In leppott/ContDataQC: Quality Control (QC) of Continous Monitoring Data
knitr::opts_chunk$set(results='asis', echo=FALSE, warning=FALSE)
# needed for trouble shooting
boo_DEBUG <- FALSE
if(boo_DEBUG==TRUE){
myConfig <- file.path(system.file(package="ContDataQC"), "extdata"
, "config.ORIG.R")
source(myConfig)
}
DATA FILE INFORMATION
# Report Info
myReportDate <- format(Sys.Date(),ContData.env$myFormat.Date)
cat(paste("**Report Date:** ",myReportDate,"\n\n",sep=""))
myUser <- Sys.getenv("USERNAME")
cat(paste("**Generated By:** ",myUser,"\n\n",sep=""))
#filename
cat("**Filename:** ",strFile,"\n\n",sep="")
mySiteID <- data.import[1,ContData.env$myName.SiteID]
cat(paste("**SiteID:** ",mySiteID,"\n\n",sep=""))
if(exists("fun.myData.DateRange.Start")==TRUE){
POR.Requested <- paste(fun.myData.DateRange.Start
," to "
,fun.myData.DateRange.End
, sep="")
} else {
POR.Requested <- "NA"
}
cat(paste("**Period of Record, Requested:** "
,POR.Requested,sep=""
,collapse="\n\n"))
myNumRecords <- nrow(data.import)
# 20170228, mod from records 10 and 11 to half way point
# myTimeDiff <- difftime(data.import[10,ContData.env$myName.DateTime]
#,data.import[11,ContData.env$myName.DateTime],units="mins")
#x <- data.import[,ContData.env$myName.DateTime]
myT <- strptime(data.import[,ContData.env$myName.DateTime]
,format=ContData.env$myFormat.DateTime)
myTimeDiff.all <- difftime(myT[-1],myT[-length(myT)],units="mins")
myTimeDiff <- median(as.vector(myTimeDiff.all),na.rm=TRUE)
cat(paste("\n\n**Period of Record, Actual:**"
,min(data.import[,ContData.env$myName.Date])
," to "
,max(data.import[,ContData.env$myName.Date])
,"\n\n"
,sep=""))
cat(paste("**Recording Interval:** ",myTimeDiff[1]," minutes\n\n",sep=""))
if(exists("strFile.DataType")==TRUE){
myDataType <- strFile.DataType
} else {
myDataType <- "NA"
}
cat(paste("**Data Type:** ",myDataType,"\n\n",sep="")) # need to do better
myParameters.ALL <- ContData.env$myNames.DataFields[ContData.env$myNames.DataFields
%in% names(data.import)==TRUE]
myParameters.Lab.ALL <- ContData.env$myNames.DataFields.Lab[ContData.env$myNames.DataFields
%in% names(data.import)==TRUE]
# Filter out Discrete
myParameters <- myParameters.ALL[!grepl(ContData.env$myPrefix.Discrete,myParameters.ALL)]
myParameters.Lab <- myParameters.Lab.ALL[
!grepl(ContData.env$myPrefix.Discrete
,myParameters.Lab.ALL)]
#cat("**Parameters:** ",paste(myParameters.Lab,", ",sep=""),"\n\n",sep="")
# above line not working, preventing pandoc conversion to WORD
#cat("**Included Parameters:** \n\n")
cat("**Parameters Included:** ",paste(myParameters.ALL
,", "
,sep="")
,"\n\n"
,sep="")
DATA SUMMARY, OVERALL
# 2.1. Records by Month/Day
# split so easier to put on paper
# number of records by month/day (split 1:15 and 16:31)
#
# format = markdown/pandoc = seems to be the same. All rows but no columns in Word.
# Have to use on table not ftable. ftable better by itself but doesn't work with knitr::kable.
# # QC, if days less than count = 15
#*Error in table. Only printing half.* (fixed for sites with <15 days)
# if((max(data.import[,"day"])-min(data.import[,"day"]))<15) {##IF.daycount.START
# myTable <- table(data.import[,"month"],data.import[,"day"])
# print(knitr::kable(myTable, format="markdown",row.names=TRUE))
# } else {
# #
# #myTable.month.day.rec.LTE15 <-
# myTable <- table(data.import[,"month"][data.import[,"day"]<=15],data.import[,"day"][data.import[,"day"]<=15])
# print(knitr::kable(myTable, format="markdown", row.names=TRUE))
# #knitr::kable(myTable.month.day.rec.LTE15, format="pandoc", caption = "Title of the table")
# cat("\n\n")
# #
# # myTable.month.day.rec.GT15 <-
# myTable <- table(data.import[,"month"][data.import[,"day"]>15],data.import[,"day"][data.import[,"day"]>15])
# print(knitr::kable(myTable, format="markdown", row.names=TRUE))
#
# }##IF.daycount.END
# Convert time interval (minutes) to number per day
records.expected <- round(24*60/as.numeric(myTimeDiff[1]),1)
cat(paste("Estimated number of records per day is ",records.expected,".",sep=""))
cat("\n\n")
cat("Number of records by year and month (with totals).")
cat("\n")
myTable <- addmargins(table(data.import[,ContData.env$myName.Yr]
,data.import[,ContData.env$myName.Mo]
, useNA = "ifany"))
print(knitr::kable(myTable, format="markdown", row.names=TRUE))
cat("\n\n")
# revert to longer table (20170228)
cat("Number of records by day and month (with totals).")
cat("\n")
myTable <- addmargins(table(data.import[,ContData.env$myName.Day]
,data.import[,ContData.env$myName.Mo]
, useNA = "ifany"))
print(knitr::kable(myTable, format="markdown", row.names=TRUE))
cat("\n\n")
#
# pandoc.table(myTable.month.day.rec.GT15,style="rmarkdown")
#
#
myTable.month.day.rec <- table(data.import[,ContData.env$myName.Mo]
,data.import[,ContData.env$myName.Day]
, useNA = "ifany")
# use apply function to count number of records not equal to the expected value
cat("\n\n")
#
myTable.DateTime.N <- aggregate(data.import[,ContData.env$myName.SiteID]
~ data.import[,ContData.env$myName.DateTime]
, data=data.import,FUN=length)
# myTable <- myTable.DateTime.N[myTable.DateTime.N[,ContData.env$myName.SiteID]!=1,]
# cat(paste0("Date and time records with more than one entry; N=",nrow(myTable),". Duplicate date.time records are shown below."))
# cat("\n")
# print(knitr::kable(myTable, format="markdown", row.names=TRUE))
# mask error, 20170307
# Missing Dates
myDateRange.Data <- seq(as.Date(min(data.import[,ContData.env$myName.Date]))
,as.Date(max(data.import[,ContData.env$myName.Date]))
,by="day")
if(exists("fun.myData.DateRange.Start")==TRUE){
myDateRange.Target <- seq(as.Date(fun.myData.DateRange.Start)
,as.Date(fun.myData.DateRange.End)
,by="day")
} else {
myDateRange.Target <- myDateRange.Data
}
myDateRange.Target.Missing <- myDateRange.Target[!myDateRange.Target %in% myDateRange.Data]
cat(paste0("Below are the dates with missing data between the min (",min(myDateRange.Data)
,") and max(",max(myDateRange.Data)," for the provided data. There are "
,length(myDateRange.Target.Missing)," records."))
cat("\n\n")
print(ifelse(length(myDateRange.Target.Missing)==0,"",myDateRange.Target.Missing))
FLAGS
Each data point is checked by each of the four QC tests below.
- Unrealistic values (Gross range): Entries are flagged if values are above or below
upper or lower limits.
- Spikes: Entries are flagged if adjacent points change by more than 'x' amount.
- Rate of Change (RoC): Entries are flagged if the RoC exceeds a given threshold (e.g., >= 3 standard deviations within 25 hours).
- Flat Line (Flat): Entries are flagged if a certain number of consecutive measurements are within a certain amount of each other (e.g., > 10 consecutive temperature measurements are within 0.01. degrees C of on another).
Flags are assigned to each data point (a single measured parameter at a unique point in time) first by each of the QC tests below. Flags are assigned the following categories;
cat(paste("* **",ContData.env$myFlagVal.Pass,"** = Pass,",sep=""))
cat("\n")
cat(paste("* **",ContData.env$myFlagVal.Suspect,"** = Suspect,",sep=""))
cat("\n")
cat(paste("* **",ContData.env$myFlagVal.Fail,"** = Fail,",sep=""))
cat("\n")
cat(paste("* **",ContData.env$myFlagVal.NoData,"** = No Data or Not Applicable (NA).",sep=""))
Overall flags are assigned by examining the results of the four tests below.
cat(paste("* **",ContData.env$myFlagVal.Pass,"** = no Fail or Suspect and at least one Pass,",sep=""))
cat("\n")
cat(paste("* **",ContData.env$myFlagVal.Suspect,"** = no Fail and at least one Suspect,",sep=""))
cat("\n")
cat(paste("* **",ContData.env$myFlagVal.Fail,"** = at least one Fail,",sep=""))
cat("\n")
cat(paste("* **",ContData.env$myFlagVal.NoData,"** = unable to perform QC check because no adjacent value(s) to compare to, either due to missing data or because the measurement was taken at the beginning or end of the deployment period.",sep=""))
The thresholds used in the tests are defined in the config file and are listed below.
kbl_caption <- "Thresholds, Quick Reference"
txt_header <- c("Analyte", "Flag", "Gross", "Spike", "Rate of Change", "Flat Line")
# WaterTemp
nam_WaterTemp <- c("myLab.WaterTemp" #1
, "myUnits.WaterTemp" #2
, "myThresh.Gross.Fail.Hi.WaterTemp" #3
, "myThresh.Gross.Fail.Lo.WaterTemp" #4
, "myThresh.Gross.Suspect.Hi.WaterTemp" #5
, "myThresh.Gross.Suspect.Lo.WaterTemp" #6
, "myThresh.Spike.Hi.WaterTemp" #7
, "myThresh.Spike.Lo.WaterTemp" #8
, "myThresh.RoC.SD.number.WaterTemp" #9
, "myThresh.RoC.SD.period.WaterTemp" #10
, "myThresh.Flat.Hi.WaterTemp" #11
, "myThresh.Flat.Lo.WaterTemp" #12
, "myThresh.Flat.Tolerance.WaterTemp" #13
)
val_WaterTemp <- mget(nam_WaterTemp, envir=ContData.env)
txt_WaterTemp_F <- c(val_WaterTemp[1]
, "Fail"
, paste0("> ",val_WaterTemp[3]," deg ",val_WaterTemp[2]
," or < ",val_WaterTemp[4]," deg ",val_WaterTemp[2])
, paste0(">= ", val_WaterTemp[7]," deg ",val_WaterTemp[2]," (+/-)")
, "NA"
, paste0("> ",val_WaterTemp[11]
," consecutive measurements within ",val_WaterTemp[13]
," units of one another"))
txt_WaterTemp_S <- c(val_WaterTemp[1]
, "Suspect"
, paste0("> ",val_WaterTemp[5]," deg ",val_WaterTemp[2]
," or < ",val_WaterTemp[6]," deg ",val_WaterTemp[2])
, paste0(">= ", val_WaterTemp[8]," deg ",val_WaterTemp[2]," (+/-)")
, paste0(">= ",val_WaterTemp[9]," standard deviations within "
,val_WaterTemp[10]," hours")
, paste0("> ",val_WaterTemp[12]
," consecutive measurements within ",val_WaterTemp[13]
," units of one another"))
# AirTemp
nam_AirTemp <- c("myLab.AirTemp" #1
, "myUnits.AirTemp" #2
, "myThresh.Gross.Fail.Hi.AirTemp" #3
, "myThresh.Gross.Fail.Lo.AirTemp" #4
, "myThresh.Gross.Suspect.Hi.AirTemp" #5
, "myThresh.Gross.Suspect.Lo.AirTemp" #6
, "myThresh.Spike.Hi.AirTemp" #7
, "myThresh.Spike.Lo.AirTemp" #8
, "myThresh.RoC.SD.number.AirTemp" #9
, "myThresh.RoC.SD.period.AirTemp" #10
, "myThresh.Flat.Hi.AirTemp" #11
, "myThresh.Flat.Lo.AirTemp" #12
, "myThresh.Flat.Tolerance.AirTemp" #13
)
val_AirTemp <- mget(nam_AirTemp, envir=ContData.env)
txt_AirTemp_F <- c(val_AirTemp[1]
, "Fail"
, paste0("> ",val_AirTemp[3]," deg ",val_AirTemp[2]
," or < ",val_AirTemp[4]," deg ",val_AirTemp[2])
, paste0(">= ", val_AirTemp[7]," deg ",val_AirTemp[2]," (+/-)")
, "NA"
, paste0("> ",val_AirTemp[11]
," consecutive measurements within ",val_AirTemp[13]
," units of one another"))
txt_AirTemp_S <- c(val_AirTemp[1]
, "Suspect"
, paste0("> ",val_AirTemp[5]," deg ",val_AirTemp[2]
," or < ",val_AirTemp[6]," deg ",val_AirTemp[2])
, paste0(">= ", val_AirTemp[8]," deg ",val_AirTemp[2]," (+/-)")
, paste0(">= ",val_AirTemp[9]," standard deviations within "
,val_AirTemp[10]," hours")
, paste0("> ",val_AirTemp[12]
," consecutive measurements within ",val_AirTemp[13]
," units of one another"))
# WaterP
nam_WaterP <- c("myLab.WaterP" #1
, "myUnits.WaterP" #2
, "myThresh.Gross.Fail.Hi.WaterP" #3
, "myThresh.Gross.Fail.Lo.WaterP" #4
, "myThresh.Gross.Suspect.Hi.WaterP" #5
, "myThresh.Gross.Suspect.Lo.WaterP" #6
, "myThresh.Spike.Hi.WaterP" #7
, "myThresh.Spike.Lo.WaterP" #8
, "myThresh.RoC.SD.number.WaterP" #9
