Package 'USGSHydroTools'

Title: Collection of functions for hydrological analysis
Description: Collection of functions for storm volume computation (Hydrovol), time series data aggregation, baseflow determination, load computations, and spatial data visualization.
Authors: Steve Corsi
Maintainer: Steve Corsi <[email protected]>
License: CC0
Version: 1.3.0
Built: 2024-10-26 02:54:35 UTC
Source: https://github.com/USGS-R/USGSHydroTools

Help Index

Collection of functions for hydrological analysis.

Description

Package: GSHydroTools
Type: Package
Version: 1.0.0
Date: 2014-01-10
License: Unlimited for this package, dependencies have more restrictive licensing.
Copyright: This software is in the public domain because it contains materials that originally came from the United States Geological Survey, an agency of the United States Department of Interior. For more information, see the official USGS copyright policy at http://www.usgs.gov/visual-id/credit_usgs.html#copyright
LazyLoad: yes

Details

Collection of functions for hydrological analysis.

Author(s)

Steve Corsi [email protected]

Compute sine and cosine terms for dates to use as seasonal terms in data analysis

Description

Function to compute seasonal sin and cosine terms from POSIXlt variable

Usage

computeSeasonal(df, date, return.var)

Arguments

df

dataframe with date included
date

string column name of date to convert in POSIXlt format
return.var

string suffix for variable names to return

Value

df

Examples

sampleData <- sampleData
sampleData$bpdate <- as.POSIXlt(sampleData$Hbpdate) #convert from POSIXct to POSIXlt
computeSeasonal(df=sampleData,date="bpdate",return.var="bdate")

Determine baseflow and events from HYSEP output

Description

Function to find the longest continuous start and end dates from the Daily dataframe. Primary use case is to find input value to use in a call to HYSEP (from package DVstats). If there are gaps in the data, the function will look for the largest continous gap.

Usage

determineHYSEPEvents(HYSEPReturn, sampleDates, percent = 0.8, value = "Flow")

Arguments

HYSEPReturn

dataframe returned from hysep function (in DVstats package)
sampleDates

dataframe with two columns "Discharge_cubic_feet_per_second" and "maxSampleTime"
percent

number to use to determine event conditions. This number will be multiplied by the flow, and if that product is greater than the calculated baseflow, the sample time will be labeled an event.
value

character name of discharge column.

Value

sampleDates dataframe

Examples

site <- "04085427"
sampleDates <- sampleDates
Start_extend <- as.character(as.Date(min(sampleDates$ActivityStartDateGiven, na.rm=TRUE))-60)
End_extend <- as.character(as.Date(max(sampleDates$ActivityStartDateGiven, na.rm=TRUE))+60)
Daily <- dataRetrieval::readNWISdv(site,'00060', Start_extend, End_extend)
Daily <- dataRetrieval::renameNWISColumns(Daily)
sampleDates <- findSampleQ(site, sampleDates, Daily)
startEnd <- getMaxStartEnd(Daily)
Start <- startEnd$Start
End <- startEnd$End
naFreeDaily <- Daily[!is.na(Daily$Flow),]
INFO <- dataRetrieval::readNWISsite(site)
DA_mi <- INFO$drain_area_va
HYSEPReturn <- exampleHYSEP
sampleDates <- determineHYSEPEvents(HYSEPReturn, sampleDates,0.8)

Example data for correlations

Description

Example data with response and predictor variables

Author(s)

Steve Corsi [email protected]

Example event dates and times.

Description

Example event begin and end dates and times to define a sampled hydrograph

Author(s)

Steve Corsi [email protected]

Example HYSEP output data.

Description

Example HYSEP output data. Needs more info.

Author(s)

Steve Corsi [email protected]

Example water quality data included in GSHydroTools

Description

Example data representing composite fecal indicator bacteria from the Menomonee River at Wauwatosa, Wisconsin

Author(s)

Steve Corsi [email protected]

Find flow for sample times

Description

Function to find flows values for given sample times. If instantaneous data is available, this function will retrieve that data, otherwise the Daily streamflow data will be used. If the sample times have a start and end time, the flow is the maximum flow in the range of the sample.

