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testCalendar.R
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testCalendar.R
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#Loading Libraries ----
library(tidyverse)
library(lubridate)
library(geosphere)
library(dplyr)
library(ggplot2)
library(forcats)
library(scales)
require(tidyverse)
require(lubridate)
require(rgdal)
require(raster)
require(sp)
require(rgeos)
source("loadData.R")
#rm(list=ls())
#Order of Ops
#Load Data /
#Filter Calendar /
#Extract and Filter Bounds /
#Extract and Filter Errernous Dates
#Update Fixes
#Update Summarization
#Combine Data-Frames
#Plot Previous
#Update Plots
gpsRawPre <- loadData("gps", "gps/pre")
gpsCalendarPre <- filterCalendar(gpsRawPre)
gpsOutBoundsPre <- extractOutBounds(gpsCalendarPre)
gpsZerosPre <- extractZeroGPS(gpsOutBoundsPre)
gpsOutBoundsPre <- filterZeroGPS(gpsOutBoundsPre)
gpsInBoundsPre <- filterOutBounds(gpsCalendarPre)
gpsFilteredPre <- speedAndTimeDifference(gpsInBoundsPre)
gpsBadDatesPre <- extractErrenousDates(gpsFilteredPre)
gpsFilteredPre <- filterErrenousDates(gpsFilteredPre)
gpsResultsPre <- gpsFixCheck(gpsFilteredPre)
#gpsSummaryPre <- gpsSummaryRaw(gpsResultsPre, gpsBadDatesPre, gpsZerosPre, gpsOutBoundsPre)
# -------------------------------------------------- #
gpsRawPost <- loadData("gps", "gps/post")
gpsCalendarPost <- filterCalendar(gpsRawPost)
gpsOutBoundsPost <- extractOutBounds(gpsCalendarPost)
gpsZerosPost <- extractZeroGPS(gpsOutBoundsPost)
gpsOutBoundsPost <- filterZeroGPS(gpsOutBoundsPost)
gpsInBoundsPost <- filterOutBounds(gpsCalendarPost)
gpsFilteredPost <- speedAndTimeDifference(gpsInBoundsPost)
gpsBadDatesPost <- extractErrenousDates(gpsFilteredPost)
gpsFilteredPost <- filterErrenousDates(gpsFilteredPost)
gpsResultsPost <- gpsFixCheck(gpsFilteredPost)
#gpsSummaryPost <- gpsSummaryRaw(gpsResultsPost, gpsBadDatesPost, gpsZerosPost, gpsOutBoundsPost)
# -------------------------------------------------- #
gpsSummaryPre <- gpsSummaryRaw(gpsResultsPre, gpsBadDatesPre, gpsZerosPre, gpsOutBoundsPre)
gpsSummaryPost <- gpsSummaryRaw(gpsResultsPost, gpsBadDatesPost, gpsZerosPost, gpsOutBoundsPost)
gpsSummaryPost <- gpsSummaryPost[-c(3), ]
gpsSummaryComb <- gpsSummaryCombine(gpsSummaryPre, gpsSummaryPost)
write.csv(gpsSummaryPre, "F:/Development/Projects/Research/TeamBeef/workingProject/output/preGPS.csv", row.names = FALSE)
write.csv(gpsSummaryPost, "F:/Development/Projects/Research/TeamBeef/workingProject/output/postGPS.csv", row.names = FALSE)
write.csv(gpsSummaryComb, "F:/Development/Projects/Research/TeamBeef/workingProject/output/combGPS.csv", row.names = FALSE)
#Testing Code ----
#Calendar Loading Functions ----
loadCalendar <- function()
{
directoryPath <- paste("raw/", "events", "/", sep="")
fileList <- list.files(directoryPath)
filePath <- paste(directoryPath, fileList[1], sep="")
events <- read.csv(filePath, stringsAsFactors = FALSE)
events$Collar_On <- as.POSIXct(events$Collar_On)
events$Collar_Off <- as.POSIXct(events$Collar_Off)
return(events)
}
#Calendar Assignment Functions ----
filterCalendar <- function(dataIn)
{
eventCalendar <- loadCalendar()
eventList <- list()
for(i in 1:nrow(eventCalendar))
{
onTime <- eventCalendar$Collar_On[i]
offTime <- eventCalendar$Collar_Off[i]
fileID <- as.factor(eventCalendar$Collar_ID[i])
BullID <- as.factor(eventCalendar$Bull_ID[i])
prefix <- "ID"
varname <- paste(prefix, fileID, sep="_")
copyFrame <- dataIn[[varname]]
copyFrame <- copyFrame[onTime < copyFrame$DateTime & offTime > copyFrame$DateTime, ]
if(is.null(copyFrame))
{}
else if(nrow(copyFrame) > 0)
{
copyFrame <- transform(copyFrame, BullID = BullID)
logNames <- names(eventList) == varname
listSum <- sum(logNames)
if(listSum > 0)
{
