", "green"),
  printLegend = TRUE, windowY = 7, windowQ = 2, windowS = 0.5,
  tinyPlot = FALSE, customPar = FALSE, lwd = 2, cex = 0.8,
  cex.axis = 1.1, cex.main = 1.1, cex.legend = 1.2, lineVal = c(1, 1,
  1), logScale = FALSE, edgeAdjust = TRUE, concLab = 1,
  usgsStyle = FALSE, ...)

Arguments

See Also

genericEGRETDotPlot, runSurvReg

Examples

date1 <- "1982-06-01"
date2 <- "1994-06-01"
date3 <- "2010-06-01"
qLow <- 0.5
qHigh <- 50
eList <- Choptank_eList

plotConcQSmooth(eList, date1, date2, date3, qLow, qHigh,
                 legendLeft = 0.6, legendTop = 0.7)
plotConcQSmooth(eList, date1, date2, date3, qLow, qHigh,
                logScale=TRUE, legendLeft = 0.6, legendTop = 0.7)

Description

This function allows the user to plot all of the data, but also to limit it in two ways. The data can be limited to only those observed concentrations collected in a specified discharge range. The data can also be limited to only those observed in certain months of the year. These two selection criteria can be combined. For example, we may only want to plot data for discharges between 100 and 500 cubic feet per second in the months of March, April and May.

Although there are a lot of optional arguments to this function, most are set to a logical default.

Data come from named list, which contains a Sample dataframe with the sample data, and an INFO dataframe with metadata.

Usage

plotConcTime(eList, qUnit = 2, yearStart = NA, yearEnd = NA,
  qLower = NA, qUpper = NA, randomCensored = FALSE, tinyPlot = FALSE,
  concMax = NA, concMin = NA, printTitle = TRUE, logScale = FALSE,
  cex = 0.8, cex.axis = 1.1, cex.main = 1.1, customPar = FALSE,
  col = "black", lwd = 1, concLab = 1, usgsStyle = FALSE, ...)

Arguments

Details

The function has two possible ways to plot censored values (e.g. "less-than values").

The default is to plot them as a vertical line that goes from the reporting limit down to the bottom of the graph.

The alternative is to set randomCensored = TRUE. In this case a random value is used for plotting the individual sample value. This random value lies between the reporting limit and zero and it is distributed as a truncated log normal based on the fitted WRTDS model.

The function makeAugmentedSample must be run first if randomCensored = TRUE. Running makeAugmentedSample requires that modelEstimation has already been run.

These random censored values are used to create more readable plots and are not used in any computations about the data set. The random censored values are shown as open circles and the non-censored data are shown as filled dots.

See Also

selectDays, genericEGRETDotPlot

Examples

eList <- Choptank_eList
# Water year:
plotConcTime(eList)
# Graphs consisting of Jun-Aug
eList <- setPA(eList, paStart=6,paLong=3)
plotConcTime(eList, 
             qUnit = 1,
             qLower = 100,
             qUpper = 10000)
plotConcTime(eList, logScale=TRUE)
plotConcTime(eList, 
             qUnit = 1, 
             qLower = 100, qUpper = 10000,
              randomCensored = TRUE)

Description

This plot is useful for visual examination of the ability of the WRTDS, or other model, to fit the data, seen in a time-series perspective. The graph is most useful when it covers a period of just a few years and not the complete record but a complete record can be done by repeated use over a series of segments.

Although there are a lot of optional arguments to this function, most are set to a logical default.

Data come from named list, which contains a Sample dataframe with the sample data, a Daily dataframe with the daily flow data, and an INFO dataframe with metadata.

Usage

plotConcTimeDaily(eList, yearStart = NA, yearEnd = NA, tinyPlot = FALSE,
  concMax = NA, printTitle = TRUE, plotGenConc = TRUE, cex = 0.8,
  cex.axis = 1.1, randomCensored = FALSE, cex.main = 1.1,
  customPar = FALSE, col = "black", lwd = 1, prettyDate = TRUE,
  usgsStyle = FALSE, ...)