, "myThresh.RoC.SD.period.WaterP" #10
, "myThresh.Flat.Hi.WaterP" #11
, "myThresh.Flat.Lo.WaterP" #12
, "myThresh.Flat.Tolerance.WaterP" #13
)
val_WaterP <- mget(nam_WaterP, envir=ContData.env)
txt_WaterP_F <- c(val_WaterP[1]
, "Fail"
, paste0("> ",val_WaterP[3]," ",val_WaterP[2]
," or < ",val_WaterP[4]," ",val_WaterP[2])
, paste0(">= ", val_WaterP[7]," ",val_WaterP[2]," (+/-)")
, "NA"
, paste0("> ",val_WaterP[11]
," consecutive measurements within ",val_WaterP[13]
," units of one another"))
txt_WaterP_S <- c(val_WaterP[1]
, "Suspect"
, paste0("> ",val_WaterP[5]," ",val_WaterP[2]
," or < ",val_WaterP[6]," ",val_WaterP[2])
, paste0(">= ", val_WaterP[8]," ",val_WaterP[2]," (+/-)")
, paste0(">= ",val_WaterP[9]," standard deviations within "
,val_WaterP[10]," hours")
, paste0("> ",val_WaterP[12]
," consecutive measurements within ",val_WaterP[13]
," units of one another"))
# AirBP
nam_AirBP <- c("myLab.AirBP" #1
, "myUnits.AirBP" #2
, "myThresh.Gross.Fail.Hi.AirBP" #3
, "myThresh.Gross.Fail.Lo.AirBP" #4
, "myThresh.Gross.Suspect.Hi.AirBP" #5
, "myThresh.Gross.Suspect.Lo.AirBP" #6
, "myThresh.Spike.Hi.AirBP" #7
, "myThresh.Spike.Lo.AirBP" #8
, "myThresh.RoC.SD.number.AirBP" #9
, "myThresh.RoC.SD.period.AirBP" #10
, "myThresh.Flat.Hi.AirBP" #11
, "myThresh.Flat.Lo.AirBP" #12
, "myThresh.Flat.Tolerance.AirBP" #13
)
val_AirBP <- mget(nam_AirBP, envir=ContData.env)
txt_AirBP_F <- c(val_AirBP[1]
, "Fail"
, paste0("> ",val_AirBP[3]," ",val_AirBP[2]
," or < ",val_AirBP[4]," ",val_AirBP[2])
, paste0(">= ", val_AirBP[7]," ",val_AirBP[2]," (+/-)")
, "NA"
, paste0("> ",val_AirBP[11]
," consecutive measurements within ",val_AirBP[13]
," units of one another"))
txt_AirBP_S <- c(val_AirBP[1]
, "Suspect"
, paste0("> ",val_AirBP[5]," ",val_AirBP[2]
," or < ",val_AirBP[6]," ",val_AirBP[2])
, paste0(">= ", val_AirBP[8]," ",val_AirBP[2]," (+/-)")
, paste0(">= ",val_AirBP[9]," standard deviations within "
,val_AirBP[10]," hours")
, paste0("> ",val_AirBP[12]
," consecutive measurements within ",val_AirBP[13]
," units of one another"))
# SensorDepth
nam_SensorDepth <- c("myLab.SensorDepth" #1
, "myUnits.SensorDepth" #2
, "myThresh.Gross.Fail.Hi.SensorDepth" #3
, "myThresh.Gross.Fail.Lo.SensorDepth" #4
, "myThresh.Gross.Suspect.Hi.SensorDepth" #5
, "myThresh.Gross.Suspect.Lo.SensorDepth" #6
, "myThresh.Spike.Hi.SensorDepth" #7
, "myThresh.Spike.Lo.SensorDepth" #8
, "myThresh.RoC.SD.number.SensorDepth" #9
, "myThresh.RoC.SD.period.SensorDepth" #10
, "myThresh.Flat.Hi.SensorDepth" #11
, "myThresh.Flat.Lo.SensorDepth" #12
, "myThresh.Flat.Tolerance.SensorDepth" #13
)
val_SensorDepth <- mget(nam_SensorDepth, envir=ContData.env)
txt_SensorDepth_F <- c(val_SensorDepth[1]
, "Fail"
, paste0("> ",val_SensorDepth[3]," ",val_SensorDepth[2]
," or < ",val_SensorDepth[4]," ",val_SensorDepth[2])
, paste0(">= ", val_SensorDepth[7]," ",val_SensorDepth[2]," (+/-)")
, "NA"
, paste0("> ",val_SensorDepth[11]
," consecutive measurements within ",val_SensorDepth[13]
," units of one another"))
txt_SensorDepth_S <- c(val_SensorDepth[1]
, "Suspect"
, paste0("> ",val_SensorDepth[5]," ",val_SensorDepth[2]
," or < ",val_SensorDepth[6]," ",val_SensorDepth[2])
, paste0(">= ", val_SensorDepth[8]," ",val_SensorDepth[2]," (+/-)")
, paste0(">= ",val_SensorDepth[9]," standard deviations within "
,val_SensorDepth[10]," hours")
, paste0("> ",val_SensorDepth[12]
," consecutive measurements within ",val_SensorDepth[13]
," units of one another"))
# Discharge
nam_Discharge <- c("myLab.Discharge" #1
, "myUnits.Discharge" #2
, "myThresh.Gross.Fail.Hi.Discharge" #3
, "myThresh.Gross.Fail.Lo.Discharge" #4
, "myThresh.Gross.Suspect.Hi.Discharge" #5
, "myThresh.Gross.Suspect.Lo.Discharge" #6
, "myThresh.Spike.Hi.Discharge" #7
, "myThresh.Spike.Lo.Discharge" #8
, "myThresh.RoC.SD.number.Discharge" #9
, "myThresh.RoC.SD.period.Discharge" #10
, "myThresh.Flat.Hi.Discharge" #11
, "myThresh.Flat.Lo.Discharge" #12
, "myThresh.Flat.Tolerance.Discharge" #13
)
val_Discharge <- mget(nam_Discharge, envir=ContData.env)
txt_Discharge_F <- c(val_Discharge[1]
, "Fail"
, paste0("> ",val_Discharge[3]," ",val_Discharge[2]
," or < ",val_Discharge[4]," ",val_Discharge[2])
, paste0(">= ", val_Discharge[7]," ",val_Discharge[2]," (+/-)")
, "NA"
, paste0("> ",val_Discharge[11]
," consecutive measurements within ",val_Discharge[13]
," units of one another"))
txt_Discharge_S <- c(val_Discharge[1]
, "Suspect"
, paste0("> ",val_Discharge[5]," ",val_Discharge[2]
," or < ",val_Discharge[6]," ",val_Discharge[2])
, paste0(">= ", val_Discharge[8]," ",val_Discharge[2]," (+/-)")
, paste0(">= ",val_Discharge[9]," standard deviations within "
,val_Discharge[10]," hours")
, paste0("> ",val_Discharge[12]
," consecutive measurements within ",val_Discharge[13]
," units of one another"))
# Cond
nam_Cond <- c("myLab.Cond" #1
, "myUnits.Cond" #2
, "myThresh.Gross.Fail.Hi.Cond" #3
, "myThresh.Gross.Fail.Lo.Cond" #4
, "myThresh.Gross.Suspect.Hi.Cond" #5
, "myThresh.Gross.Suspect.Lo.Cond" #6
, "myThresh.Spike.Hi.Cond" #7
, "myThresh.Spike.Lo.Cond" #8
, "myThresh.RoC.SD.number.Cond" #9
, "myThresh.RoC.SD.period.Cond" #10
, "myThresh.Flat.Hi.Cond" #11
, "myThresh.Flat.Lo.Cond" #12
, "myThresh.Flat.Tolerance.Cond" #13
)
val_Cond <- mget(nam_Cond, envir=ContData.env)
txt_Cond_F <- c(val_Cond[1]
, "Fail"
, paste0("> ",val_Cond[3]," ",val_Cond[2]
," or < ",val_Cond[4]," ",val_Cond[2])
, paste0(">= ", val_Cond[7]," ",val_Cond[2]," (+/-)")
, "NA"
, paste0("> ",val_Cond[11]
," consecutive measurements within ",val_Cond[13]
," units of one another"))
txt_Cond_S <- c(val_Cond[1]
, "Suspect"
, paste0("> ",val_Cond[5]," ",val_Cond[2]
," or < ",val_Cond[6]," ",val_Cond[2])
, paste0(">= ", val_Cond[8]," ",val_Cond[2]," (+/-)")
, paste0(">= ",val_Cond[9]," standard deviations within "
,val_Cond[10]," hours")
, paste0("> ",val_Cond[12]
," consecutive measurements within ",val_Cond[13]
," units of one another"))
# DO, mg/L
nam_DO <- c("myLab.DO" #1
, "myUnits.DO" #2
, "myThresh.Gross.Fail.Hi.DO" #3
, "myThresh.Gross.Fail.Lo.DO" #4
, "myThresh.Gross.Suspect.Hi.DO" #5
, "myThresh.Gross.Suspect.Lo.DO" #6
, "myThresh.Spike.Hi.DO" #7
, "myThresh.Spike.Lo.DO" #8
, "myThresh.RoC.SD.number.DO" #9
, "myThresh.RoC.SD.period.DO" #10
, "myThresh.Flat.Hi.DO" #11
, "myThresh.Flat.Lo.DO" #12
, "myThresh.Flat.Tolerance.DO" #13
)
val_DO <- mget(nam_DO, envir=ContData.env)
txt_DO_F <- c(val_DO[1]
, "Fail"
, paste0("> ",val_DO[3]," ",val_DO[2]
," or < ",val_DO[4]," ",val_DO[2])
, paste0(">= ", val_DO[7]," ",val_DO[2]," (+/-)")
, "NA"
, paste0("> ",val_DO[11]
," consecutive measurements within ",val_DO[13]
," units of one another"))
txt_DO_S <- c(val_DO[1]
, "Suspect"
, paste0("> ",val_DO[5]," ",val_DO[2]
," or < ",val_DO[6]," ",val_DO[2])
, paste0(">= ", val_DO[8]," ",val_DO[2]," (+/-)")
, paste0(">= ",val_DO[9]," standard deviations within "
,val_DO[10]," hours")
, paste0("> ",val_DO[12]
," consecutive measurements within ",val_DO[13]
," units of one another"))
# DO, adj
nam_DO.adj <- c("myLab.DO.adj" #1
, "myUnits.DO.adj" #2
, "myThresh.Gross.Fail.Hi.DO.adj" #3
, "myThresh.Gross.Fail.Lo.DO.adj" #4
, "myThresh.Gross.Suspect.Hi.DO.adj" #5
, "myThresh.Gross.Suspect.Lo.DO.adj" #6
, "myThresh.Spike.Hi.DO.adj" #7
, "myThresh.Spike.Lo.DO.adj" #8
, "myThresh.RoC.SD.number.DO.adj" #9
, "myThresh.RoC.SD.period.DO.adj" #10
, "myThresh.Flat.Hi.DO.adj" #11
, "myThresh.Flat.Lo.DO.adj" #12
, "myThresh.Flat.Tolerance.DO.adj" #13
)
val_DO.adj <- mget(nam_DO.adj, envir=ContData.env)
txt_DO_F.adj <- c(val_DO.adj[1]
, "Fail"
, paste0("> ",val_DO.adj[3]," ",val_DO.adj[2]
," or < ",val_DO.adj[4]," ",val_DO.adj[2])
, paste0(">= ", val_DO.adj[7]," ",val_DO.adj[2]," (+/-)")
, "NA"
, paste0("> ",val_DO.adj[11]
," consecutive measurements within ",val_DO.adj[13]
," units of one another"))
txt_DO_S.adj <- c(val_DO.adj[1]
, "Suspect"
, paste0("> ",val_DO.adj[5]," ",val_DO.adj[2]
," or < ",val_DO.adj[6]," ",val_DO.adj[2])
, paste0(">= ", val_DO.adj[8]," ",val_DO.adj[2]," (+/-)")
, paste0(">= ",val_DO.adj[9]," standard deviations within "
,val_DO.adj[10]," hours")
, paste0("> ",val_DO.adj[12]
," consecutive measurements within ",val_DO.adj[13]
," units of one another"))
# DO, pct sat
nam_DO.pctsat <- c("myLab.DO.pctsat" #1
, "myUnits.DO.pctsat" #2
, "myThresh.Gross.Fail.Hi.DO.pctsat" #3
, "myThresh.Gross.Fail.Lo.DO.pctsat" #4
, "myThresh.Gross.Suspect.Hi.DO.pctsat" #5
, "myThresh.Gross.Suspect.Lo.DO.pctsat" #6
, "myThresh.Spike.Hi.DO.pctsat" #7
, "myThresh.Spike.Lo.DO.pctsat" #8
, "myThresh.RoC.SD.number.DO.pctsat" #9
, "myThresh.RoC.SD.period.DO.pctsat" #10
, "myThresh.Flat.Hi.DO.pctsat" #11
, "myThresh.Flat.Lo.DO.pctsat" #12
, "myThresh.Flat.Tolerance.DO.pctsat" #13
)
val_DO.pctsat <- mget(nam_DO.pctsat, envir=ContData.env)
txt_DO_F.pctsat <- c(val_DO.pctsat[1]
, "Fail"
, paste0("> ",val_DO.pctsat[3]," ",val_DO.pctsat[2]
," or < ",val_DO.pctsat[4]," ",val_DO.pctsat[2])
, paste0(">= ", val_DO.pctsat[7]," ",val_DO.pctsat[2]," (+/-)")
, "NA"
, paste0("> ",val_DO.pctsat[11]
," consecutive measurements within ",val_DO.pctsat[13]
," units of one another"))
txt_DO_S.pctsat <- c(val_DO.pctsat[1]
, "Suspect"
, paste0("> ",val_DO.pctsat[5]," ",val_DO.pctsat[2]
," or < ",val_DO.pctsat[6]," ",val_DO.pctsat[2])
, paste0(">= ", val_DO.pctsat[8]," ",val_DO[2]," (+/-)")
, paste0(">= ",val_DO.pctsat[9]," standard deviations within "
,val_DO.pctsat[10]," hours")
, paste0("> ",val_DO.pctsat[12]
," consecutive measurements within ",val_DO.pctsat[13]
," units of one another"))
# pH
nam_pH <- c("myLab.pH" #1
, "myUnits.pH" #2
, "myThresh.Gross.Fail.Hi.pH" #3
, "myThresh.Gross.Fail.Lo.pH" #4
, "myThresh.Gross.Suspect.Hi.pH" #5
, "myThresh.Gross.Suspect.Lo.pH" #6
, "myThresh.Spike.Hi.pH" #7
, "myThresh.Spike.Lo.pH" #8
, "myThresh.RoC.SD.number.pH" #9
, "myThresh.RoC.SD.period.pH" #10
, "myThresh.Flat.Hi.pH" #11
, "myThresh.Flat.Lo.pH" #12
, "myThresh.Flat.Tolerance.pH" #13
)
val_pH <- mget(nam_pH, envir=ContData.env)
txt_pH_F <- c(val_pH[1]
, "Fail"
, paste0("> ",val_pH[3]," ",val_pH[2]
," or < ",val_pH[4]," ",val_pH[2])
, paste0(">= ", val_pH[7]," ",val_pH[2]," (+/-)")
, "NA"
, paste0("> ",val_pH[11]
," consecutive measurements within ",val_pH[13]
," units of one another"))
txt_pH_S <- c(val_pH[1]
, "Suspect"
, paste0("> ",val_pH[5]," ",val_pH[2]
," or < ",val_pH[6]," ",val_pH[2])
, paste0(">= ", val_pH[8]," ",val_pH[2]," (+/-)")
, paste0(">= ",val_pH[9]," standard deviations within "
,val_pH[10]," hours")
, paste0("> ",val_pH[12]
," consecutive measurements within ",val_pH[13]
," units of one another"))
# Turbidity
nam_Turbidity <- c("myLab.Turbidity" #1
, "myUnits.Turbidity" #2
, "myThresh.Gross.Fail.Hi.Turbidity" #3
, "myThresh.Gross.Fail.Lo.Turbidity" #4
, "myThresh.Gross.Suspect.Hi.Turbidity" #5
, "myThresh.Gross.Suspect.Lo.Turbidity" #6
, "myThresh.Spike.Hi.Turbidity" #7
, "myThresh.Spike.Lo.Turbidity" #8
, "myThresh.RoC.SD.number.Turbidity" #9
, "myThresh.RoC.SD.period.Turbidity" #10
, "myThresh.Flat.Hi.Turbidity" #11
, "myThresh.Flat.Lo.Turbidity" #12
, "myThresh.Flat.Tolerance.Turbidity" #13