Usage

findSampleQ(site, sampleDates, localDaily, value = "Flow")

Arguments

site

string USGS identification number
sampleDates

dataframe with two columns "ActivityStartDateGiven" and "ActivityEndDateGiven"
localDaily

dataframe returned from dataRetrieval
value

character name of discharge column

Value

sampleDates

Examples

site <- "04085427"
sampleDates <- sampleDates
Start_extend <- as.character(as.Date(min(sampleDates$ActivityStartDateGiven, na.rm=TRUE))-60)
End_extend <- as.character(as.Date(max(sampleDates$ActivityStartDateGiven, na.rm=TRUE))+60)
Daily <- dataRetrieval::readNWISdv(site,'00060', Start_extend, End_extend)
Daily <- dataRetrieval::renameNWISColumns(Daily)
sampleDates <- findSampleQ(site, sampleDates, Daily)

Example flow data.

Description

Example instantaneous (unit value) flow data from Menomonee River at Wauwatosa, WI.

Author(s)

Steve Corsi [email protected]

Find maximum start and end dates from Daily dataframe

Description

Function to find the longest continuous start and end dates from the Daily dataframe. Primary use case is to find input value to use in a call to HYSEP (from package DVStats). If there are gaps in the data, the function will look for the largest continous gap.

Usage

getMaxStartEnd(localDaily, value = "Flow", date = "Date")

Arguments

localDaily

dataframe returned from dataRetrieval
value

character name of discharge column
date

character name of date column

Value

named list with Start and End values

Examples

site <- "04085427"
sampleDates <- sampleDates
Start_extend <- as.character(as.Date(min(sampleDates$ActivityStartDateGiven, na.rm=TRUE))-60)
End_extend <- as.character(as.Date(max(sampleDates$ActivityStartDateGiven, na.rm=TRUE))+60)
Daily <- dataRetrieval::readNWISdv(site,'00060', Start_extend, End_extend)
Daily <- dataRetrieval::renameNWISColumns(Daily)
startEnd <- getMaxStartEnd(Daily)
Start <- startEnd$Start
End <- startEnd$End

Hydrovol

Description

Computes volumes and max discharge for hydrographs given the discharge time series and the begin and end dates and times of the hydrographs. Dates must be in POSIXct format.

Usage

Hydrovol(
  dfQ,
  Q = "Q",
  time = "pdate",
  df.dates,
  bdate = "bpdate",
  edate = "epdate",
  volume = "event.vol",
  Qmax = "Qmax",
  duration = "Eduration"
)

Arguments

dfQ

dataframe with Q and time
Q

string name of column in dfQ with Q, defaults to "Q"
time

string name of column in dfQ with POSIXct time, defaults to "pdate"
df.dates

dataframe with begin and end dates/times in POSIXct format
bdate

string begin date in POSIXct column name, defaults to "bpdate"
edate

string end date in POSIXct column name, defaults to "epdate"
volume

string name of resulting volume variable, defaults to "event.vol"
Qmax

string name of Qmax variable, defaults to "Qmax"
duration

string name of resulting duration variable, defaults to "Eduration"

Value

df.dates2 dataframe

Examples

sampleData <- sampleData
flowData <- flowData
Hydrovol(dfQ=flowData,Q="Q",time="pdate",
         df.dates=sampleData,bdate="Hbpdate",edate="Hepdate")

LoadCompEvent

Description

Computation of loadings for event periods using individual discrete samples. Results in added columns to the event data frame that represent the event loadings in the original mass units from the concentration variable and the flow-weighted event mean concentration. maximum flow in the original flow units, volumes in the original volume units from the flow variable, and loadings in the original mass units from the concentration variable.

Usage

LoadCompEvent(
  df.samples,
  Conc,
  sample.time,
  Conc2liters,
  df.Q,
  Q,
  Q.time,
  Q2liters,
  df.events,
  event.bdate,
  event.edate
)

Arguments

df.samples

dataframe with discrete sample results and dates/times
Conc

string column name in df.samples with the concentration results
sample.time

string column name in df.samples with sample dates/times in POSIXct format
Conc2liters

numeric conversion factor that converts the concentrations to a units/liter
df.Q

dataframe with Q and date/time
Q

string name of column in dfQ with Q
Q.time

string name of column in dfQ with date/time in POSIXct format
Q2liters

numeric conversion factor that converts flow to rate per liters
df.events

dataframe with begin and end dates defining the event period
event.bdate

character string with name of variable defining beginning date for events in POSIXct format
event.edate

character string with name of variable defining end date for events in POSIXct format

Value

df.load

Examples

WQdata <- WQdata
flowData <- flowData
events <- events
LoadCompEvent(df.samples=WQdata,Conc="Total_P",sample.time="dateTime",Conc2liters=1,
df.Q=flowData,Q="Q",Q.time="pdate",Q2liters=28.3168466,
df.events=events,event.bdate="pbdate",event.edate="pedate")

LoadInstantaneous

Description

Computation of loadings for individual discrete samples. Results in added columns to the concentration data frame that represent the maximum flow in the original flow units, volumes in the original volume units from the flow variable, and loadings in the original mass units from the concentration variable.