Arguments

See Also

selectDays, genericEGRETDotPlot

Examples

eList <- Choptank_eList
# Water year:
plotConcTimeDaily(eList)
plotConcTimeDaily(eList, 
                  yearStart = 1998,
                  yearEnd = 2001,
                  plotGenConc = FALSE)

Description

These plots show how the concentration-time relationship is changing over flow.

This plot can also help identify situations where the windowY may be too small. If there are substantial oscillations of some of the curves, then the windowY should be increased. Alternatively, windowY may be too large. This can be seen when the windowY is reduced (say to 4.0). A good choice of windowY would be a value just great enough to damp out oscillations in the curves.

Although there are a lot of optional arguments to this function, most are set to a logical default.

Data come from named list, which contains a Sample dataframe with the sample data and an INFO dataframe with metadata.

Usage

plotConcTimeSmooth(eList, q1, q2, q3, centerDate, yearStart, yearEnd,
  qUnit = 2, legendLeft = 0, legendTop = 0, concMax = NA,
  concMin = NA, bw = FALSE, printTitle = TRUE, colors = c("black",
  "red", "green"), printValues = FALSE, tinyPlot = FALSE, concLab = 1,
  monthLab = 1, minNumObs = 100, minNumUncen = 50, windowY = 10,
  windowQ = 2, windowS = 0.5, cex.main = 1.1, lwd = 2,
  printLegend = TRUE, cex.legend = 1.2, cex = 0.8, cex.axis = 1.1,
  customPar = FALSE, lineVal = c(1, 1, 1), logScale = FALSE,
  edgeAdjust = TRUE, usgsStyle = FALSE, ...)

Arguments

See Also

genericEGRETDotPlot, runSurvReg

Examples

q1 <- 1
q2 <- 10
q3 <- 100
centerDate <- "07-01"
yearStart <- 1990
yearEnd <- 2010
eList <- Choptank_eList
plotConcTimeSmooth(eList, q1, q2,q3, centerDate, 
                   yearStart, yearEnd, legendLeft = 1997, 
                   legendTop = 0.44, cex.legend = 0.9)
plotConcTimeSmooth(eList, q1, q2,q3, centerDate, yearStart, 
                   yearEnd, logScale = TRUE, legendLeft = 1994, 
                   legendTop = 0.4, cex.legend = 0.9)

Description

These plots are normally used for plotting the estimated concentration surface (whatSurface = 3) but can be used to explore the estimated surfaces for the log of concentration or for the standard error (in log space) which is what determines the bias correction. The plots are often more interpretable when the time limits for the plot are less than a decade. To explore changes over a long time period it is best to do this multiple times, for various time slices of 2 years (for example) or to use the function plotDiffContours.

Although there are a lot of optional arguments to this function, most are set to a logical default.

Obtaining a plot that provides good insight it is useful to experiment with several of the arguments such as yearStart, yearEnd, qBottom, qTop, and contourLevels.

Data come from named list, which contains a Sample dataframe with the sample data, a Daily dataframe with the daily flow data, and an INFO dataframe with metadata.

Usage

plotContours(eList, yearStart, yearEnd, qBottom = NA, qTop = NA,
  whatSurface = 3, qUnit = 2, contourLevels = NA, span = 60,
  pval = 0.05, printTitle = TRUE, vert1 = NA, vert2 = NA, horiz = NA,
  tcl = 0.03, flowDuration = TRUE, customPar = FALSE, yTicks = NA,
  tick.lwd = 1, usgsStyle = FALSE, lwd = 2, cex.main = 1,
  cex.axis = 1, concLab = 1,
  color.palette = grDevices::colorRampPalette(c("white", "gray", "blue",
  "red")), ...)