)
val_Turbidity <- mget(nam_Turbidity, envir=ContData.env)
txt_Turbidity_F <- c(val_Turbidity[1]
, "Fail"
, paste0("> ",val_Turbidity[3]," ",val_Turbidity[2]
," or < ",val_Turbidity[4]," ",val_Turbidity[2])
, paste0(">= ", val_Turbidity[7]," ",val_Turbidity[2]," (+/-)")
, "NA"
, paste0("> ",val_Turbidity[11]
," consecutive measurements within ",val_Turbidity[13]
," units of one another"))
txt_Turbidity_S <- c(val_Turbidity[1]
, "Suspect"
, paste0("> ",val_Turbidity[5]," ",val_Turbidity[2]
," or < ",val_Turbidity[6]," ",val_Turbidity[2])
, paste0(">= ", val_Turbidity[8]," ",val_Turbidity[2]," (+/-)")
, paste0(">= ",val_Turbidity[9]," standard deviations within "
,val_Turbidity[10]," hours")
, paste0("> ",val_Turbidity[12]
," consecutive measurements within ",val_Turbidity[13]
," units of one another"))
# Chlorophylla
nam_Chlorophylla <- c("myLab.Chlorophylla" #1
, "myUnits.Chlorophylla" #2
, "myThresh.Gross.Fail.Hi.Chlorophylla" #3
, "myThresh.Gross.Fail.Lo.Chlorophylla" #4
, "myThresh.Gross.Suspect.Hi.Chlorophylla" #5
, "myThresh.Gross.Suspect.Lo.Chlorophylla" #6
, "myThresh.Spike.Hi.Chlorophylla" #7
, "myThresh.Spike.Lo.Chlorophylla" #8
, "myThresh.RoC.SD.number.Chlorophylla" #9
, "myThresh.RoC.SD.period.Chlorophylla" #10
, "myThresh.Flat.Hi.Chlorophylla" #11
, "myThresh.Flat.Lo.Chlorophylla" #12
, "myThresh.Flat.Tolerance.Chlorophylla" #13
)
val_Chlorophylla <- mget(nam_Chlorophylla, envir=ContData.env)
txt_Chlorophylla_F <- c(val_Chlorophylla[1]
, "Fail"
, paste0("> ",val_Chlorophylla[3]," ",val_Chlorophylla[2]
," or < ",val_Chlorophylla[4]," ",val_Chlorophylla[2])
, paste0(">= ", val_Chlorophylla[7]," ",val_Chlorophylla[2]," (+/-)")
, "NA"
, paste0("> ",val_Chlorophylla[11]
," consecutive measurements within ",val_Chlorophylla[13]
," units of one another"))
txt_Chlorophylla_S <- c(val_Chlorophylla[1]
, "Suspect"
, paste0("> ",val_Chlorophylla[5]," ",val_Chlorophylla[2]
," or < ",val_Chlorophylla[6]," ",val_Chlorophylla[2])
, paste0(">= ", val_Chlorophylla[8]," ",val_Chlorophylla[2]," (+/-)")
, paste0(">= ",val_Chlorophylla[9]," standard deviations within "
,val_Chlorophylla[10]," hours")
, paste0("> ",val_Chlorophylla[12]
," consecutive measurements within ",val_Chlorophylla[13]
," units of one another"))
# WaterLevel
nam_WaterLevel <- c("myLab.WaterLevel" #1
, "myUnits.WaterLevel" #2
, "myThresh.Gross.Fail.Hi.WaterLevel" #3
, "myThresh.Gross.Fail.Lo.WaterLevel" #4
, "myThresh.Gross.Suspect.Hi.WaterLevel" #5
, "myThresh.Gross.Suspect.Lo.WaterLevel" #6
#, "myThresh.Spike.Hi.WaterLevel" #7 -removed
#, "myThresh.Spike.Lo.WaterLevel" #8 - removed
, "myThresh.RoC.SD.number.WaterLevel" #9 - 7
, "myThresh.RoC.SD.period.WaterLevel" #10 - 8
, "myThresh.Flat.Hi.WaterLevel" #11 - 9
, "myThresh.Flat.Lo.WaterLevel" #12 - 10
, "myThresh.Flat.Tolerance.WaterLevel" #13 - 11
)
val_WaterLevel <- mget(nam_WaterLevel, envir=ContData.env)
txt_WaterLevel_F <- c(val_WaterLevel[1]
, "Fail"
, paste0("> ",val_WaterLevel[3]," ",val_WaterLevel[2]
," or < ",val_WaterLevel[4]," ",val_WaterLevel[2])
, "NA"
, "NA"
, paste0("> ",val_WaterLevel[9]
," consecutive measurements within ",val_WaterLevel[11]
," units of one another"))
txt_WaterLevel_S <- c(val_WaterLevel[1]
, "Suspect"
, paste0("> ",val_WaterLevel[5]," ",val_WaterLevel[2]
," or < ",val_WaterLevel[6]," ",val_WaterLevel[2])
, "NA"
, paste0(">= ",val_WaterLevel[7]," standard deviations within "
,val_WaterLevel[8]," hours")
, paste0("> ",val_WaterLevel[10]
," consecutive measurements within ",val_WaterLevel[11]
," units of one another"))
# Salinity
nam_Salinity <- c("myLab.Salinity" #1
, "myUnits.Salinity" #2
, "myThresh.Gross.Fail.Hi.Salinity" #3
, "myThresh.Gross.Fail.Lo.Salinity" #4
, "myThresh.Gross.Suspect.Hi.Salinity" #5
, "myThresh.Gross.Suspect.Lo.Salinity" #6
, "myThresh.Spike.Hi.Salinity" #7
, "myThresh.Spike.Lo.Salinity" #8
, "myThresh.RoC.SD.number.Salinity" #9
, "myThresh.RoC.SD.period.Salinity" #10
, "myThresh.Flat.Hi.Salinity" #11
, "myThresh.Flat.Lo.Salinity" #12
, "myThresh.Flat.Tolerance.Salinity" #13
)
val_Salinity <- mget(nam_Salinity, envir=ContData.env)
txt_Salinity_F <- c(val_Salinity[1]
, "Fail"
, paste0("> ",val_Salinity[3]," ",val_Salinity[2]
," or < ",val_Salinity[4]," ",val_Salinity[2])
, "NA"
, "NA"
, paste0("> ",val_Salinity[11]
," consecutive measurements within ",val_Salinity[13]
," units of one another"))
txt_Salinity_S <- c(val_Salinity[1]
, "Suspect"
, paste0("> ",val_Salinity[5]," ",val_Salinity[2]
," or < ",val_Salinity[6]," ",val_Salinity[2])
, "NA"
, paste0(">= ",val_Salinity[9]," standard deviations within "
,val_Salinity[10]," hours")
, paste0("> ",val_Salinity[12]
," consecutive measurements within ",val_Salinity[13]
," units of one another"))
# Create Table
## Display only those parameters used in current dataset (data.import)
# QC
boo.QC <- FALSE
if(boo.QC==TRUE){##IF.boo.QC.START
# Ensure have ContDat.env loaded
myConfig <- file.path(system.file(package="ContDataQC"), "extdata", "config.ORIG.R")
source(myConfig)
# load dat.import
fn.import <- file.path(getwd(), "Data1_RAW", "test2_AW_20130426_20130725.csv")
data.import <- read.csv(fn.import)
}##IF.boo.QC.END
#
# Check for Presence of each Analyte in data
# if not present convert to NULL
# if NULL then won't be included in result data.frame
if(!(ContData.env$myName.WaterTemp %in% names(data.import))){
txt_WaterTemp_F <- NULL
txt_WaterTemp_S <- NULL
}
#
if(!(ContData.env$myName.AirTemp %in% names(data.import))){
txt_AirTemp_F <- NULL
txt_AirTemp_S <- NULL
}
#
if(!(ContData.env$myName.WaterP %in% names(data.import))){
txt_WaterP_F <- NULL
txt_WaterP_S <- NULL
}
#
if(!(ContData.env$myName.AirBP %in% names(data.import))){
txt_AirBP_F <- NULL
txt_AirBP_S <- NULL
}
#
if(!(ContData.env$myName.SensorDepth %in% names(data.import))){
txt_SensorDepth_F <- NULL
txt_SensorDepth_S <- NULL
}
#
if(!(ContData.env$myName.Discharge %in% names(data.import))){
txt_Discharge_F <- NULL
txt_Discharge_S <- NULL
}
#
if(!(ContData.env$myName.Cond %in% names(data.import))){
txt_Cond_F <- NULL
txt_Cond_S <- NULL
}
#
if(!(ContData.env$myName.DO %in% names(data.import))){
txt_DO_F <- NULL
txt_DO_S <- NULL
}
#
if(!(ContData.env$myName.DO.adj %in% names(data.import))){
txt_DO_F.adj <- NULL
txt_DO_S.adj <- NULL
}
#
if(!(ContData.env$myName.DO.pctsat %in% names(data.import))){
txt_DO_F.pctsat <- NULL
txt_DO_S.pctsat <- NULL
}
#
if(!(ContData.env$myName.pH %in% names(data.import))){
txt_pH_F <- NULL
txt_pH_S <- NULL
}
#
if(!(ContData.env$myName.Turbidity %in% names(data.import))){
txt_Turbidity_F <- NULL
txt_Turbidity_S <- NULL
}
#
if(!(ContData.env$myName.Chlorophylla %in% names(data.import))){
txt_Chlorophylla_F <- NULL
txt_Chlorophylla_S <- NULL
}
#
if(!(ContData.env$myName.WaterLevel %in% names(data.import))){
txt_WaterLevel_F <- NULL
txt_WaterLevel_S <- NULL
}
#
if(!(ContData.env$myName.Salinity %in% names(data.import))){
txt_Salinity_F <- NULL
txt_Salinity_S <- NULL
}
# Table
df_Env <- data.frame(rbind(txt_WaterTemp_F, txt_WaterTemp_S
,txt_AirTemp_F, txt_AirTemp_S
, txt_WaterP_F, txt_WaterP_S
, txt_AirBP_F, txt_AirBP_S
, txt_SensorDepth_F, txt_SensorDepth_S
, txt_Discharge_F, txt_Discharge_S
, txt_Cond_F, txt_Cond_S
, txt_DO_F, txt_DO_S
, txt_DO_F.adj, txt_DO_S.adj
, txt_DO_F.pctsat, txt_DO_S.pctsat
, txt_pH_F, txt_pH_S
, txt_Turbidity_F, txt_Turbidity_S
, txt_Chlorophylla_F, txt_Chlorophylla_S
, txt_WaterLevel_F, txt_WaterLevel_S
, txt_Salinity_F, txt_Salinity_S
)
, row.names = NULL)
names(df_Env) <- txt_header
knitr::kable(df_Env, caption = kbl_caption)
Overall flags by parameter
#
for (j in 1:length(myParameters)) {##FOR.j.START
# #
#j.num <- match(j,myParameters)
# cat(paste(myParameters.Lab[j.num],"/n/n",sep=""))
# ISSUE WITH knitr::kable INSIDE OF LOOPS
cat(paste("## ",myParameters[j],"\n\n",sep=""))
colnum <- match(paste("Flag",myParameters[j],sep="."),names(data.import))
#print(myTable.Flags.Overall <- ftable(data.import[,colnum]))
myTable <- addmargins(table(data.import[,colnum]
, useNA = "ifany")
,1)
print(knitr::kable(t(as.matrix(myTable)), format = "markdown"))
# future mod, 1,3,4,9 to Pass, Suspect, Fail, NA
# exclude=NA to get all
# future mod, 2nd line in table to include Percent of row total
# future mod, sort levels
cat("\n\n")
# #
}##FOR.j.END
QC TESTS, BY PARAMETER
# 3. Data Info, by Parameter
# Individual Data Type Report
# QC
#i <- myParameters[1]
for (i in myParameters) {##FOR.i.START
# #
i.num <- match(i,myParameters)
myTitle.Sub <- myParameters.Lab[i.num]
cat("## QC TESTS, ",i,"\n\n",sep="")
#cat(paste("**QC TESTS,",myTitle.Sub,sep=""))
# 3.1. Flags, overall
cat(myTitle.Sub <- "### Flags")
cat("\n\n")
cat("#### Flag summary, overall number of records.")
cat("\n\n")
myTable <- addmargins(table(data.import[,paste("Flag",i,sep=".")]
, useNA = "ifany")
,1)
print(knitr::kable(t(as.matrix(myTable)), format = "markdown"))
cat("\n\n")
#
# 3.1. Number of Records (revised 20170228)
# Convert time interval (minutes) to number per day
records.expected <- round(24*60/as.numeric(myTimeDiff[1]),1)
cat(paste("Estimated number of records per day is ",records.expected,".",sep=""))
cat("\n\n")
#
cat("Number of records by year and month (with totals)")
cat("\n")
myTable <- addmargins(table(data.import[,ContData.env$myName.Yr]
,data.import[,ContData.env$myName.Mo]
, useNA = "ifany"))
print(knitr::kable(myTable, format="markdown", row.names=TRUE))
cat("\n\n")
#
cat("Number of records by day and month (with totals)")
cat("\n")
myTable <- addmargins(table(data.import[,ContData.env$myName.Day]
,data.import[,ContData.env$myName.Mo]
, useNA = "ifany"))
print(knitr::kable(myTable, format="markdown", row.names=TRUE))
cat("\n\n")
#
# identify days/months where not the expected number of records
# (expect first and last day)
# print("days where not the expected number of records")
#
# 3.2. Flags by QC Test
cat("\n\n")
cat("#### Flags by QC Test")
cat("\n\n")
cat("##### Flags, Gross\n\n")
myTable <- addmargins(table(data.import[, paste(ContData.env$myName.Flag
,"Gross"
,i
,sep=".")]
, useNA = "ifany")
,1)
print(knitr::kable(t(as.matrix(myTable)), format = "markdown"))
cat("\n\n")
cat("##### Flags, Spike\n\n")
myTable <- addmargins(table(data.import[, paste(ContData.env$myName.Flag
,"Spike"
,i
,sep=".")]
, useNA = "ifany")
,1)
print(knitr::kable(t(as.matrix(myTable)), format = "markdown"))
cat("\n\n")
cat("##### Flags, RoC\n\n")
myTable <- addmargins(table(data.import[, paste(ContData.env$myName.Flag
,"RoC"
,i
,sep=".")]
, useNA = "ifany")
,1)
print(knitr::kable(t(as.matrix(myTable)), format = "markdown"))
cat("\n\n")
cat("##### Flags, Flat\n\n")
myTable <- addmargins(table(data.import[, paste(ContData.env$myName.Flag
,"Flat"
,i
,sep=".")]
, useNA = "ifany")
,1)
print(knitr::kable(t(as.matrix(myTable)), format = "markdown"))
cat("\n\n")
cat("QC Test Flag fields are saved in the data file so the user can identify data points that have been flagged as suspect or fail.")
#
#myFlagTests <- c("Gross","Spike","RoC","Flat")
#(myTable.Flags.Flat <- ftable(data.import[,paste("Flag",myFlagTests,i,sep=".")]))