Usage

LoadInstantaneous(
  df.samples,
  Conc,
  sample.time,
  Conc2liters,
  df.Q,
  Q,
  Q.time,
  Q2liters
)

Arguments

df.samples

dataframe with discrete sample results and dates/times
Conc

string column name in df.samples with the concentration results
sample.time

string column name in df.samples with sample dates/times in POSIXct format
Conc2liters

numeric conversion factor that converts the concentrations to a units/liter
df.Q

dataframe with Q and date/time
Q

string name of column in dfQ with Q
Q.time

string name of column in dfQ with date/time in POSIXct format
Q2liters

numeric conversion factor that converts flow to rate per liters

Value

df.load

Examples

WQdata <- WQdata
flowData <- flowData
LoadInstantaneous(df.samples=WQdata, Conc="Total_P",
         sample.time="dateTime", Conc2liters=1, 
         df.Q=flowData, Q="Q", Q.time="pdate", Q2liters=28.3168466)

multiCor compute correlation coefficients for one response variable vs multiple predictor (independent) variables. The output is a dataframe ordered by highest to lowest correlation

Description

multiCor compute correlation coefficients for one response variable vs multiple predictor (independent) variables. The output is a dataframe ordered by highest to lowest correlation

Usage

multiCor(df, response, IVs, method = "spearman")

Arguments

df

is dataframe with response variable and predictor variables
response

is a character string that is the name of the response variable in df
IVs

is a vector of character strings that are the independent variables in df
method

is either "spearman" (nonparametric) or "pearson" (parametric)

Value

z dataframe with the variable name in column 1 and correlation coefficient in column 2. The dataframe is ordered from greatest correlation to least correlation.

Examples

data <- dfOptical
multiCor(data,"logEColi",names(data)[-1],"spearman")

Plot baseflow/event plot

Description

Plot output of flow, with daily and instantaneous flow (when available).

Usage

plotBaseflow(
  sampleDates,
  Daily,
  INFO,
  site,
  HYSEPReturn,
  baseflowColumns = "flowConditionHYSEP_localMin",
  HYSEPcolNames = "LocalMin",
  xlabel = TRUE,
  showLegend = TRUE,
  plotTitle = TRUE,
  instantFlow = NA,
  whatDischarge,
  value = "Flow",
  valueInst = "Flow_Inst"
)

Arguments

sampleDates

dataframe with two columns "Discharge_cubic_feet_per_second" and "maxSampleTime"
Daily

dataframe from getNWISDaily function in the dataRetrieval package
INFO

dataframe from getNWISInfo function in dataRetrieval package. Alternatively, a dataframe with a column "station.nm"
site

string USGS site identification
HYSEPReturn

dataframe with one column Dates, and at least 1 column of baseflow
baseflowColumns

string. Names of columns in the sampleDates dataframe with "Baseflow" or "Event" indicators.
HYSEPcolNames

string. Name of column in HYSEPReturn.
xlabel

logical. Whether or not to print x label
showLegend

logical. Whether or not to print legend
plotTitle

logical. Whether or not to print title
instantFlow

dataframe returned from retrieveUnitNWISData. If none available, NA.
whatDischarge

dataframe returned from whatNWISdata
value

character name of discharge column in Daily
valueInst

character name of discharge column in instantFlow

Examples

site <- "04085427"
sampleDates <- sampleDates
Start_extend <- as.character(as.Date(min(sampleDates$ActivityStartDateGiven, na.rm=TRUE))-60)
End_extend <- as.character(as.Date(max(sampleDates$ActivityStartDateGiven, na.rm=TRUE))+60)
Daily <- dataRetrieval::readNWISdv(site,'00060', Start_extend, End_extend)
Daily <- dataRetrieval::renameNWISColumns(Daily)
sampleDates <- findSampleQ(site, sampleDates, Daily)
startEnd <- getMaxStartEnd(Daily)
Start <- startEnd$Start
End <- startEnd$End
naFreeDaily <- Daily[!is.na(Daily$Flow),]
INFO <- dataRetrieval::readNWISsite(site)
DA_mi <- as.numeric(INFO$drain_area_va)
HYSEPReturn <- exampleHYSEP
sampleDates <- determineHYSEPEvents(HYSEPReturn, sampleDates,0.8)
whatDischarge <- dataRetrieval::whatNWISdata(siteNumber = site)
whatDischarge <-  whatDischarge[whatDischarge$parm_cd == "00060", ]
Start <- as.character(as.Date(min(sampleDates$ActivityStartDateGiven, na.rm=TRUE)))
End <- as.character(as.Date(max(sampleDates$ActivityStartDateGiven, na.rm=TRUE)))