Arguments

Examples

yearStart <- 2002
yearEnd <- 2010
qBottom <- 0.5
qTop<- 20
clevel <- seq(0,2,0.25)
eList <- Choptank_eList
plotContours(eList, yearStart, yearEnd, qBottom, qTop, 
             contourLevels = clevel)  
plotContours(eList, yearStart, yearEnd, qBottom, qTop = 50, 
             contourLevels = clevel, flowDuration = FALSE) 
colors <- grDevices::colorRampPalette(c("white","black"))
plotContours(eList, yearStart, yearEnd, qBottom, qTop = 50, 
             contourLevels = clevel, color.palette = colors, 
             flowDuration = FALSE)

Description

These plots are normally used for plotting changes in the estimated concentration surface (whatSurface=3) but can be used to explore the changes in estimated surfaces for the log of concentration or for the standard error (in log space) which is what determines the bias correction.

The difference can be shown either as an arithmetic difference or as a percentage difference.

Although there are a lot of optional arguments to this function, most are set to a logical default.

Data come from named list, which contains a Sample dataframe with the sample data, a Daily dataframe with the daily flow data, and an INFO dataframe with metadata.

Usage

plotDiffContours(eList, year0, year1, qBottom = NA, qTop = NA,
  maxDiff = NA, whatSurface = 3, tcl = 0.03, qUnit = 2, span = 60,
  pval = 0.05, printTitle = TRUE, plotPercent = FALSE, vert1 = NA,
  vert2 = NA, horiz = NA, flowDuration = TRUE, yTicks = NA,
  tick.lwd = 1, lwd = 2, cex.main = 0.95, cex.axis = 1,
  customPar = FALSE, usgsStyle = FALSE,
  color.palette = grDevices::colorRampPalette(c("blue", "white", "red")),
  concLab = 1, monthLab = 1, ...)

Arguments

Examples

year0 <- 1990
year1 <- 2009
qBottom <- 0.5
qTop <- 20
maxDiff<-0.5
eList <- Choptank_eList
plotDiffContours(eList, year0, year1, qBottom, qTop, maxDiff = 0.5)
plotDiffContours(eList, year0, year1, qBottom, qTop, maxDiff = 50, plotPercent = TRUE)

Description

A part of the flowHistory system. The index of the flow statistics is istat. These statistics are: (1) 1-day minimum, (2) 7-day minimum, (3) 30-day minimum, (4) median (5) mean, (6) 30-day maximum, (7) 7-day maximum, and (8) 1-day maximum

Although there are a lot of optional arguments to this function, most are set to a logical default.

Data come from named list, which contains a Daily dataframe with the daily flow data, and an INFO dataframe with metadata.

Usage

plotFlowSingle(eList, istat, yearStart = NA, yearEnd = NA, qMax = NA,
  printTitle = TRUE, tinyPlot = FALSE, customPar = FALSE,
  runoff = FALSE, qUnit = 1, printStaName = TRUE, printPA = TRUE,
  usgsStyle = FALSE, printIstat = TRUE, cex = 0.8, cex.axis = 1.1,
  cex.main = 1.1, lwd = 2, col = "black", ...)

Arguments

Details

The curve plotted on the graph is a loess smooth of the data. This smooth is computed on the logs of the data and then transformed back to plot. The width of the smoothing window is 20 years on either side of the year being plotted However, the window width can be adjusted using setPA function.

See Also

makeAnnualSeries, genericEGRETDotPlot

Examples

eList <- Choptank_eList
# Water year:
plotFlowSingle(eList, 1)
# Graphs consisting of Jun-Aug
eList <- setPA(eList, paStart=6,paLong=3)
plotFlowSingle(eList, 1)

Description

The annual results reported are for a specified "period of analysis" which can be an entire water year, a calendar, a season or even an individual month. The user specifies this period of analysis in the call to setupYears. Values plotted express a flux rate, such as thousand kg per year For a period of analysis that is less than a year, this does not equal the mass transported over the period of analysis

Although there are a lot of optional arguments to this function, most are set to a logical default.

Data come from named list (eList), which contains a Daily dataframe with the daily flow data, and an INFO dataframe with metadata.