eventList[[varname]] <- rbind(eventList[[varname]], copyFrame)
}
else
{
eventList[[varname]] <- copyFrame
}
}
}
return(eventList)
}
#DateTime Difference Function ----
speedAndTimeDifference <- function(dataIn)
{
dataList <- list()
for(i in 1:length(dataIn))
{
copyFrame <- dataIn[[i]]
if(is.null(copyFrame))
{}
else if(nrow(copyFrame) > 0)
{
fileID <- levels(copyFrame$CollarID[1])
prefix <- "ID"
varname <- paste(prefix, fileID, sep="_")
timeFirst <- copyFrame[1:(nrow(copyFrame) - 1), ]
timeSecond <- copyFrame[2:nrow(copyFrame), ]
p1 <- copyFrame[1:(nrow(copyFrame) - 1), ]
p2 <- copyFrame[2:nrow(copyFrame), ]
#Time Difference
timeFirst <- as.numeric(timeFirst$DateTime)
timeSecond <- as.numeric(timeSecond$DateTime)
time <- timeSecond - timeFirst
copyFrame <- copyFrame[2:nrow(copyFrame), ]
copyFrame <- cbind(copyFrame, TimeDifference = time)
#Speed
# p1GPS <- p1 %>% select(Latitude, Longitude)
# p2GPS <- p2 %>% select(Latitude, Longitude)
p1GPS <- p1[c('Longitude', "Latitude")]
p2GPS <- p2[c('Longitude', "Latitude")]
distance <- distGeo(p1GPS, p2GPS)
speed <- abs(distance / time)
copyFrame <- cbind(copyFrame, Speed = speed)
copyFrame$DateTime <- date(copyFrame$DateTime)
#Loading In
logNames <- names(dataList) == varname
listSum <- sum(logNames)
if(listSum > 0)
{
dataList[[varname]] <- rbind(dataList[[varname]], copyFrame)
}
else
{
dataList[[varname]] <- copyFrame
}
}
else if(nrow(copyFrame) == 0)
{
varname <- names(dataIn[i])
copyFrame <- cbind(copyFrame, TimeDifference = numeric(0))
copyFrame <- cbind(copyFrame, Speed = numeric(0))
logNames <- names(dataList) == varname
listSum <- sum(logNames)
if(listSum > 0)
{
dataList[[varname]] <- rbind(dataList[[varname]], copyFrame)
}
else
{
dataList[[varname]] <- copyFrame
}
}
}
return(dataList)
}
timeDifference <- function(dataIn)
{
dataList <- list()
for(i in 1:length(dataIn))
{
copyFrame <- dataIn[[i]]
fileID <- levels(copyFrame$CollarID[1])
prefix <- "ID"
varname <- paste(prefix, fileID, sep="_")
timeFirst <- copyFrame[1:(nrow(copyFrame) - 1), ]
timeSecond <- copyFrame[2:nrow(copyFrame), ]
#Time Difference
timeFirst <- as.numeric(timeFirst$DateTime)
timeSecond <- as.numeric(timeSecond$DateTime)
time <- timeSecond - timeFirst
copyFrame <- copyFrame[2:nrow(copyFrame), ]
copyFrame <- cbind(copyFrame, TimeDifference = time)
copyFrame$DateTime <- date(copyFrame$DateTime)
#Loading In
logNames <- names(dataList) == varname
listSum <- sum(logNames)
if(listSum > 0)
{
dataList[[varname]] <- rbind(dataList[[varname]], copyFrame)
}
else
{
dataList[[varname]] <- copyFrame
}
}
return(dataList)
}
#GPS Zero Filter Functions ----
extractZeroGPS <- function(dataIn)
{
dataList <- list()
for(i in 1:length(dataIn))
{
copyFrame <- dataIn[[i]]
fileID <- levels(copyFrame$CollarID[1])
prefix <- "ID"
varname <- paste(prefix, fileID, sep="_")
copyFrame <- copyFrame[copyFrame$Latitude == 0 | copyFrame$Longitude == 0 | copyFrame$Altitude == 0,]
logNames <- names(dataList) == varname
listSum <- sum(logNames)
if(listSum > 0)
{
dataList[[varname]] <- rbind(dataList[[varname]], copyFrame)
}
else
{
dataList[[varname]] <- copyFrame
}
}
return(dataList)
}
filterZeroGPS <- function(dataIn)
{
dataList <- list()
for(i in 1:length(dataIn))
{
copyFrame <- dataIn[[i]]
fileID <- levels(copyFrame$CollarID[1])
prefix <- "ID"
varname <- paste(prefix, fileID, sep="_")
copyFrame <- copyFrame[copyFrame$Latitude != 0 | copyFrame$Longitude != 0 | copyFrame$Altitude != 0,]
logNames <- names(dataList) == varname
listSum <- sum(logNames)
if(listSum > 0)
{
dataList[[varname]] <- rbind(dataList[[varname]], copyFrame)
}
else
{
dataList[[varname]] <- copyFrame
}
}
return(dataList)
}
#Errenous Dates Selection Function ----
#this kind of works but there is a lot that it may be missing.