if ("uv" %in% whatDischarge$data_type_cd){
  if(any(whatDischarge$begin_date[whatDischarge$data_type_cd == "uv"] < End, na.rm = TRUE)){
    instantFlow <- dataRetrieval::readNWISuv(site,"00060",Start,End)
    instantFlow <- dataRetrieval::renameNWISColumns(instantFlow)
  }
}
plotBaseflow(sampleDates,Daily,INFO,site,HYSEPReturn,
             baseflowColumns="flowConditionHYSEP_localMin",
             HYSEPcolNames = "LocalMin",plotTitle=TRUE,
             instantFlow=instantFlow,whatDischarge=whatDischarge,xlabel=FALSE)

plotHydroConc

Description

Function to generate a two panel graph with hydrograph(s) in the top panel and concentration or other water quality parameter (e.g. flux) in the lower panel with corresponding date axes for the top and bottom panels

Usage

plotHydroConc(
  Q,
  QVars,
  QDateVars,
  smooth,
  sites,
  Conc,
  CVars,
  CDateVar,
  CVarsDisplay,
  dates,
  sampDates,
  eventDates,
  Qcols,
  Ccols,
  leftBuffer,
  rightBuffer,
  concLines = TRUE,
  Qylab = "Discharge (cfs)",
  Cylab = "Concentration",
  title1 = "Flow",
  title2 = "and concentration"
)

Arguments

Q

dataframe list with flow variables and POSIXct date variables. The list contains one file per Q record (usually one dataframe per site).
QVars

vector of strings that signify the column names that represent the flow variables in the dataframes defined in Q
QDateVars

vector of strings that signify the column names that represent the POSIXct date variables in the dataframes defined in Q
smooth

Boolean vector to trigger lowess smooth in graphing rather than direct flow graphing (useful for sites impacted by seiche)
sites

Sites for Q data. This will be used in the legend of the Q panel
Conc

dataframe with variables to be plotted in the second panel
CVars

vector of strings that represent the variables in Conc to include in the graphs in the second panel
CDateVar

column name in Conc for sample dates and times in POSIXct
CVarsDisplay

variable names from CVars to be displayed on the legend
dates

dataframe with beginning and ending sample dates and times and beginning and ending hydrograph dates and times
sampDates

vector of length two that contains strings representing the column names for beginning and ending dates and times for samples
eventDates

vector of length two that contains strings representing the column names for beginning and ending dates and times for the sampling event. This is the hydrograph segment that the samples are intended to represent (often a runoff hydrograph or a baseflow period)
Qcols

vector of colors for ploting hydrographs from the Q dataframe
Ccols

vector of colors for plotting from the Conc dataframe
leftBuffer

time in days to plot before the beginning of the event period
rightBuffer

time in days to plot after the end of the event period
concLines

Boolean variable to signify whether a line should be drawn between consecutive data points from the Conc dataframe in the second graph panel
Qylab

y-axis label for the first graph panel
Cylab

y-axis label for the second graph panel
title1

Line 1 of plot title
title2

Line 2 of plot title

Examples

#Add example

Plot 3 baseflow/event plots from hysep output

Description

Plot sliding, fixed, and local_min output of flow, with daily and instantaneous flow (when available).

Usage

plotHYSEPOverview(
  sampleDates,
  Daily,
  INFO,
  site,
  HYSEPReturn,
  baseflowColumns = c("flowConditionHYSEP_localMin", "flowConditionHYSEP_Fixed",
    "flowConditionHYSEP_Sliding"),
  HYSEPcolNames = c("LocalMin", "Fixed", "Sliding")
)