Usage

plotFluxHist(eList, yearStart = NA, yearEnd = NA, fluxUnit = 9,
  fluxMax = NA, printTitle = TRUE, usgsStyle = FALSE,
  plotFlowNorm = TRUE, plotAnnual = TRUE, plotGenFlux = FALSE,
  tinyPlot = FALSE, col = "black", col.pred = "green", col.gen = "red",
  cex = 0.8, cex.axis = 1.1, cex.main = 1.1, lwd = 2,
  customPar = FALSE, ...)

Arguments

See Also

setupYears

Examples

yearStart <- 2001
yearEnd <- 2010
eList <- Choptank_eList
# Water year:

plotFluxHist(eList)
plotFluxHist(eList, yearStart, yearEnd, fluxUnit = 1)
plotFluxHist(eList, yearStart, yearEnd, fluxUnit = 'kgDay')

Description

Data come from named list, which contains a Sample dataframe with the sample data, and an INFO dataframe with metadata.

Although there are a lot of optional arguments to this function, most are set to a logical default.

Usage

plotFluxPred(eList, fluxUnit = 3, fluxMax = NA, printTitle = TRUE,
  oneToOneLine = TRUE, customPar = FALSE, col = "black", lwd = 1,
  cex = 0.8, cex.axis = 1.1, cex.main = 1.1, tinyPlot = FALSE,
  usgsStyle = FALSE, logScale = FALSE, randomCensored = FALSE, ...)

Arguments

See Also

selectDays, genericEGRETDotPlot

Examples

eList <- Choptank_eList
# Water year:
plotFluxPred(eList)
plotFluxPred(eList, fluxUnit = 'poundsDay')
plotFluxPred(eList, logScale=TRUE)
# Graphs consisting of Jun-Aug
eList <- setPA(eList, paStart=6,paLong=3)
plotFluxPred(eList)

Description

Concentration and discharge data used to compute flux come from a data frame named Sample which contains the sample data. The metadata come from a data frame named INFO.

Although there are a lot of optional arguments to this function, most are set to a logical default.

Data come from named list, which contains a Sample dataframe with the sample data, and an INFO dataframe with metadata.

Usage

plotFluxQ(eList, qUnit = 2, logScale = TRUE, fluxUnit = 3,
  tinyPlot = FALSE, fluxMax = NA, fluxMin = NA, col = "black",
  lwd = 1, printTitle = TRUE, usgsStyle = FALSE, cex = 0.8,
  cex.axis = 1.1, cex.main = 1.1, customPar = FALSE, ...)

Arguments

See Also

selectDays, genericEGRETDotPlot

Examples

eList <- Choptank_eList
# Water year:
plotFluxQ(eList, qUnit = 1, fluxUnit = 1)
plotFluxQ(eList, fluxUnit = 'kgDay')
plotFluxQ(eList)
# Graphs consisting of Jun-Aug
eList <- setPA(eList, paStart=6,paLong=3)
plotFluxQ(eList)

Description

This plot is useful for visual examination of the ability of the WRTDS, or other model, to fit the data, as seen in a time-series perspective.

Although there are a lot of optional arguments to this function, most are set to a logical default.

Data come from named list, which contains a Sample dataframe with the sample data, a Daily dataframe with the daily flow data, and an INFO dataframe with metadata.

Usage

plotFluxTimeDaily(eList, yearStart = NA, yearEnd = NA, tinyPlot = FALSE,
  fluxUnit = 3, fluxMax = NA, randomCensored = FALSE,
  printTitle = TRUE, plotGenFlux = TRUE, usgsStyle = FALSE, cex = 0.8,
  cex.axis = 1.1, cex.main = 1.1, customPar = FALSE, col = "black",
  lwd = 1, prettyDate = TRUE, ...)

Arguments

See Also

selectDays, genericEGRETDotPlot

Examples

eList <- Choptank_eList
# Water year:
plotFluxTimeDaily(eList)
plotFluxTimeDaily(eList, 2001,2009)