#
cat(paste("\n\n Test results marked as ",ContData.env$myFlagVal.NoData," (No/Missing Data) if unable to calculate the end point needed for the test. For example, the first record does not have a previous record for comparison for the Gross QC Test. QC Test flags are saved in the data file.",sep=""))
#
# 3.3. Plot
data.plot <- data.import
#
plot_format <- "ggplot" # "base" or "ggplot"
#
if(plot_format=="base"){##IF~plot_format~START
# plot, base ####
# cheat on Date/Time axis
n.Total <- length(data.plot[,ContData.env$myName.Date])
pct <- c(20,40,60,80,100)*.01
myAT <- c(1,round(n.Total * pct,0))
myLab <- data.plot[,ContData.env$myName.Date][myAT]
#
myPlot.Y <- na.omit(as.numeric(data.plot[,i]))
#20170518, v2.0.1.9008, coming in as text add na.omit(as.numeric(x))
myPlot.Ylab <- myParameters.Lab[i.num]
plot(myPlot.Y,type="l",main=mySiteID,xlab=ContData.env$myLab.Date
,ylab=myPlot.Ylab,col="gray", xaxt="n")
axis(1,at=myAT,labels=myLab,tick=TRUE)
# Add discrete (only if present)
myDiscrete <- paste(ContData.env$myPrefix.Discrete,i,sep=".")
if (myDiscrete %in% myParameters.ALL == TRUE){##IF.Discrete.START
data.plot[,myDiscrete] <- as.numeric(data.plot[,myDiscrete]) # coerce "NA" to NA
data.plot.points <- na.omit(data.plot[,c(ContData.env$myName.DateTime,myDiscrete)])
# remove NA
myPoints.X <- as.numeric(rownames(data.plot.points))
myPoints.Y <- data.plot.points[,myDiscrete]
points(myPoints.Y~myPoints.X,col="black",pch=19)
}##IF.Discrete.END
cat("\n\n")
cat("Discrete measurements, if any, show up as points on the plot.")
cat("\n\n")
} else if(plot_format=="ggplot"){
# plot, ggplot ####
data.plot[, ContData.env$myName.DateTime] <- as.POSIXct(data.plot[, ContData.env$myName.DateTime])
# ggplot, main
scale_lab <- c("Continuous", "Discrete")
scale_col <- c("dark gray", "black")
scale_shape <- c(21, 24)
scale_fill <- scale_col
p_i <- ggplot2::ggplot(data=data.plot, ggplot2::aes_string(x=ContData.env$myName.DateTime, y=i)) +
ggplot2::geom_point(ggplot2::aes(color="continuous"
, shape="continuous"
, fill="continuous"), na.rm=TRUE) +
ggplot2::scale_x_datetime(date_labels = "%Y-%m-%d") +
ggplot2::labs(title=mySiteID, x=ContData.env$myLab.Date, y=myParameters.Lab[i.num]) +
ggplot2::theme(plot.title=ggplot2::element_text(hjust=0.5)
, plot.subtitle=ggplot2::element_text(hjust=0.5))
# ggplot, discrete points
# Add discrete (only if present)
myDiscrete <- paste(ContData.env$myPrefix.Discrete,i,sep=".")
if (myDiscrete %in% myParameters.ALL == TRUE){##IF.Discrete.START
p_i <- p_i + ggplot2::geom_point(data=data.plot
, ggplot2::aes_q(x=as.name(ContData.env$myName.DateTime)
, y=as.name(myDiscrete)
, color="discrete"
, shape="discrete"
, fill="discrete"), na.rm=TRUE)
} else {
p_i <- p_i + ggplot2::geom_blank(ggplot2::aes(color="discrete"
, shape="discrete"
, fill="discrete"))
}##IF.Discrete.END
# ggplot, Legend
p_i <- p_i +
ggplot2::scale_color_manual(name="Data Type", labels=scale_lab, values=scale_col) +
ggplot2::scale_shape_manual(name="Data Type", labels=scale_lab, values=scale_shape) +
ggplot2::scale_fill_manual(name="Data Type", labels=scale_lab, values=scale_fill)
# ggplot, show
print(p_i)
cat("\n\n")
}##IF~plot_format~END
#
}##FOR.i.END
PLOTS, MULTIPARAMETER
Multiparameter plots if exist in the data.
# BOTH plots
# 3.3. Plot
data.plot <- data.import
plot_format <- "ggplot" # "base" or "ggplot"
# Plot
if(plot_format=="base") {##IF~plot_format~START
# plot, base ####
# cheat on Date/Time axis
n.Total <- length(data.plot[,ContData.env$myName.Date])
pct <- c(20,40,60,80,100)*.01
myAT <- c(1,round(n.Total * pct,0))
myLab <- data.plot[,ContData.env$myName.Date][myAT]
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Need to check for parameters before plot
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Temp, Air vs. Water
if (ContData.env$myName.AirTemp %in% myParameters &
ContData.env$myName.WaterTemp %in% myParameters == TRUE) {
cat("## PLOT, Temperature (Air vs. Water) \n\n" )
#
par.orig <- par(no.readonly=TRUE) # save original par settings
layout(rbind(1,2),heights=c(7,1))
#
myPlot.Y <- na.omit(as.numeric(data.plot[,ContData.env$myName.AirTemp]))
# 20170518, v2.0.1.9008
myPlot.Y2 <- na.omit(as.numeric(data.plot[
,ContData.env$myName.WaterTemp]))
# 20170518, v2.0.1.9008
myPlot.Ylab <- ContData.env$myLab.Temp.BOTH
plot(myPlot.Y
,type="l"
,main=mySiteID
,xlab=ContData.env$myLab.Date
,ylab=myPlot.Ylab
,col="green"
, xaxt="n")
# Revised myAT for lots of NA (20170518)
data.length <- length(myPlot.Y)
myAT <- c(1,round(data.length * pct,0))
#
axis(1,at=myAT,labels=myLab,tick=TRUE)
lines(myPlot.Y2,type="l",col="blue")
#legend(x="bottomright",lty=1,col=c("green","blue")
# ,legend=c("air","water"))
# Add discrete, AirTemp (only if present)
myDiscrete <- paste(ContData.env$myPrefix.Discrete
,ContData.env$myName.AirTemp
,sep=".")
if (myDiscrete %in% myParameters.ALL == TRUE){##IF.Discrete.START
data.plot[,myDiscrete] <- as.numeric(data.plot[,myDiscrete])
# coerce "NA" to NA
data.plot.points <-
na.omit(data.plot[,c(ContData.env$myName.DateTime,myDiscrete)])
# remove NA
myPoints.X <- as.numeric(rownames(data.plot.points))
myPoints.Y <- data.plot.points[,myDiscrete]
points(myPoints.Y~myPoints.X,col="green",pch=19)
}##IF.Discrete.END
# Add discrete, Water.Temp (only if present)
myDiscrete <- paste(ContData.env$myPrefix.Discrete
,ContData.env$myName.WaterTemp
,sep=".")
if (myDiscrete %in% myParameters.ALL == TRUE){
data.plot[,myDiscrete] <- as.numeric(data.plot[,myDiscrete])
# coerce "NA" to NA
data.plot.points <-
na.omit(data.plot[,c(ContData.env$myName.DateTime,myDiscrete)])
# remove NA
myPoints.X <- as.numeric(rownames(data.plot.points))
myPoints.Y <- data.plot.points[,myDiscrete]
points(myPoints.Y~myPoints.X,col="blue",pch=19)
}##IF.Discrete.END
#
# LEGEND
par(mar=c(0,0,0,0))
plot.new()
legend(x="center"
,lty=1
,col=c("green","blue")
,legend=c("air","water")
,bty="n")
#
par(par.orig) # return to original par settings
#
# Future mod, add points to legend
cat("\n\n")
cat("Discrete measurements, if any, show up as points on the plot.")
cat("\n\n")
#
}##IF.Temp.END
#
# Water, Temp vs Sensor Depth
if (ContData.env$myName.WaterTemp %in% myParameters
& ContData.env$myName.SensorDepth %in% myParameters == TRUE) {
cat("## PLOT, Sensor Depth vs. Water Temperature \n\n")
#
par.orig <- par(no.readonly=TRUE) # save original par settings
layout(rbind(1,2),heights=c(7,1))
par(oma=c(0,0,0,2))
#
myPlot.Y <- na.omit(as.numeric(data.plot[
,ContData.env$myName.WaterTemp]))
# 20170518, v2.0.1.9008
myPlot.Ylab <- ContData.env$myLab.WaterTemp
myPlot.Y2 <- na.omit(as.numeric(data.plot[
,ContData.env$myName.SensorDepth]))
# 20170518, v2.0.1.9008
myPlot.Y2lab <- ContData.env$myLab.SensorDepth
#
plot(myPlot.Y
,type="l"
,main=mySiteID
,xlab=ContData.env$myLab.Date
,ylab=myPlot.Ylab
,col="blue"
, xaxt="n")
# Revised myAT for lots of NA (20170518)
data.length <- length(myPlot.Y)
myAT <- c(1,round(data.length * pct,0))
#
axis(1,at=myAT,labels=myLab,tick=TRUE)
# Add discrete, Water.Temp (only if present)
myDiscrete <- paste(ContData.env$myPrefix.Discrete
,ContData.env$myName.WaterTemp
,sep=".")
if (myDiscrete %in% myParameters.ALL == TRUE){
data.plot[,myDiscrete] <- as.numeric(data.plot[,myDiscrete])
# coerce "NA" to NA
data.plot.points <- na.omit(data.plot[
,c(ContData.env$myName.DateTime,myDiscrete)]) # remove NA
myPoints.X <- as.numeric(rownames(data.plot.points))
myPoints.Y <- data.plot.points[,myDiscrete]
points(myPoints.Y~myPoints.X,col="blue",pch=19)
}##IF.Discrete.END
#
# Add 2nd y axis (2nd color is black)
par(new=TRUE)
plot(myPlot.Y2,type="l",col="black",axes=FALSE,ann=FALSE)
axis(4)
mtext(myPlot.Y2lab,side=4,line=2.5)
# Add discrete, SensorDepth (only if present)
myDiscrete <- paste(ContData.env$myPrefix.Discrete
,ContData.env$myName.SensorDepth,sep=".")
if (myDiscrete %in% myParameters.ALL == TRUE){
data.plot[,myDiscrete] <- as.numeric(data.plot[,myDiscrete])
# coerce "NA" to NA
data.plot.points <- na.omit(data.plot[
,c(ContData.env$myName.DateTime,myDiscrete)]) # remove NA
myPoints.X <- as.numeric(rownames(data.plot.points))
myPoints.Y <- data.plot.points[,myDiscrete]
points(myPoints.Y~myPoints.X,col="black",pch=19)
}##IF.Discrete.END
#
# LEGEND
par(mar=c(0,0,0,0))
plot.new()
legend(x="center"
,lty=1
,col=c("blue","black")
,legend=c("temperature"
,"sensor depth")
,bty="n")
#
# Future mod, add points to legend
#
par(par.orig) # return to original par settings
cat("\n\n")
cat("Discrete measurements, if any, show up as points on the plot.")
cat("\n\n")
}##IF.Temp_Level.END
} else if(plot_format=="ggplot"){
# plot, ggplot ####
# date to POSIX
data.plot[, ContData.env$myName.DateTime] <-
as.POSIXct(data.plot[, ContData.env$myName.DateTime])
#
# Temp, Air vs. Water
if (ContData.env$myName.AirTemp %in% myParameters &
ContData.env$myName.WaterTemp %in% myParameters ) {
cat("## PLOT, Temperature (Air vs. Water) \n\n" )
#
# ggplot, main
scale_lab <- c("Air", "Water")
scale_col <- c("green", "blue")
scale_fill <- scale_col
scale_shape <- c(21, 21)
p2_t_aw <- ggplot2::ggplot(data=data.plot, ggplot2::aes(color="a"
, fill="b"
, shape="c")) +
ggplot2::geom_path(ggplot2::aes_q(x=as.name(ContData.env$myName.DateTime)
,y=as.name(ContData.env$myName.AirTemp)
, color="air")
, na.rm=TRUE) +
ggplot2::geom_path(ggplot2::aes_q(x=as.name(ContData.env$myName.DateTime)
,y=as.name(ContData.env$myName.WaterTemp)
, color="water")
, na.rm=TRUE) +
ggplot2::scale_x_datetime(date_labels = "%Y-%m-%d") +
ggplot2::labs(title=mySiteID
, x=ContData.env$myLab.Date
, y=ContData.env$myLab.Temp.BOTH) +
ggplot2::theme(plot.title=ggplot2::element_text(hjust=0.5)
, plot.subtitle=ggplot2::element_text(hjust=0.5))
# ggplot, discrete points
## skip
# ggplot, Legend
p2_t_aw <- p2_t_aw +
ggplot2::scale_color_manual(name="Data Type"
, labels=scale_lab
, values=scale_col)
# ggplot, show
print(p2_t_aw)
cat("\n\n")
#
}##IF.Temp.END
#
# Water, Temp vs Sensor Depth
if (ContData.env$myName.WaterTemp %in% myParameters &
ContData.env$myName.SensorDepth %in% myParameters) {
cat("## PLOT, Sensor Depth vs. Water Temperature \n\n")
#
# ggplot, main
scale_lab <- c("Water Temp", "Sensor Depth")
scale_col <- c("blue", "black")
scale_fill <- scale_col
scale_shape <- c(21, 21)
# ggplot intentionaly not designed for dual y plotting
min_diff <-
min(data.plot[, ContData.env$myName.WaterTemp], na.rm=TRUE) -
min(data.plot[, ContData.env$myName.SensorDepth], na.rm=TRUE)
rd_y1 <- diff(range(data.plot[, ContData.env$myName.WaterTemp]
, na.rm = TRUE))
rd_y2 <- diff(range(data.plot[, ContData.env$myName.SensorDepth]
, na.rm = TRUE))
min_y1 <- min(data.plot[, ContData.env$myName.WaterTemp])
max_y1 <- max(data.plot[, ContData.env$myName.WaterTemp])
avg_y1 <- mean(data.plot[, ContData.env$myName.WaterTemp])
data.plot[, "depth4plot"] <-
(data.plot[, ContData.env$myName.SensorDepth] * rd_y1/rd_y2) +
min_diff
p3_td <- ggplot2::ggplot(data=data.plot) +
ggplot2::geom_path(ggplot2::aes_q(x=as.name(ContData.env$myName.DateTime)
,y=as.name(ContData.env$myName.WaterTemp)
, color="water")
, na.rm=TRUE) +
ggplot2::geom_path(ggplot2::aes_q(x=as.name(ContData.env$myName.DateTime)
,y=as.name("depth4plot")
, color="depth"), na.rm = TRUE) +
ggplot2::scale_x_datetime(date_labels = "%Y-%m-%d") +
ggplot2::labs(title=mySiteID
, x=ContData.env$myLab.Date
, y=ContData.env$myLab.WaterTemp) +
ggplot2::theme(plot.title=ggplot2::element_text(hjust=0.5)
, plot.subtitle=ggplot2::element_text(hjust=0.5))
# ggplot, add 2nd Y
p3_td <- p3_td + ggplot2::scale_y_continuous(sec.axis = ggplot2::sec_axis(
trans=~./(rd_y1/rd_y2)-(min_diff/(rd_y1/rd_y2))
, name=ContData.env$myLab.SensorDepth))
# ggplot, discrete points
## skip
# ggplot, Legend
## need to reverse legend items to be correct.
p3_td <- p3_td +
ggplot2::scale_color_manual(name="Data Type"
, labels=rev(scale_lab)
, values=rev(scale_col))
# ggplot, show
print(p3_td)
cat("\n\n")
#
}##IF.Temp_SensorDepth.END
# Water, Temp vs Sensor Depth
if (ContData.env$myName.WaterTemp %in% myParameters &
ContData.env$myName.WaterLevel %in% myParameters) {
cat("## PLOT, Water Level vs. Water Temperature \n\n")
#
# ggplot, main
scale_lab <- c("Water Temp", "Water Level")
scale_col <- c("blue", "black")
scale_fill <- scale_col
scale_shape <- c(21, 21)
# ggplot intentionaly not designed for dual y plotting
min_diff <-
min(data.plot[, ContData.env$myName.WaterTemp], na.rm=TRUE) -
min(data.plot[, ContData.env$myName.WaterLevel], na.rm=TRUE)
rd_y1 <- diff(range(data.plot[, ContData.env$myName.WaterTemp]
, na.rm = TRUE))
rd_y2 <- diff(range(data.plot[, ContData.env$myName.WaterLevel]
, na.rm = TRUE))
min_y1 <- min(data.plot[, ContData.env$myName.WaterTemp])
max_y1 <- max(data.plot[, ContData.env$myName.WaterTemp])
avg_y1 <- mean(data.plot[, ContData.env$myName.WaterTemp])
data.plot[, "depth4plot"] <-
(data.plot[, ContData.env$myName.WaterLevel] * rd_y1/rd_y2) +
min_diff
p4_td <- ggplot2::ggplot(data=data.plot) +
ggplot2::geom_path(ggplot2::aes_q(x=as.name(ContData.env$myName.DateTime)
,y=as.name(ContData.env$myName.WaterTemp)
, color="water")
, na.rm=TRUE) +
ggplot2::geom_path(ggplot2::aes_q(x=as.name(ContData.env$myName.DateTime)
,y=as.name("depth4plot")
, color="depth"), na.rm = TRUE) +
ggplot2::scale_x_datetime(date_labels = "%Y-%m-%d") +
ggplot2::labs(title=mySiteID
, x=ContData.env$myLab.Date
, y=ContData.env$myLab.WaterTemp) +
ggplot2::theme(plot.title=ggplot2::element_text(hjust=0.5)
, plot.subtitle=ggplot2::element_text(hjust=0.5))
# ggplot, add 2nd Y
p4_td <- p4_td + ggplot2::scale_y_continuous(sec.axis = ggplot2::sec_axis(
trans=~./(rd_y1/rd_y2)-(min_diff/(rd_y1/rd_y2))
, name=ContData.env$myLab.WaterLevel))
# ggplot, discrete points
## skip
# ggplot, Legend
## need to reverse legend items to be correct.
p4_td <- p4_td +
ggplot2::scale_color_manual(name="Data Type"
, labels=rev(scale_lab)
, values=rev(scale_col))
# ggplot, show
print(p4_td)
cat("\n\n")
#
}##IF.Temp_Level.END
}##IF~plot_format~START
# '''
leppott/ContDataQC documentation built on Jan. 5, 2025, 10:12 a.m.