Arguments

sampleDates

dataframe with two columns "Discharge_cubic_feet_per_second" and "maxSampleTime"
Daily

dataframe from getNWISDaily function in the dataRetrieval package
INFO

dataframe from getNWISSiteInfo function in dataRetrieval package. Alternatively, a dataframe with a column "station.nm"
site

string USGS site identification
HYSEPReturn

dataframe with one column Dates, and 3 columns of baseflow as defined by HYSEPcolNames
baseflowColumns

sting vector length of 3. Names of columns with "Baseflow" or "Event" indicators.
HYSEPcolNames

sting vector length of 3. Names of columns in HYSEPReturn

Value

sampleDates dataframe

Examples

site <- "04085427"
sampleDates <- sampleDates
Start_extend <- as.character(as.Date(min(sampleDates$ActivityStartDateGiven, na.rm=TRUE))-60)
End_extend <- as.character(as.Date(max(sampleDates$ActivityStartDateGiven, na.rm=TRUE))+60)
Daily <- dataRetrieval::readNWISdv(site,'00060', Start_extend, End_extend)
Daily <- dataRetrieval::renameNWISColumns(Daily)
sampleDates <- findSampleQ(site, sampleDates, Daily)
startEnd <- getMaxStartEnd(Daily)
Start <- startEnd$Start
End <- startEnd$End
naFreeDaily <- Daily[!is.na(Daily$Flow),]
INFO <- dataRetrieval::readNWISsite(siteNumber = site)
DA_mi <- as.numeric(INFO$drain_area_va)
HYSEPReturn <- exampleHYSEP
sampleDates <- determineHYSEPEvents(HYSEPReturn, sampleDates,0.8)
plotHYSEPOverview(sampleDates,Daily,INFO,site,HYSEPReturn)

Example sample data.

Description

Example sample data. Needs more info.

Author(s)

Steve Corsi [email protected]

Example sample dates.

Description

Example sample dates data.

Author(s)

Steve Corsi [email protected]

Rivers in US.

Description

River shapefiles from http://dds.cr.usgs.gov/pub/data/nationalatlas/hydro0m_shp_nt00300.tar.gz

Lakes in US

Description

Lake shapefiles from http://dds.cr.usgs.gov/pub/data/nationalatlas/hydro0m_shp_nt00300.tar.gz

Political boundaries.

Description

Political boundary shapefiles from http://dds.cr.usgs.gov/pub/data/nationalatlas/bound0m_shp_nt00298.tar.gz

Example USGS site information

Description

Station name, coordinates, label offsets, and line offsets for positioning the labels on a map and lines from the data points to the labels

Example USGS site information

Description

Raw data pulled from NWIS

Summarize dataDF by group

Description

Mapping routine that displays spatial dataDF variability by color differences. over layers with political boundaries, hydrologic polygons, and hydrologic lines.

Usage

summarizedataDF(dataDF, colGroup, colValue, colDate)

Arguments

dataDF

dataframe with columns defined by colGroup (grouping column, such as site ID), colValue (value column), and optionally colDate (date columns)
colGroup

string defines grouping column in dataDF
colValue

string defines value column in dataDF
colDate

string defines date column in dataDF. If colDate = NA, the calculations for start and end date are ignored.

Value

dataframe with count, mean, median, min, max, start(date/time), end(date/time), and number of non-detects (nd) defined as number of NA's grouped by colGroup

Examples

df <- data.frame(site=c("1","x","2","1","x","2"),
                 conc=c(2,3,4,5,NA,7),
                 dates=as.Date(c("2011-01-01","2011-01-01",
                   "2011-01-01","2011-01-02","2011-01-02","2011-01-02")))
sumDF <- summarizedataDF(df, "site", "conc", "dates")

Compute various antecedent summary statistics from time series data for specified windows of time

Description

Compute various stats for time series data over a period of time Originally scripted for NOAA Great Lakes model from GDP for given set of dates and time periods, but could be used for any time series. File format must include the POSIX formatted date (yyyy-mm-ddThh:mm:ssZ), and then columns of values with the time series data

read date with format mm/dd/yy hh:mm (use koepkeSM$date <- as.POSIXct(koepkeSM$Date,"

read date with format mm/dd/yyyy hh:mm cedardates$psdate <- as.POSIXct(cedardates$Startdate," cedardates$parfdate <- as.POSIXct(cedardates$Enddate,"

Subset the data by begin and end date (can also assign to a df if you like) then define min mean median and max for the subset. Do this for all date periods in the file.