#should look into a little more
extractErrenousDates <- function(dataIn)
{
dataList <- list()
for(i in 1:length(dataIn))
{
copyFrame <- dataIn[[i]]
fileID <- levels(copyFrame$CollarID[1])
prefix <- "ID"
varname <- paste(prefix, fileID, sep="_")
if(nrow(copyFrame) > 0)
copyFrame <- copyFrame[copyFrame$TimeDifference < 0 | copyFrame$TimeDifference >= 3600,]
logNames <- names(dataList) == varname
listSum <- sum(logNames)
if(listSum > 0)
{
dataList[[varname]] <- rbind(dataList[[varname]], copyFrame)
}
else
{
dataList[[varname]] <- copyFrame
}
}
return(dataList)
}
filterErrenousDates <- function(dataIn)
{
dataList <- list()
for(i in 1:length(dataIn))
{
copyFrame <- dataIn[[i]]
fileID <- levels(copyFrame$CollarID[1])
prefix <- "ID"
varname <- paste(prefix, fileID, sep="_")
if(nrow(copyFrame) > 0)
copyFrame <- copyFrame[copyFrame$TimeDifference > 0 & copyFrame$TimeDifference < 3600,]
logNames <- names(dataList) == varname
listSum <- sum(logNames)
if(listSum > 0)
{
dataList[[varname]] <- rbind(dataList[[varname]], copyFrame)
}
else
{
dataList[[varname]] <- copyFrame
}
}
return(dataList)
}
#Early, Late and Missing Fixes ----
gpsFixCheck <- function(dataIn)
{
eventCalendar <- loadCalendar()
dataList <- list()
for(j in 1:nrow(eventCalendar))
{
outputFrame <- data.frame(Dates = character(0), Expected_Fixes = numeric(0), On_Time_Fix = numeric(0), Early_Fix = numeric(0), Late_Fix = numeric(0), Missing_Fix = numeric(0), CollarID = numeric(0), BullID = numeric(0))
start <- eventCalendar$Collar_On[j]
end <- eventCalendar$Collar_Off[j]
expectedFixes <- data.frame(dates = seq(start, end, by = 300))
dateSequence <- data.frame(dates = seq(as.Date(start), as.Date(end), by="days"))
CollarID <- as.factor(eventCalendar$Collar_ID[j])
BullID <- as.factor(eventCalendar$Bull_ID[j])
prefix <- "ID"
varname <- paste(prefix, CollarID, sep="_")
copyFrame <- dataIn[[varname]]
for(i in 1:nrow(dateSequence))
{
dateFrame <- copyFrame[copyFrame$DateTime == dateSequence$dates[i], ]
expFix <- length(expectedFixes[as.Date(expectedFixes$dates) == dateSequence$dates[i], ] )
date <- dateSequence$dates[i]
if(is.null(copyFrame))
{
fix <- 0
early <- 0
late <- 0
missing <- expFix
}
else if(nrow(copyFrame) == 0)
{
fix <- 0
early <- 0
late <- 0
missing <- expFix
}
else if(nrow(copyFrame) > 0)
{
fix <- nrow(dateFrame[dateFrame$TimeDifference >= 270 & dateFrame$TimeDifference <= 330, ])
early <- nrow(dateFrame[dateFrame$TimeDifference < 270, ])
late <- nrow(dateFrame[dateFrame$TimeDifference > 330, ])
missing <- expFix - (fix + early + late)
}
holdFrame <- data.frame(date, expFix, fix, early, late, missing, CollarID, BullID)
outputFrame <- rbind(outputFrame, holdFrame)
}
#change date when dealing with pre-breeding season 2020-06-18, 07-13 and 09-20
outputFrame <- outputFrame[as.Date(outputFrame$date) >= "2020-07-13" & as.Date(outputFrame$date) <= "2020-09-20", ]
outputFrame <- arrange(outputFrame, date)
logNames <- names(dataList) == varname
listSum <- sum(logNames)
if(listSum > 0)
{
dataList[[varname]] <- rbind(dataList[[varname]], outputFrame)
}
else
{
dataList[[varname]] <- outputFrame