R Package Documentation
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knitr::opts_chunk$set(results='asis', echo=FALSE, warning=FALSE) # needed for trouble shooting boo_DEBUG <- FALSE if(boo_DEBUG==TRUE){ myConfig <- file.path(system.file(package="ContDataQC"), "extdata" , "config.ORIG.R") source(myConfig) }
DATA FILE INFORMATION
# Report Info myReportDate <- format(Sys.Date(),ContData.env$myFormat.Date) cat(paste("**Report Date:** ",myReportDate,"\n\n",sep="")) myUser <- Sys.getenv("USERNAME") cat(paste("**Generated By:** ",myUser,"\n\n",sep="")) #filename cat("**Filename:** ",strFile,"\n\n",sep="") mySiteID <- data.import[1,ContData.env$myName.SiteID] cat(paste("**SiteID:** ",mySiteID,"\n\n",sep="")) if(exists("fun.myData.DateRange.Start")==TRUE){ POR.Requested <- paste(fun.myData.DateRange.Start ," to " ,fun.myData.DateRange.End , sep="") } else { POR.Requested <- "NA" } cat(paste("**Period of Record, Requested:** " ,POR.Requested,sep="" ,collapse="\n\n")) myNumRecords <- nrow(data.import) # 20170228, mod from records 10 and 11 to half way point # myTimeDiff <- difftime(data.import[10,ContData.env$myName.DateTime] #,data.import[11,ContData.env$myName.DateTime],units="mins") #x <- data.import[,ContData.env$myName.DateTime] myT <- strptime(data.import[,ContData.env$myName.DateTime] ,format=ContData.env$myFormat.DateTime) myTimeDiff.all <- difftime(myT[-1],myT[-length(myT)],units="mins") myTimeDiff <- median(as.vector(myTimeDiff.all),na.rm=TRUE) cat(paste("\n\n**Period of Record, Actual:**" ,min(data.import[,ContData.env$myName.Date]) ," to " ,max(data.import[,ContData.env$myName.Date]) ,"\n\n" ,sep="")) cat(paste("**Recording Interval:** ",myTimeDiff[1]," minutes\n\n",sep="")) if(exists("strFile.DataType")==TRUE){ myDataType <- strFile.DataType } else { myDataType <- "NA" } cat(paste("**Data Type:** ",myDataType,"\n\n",sep="")) # need to do better myParameters.ALL <- ContData.env$myNames.DataFields[ContData.env$myNames.DataFields %in% names(data.import)==TRUE] myParameters.Lab.ALL <- ContData.env$myNames.DataFields.Lab[ContData.env$myNames.DataFields %in% names(data.import)==TRUE] # Filter out Discrete myParameters <- myParameters.ALL[!grepl(ContData.env$myPrefix.Discrete,myParameters.ALL)] myParameters.Lab <- myParameters.Lab.ALL[ !grepl(ContData.env$myPrefix.Discrete ,myParameters.Lab.ALL)] #cat("**Parameters:** ",paste(myParameters.Lab,", ",sep=""),"\n\n",sep="") # above line not working, preventing pandoc conversion to WORD #cat("**Included Parameters:** \n\n") cat("**Parameters Included:** ",paste(myParameters.ALL ,", " ,sep="") ,"\n\n" ,sep="")
DATA SUMMARY, OVERALL
# 2.1. Records by Month/Day # split so easier to put on paper # number of records by month/day (split 1:15 and 16:31) # # format = markdown/pandoc = seems to be the same. All rows but no columns in Word. # Have to use on table not ftable. ftable better by itself but doesn't work with knitr::kable. # # QC, if days less than count = 15 #*Error in table. Only printing half.* (fixed for sites with <15 days) # if((max(data.import[,"day"])-min(data.import[,"day"]))<15) {##IF.daycount.START # myTable <- table(data.import[,"month"],data.import[,"day"]) # print(knitr::kable(myTable, format="markdown",row.names=TRUE)) # } else { # # # #myTable.month.day.rec.LTE15 <- # myTable <- table(data.import[,"month"][data.import[,"day"]<=15],data.import[,"day"][data.import[,"day"]<=15]) # print(knitr::kable(myTable, format="markdown", row.names=TRUE)) # #knitr::kable(myTable.month.day.rec.LTE15, format="pandoc", caption = "Title of the table") # cat("\n\n") # # # # myTable.month.day.rec.GT15 <- # myTable <- table(data.import[,"month"][data.import[,"day"]>15],data.import[,"day"][data.import[,"day"]>15]) # print(knitr::kable(myTable, format="markdown", row.names=TRUE)) # # }##IF.daycount.END # Convert time interval (minutes) to number per day records.expected <- round(24*60/as.numeric(myTimeDiff[1]),1) cat(paste("Estimated number of records per day is ",records.expected,".",sep="")) cat("\n\n") cat("Number of records by year and month (with totals).") cat("\n") myTable <- addmargins(table(data.import[,ContData.env$myName.Yr] ,data.import[,ContData.env$myName.Mo] , useNA = "ifany")) print(knitr::kable(myTable, format="markdown", row.names=TRUE)) cat("\n\n") # revert to longer table (20170228) cat("Number of records by day and month (with totals).") cat("\n") myTable <- addmargins(table(data.import[,ContData.env$myName.Day] ,data.import[,ContData.env$myName.Mo] , useNA = "ifany")) print(knitr::kable(myTable, format="markdown", row.names=TRUE)) cat("\n\n") # # pandoc.table(myTable.month.day.rec.GT15,style="rmarkdown") # # myTable.month.day.rec <- table(data.import[,ContData.env$myName.Mo] ,data.import[,ContData.env$myName.Day] , useNA = "ifany") # use apply function to count number of records not equal to the expected value cat("\n\n") # myTable.DateTime.N <- aggregate(data.import[,ContData.env$myName.SiteID] ~ data.import[,ContData.env$myName.DateTime] , data=data.import,FUN=length) # myTable <- myTable.DateTime.N[myTable.DateTime.N[,ContData.env$myName.SiteID]!=1,] # cat(paste0("Date and time records with more than one entry; N=",nrow(myTable),". Duplicate date.time records are shown below.")) # cat("\n") # print(knitr::kable(myTable, format="markdown", row.names=TRUE)) # mask error, 20170307 # Missing Dates myDateRange.Data <- seq(as.Date(min(data.import[,ContData.env$myName.Date])) ,as.Date(max(data.import[,ContData.env$myName.Date])) ,by="day") if(exists("fun.myData.DateRange.Start")==TRUE){ myDateRange.Target <- seq(as.Date(fun.myData.DateRange.Start) ,as.Date(fun.myData.DateRange.End) ,by="day") } else { myDateRange.Target <- myDateRange.Data } myDateRange.Target.Missing <- myDateRange.Target[!myDateRange.Target %in% myDateRange.Data] cat(paste0("Below are the dates with missing data between the min (",min(myDateRange.Data) ,") and max(",max(myDateRange.Data)," for the provided data. There are " ,length(myDateRange.Target.Missing)," records.")) cat("\n\n") print(ifelse(length(myDateRange.Target.Missing)==0,"",myDateRange.Target.Missing))
FLAGS
Each data point is checked by each of the four QC tests below.
- Unrealistic values (Gross range): Entries are flagged if values are above or below upper or lower limits.
- Spikes: Entries are flagged if adjacent points change by more than 'x' amount.
- Rate of Change (RoC): Entries are flagged if the RoC exceeds a given threshold (e.g., >= 3 standard deviations within 25 hours).
- Flat Line (Flat): Entries are flagged if a certain number of consecutive measurements are within a certain amount of each other (e.g., > 10 consecutive temperature measurements are within 0.01. degrees C of on another).
Flags are assigned to each data point (a single measured parameter at a unique point in time) first by each of the QC tests below. Flags are assigned the following categories;
cat(paste("* **",ContData.env$myFlagVal.Pass,"** = Pass,",sep="")) cat("\n") cat(paste("* **",ContData.env$myFlagVal.Suspect,"** = Suspect,",sep="")) cat("\n") cat(paste("* **",ContData.env$myFlagVal.Fail,"** = Fail,",sep="")) cat("\n") cat(paste("* **",ContData.env$myFlagVal.NoData,"** = No Data or Not Applicable (NA).",sep=""))
Overall flags are assigned by examining the results of the four tests below.
cat(paste("* **",ContData.env$myFlagVal.Pass,"** = no Fail or Suspect and at least one Pass,",sep="")) cat("\n") cat(paste("* **",ContData.env$myFlagVal.Suspect,"** = no Fail and at least one Suspect,",sep="")) cat("\n") cat(paste("* **",ContData.env$myFlagVal.Fail,"** = at least one Fail,",sep="")) cat("\n") cat(paste("* **",ContData.env$myFlagVal.NoData,"** = unable to perform QC check because no adjacent value(s) to compare to, either due to missing data or because the measurement was taken at the beginning or end of the deployment period.",sep=""))
The thresholds used in the tests are defined in the config file and are listed below.
kbl_caption <- "Thresholds, Quick Reference" txt_header <- c("Analyte", "Flag", "Gross", "Spike", "Rate of Change", "Flat Line") # WaterTemp nam_WaterTemp <- c("myLab.WaterTemp" #1 , "myUnits.WaterTemp" #2 , "myThresh.Gross.Fail.Hi.WaterTemp" #3 , "myThresh.Gross.Fail.Lo.WaterTemp" #4 , "myThresh.Gross.Suspect.Hi.WaterTemp" #5 , "myThresh.Gross.Suspect.Lo.WaterTemp" #6 , "myThresh.Spike.Hi.WaterTemp" #7 , "myThresh.Spike.Lo.WaterTemp" #8 , "myThresh.RoC.SD.number.WaterTemp" #9 , "myThresh.RoC.SD.period.WaterTemp" #10 , "myThresh.Flat.Hi.WaterTemp" #11 , "myThresh.Flat.Lo.WaterTemp" #12 , "myThresh.Flat.Tolerance.WaterTemp" #13 ) val_WaterTemp <- mget(nam_WaterTemp, envir=ContData.env) txt_WaterTemp_F <- c(val_WaterTemp[1] , "Fail" , paste0("> ",val_WaterTemp[3]," deg ",val_WaterTemp[2] ," or < ",val_WaterTemp[4]," deg ",val_WaterTemp[2]) , paste0(">= ", val_WaterTemp[7]," deg ",val_WaterTemp[2]," (+/-)") , "NA" , paste0("> ",val_WaterTemp[11] ," consecutive measurements within ",val_WaterTemp[13] ," units of one another")) txt_WaterTemp_S <- c(val_WaterTemp[1] , "Suspect" , paste0("> ",val_WaterTemp[5]," deg ",val_WaterTemp[2] ," or < ",val_WaterTemp[6]," deg ",val_WaterTemp[2]) , paste0(">= ", val_WaterTemp[8]," deg ",val_WaterTemp[2]," (+/-)") , paste0(">= ",val_WaterTemp[9]," standard deviations within " ,val_WaterTemp[10]," hours") , paste0("> ",val_WaterTemp[12] ," consecutive measurements within ",val_WaterTemp[13] ," units of one another")) # AirTemp nam_AirTemp <- c("myLab.AirTemp" #1 , "myUnits.AirTemp" #2 , "myThresh.Gross.Fail.Hi.AirTemp" #3 , "myThresh.Gross.Fail.Lo.AirTemp" #4 , "myThresh.Gross.Suspect.Hi.AirTemp" #5 , "myThresh.Gross.Suspect.Lo.AirTemp" #6 , "myThresh.Spike.Hi.AirTemp" #7 , "myThresh.Spike.Lo.AirTemp" #8 , "myThresh.RoC.SD.number.AirTemp" #9 , "myThresh.RoC.SD.period.AirTemp" #10 , "myThresh.Flat.Hi.AirTemp" #11 , "myThresh.Flat.Lo.AirTemp" #12 , "myThresh.Flat.Tolerance.AirTemp" #13 ) val_AirTemp <- mget(nam_AirTemp, envir=ContData.env) txt_AirTemp_F <- c(val_AirTemp[1] , "Fail" , paste0("> ",val_AirTemp[3]," deg ",val_AirTemp[2] ," or < ",val_AirTemp[4]," deg ",val_AirTemp[2]) , paste0(">= ", val_AirTemp[7]," deg ",val_AirTemp[2]," (+/-)") , "NA" , paste0("> ",val_AirTemp[11] ," consecutive measurements within ",val_AirTemp[13] ," units of one another")) txt_AirTemp_S <- c(val_AirTemp[1] , "Suspect" , paste0("> ",val_AirTemp[5]," deg ",val_AirTemp[2] ," or < ",val_AirTemp[6]," deg ",val_AirTemp[2]) , paste0(">= ", val_AirTemp[8]," deg ",val_AirTemp[2]," (+/-)") , paste0(">= ",val_AirTemp[9]," standard deviations within " ,val_AirTemp[10]," hours") , paste0("> ",val_AirTemp[12] ," consecutive measurements within ",val_AirTemp[13] ," units of one another")) # WaterP nam_WaterP <- c("myLab.WaterP" #1 , "myUnits.WaterP" #2 , "myThresh.Gross.Fail.Hi.WaterP" #3 , "myThresh.Gross.Fail.Lo.WaterP" #4 , "myThresh.Gross.Suspect.Hi.WaterP" #5 , "myThresh.Gross.Suspect.Lo.WaterP" #6 , "myThresh.Spike.Hi.WaterP" #7 , "myThresh.Spike.Lo.WaterP" #8 , "myThresh.RoC.SD.number.WaterP" #9 , "myThresh.RoC.SD.period.WaterP" #10 , "myThresh.Flat.Hi.WaterP" #11 , "myThresh.Flat.Lo.WaterP" #12 , "myThresh.Flat.Tolerance.WaterP" #13 ) val_WaterP <- mget(nam_WaterP, envir=ContData.env) txt_WaterP_F <- c(val_WaterP[1] , "Fail" , paste0("> ",val_WaterP[3]," ",val_WaterP[2] ," or < ",val_WaterP[4]," ",val_WaterP[2]) , paste0(">= ", val_WaterP[7]," ",val_WaterP[2]," (+/-)") , "NA" , paste0("> ",val_WaterP[11] ," consecutive measurements within ",val_WaterP[13] ," units of one another")) txt_WaterP_S <- c(val_WaterP[1] , "Suspect" , paste0("> ",val_WaterP[5]," ",val_WaterP[2] ," or < ",val_WaterP[6]," ",val_WaterP[2]) , paste0(">= ", val_WaterP[8]," ",val_WaterP[2]," (+/-)") , paste0(">= ",val_WaterP[9]," standard deviations within " ,val_WaterP[10]," hours") , paste0("> ",val_WaterP[12] ," consecutive measurements within ",val_WaterP[13] ," units of one another")) # AirBP nam_AirBP <- c("myLab.AirBP" #1 , "myUnits.AirBP" #2 , "myThresh.Gross.Fail.Hi.AirBP" #3 , "myThresh.Gross.Fail.Lo.AirBP" #4 , "myThresh.Gross.Suspect.Hi.AirBP" #5 , "myThresh.Gross.Suspect.Lo.AirBP" #6 , "myThresh.Spike.Hi.AirBP" #7 , "myThresh.Spike.Lo.AirBP" #8 , "myThresh.RoC.SD.number.AirBP" #9 , "myThresh.RoC.SD.period.AirBP" #10 , "myThresh.Flat.Hi.AirBP" #11 , "myThresh.Flat.Lo.AirBP" #12 , "myThresh.Flat.Tolerance.AirBP" #13 ) val_AirBP <- mget(nam_AirBP, envir=ContData.env) txt_AirBP_F <- c(val_AirBP[1] , "Fail" , paste0("> ",val_AirBP[3]," ",val_AirBP[2] ," or < ",val_AirBP[4]," ",val_AirBP[2]) , paste0(">= ", val_AirBP[7]," ",val_AirBP[2]," (+/-)") , "NA" , paste0("> ",val_AirBP[11] ," consecutive measurements within ",val_AirBP[13] ," units of one another")) txt_AirBP_S <- c(val_AirBP[1] , "Suspect" , paste0("> ",val_AirBP[5]," ",val_AirBP[2] ," or < ",val_AirBP[6]," ",val_AirBP[2]) , paste0(">= ", val_AirBP[8]," ",val_AirBP[2]," (+/-)") , paste0(">= ",val_AirBP[9]," standard deviations within " ,val_AirBP[10]," hours") , paste0("> ",val_AirBP[12] ," consecutive measurements within ",val_AirBP[13] ," units of one another")) # SensorDepth nam_SensorDepth <- c("myLab.SensorDepth" #1 , "myUnits.SensorDepth" #2 , "myThresh.Gross.Fail.Hi.SensorDepth" #3 , "myThresh.Gross.Fail.Lo.SensorDepth" #4 , "myThresh.Gross.Suspect.Hi.SensorDepth" #5 , "myThresh.Gross.Suspect.Lo.SensorDepth" #6 , "myThresh.Spike.Hi.SensorDepth" #7 , "myThresh.Spike.Lo.SensorDepth" #8 , "myThresh.RoC.SD.number.SensorDepth" #9 , "myThresh.RoC.SD.period.SensorDepth" #10 , "myThresh.Flat.Hi.SensorDepth" #11 , "myThresh.Flat.Lo.SensorDepth" #12 , "myThresh.Flat.Tolerance.SensorDepth" #13 ) val_SensorDepth <- mget(nam_SensorDepth, envir=ContData.env) txt_SensorDepth_F <- c(val_SensorDepth[1] , "Fail" , paste0("> ",val_SensorDepth[3]," ",val_SensorDepth[2] ," or < ",val_SensorDepth[4]," ",val_SensorDepth[2]) , paste0(">= ", val_SensorDepth[7]," ",val_SensorDepth[2]," (+/-)") , "NA" , paste0("> ",val_SensorDepth[11] ," consecutive measurements within ",val_SensorDepth[13] ," units of one another")) txt_SensorDepth_S <- c(val_SensorDepth[1] , "Suspect" , paste0("> ",val_SensorDepth[5]," ",val_SensorDepth[2] ," or < ",val_SensorDepth[6]," ",val_SensorDepth[2]) , paste0(">= ", val_SensorDepth[8]," ",val_SensorDepth[2]," (+/-)") , paste0(">= ",val_SensorDepth[9]," standard deviations within " ,val_SensorDepth[10]," hours") , paste0("> ",val_SensorDepth[12] ," consecutive measurements within ",val_SensorDepth[13] ," units of one another")) # Discharge nam_Discharge <- c("myLab.Discharge" #1 , "myUnits.Discharge" #2 , "myThresh.Gross.Fail.Hi.Discharge" #3 , "myThresh.Gross.Fail.Lo.Discharge" #4 , "myThresh.Gross.Suspect.Hi.Discharge" #5 , "myThresh.Gross.Suspect.Lo.Discharge" #6 , "myThresh.Spike.Hi.Discharge" #7 , "myThresh.Spike.Lo.Discharge" #8 , "myThresh.RoC.SD.number.Discharge" #9 , "myThresh.RoC.SD.period.Discharge" #10 , "myThresh.Flat.Hi.Discharge" #11 , "myThresh.Flat.Lo.Discharge" #12 , "myThresh.Flat.Tolerance.Discharge" #13 ) val_Discharge <- mget(nam_Discharge, envir=ContData.env) txt_Discharge_F <- c(val_Discharge[1] , "Fail" , paste0("> ",val_Discharge[3]," ",val_Discharge[2] ," or < ",val_Discharge[4]," ",val_Discharge[2]) , paste0(">= ", val_Discharge[7]," ",val_Discharge[2]," (+/-)") , "NA" , paste0("> ",val_Discharge[11] ," consecutive measurements within ",val_Discharge[13] ," units of one another")) txt_Discharge_S <- c(val_Discharge[1] , "Suspect" , paste0("> ",val_Discharge[5]," ",val_Discharge[2] ," or < ",val_Discharge[6]," ",val_Discharge[2]) , paste0(">= ", val_Discharge[8]," ",val_Discharge[2]," (+/-)") , paste0(">= ",val_Discharge[9]," standard deviations within " ,val_Discharge[10]," hours") , paste0("> ",val_Discharge[12] ," consecutive measurements within ",val_Discharge[13] ," units of one another")) # Cond nam_Cond <- c("myLab.Cond" #1 , "myUnits.Cond" #2 , "myThresh.Gross.Fail.Hi.Cond" #3 , "myThresh.Gross.Fail.Lo.Cond" #4 , "myThresh.Gross.Suspect.Hi.Cond" #5 , "myThresh.Gross.Suspect.Lo.Cond" #6 , "myThresh.Spike.Hi.Cond" #7 , "myThresh.Spike.Lo.Cond" #8 , "myThresh.RoC.SD.number.Cond" #9 , "myThresh.RoC.SD.period.Cond" #10 , "myThresh.Flat.Hi.Cond" #11 , "myThresh.Flat.Lo.Cond" #12 , "myThresh.Flat.Tolerance.Cond" #13 ) val_Cond <- mget(nam_Cond, envir=ContData.env) txt_Cond_F <- c(val_Cond[1] , "Fail" , paste0("> ",val_Cond[3]," ",val_Cond[2] ," or < ",val_Cond[4]," ",val_Cond[2]) , paste0(">= ", val_Cond[7]," ",val_Cond[2]," (+/-)") , "NA" , paste0("> ",val_Cond[11] ," consecutive measurements within ",val_Cond[13] ," units of one another")) txt_Cond_S <- c(val_Cond[1] , "Suspect" , paste0("> ",val_Cond[5]," ",val_Cond[2] ," or < ",val_Cond[6]," ",val_Cond[2]) , paste0(">= ", val_Cond[8]," ",val_Cond[2]," (+/-)") , paste0(">= ",val_Cond[9]," standard deviations within " ,val_Cond[10]," hours") , paste0("> ",val_Cond[12] ," consecutive measurements within ",val_Cond[13] ," units of one another")) # DO, mg/L nam_DO <- c("myLab.DO" #1 , "myUnits.DO" #2 , "myThresh.Gross.Fail.Hi.DO" #3 , "myThresh.Gross.Fail.Lo.DO" #4 , "myThresh.Gross.Suspect.Hi.DO" #5 , "myThresh.Gross.Suspect.Lo.DO" #6 , "myThresh.Spike.Hi.DO" #7 , "myThresh.Spike.Lo.DO" #8 , "myThresh.RoC.SD.number.DO" #9 , "myThresh.RoC.SD.period.DO" #10 , "myThresh.Flat.Hi.DO" #11 , "myThresh.Flat.Lo.DO" #12 , "myThresh.Flat.Tolerance.DO" #13 ) val_DO <- mget(nam_DO, envir=ContData.env) txt_DO_F <- c(val_DO[1] , "Fail" , paste0("> ",val_DO[3]," ",val_DO[2] ," or < ",val_DO[4]," ",val_DO[2]) , paste0(">= ", val_DO[7]," ",val_DO[2]," (+/-)") , "NA" , paste0("> ",val_DO[11] ," consecutive measurements within ",val_DO[13] ," units of one another")) txt_DO_S <- c(val_DO[1] , "Suspect" , paste0("> ",val_DO[5]," ",val_DO[2] ," or < ",val_DO[6]," ",val_DO[2]) , paste0(">= ", val_DO[8]," ",val_DO[2]," (+/-)") , paste0(">= ",val_DO[9]," standard deviations within " ,val_DO[10]," hours") , paste0("> ",val_DO[12] ," consecutive measurements within ",val_DO[13] ," units of one another")) # DO, adj nam_DO.adj <- c("myLab.DO.adj" #1 , "myUnits.DO.adj" #2 , "myThresh.Gross.Fail.Hi.DO.adj" #3 , "myThresh.Gross.Fail.Lo.DO.adj" #4 , "myThresh.Gross.Suspect.Hi.DO.adj" #5 , "myThresh.Gross.Suspect.Lo.DO.adj" #6 , "myThresh.Spike.Hi.DO.adj" #7 , "myThresh.Spike.Lo.DO.adj" #8 , "myThresh.RoC.SD.number.DO.adj" #9 , "myThresh.RoC.SD.period.DO.adj" #10 , "myThresh.Flat.Hi.DO.adj" #11 , "myThresh.Flat.Lo.DO.adj" #12 , "myThresh.Flat.Tolerance.DO.adj" #13 ) val_DO.adj <- mget(nam_DO.adj, envir=ContData.env) txt_DO_F.adj <- c(val_DO.adj[1] , "Fail" , paste0("> ",val_DO.adj[3]," ",val_DO.adj[2] ," or < ",val_DO.adj[4]," ",val_DO.adj[2]) , paste0(">= ", val_DO.adj[7]," ",val_DO.adj[2]," (+/-)") , "NA" , paste0("> ",val_DO.adj[11] ," consecutive measurements within ",val_DO.adj[13] ," units of one another")) txt_DO_S.adj <- c(val_DO.adj[1] , "Suspect" , paste0("> ",val_DO.adj[5]," ",val_DO.adj[2] ," or < ",val_DO.adj[6]," ",val_DO.adj[2]) , paste0(">= ", val_DO.adj[8]," ",val_DO.adj[2]," (+/-)") , paste0(">= ",val_DO.adj[9]," standard deviations within " ,val_DO.adj[10]," hours") , paste0("> ",val_DO.adj[12] ," consecutive measurements within ",val_DO.adj[13] ," units of one another")) # DO, pct sat nam_DO.pctsat <- c("myLab.DO.pctsat" #1 , "myUnits.DO.pctsat" #2 , "myThresh.Gross.Fail.Hi.DO.pctsat" #3 , "myThresh.Gross.Fail.Lo.DO.pctsat" #4 , "myThresh.Gross.Suspect.Hi.DO.pctsat" #5 , "myThresh.Gross.Suspect.Lo.DO.pctsat" #6 , "myThresh.Spike.Hi.DO.pctsat" #7 , "myThresh.Spike.Lo.DO.pctsat" #8 , "myThresh.RoC.SD.number.DO.pctsat" #9 , "myThresh.RoC.SD.period.DO.pctsat" #10 , "myThresh.Flat.Hi.DO.pctsat" #11 , "myThresh.Flat.Lo.DO.pctsat" #12 , "myThresh.Flat.Tolerance.DO.pctsat" #13 ) val_DO.pctsat <- mget(nam_DO.pctsat, envir=ContData.env) txt_DO_F.pctsat <- c(val_DO.pctsat[1] , "Fail" , paste0("> ",val_DO.pctsat[3]," ",val_DO.pctsat[2] ," or < ",val_DO.pctsat[4]," ",val_DO.pctsat[2]) , paste0(">= ", val_DO.pctsat[7]," ",val_DO.pctsat[2]," (+/-)") , "NA" , paste0("> ",val_DO.pctsat[11] ," consecutive measurements within ",val_DO.pctsat[13] ," units of one another")) txt_DO_S.pctsat <- c(val_DO.pctsat[1] , "Suspect" , paste0("> ",val_DO.pctsat[5]," ",val_DO.pctsat[2] ," or < ",val_DO.pctsat[6]," ",val_DO.pctsat[2]) , paste0(">= ", val_DO.pctsat[8]," ",val_DO[2]," (+/-)") , paste0(">= ",val_DO.pctsat[9]," standard deviations within " ,val_DO.pctsat[10]," hours") , paste0("> ",val_DO.pctsat[12] ," consecutive measurements within ",val_DO.pctsat[13] ," units of one another")) # pH nam_pH <- c("myLab.pH" #1 , "myUnits.pH" #2 , "myThresh.Gross.Fail.Hi.pH" #3 , "myThresh.Gross.Fail.Lo.pH" #4 , "myThresh.Gross.Suspect.Hi.pH" #5 , "myThresh.Gross.Suspect.Lo.pH" #6 , "myThresh.Spike.Hi.pH" #7 , "myThresh.Spike.Lo.pH" #8 , "myThresh.RoC.SD.number.pH" #9 , "myThresh.RoC.SD.period.pH" #10 , "myThresh.Flat.Hi.pH" #11 , "myThresh.Flat.Lo.pH" #12 , "myThresh.Flat.Tolerance.pH" #13 ) val_pH <- mget(nam_pH, envir=ContData.env) txt_pH_F <- c(val_pH[1] , "Fail" , paste0("> ",val_pH[3]," ",val_pH[2] ," or < ",val_pH[4]," ",val_pH[2]) , paste0(">= ", val_pH[7]," ",val_pH[2]," (+/-)") , "NA" , paste0("> ",val_pH[11] ," consecutive measurements within ",val_pH[13] ," units of one another")) txt_pH_S <- c(val_pH[1] , "Suspect" , paste0("> ",val_pH[5]," ",val_pH[2] ," or < ",val_pH[6]," ",val_pH[2]) , paste0(">= ", val_pH[8]," ",val_pH[2]," (+/-)") , paste0(">= ",val_pH[9]," standard deviations within " ,val_pH[10]," hours") , paste0("> ",val_pH[12] ," consecutive measurements within ",val_pH[13] ," units of one another")) # Turbidity nam_Turbidity <- c("myLab.Turbidity" #1 , "myUnits.Turbidity" #2 , "myThresh.Gross.Fail.Hi.Turbidity" #3 , "myThresh.Gross.Fail.Lo.Turbidity" #4 , "myThresh.Gross.Suspect.Hi.Turbidity" #5 , "myThresh.Gross.Suspect.Lo.Turbidity" #6 , "myThresh.Spike.Hi.Turbidity" #7 , "myThresh.Spike.Lo.Turbidity" #8 , "myThresh.RoC.SD.number.Turbidity" #9 , "myThresh.RoC.SD.period.Turbidity" #10 , "myThresh.Flat.Hi.Turbidity" #11 , "myThresh.Flat.Lo.Turbidity" #12 , "myThresh.Flat.Tolerance.Turbidity" #13 ) val_Turbidity <- mget(nam_Turbidity, envir=ContData.env) txt_Turbidity_F <- c(val_Turbidity[1] , "Fail" , paste0("> ",val_Turbidity[3]," ",val_Turbidity[2] ," or < ",val_Turbidity[4]," ",val_Turbidity[2]) , paste0(">= ", val_Turbidity[7]," ",val_Turbidity[2]," (+/-)") , "NA" , paste0("> ",val_Turbidity[11] ," consecutive measurements within ",val_Turbidity[13] ," units of one another")) txt_Turbidity_S <- c(val_Turbidity[1] , "Suspect" , paste0("> ",val_Turbidity[5]," ",val_Turbidity[2] ," or < ",val_Turbidity[6]," ",val_Turbidity[2]) , paste0(">= ", val_Turbidity[8]," ",val_Turbidity[2]," (+/-)") , paste0(">= ",val_Turbidity[9]," standard deviations within " ,val_Turbidity[10]," hours") , paste0("> ",val_Turbidity[12] ," consecutive measurements within ",val_Turbidity[13] ," units of one another")) # Chlorophylla nam_Chlorophylla <- c("myLab.Chlorophylla" #1 , "myUnits.Chlorophylla" #2 , "myThresh.Gross.Fail.Hi.Chlorophylla" #3 , "myThresh.Gross.Fail.Lo.Chlorophylla" #4 , "myThresh.Gross.Suspect.Hi.Chlorophylla" #5 , "myThresh.Gross.Suspect.Lo.Chlorophylla" #6 , "myThresh.Spike.Hi.Chlorophylla" #7 , "myThresh.Spike.Lo.Chlorophylla" #8 , "myThresh.RoC.SD.number.Chlorophylla" #9 , "myThresh.RoC.SD.period.Chlorophylla" #10 , "myThresh.Flat.Hi.Chlorophylla" #11 , "myThresh.Flat.Lo.Chlorophylla" #12 , "myThresh.Flat.Tolerance.Chlorophylla" #13 ) val_Chlorophylla <- mget(nam_Chlorophylla, envir=ContData.env) txt_Chlorophylla_F <- c(val_Chlorophylla[1] , "Fail" , paste0("> ",val_Chlorophylla[3]," ",val_Chlorophylla[2] ," or < ",val_Chlorophylla[4]," ",val_Chlorophylla[2]) , paste0(">= ", val_Chlorophylla[7]," ",val_Chlorophylla[2]," (+/-)") , "NA" , paste0("> ",val_Chlorophylla[11] ," consecutive measurements within ",val_Chlorophylla[13] ," units of one another")) txt_Chlorophylla_S <- c(val_Chlorophylla[1] , "Suspect" , paste0("> ",val_Chlorophylla[5]," ",val_Chlorophylla[2] ," or < ",val_Chlorophylla[6]," ",val_Chlorophylla[2]) , paste0(">= ", val_Chlorophylla[8]," ",val_Chlorophylla[2]," (+/-)") , paste0(">= ",val_Chlorophylla[9]," standard deviations within " ,val_Chlorophylla[10]," hours") , paste0("> ",val_Chlorophylla[12] ," consecutive