Usage

TSstats(
  df,
  date = "date",
  varnames,
  dates,
  starttime = "psdate",
  times = c(1, 2),
  units = "hours",
  stats.return = c("mean"),
  subdfvar = "",
  subdfvalue = "",
  subdatesvar = "",
  subdatesvalue = "",
  out.varname = ""
)

Arguments

df

dataframe Unit values file
date

string Date column in POSIX format in unit values file
varnames

string Column name with unit values
dates

dataframe File with sample dates
starttime

string Column in sample dates file with dates in POSIX format, defaults to "psdate"
times

vector to define desired processing times. Zero indicates then nearest or nearest previous value. Default is hours, but can be specified using "units" variable
units

string Units of times vector. Can be any of the following: "minutes","min","mins","hours","hr","hrs","day","days","week","weeks"
stats.return

string Options include "mean","max","min","median","sum","sd","maxdiff","difference",nearest","nearprev" maxdiff is the maximum value minus the minimum value for the time period, difference is the latest minus the first value, nearest is the closest value in time, nearprev is the closest value previous to the specified time, nearest and nearprev require a 0 in the times vector,
subdfvar

string column name in UVdf with names of parameters, default is ""
subdfvalue

string Optional: value of varname to use in subsetting df, default is ""
subdatesvar

string Optional: subset dates data frame by a value in this column, default is ""
subdatesvalue

string Optional: value to use in subsetting
out.varname

string

Value

dates dataframe

Examples

flowData <- flowData
sampleData <- sampleData
TSstats(df=flowData,date="pdate",varnames="Q",
        dates=sampleData,starttime="Hbpdate",times=c(1,3,6,12,24),
        units="hrs",stats.return=c("mean","max","sd"),out.varname="Q")

Compute various time-series summary statistics between specified time periods

Description

Compute various stats for time series data over a period of time Can be used for time series data with equally spaced time increments. File format must include the POSIXct formatted date and columns of values with the time series data

Usage

TSstormstats(
  df,
  date = "pdate",
  varname,
  dates,
  starttime = "Ebpdate",
  endtime = "Eepdate",
  stats.return = c("mean"),
  subdfvar = "",
  subdfvalue = "",
  subdatesvar = "",
  subdatesvalue = "",
  out.varname = ""
)

Arguments

df

dataframe with unit values values and date/time in POSIX
date

string name of POSIX date column
varname

string column with unit values in df
dates

dataframe with sample dates
starttime

string Column in sample dates data fram with dates in POSIX format used for extracting summary data from dates dataframe This date serves as the beginning date of the summary period, default is "psdate"
endtime

string Column in sample dates data fram with dates in POSIX format used for extracting summary data from dates dataframe This date serves as the ending date of the summary period, default is "pedate"
stats.return

string vector Options include = c("mean","max","min","median","sum") specification of stats to apply to the time series data. Current options include mean, max, min, median, sum, difference, nearest, and nearprev. difference is the latest minus the first value, nearest is the closest value in time to starttime, nearprev is the closest value previous to starttime, nearest and nearprev require a 0 in the times vector.
subdfvar

string subset df data frame by a value in this column
subdfvalue

string value to use in subsetting df
subdatesvar

string subset dates data frame by a value in this column
subdatesvalue

string value to use in subsetting
out.varname

string variable name for resulting column

Value

dates dataframe

Examples

flowData <- flowData
sampleData <- sampleData
TSstormstats(df=flowData,date="pdate",varname="Q",
      dates=sampleData,starttime="Hbpdate",endtime="Hepdate",
      stats.return=c("mean","max","sd"),out.varname="Q")

Combine multiple samples into one composite sample (loads and concentrations)

Description

function to composite samples weighted by the associated volume the result is a volume-weighted concentration and summation of volumes

Usage

WQcompos(df.samples, sampleID, parms, volume = "Evolume", bdate, edate, codes)

Arguments

df.samples

dataframe with sample results and volumes
sampleID

character variable name for the IDs for compositing samples (multiple samples will have the same ID)
parms

vector Parameters to composite
volume

character variable name for the volume, defaults to "Evolume"
bdate

character variable name for the beginning of event times for each sample
edate

character variable name for the ending of event times for each sample
codes

a vector of character variable names for the values that should be pasted together into one string when combining samples (lab IDs are common here)

Value

IDdf dataframe

Examples

flowData <- flowData
FIBdata <- FIBdata
FIBcomposData <- Hydrovol(dfQ=flowData,Q="Q",time="pdate",
       df.dates=FIBdata,bdate="SSdate",edate="SEdate")
WQcompos(df.samples=FIBcomposData,sampleID="SampleID",
       parms=c("Ecoli","Enterococci"), volume="event.vol",
       bdate="SSdate",edate="SEdate",codes="SampleID")

Example water quality data included in GSHydroTools

Description

Example data representing discrete total phosphorus concentrtions from the Menomonee River at Wauwatosa, Wisconsin

Author(s)

Steve Corsi [email protected]