}
}
return(dataList)
}
#GPS Histogram Plotting ----
gpsHistograms <- function(dataIn)
{
for(j in 1:length(dataIn))
{
#set to i in the loop
copyFrame <- dataIn[[j]]
#Binwidth by Freedman-Diaconis Rule
#change type depending on graph
for(i in 1:length(levels(copyFrame$BullID)))
{
CollarID <- levels(copyFrame$CollarID)
CollarID <- CollarID[1]
BullID <- levels(copyFrame$BullID)
BullID <- BullID[i]
copyFrame <- copyFrame[copyFrame$BullID == BullID, ]
titleFix <- paste("Collar", CollarID, "Bull", BullID, "Histogram of Fix Rate", sep=" ")
titleNoFix <- paste("Collar", CollarID, "Bull", BullID, "Histogram of No Fix Rate", sep=" ")
titleMissing <- paste("Collar", CollarID, "Bull", BullID, "Histogram of Missing Fix Rate", sep=" ")
#The Freedman-Diaconis RUle for Bin Width Calculation
# x <- copyFrame$missing
# bw <- 2 * IQR(x) / length(x)^(1/3)
print(qplot(copyFrame$fix,
geom="histogram",
binwidth=20,
main=titleFix,
xlab="Daily Number of On Time Fixes",
fill=I("blue"),
col=I("black")))
print(qplot(copyFrame$noFix,
geom="histogram",
binwidth=10,
main=titleNoFix,
xlab="Daily Number of No Fixes",
fill=I("cyan"),
col=I("black")))
print(qplot(copyFrame$missing,
geom="histogram",
binwidth=20,
main=titleMissing,
xlab="Daily Number of Missing Fixes",
fill=I("green"),
col=I("black")))
}
}
}
#GPS Pie Chart ----
gpsPie <- function(dataIn)
{
for(j in 1:length(dataIn))
{
copyFrame <- dataIn[[j]]
#add condition to break if a level is NA
for(i in 1:length(levels(copyFrame$BullID)))
{
CollarID <- levels(copyFrame$CollarID)
CollarID <- CollarID[1]
BullID <- levels(copyFrame$BullID)
BullID <- BullID[i]
copyFrame <- copyFrame[copyFrame$BullID == BullID, ]
fix <- mean(copyFrame$fix) / mean(copyFrame$expFix) * 100
nofix <- mean(copyFrame$noFix) / mean(copyFrame$expFix) * 100
earlyfix <- mean(copyFrame$early) / mean(copyFrame$expFix) * 100
latefix <- mean(copyFrame$late) / mean(copyFrame$expFix) * 100
missingfix <- mean(copyFrame$missing) / mean(copyFrame$expFix) * 100
outfix <- mean(copyFrame$outBounds) / mean(copyFrame$expFix) * 100
data <- data.frame(Type = c("Fix", "noFix", "earlyFix", "lateFix", "missingFix", "outOfBounds"),
values = c(fix, nofix, earlyfix, latefix, missingfix, outfix))
data <- cbind(data, labels = signif(data$values, digits = 4))
data$label <- paste0(data$label, "%")
title <- paste("Collar", CollarID, "Bull", BullID, "Pie Chart Average % Summary Post-Breeding", sep=" ")
p <- ggplot(data,aes(x=1,y=values,fill=Type)) + geom_bar(stat="identity", color = "black")
p <- p + coord_polar(theta='y')+ theme(axis.ticks=element_blank(),
axis.text.y=element_blank(),
axis.text.x=element_text(colour='black'),
axis.title=element_blank())
# p <- p + scale_y_continuous(breaks=cumsum(data$values) - data$values / 2, labels= data$label)
p <- p + ggtitle(title)
print(p)
}
}
}
#Acclerometer Summarization ----
acclAnalysis <- function(dataIn)
{
eventCalendar <- loadCalendar()
dataList <- list()
for(j in 1:nrow(eventCalendar))
{
outputFrame <- data.frame(Dates = character(0), Daily_Frequency = numeric(0), Unexpected_Freqeuncy = numeric(0), Percent_Missing = numeric(0), avgX = numeric(0), avgY = numeric(0), avgZ = numeric(0))