measurements within ",val_Chlorophylla[13] ," units of one another")) # WaterLevel nam_WaterLevel <- c("myLab.WaterLevel" #1 , "myUnits.WaterLevel" #2 , "myThresh.Gross.Fail.Hi.WaterLevel" #3 , "myThresh.Gross.Fail.Lo.WaterLevel" #4 , "myThresh.Gross.Suspect.Hi.WaterLevel" #5 , "myThresh.Gross.Suspect.Lo.WaterLevel" #6 #, "myThresh.Spike.Hi.WaterLevel" #7 -removed #, "myThresh.Spike.Lo.WaterLevel" #8 - removed , "myThresh.RoC.SD.number.WaterLevel" #9 - 7 , "myThresh.RoC.SD.period.WaterLevel" #10 - 8 , "myThresh.Flat.Hi.WaterLevel" #11 - 9 , "myThresh.Flat.Lo.WaterLevel" #12 - 10 , "myThresh.Flat.Tolerance.WaterLevel" #13 - 11 ) val_WaterLevel <- mget(nam_WaterLevel, envir=ContData.env) txt_WaterLevel_F <- c(val_WaterLevel[1] , "Fail" , paste0("> ",val_WaterLevel[3]," ",val_WaterLevel[2] ," or < ",val_WaterLevel[4]," ",val_WaterLevel[2]) , "NA" , "NA" , paste0("> ",val_WaterLevel[9] ," consecutive measurements within ",val_WaterLevel[11] ," units of one another")) txt_WaterLevel_S <- c(val_WaterLevel[1] , "Suspect" , paste0("> ",val_WaterLevel[5]," ",val_WaterLevel[2] ," or < ",val_WaterLevel[6]," ",val_WaterLevel[2]) , "NA" , paste0(">= ",val_WaterLevel[7]," standard deviations within " ,val_WaterLevel[8]," hours") , paste0("> ",val_WaterLevel[10] ," consecutive measurements within ",val_WaterLevel[11] ," units of one another")) # Salinity nam_Salinity <- c("myLab.Salinity" #1 , "myUnits.Salinity" #2 , "myThresh.Gross.Fail.Hi.Salinity" #3 , "myThresh.Gross.Fail.Lo.Salinity" #4 , "myThresh.Gross.Suspect.Hi.Salinity" #5 , "myThresh.Gross.Suspect.Lo.Salinity" #6 , "myThresh.Spike.Hi.Salinity" #7 , "myThresh.Spike.Lo.Salinity" #8 , "myThresh.RoC.SD.number.Salinity" #9 , "myThresh.RoC.SD.period.Salinity" #10 , "myThresh.Flat.Hi.Salinity" #11 , "myThresh.Flat.Lo.Salinity" #12 , "myThresh.Flat.Tolerance.Salinity" #13 ) val_Salinity <- mget(nam_Salinity, envir=ContData.env) txt_Salinity_F <- c(val_Salinity[1] , "Fail" , paste0("> ",val_Salinity[3]," ",val_Salinity[2] ," or < ",val_Salinity[4]," ",val_Salinity[2]) , "NA" , "NA" , paste0("> ",val_Salinity[11] ," consecutive measurements within ",val_Salinity[13] ," units of one another")) txt_Salinity_S <- c(val_Salinity[1] , "Suspect" , paste0("> ",val_Salinity[5]," ",val_Salinity[2] ," or < ",val_Salinity[6]," ",val_Salinity[2]) , "NA" , paste0(">= ",val_Salinity[9]," standard deviations within " ,val_Salinity[10]," hours") , paste0("> ",val_Salinity[12] ," consecutive measurements within ",val_Salinity[13] ," units of one another")) # Create Table ## Display only those parameters used in current dataset (data.import) # QC boo.QC <- FALSE if(boo.QC==TRUE){##IF.boo.QC.START # Ensure have ContDat.env loaded myConfig <- file.path(system.file(package="ContDataQC"), "extdata", "config.ORIG.R") source(myConfig) # load dat.import fn.import <- file.path(getwd(), "Data1_RAW", "test2_AW_20130426_20130725.csv") data.import <- read.csv(fn.import) }##IF.boo.QC.END # # Check for Presence of each Analyte in data # if not present convert to NULL # if NULL then won't be included in result data.frame if(!(ContData.env$myName.WaterTemp %in% names(data.import))){ txt_WaterTemp_F <- NULL txt_WaterTemp_S <- NULL } # if(!(ContData.env$myName.AirTemp %in% names(data.import))){ txt_AirTemp_F <- NULL txt_AirTemp_S <- NULL } # if(!(ContData.env$myName.WaterP %in% names(data.import))){ txt_WaterP_F <- NULL txt_WaterP_S <- NULL } # if(!(ContData.env$myName.AirBP %in% names(data.import))){ txt_AirBP_F <- NULL txt_AirBP_S <- NULL } # if(!(ContData.env$myName.SensorDepth %in% names(data.import))){ txt_SensorDepth_F <- NULL txt_SensorDepth_S <- NULL } # if(!(ContData.env$myName.Discharge %in% names(data.import))){ txt_Discharge_F <- NULL txt_Discharge_S <- NULL } # if(!(ContData.env$myName.Cond %in% names(data.import))){ txt_Cond_F <- NULL txt_Cond_S <- NULL } # if(!(ContData.env$myName.DO %in% names(data.import))){ txt_DO_F <- NULL txt_DO_S <- NULL } # if(!(ContData.env$myName.DO.adj %in% names(data.import))){ txt_DO_F.adj <- NULL txt_DO_S.adj <- NULL } # if(!(ContData.env$myName.DO.pctsat %in% names(data.import))){ txt_DO_F.pctsat <- NULL txt_DO_S.pctsat <- NULL } # if(!(ContData.env$myName.pH %in% names(data.import))){ txt_pH_F <- NULL txt_pH_S <- NULL } # if(!(ContData.env$myName.Turbidity %in% names(data.import))){ txt_Turbidity_F <- NULL txt_Turbidity_S <- NULL } # if(!(ContData.env$myName.Chlorophylla %in% names(data.import))){ txt_Chlorophylla_F <- NULL txt_Chlorophylla_S <- NULL } # if(!(ContData.env$myName.WaterLevel %in% names(data.import))){ txt_WaterLevel_F <- NULL txt_WaterLevel_S <- NULL } # if(!(ContData.env$myName.Salinity %in% names(data.import))){ txt_Salinity_F <- NULL txt_Salinity_S <- NULL } # Table df_Env <- data.frame(rbind(txt_WaterTemp_F, txt_WaterTemp_S ,txt_AirTemp_F, txt_AirTemp_S , txt_WaterP_F, txt_WaterP_S , txt_AirBP_F, txt_AirBP_S , txt_SensorDepth_F, txt_SensorDepth_S , txt_Discharge_F, txt_Discharge_S , txt_Cond_F, txt_Cond_S , txt_DO_F, txt_DO_S , txt_DO_F.adj, txt_DO_S.adj , txt_DO_F.pctsat, txt_DO_S.pctsat , txt_pH_F, txt_pH_S , txt_Turbidity_F, txt_Turbidity_S , txt_Chlorophylla_F, txt_Chlorophylla_S , txt_WaterLevel_F, txt_WaterLevel_S , txt_Salinity_F, txt_Salinity_S ) , row.names = NULL) names(df_Env) <- txt_header knitr::kable(df_Env, caption = kbl_caption)
Overall flags by parameter
# for (j in 1:length(myParameters)) {##FOR.j.START # # #j.num <- match(j,myParameters) # cat(paste(myParameters.Lab[j.num],"/n/n",sep="")) # ISSUE WITH knitr::kable INSIDE OF LOOPS cat(paste("## ",myParameters[j],"\n\n",sep="")) colnum <- match(paste("Flag",myParameters[j],sep="."),names(data.import)) #print(myTable.Flags.Overall <- ftable(data.import[,colnum])) myTable <- addmargins(table(data.import[,colnum] , useNA = "ifany") ,1) print(knitr::kable(t(as.matrix(myTable)), format = "markdown")) # future mod, 1,3,4,9 to Pass, Suspect, Fail, NA # exclude=NA to get all # future mod, 2nd line in table to include Percent of row total # future mod, sort levels cat("\n\n") # # }##FOR.j.END
QC TESTS, BY PARAMETER
# 3. Data Info, by Parameter # Individual Data Type Report # QC #i <- myParameters[1] for (i in myParameters) {##FOR.i.START # # i.num <- match(i,myParameters) myTitle.Sub <- myParameters.Lab[i.num] cat("## QC TESTS, ",i,"\n\n",sep="") #cat(paste("**QC TESTS,",myTitle.Sub,sep="")) # 3.1. Flags, overall cat(myTitle.Sub <- "### Flags") cat("\n\n") cat("#### Flag summary, overall number of records.") cat("\n\n") myTable <- addmargins(table(data.import[,paste("Flag",i,sep=".")] , useNA = "ifany") ,1) print(knitr::kable(t(as.matrix(myTable)), format = "markdown")) cat("\n\n") # # 3.1. Number of Records (revised 20170228) # Convert time interval (minutes) to number per day records.expected <- round(24*60/as.numeric(myTimeDiff[1]),1) cat(paste("Estimated number of records per day is ",records.expected,".",sep="")) cat("\n\n") # cat("Number of records by year and month (with totals)") cat("\n") myTable <- addmargins(table(data.import[,ContData.env$myName.Yr] ,data.import[,ContData.env$myName.Mo] , useNA = "ifany")) print(knitr::kable(myTable, format="markdown", row.names=TRUE)) cat("\n\n") # cat("Number of records by day and month (with totals)") cat("\n") myTable <- addmargins(table(data.import[,ContData.env$myName.Day] ,data.import[,ContData.env$myName.Mo] , useNA = "ifany")) print(knitr::kable(myTable, format="markdown", row.names=TRUE)) cat("\n\n") # # identify days/months where not the expected number of records # (expect first and last day) # print("days where not the expected number of records") # # 3.2. Flags by QC Test cat("\n\n") cat("#### Flags by QC Test") cat("\n\n") cat("##### Flags, Gross\n\n") myTable <- addmargins(table(data.import[, paste(ContData.env$myName.Flag ,"Gross" ,i ,sep=".")] , useNA = "ifany") ,1) print(knitr::kable(t(as.matrix(myTable)), format = "markdown")) cat("\n\n") cat("##### Flags, Spike\n\n") myTable <- addmargins(table(data.import[, paste(ContData.env$myName.Flag ,"Spike" ,i ,sep=".")] , useNA = "ifany") ,1) print(knitr::kable(t(as.matrix(myTable)), format = "markdown")) cat("\n\n") cat("##### Flags, RoC\n\n") myTable <- addmargins(table(data.import[, paste(ContData.env$myName.Flag ,"RoC" ,i ,sep=".")] , useNA = "ifany") ,1) print(knitr::kable(t(as.matrix(myTable)), format = "markdown")) cat("\n\n") cat("##### Flags, Flat\n\n") myTable <- addmargins(table(data.import[, paste(ContData.env$myName.Flag ,"Flat" ,i ,sep=".")] , useNA = "ifany") ,1) print(knitr::kable(t(as.matrix(myTable)), format = "markdown")) cat("\n\n") cat("QC Test Flag fields are saved in the data file so the user can identify data points that have been flagged as suspect or fail.") # #myFlagTests <- c("Gross","Spike","RoC","Flat") #(myTable.Flags.Flat <- ftable(data.import[,paste("Flag",myFlagTests,i,sep=".")])) # cat(paste("\n\n Test results marked as ",ContData.env$myFlagVal.NoData," (No/Missing Data) if unable to calculate the end point needed for the test. For example, the first record does not have a previous record for comparison for the Gross QC Test. QC Test flags are saved in the data file.",sep="")) # # 3.3. Plot data.plot <- data.import # plot_format <- "ggplot" # "base" or "ggplot" # if(plot_format=="base"){##IF~plot_format~START # plot, base #### # cheat on Date/Time axis n.Total <- length(data.plot[,ContData.env$myName.Date]) pct <- c(20,40,60,80,100)*.01 myAT <- c(1,round(n.Total * pct,0)) myLab <- data.plot[,ContData.env$myName.Date][myAT] # myPlot.Y <- na.omit(as.numeric(data.plot[,i])) #20170518, v2.0.1.9008, coming in as text add na.omit(as.numeric(x)) myPlot.Ylab <- myParameters.Lab[i.num] plot(myPlot.Y,type="l",main=mySiteID,xlab=ContData.env$myLab.Date ,ylab=myPlot.Ylab,col="gray", xaxt="n") axis(1,at=myAT,labels=myLab,tick=TRUE) # Add discrete (only if present) myDiscrete <- paste(ContData.env$myPrefix.Discrete,i,sep=".") if (myDiscrete %in% myParameters.ALL == TRUE){##IF.Discrete.START data.plot[,myDiscrete] <- as.numeric(data.plot[,myDiscrete]) # coerce "NA" to NA data.plot.points <- na.omit(data.plot[,c(ContData.env$myName.DateTime,myDiscrete)]) # remove NA myPoints.X <- as.numeric(rownames(data.plot.points)) myPoints.Y <- data.plot.points[,myDiscrete] points(myPoints.Y~myPoints.X,col="black",pch=19) }##IF.Discrete.END cat("\n\n") cat("Discrete measurements, if any, show up as points on the plot.") cat("\n\n") } else if(plot_format=="ggplot"){ # plot, ggplot #### data.plot[, ContData.env$myName.DateTime] <- as.POSIXct(data.plot[, ContData.env$myName.DateTime]) # ggplot, main scale_lab <- c("Continuous", "Discrete") scale_col <- c("dark gray", "black") scale_shape <- c(21, 24) scale_fill <- scale_col p_i <- ggplot2::ggplot(data=data.plot, ggplot2::aes_string(x=ContData.env$myName.DateTime, y=i)) + ggplot2::geom_point(ggplot2::aes(color="continuous" , shape="continuous" , fill="continuous"), na.rm=TRUE) + ggplot2::scale_x_datetime(date_labels = "%Y-%m-%d") + ggplot2::labs(title=mySiteID, x=ContData.env$myLab.Date, y=myParameters.Lab[i.num]) + ggplot2::theme(plot.title=ggplot2::element_text(hjust=0.5) , plot.subtitle=ggplot2::element_text(hjust=0.5)) # ggplot, discrete points # Add discrete (only if present) myDiscrete <- paste(ContData.env$myPrefix.Discrete,i,sep=".") if (myDiscrete %in% myParameters.ALL == TRUE){##IF.Discrete.START p_i <- p_i + ggplot2::geom_point(data=data.plot , ggplot2::aes_q(x=as.name(ContData.env$myName.DateTime) , y=as.name(myDiscrete) , color="discrete" , shape="discrete" , fill="discrete"), na.rm=TRUE) } else { p_i <- p_i + ggplot2::geom_blank(ggplot2::aes(color="discrete" , shape="discrete" , fill="discrete")) }##IF.Discrete.END # ggplot, Legend p_i <- p_i + ggplot2::scale_color_manual(name="Data Type", labels=scale_lab, values=scale_col) + ggplot2::scale_shape_manual(name="Data Type", labels=scale_lab, values=scale_shape) + ggplot2::scale_fill_manual(name="Data Type", labels=scale_lab, values=scale_fill) # ggplot, show print(p_i) cat("\n\n") }##IF~plot_format~END # }##FOR.i.END
PLOTS, MULTIPARAMETER
Multiparameter plots if exist in the data.
# BOTH plots # 3.3. Plot data.plot <- data.import plot_format <- "ggplot" # "base" or "ggplot" # Plot if(plot_format=="base") {##IF~plot_format~START # plot, base #### # cheat on Date/Time axis n.Total <- length(data.plot[,ContData.env$myName.Date]) pct <- c(20,40,60,80,100)*.01 myAT <- c(1,round(n.Total * pct,0)) myLab <- data.plot[,ContData.env$myName.Date][myAT] #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Need to check for parameters before plot #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Temp, Air vs. Water if (ContData.env$myName.AirTemp %in% myParameters & ContData.env$myName.WaterTemp %in% myParameters == TRUE) { cat("## PLOT, Temperature (Air vs. Water) \n\n" ) # par.orig <- par(no.readonly=TRUE) # save original par settings layout(rbind(1,2),heights=c(7,1)) # myPlot.Y <- na.omit(as.numeric(data.plot[,ContData.env$myName.AirTemp])) # 20170518, v2.0.1.9008 myPlot.Y2 <- na.omit(as.numeric(data.plot[ ,ContData.env$myName.WaterTemp])) # 20170518, v2.0.1.9008 myPlot.Ylab <- ContData.env$myLab.Temp.BOTH plot(myPlot.Y ,type="l" ,main=mySiteID ,xlab=ContData.env$myLab.Date ,ylab=myPlot.Ylab ,col="green" , xaxt="n") # Revised myAT for lots of NA (20170518) data.length <- length(myPlot.Y) myAT <- c(1,round(data.length * pct,0)) # axis(1,at=myAT,labels=myLab,tick=TRUE) lines(myPlot.Y2,type="l",col="blue") #legend(x="bottomright",lty=1,col=c("green","blue") # ,legend=c("air","water")) # Add discrete, AirTemp (only if present) myDiscrete <- paste(ContData.env$myPrefix.Discrete ,ContData.env$myName.AirTemp ,sep=".") if (myDiscrete %in% myParameters.ALL == TRUE){##IF.Discrete.START data.plot[,myDiscrete] <- as.numeric(data.plot[,myDiscrete]) # coerce "NA" to NA data.plot.points <- na.omit(data.plot[,c(ContData.env$myName.DateTime,myDiscrete)]) # remove NA myPoints.X <- as.numeric(rownames(data.plot.points)) myPoints.Y <- data.plot.points[,myDiscrete] points(myPoints.Y~myPoints.X,col="green",pch=19) }##IF.Discrete.END # Add discrete, Water.Temp (only if present) myDiscrete <- paste(ContData.env$myPrefix.Discrete ,ContData.env$myName.WaterTemp ,sep=".") if (myDiscrete %in% myParameters.ALL == TRUE){ data.plot[,myDiscrete] <- as.numeric(data.plot[,myDiscrete]) # coerce "NA" to NA data.plot.points <- na.omit(data.plot[,c(ContData.env$myName.DateTime,myDiscrete)]) # remove NA myPoints.X <- as.numeric(rownames(data.plot.points)) myPoints.Y <- data.plot.points[,myDiscrete] points(myPoints.Y~myPoints.X,col="blue",pch=19) }##IF.Discrete.END # # LEGEND par(mar=c(0,0,0,0)) plot.new() legend(x="center" ,lty=1 ,col=c("green","blue") ,legend=c("air","water") ,bty="n") # par(par.orig) # return to original par settings # # Future mod, add points to legend cat("\n\n") cat("Discrete measurements, if any, show up as points on the plot.") cat("\n\n") # }##IF.Temp.END # # Water, Temp vs Sensor Depth if (ContData.env$myName.WaterTemp %in% myParameters & ContData.env$myName.SensorDepth %in% myParameters == TRUE) { cat("## PLOT, Sensor Depth vs. Water Temperature \n\n") # par.orig <- par(no.readonly=TRUE) # save original par settings layout(rbind(1,2),heights=c(7,1)) par(oma=c(0,0,0,2)) # myPlot.Y <- na.omit(as.numeric(data.plot[ ,ContData.env$myName.WaterTemp])) # 20170518, v2.0.1.9008 myPlot.Ylab <- ContData.env$myLab.WaterTemp myPlot.Y2 <- na.omit(as.numeric(data.plot[ ,ContData.env$myName.SensorDepth])) # 20170518, v2.0.1.9008 myPlot.Y2lab <- ContData.env$myLab.SensorDepth # plot(myPlot.Y ,type="l" ,main=mySiteID ,xlab=ContData.env$myLab.Date ,ylab=myPlot.Ylab ,col="blue" , xaxt="n") # Revised myAT for lots of NA (20170518) data.length <- length(myPlot.Y) myAT <- c(1,round(data.length * pct,0)) # axis(1,at=myAT,labels=myLab,tick=TRUE) # Add discrete, Water.Temp (only if present) myDiscrete <- paste(ContData.env$myPrefix.Discrete ,ContData.env$myName.WaterTemp ,sep=".") if (myDiscrete %in% myParameters.ALL == TRUE){ data.plot[,myDiscrete] <- as.numeric(data.plot[,myDiscrete]) # coerce "NA" to NA data.plot.points <- na.omit(data.plot[ ,c(ContData.env$myName.DateTime,myDiscrete)]) # remove NA myPoints.X <- as.numeric(rownames(data.plot.points)) myPoints.Y <- data.plot.points[,myDiscrete] points(myPoints.Y~myPoints.X,col="blue",pch=19) }##IF.Discrete.END # # Add 2nd y axis (2nd color is black) par(new=TRUE) plot(myPlot.Y2,type="l",col="black",axes=FALSE,ann=FALSE) axis(4) mtext(myPlot.Y2lab,side=4,line=2.5) # Add discrete, SensorDepth (only if present) myDiscrete <- paste(ContData.env$myPrefix.Discrete ,ContData.env$myName.SensorDepth,sep=".") if (myDiscrete %in% myParameters.ALL == TRUE){ data.plot[,myDiscrete] <- as.numeric(data.plot[,myDiscrete]) # coerce "NA" to NA data.plot.points <- na.omit(data.plot[ ,c(ContData.env$myName.DateTime,myDiscrete)]) # remove NA myPoints.X <- as.numeric(rownames(data.plot.points)) myPoints.Y <- data.plot.points[,myDiscrete] points(myPoints.Y~myPoints.X,col="black",pch=19) }##IF.Discrete.END # # LEGEND par(mar=c(0,0,0,0)) plot.new() legend(x="center" ,lty=1 ,col=c("blue","black") ,legend=c("temperature" ,"sensor depth") ,bty="n") # # Future mod, add points to legend # par(par.orig) # return to original par settings cat("\n\n") cat("Discrete measurements, if any, show up as points on the plot.") cat("\n\n") }##IF.Temp_Level.END } else if(plot_format=="ggplot"){ # plot, ggplot #### # date to POSIX data.plot[, ContData.env$myName.DateTime] <- as.POSIXct(data.plot[, ContData.env$myName.DateTime]) # # Temp, Air vs. Water if (ContData.env$myName.AirTemp %in% myParameters & ContData.env$myName.WaterTemp %in% myParameters ) { cat("## PLOT, Temperature (Air vs. Water) \n\n" ) # # ggplot, main scale_lab <- c("Air", "Water") scale_col <- c("green", "blue") scale_fill <- scale_col scale_shape <- c(21, 21) p2_t_aw <- ggplot2::ggplot(data=data.plot, ggplot2::aes(color="a" , fill="b" , shape="c")) + ggplot2::geom_path(ggplot2::aes_q(x=as.name(ContData.env$myName.DateTime) ,y=as.name(ContData.env$myName.AirTemp) , color="air") , na.rm=TRUE) + ggplot2::geom_path(ggplot2::aes_q(x=as.name(ContData.env$myName.DateTime) ,y=as.name(ContData.env$myName.WaterTemp) , color="water") , na.rm=TRUE) + ggplot2::scale_x_datetime(date_labels = "%Y-%m-%d") + ggplot2::labs(title=mySiteID , x=ContData.env$myLab.Date , y=ContData.env$myLab.Temp.BOTH) + ggplot2::theme(plot.title=ggplot2::element_text(hjust=0.5) , plot.subtitle=ggplot2::element_text(hjust=0.5)) # ggplot, discrete points ## skip # ggplot, Legend p2_t_aw <- p2_t_aw + ggplot2::scale_color_manual(name="Data Type" , labels=scale_lab , values=scale_col) # ggplot, show print(p2_t_aw) cat("\n\n") # }##IF.Temp.END # # Water, Temp vs Sensor Depth if (ContData.env$myName.WaterTemp %in% myParameters & ContData.env$myName.SensorDepth %in% myParameters) { cat("## PLOT, Sensor Depth vs. Water Temperature \n\n") # # ggplot, main scale_lab <- c("Water Temp", "Sensor Depth") scale_col <- c("blue", "black") scale_fill <- scale_col scale_shape <- c(21, 21) # ggplot intentionaly not designed for dual y plotting min_diff <- min(data.plot[, ContData.env$myName.WaterTemp], na.rm=TRUE) - min(data.plot[, ContData.env$myName.SensorDepth], na.rm=TRUE) rd_y1 <- diff(range(data.plot[, ContData.env$myName.WaterTemp] , na.rm = TRUE)) rd_y2 <- diff(range(data.plot[, ContData.env$myName.SensorDepth] , na.rm = TRUE)) min_y1 <- min(data.plot[, ContData.env$myName.WaterTemp]) max_y1 <- max(data.plot[, ContData.env$myName.WaterTemp]) avg_y1 <- mean(data.plot[, ContData.env$myName.WaterTemp]) data.plot[, "depth4plot"] <- (data.plot[, ContData.env$myName.SensorDepth] * rd_y1/rd_y2) + min_diff p3_td <- ggplot2::ggplot(data=data.plot) + ggplot2::geom_path(ggplot2::aes_q(x=as.name(ContData.env$myName.DateTime) ,y=as.name(ContData.env$myName.WaterTemp) , color="water") , na.rm=TRUE) + ggplot2::geom_path(ggplot2::aes_q(x=as.name(ContData.env$myName.DateTime) ,y=as.name("depth4plot") , color="depth"), na.rm = TRUE) + ggplot2::scale_x_datetime(date_labels = "%Y-%m-%d") + ggplot2::labs(title=mySiteID , x=ContData.env$myLab.Date , y=ContData.env$myLab.WaterTemp) + ggplot2::theme(plot.title=ggplot2::element_text(hjust=0.5) , plot.subtitle=ggplot2::element_text(hjust=0.5)) # ggplot, add 2nd Y p3_td <- p3_td + ggplot2::scale_y_continuous(sec.axis = ggplot2::sec_axis( trans=~./(rd_y1/rd_y2)-(min_diff/(rd_y1/rd_y2)) , name=ContData.env$myLab.SensorDepth)) # ggplot, discrete points ## skip # ggplot, Legend ## need to reverse legend items to be correct. p3_td <- p3_td + ggplot2::scale_color_manual(name="Data Type" , labels=rev(scale_lab) , values=rev(scale_col)) # ggplot, show print(p3_td) cat("\n\n") # }##IF.Temp_SensorDepth.END # Water, Temp vs Sensor Depth if (ContData.env$myName.WaterTemp %in% myParameters & ContData.env$myName.WaterLevel %in% myParameters) { cat("## PLOT, Water Level vs. Water Temperature \n\n") # # ggplot, main scale_lab <- c("Water Temp", "Water Level") scale_col <- c("blue", "black") scale_fill <- scale_col scale_shape <- c(21, 21) # ggplot intentionaly not designed for dual y plotting min_diff <- min(data.plot[, ContData.env$myName.WaterTemp], na.rm=TRUE) - min(data.plot[, ContData.env$myName.WaterLevel], na.rm=TRUE) rd_y1 <- diff(range(data.plot[, ContData.env$myName.WaterTemp] , na.rm = TRUE)) rd_y2 <- diff(range(data.plot[, ContData.env$myName.WaterLevel] , na.rm = TRUE)) min_y1 <- min(data.plot[, ContData.env$myName.WaterTemp]) max_y1 <- max(data.plot[, ContData.env$myName.WaterTemp]) avg_y1 <- mean(data.plot[, ContData.env$myName.WaterTemp]) data.plot[, "depth4plot"] <- (data.plot[, ContData.env$myName.WaterLevel] * rd_y1/rd_y2) + min_diff p4_td <- ggplot2::ggplot(data=data.plot) + ggplot2::geom_path(ggplot2::aes_q(x=as.name(ContData.env$myName.DateTime) ,y=as.name(ContData.env$myName.WaterTemp) , color="water") , na.rm=TRUE) + ggplot2::geom_path(ggplot2::aes_q(x=as.name(ContData.env$myName.DateTime) ,y=as.name("depth4plot") , color="depth"), na.rm = TRUE) + ggplot2::scale_x_datetime(date_labels = "%Y-%m-%d") + ggplot2::labs(title=mySiteID , x=ContData.env$myLab.Date , y=ContData.env$myLab.WaterTemp) + ggplot2::theme(plot.title=ggplot2::element_text(hjust=0.5) , plot.subtitle=ggplot2::element_text(hjust=0.5)) # ggplot, add 2nd Y p4_td <- p4_td + ggplot2::scale_y_continuous(sec.axis = ggplot2::sec_axis( trans=~./(rd_y1/rd_y2)-(min_diff/(rd_y1/rd_y2)) , name=ContData.env$myLab.WaterLevel)) # ggplot, discrete points ## skip # ggplot, Legend ## need to reverse legend items to be correct. p4_td <- p4_td + ggplot2::scale_color_manual(name="Data Type" , labels=rev(scale_lab) , values=rev(scale_col)) # ggplot, show print(p4_td) cat("\n\n") # }##IF.Temp_Level.END }##IF~plot_format~START # '''
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