start <- eventCalendar$Collar_On[j]
end <- eventCalendar$Collar_Off[j]
expectedFixes <- data.frame(dates = seq(start, end, by = 2))
dateSequence <- data.frame(dates = seq(as.Date(start), as.Date(end), by="days"))
CollarID <- as.factor(eventCalendar$Collar_ID[j])
BullID <- as.factor(eventCalendar$Bull_ID[j])
prefix <- "ID"
varname <- paste(prefix, CollarID, sep="_")
copyFrame <- dataIn[[varname]]
for(i in 1:nrow(dateSequence))
{
dateFrame <- copyFrame[copyFrame$DateTime == dateSequence$dates[i], ]
expFix <- length(expectedFixes[as.Date(expectedFixes$dates) == dateSequence$dates[i], ] )
date <- dateSequence$dates[i]
if(nrow(dateFrame) == 0 | is.null(dateFrame))
{
avgFreqeuncy <- 0
unexpectedFrequency <- 0
perMissing <- 100
avgX <- 0
avgY <- 0
avgZ <- 0
}
else
{
avgFreqeuncy <- mean(dateFrame$TimeDifference)
unexpectedFrequency <- nrow(dateFrame[dateFrame$TimeDifference != 2, ])
perMissing <- ((expFix - nrow(dateFrame)) / expFix) * 100
avgX <- mean(dateFrame$X)
avgY <- mean(dateFrame$Y)
avgZ <- mean(dateFrame$Z)
}
holdFrame <- data.frame(date, avgFreqeuncy, unexpectedFrequency, perMissing, avgX, avgY, avgZ, CollarID, BullID)
outputFrame <- rbind(outputFrame, holdFrame)
}
logNames <- names(dataList) == varname
listSum <- sum(logNames)
if(listSum > 0)
{
dataList[[varname]] <- rbind(dataList[[varname]], outputFrame)
}
else
{
dataList[[varname]] <- outputFrame
}
}
return(dataList)
}
#Summarization ----
gpsSummary <- function(results, badDates)
{
outputFrame <- data.frame(ID = character(0), expFix = numeric(0), Fix = numeric(0), noFix = numeric(0), early = numeric(0), late = numeric(0), missing = numeric(0), outBounds = numeric(0), errenousDates = numeric(0))
for(j in 1:length(results))
{
copyFrameResults <- results[[j]]
CollarID <- as.factor(copyFrameResults$CollarID[1])
prefix <- "ID"
varname <- paste(prefix, CollarID, sep="_")
copyFrameDates <- badDates[[varname]]
for(i in 1:length(levels(copyFrameResults$BullID)))
{
BullID <- levels(copyFrameResults$BullID)
BullID <- BullID[i]
holdFrameResults <- copyFrameResults[copyFrameResults$BullID == BullID, ]
holdFrameDates <- copyFrameDates[copyFrameDates$BullID == BullID, ]
if(is.null(holdFrameResults) | is.null(holdFrameDates))
{}
else if(nrow(holdFrameResults) == 0)
{
expFix <- 0
fix <- 0
noFix <- 0
early <- 0
late <- 0
missing <- 0
outBounds <- 0
errenousDates <- nrow(holdFrameDates)
}
else if(nrow(holdFrameResults) > 0)
{
expFix <- sum(holdFrameResults$expFix)
fix <- sum(holdFrameResults$fix)
noFix <- sum(holdFrameResults$noFix)
early <- sum(holdFrameResults$early)
late <- sum(holdFrameResults$late)
missing <- sum(holdFrameResults$missing)
outBounds <- sum(holdFrameResults$outBounds)
errenousDates <- nrow(holdFrameDates)
}
ID <- paste("CollarID", CollarID, "BullID", BullID, sep=" ")
holdFrame <- data.frame(ID, expFix, fix, noFix, early, late, missing, outBounds, errenousDates)
outputFrame <- rbind(outputFrame, holdFrame)
}
}
sumFix <- (outputFrame$fix + outputFrame$noFix + outputFrame$early + outputFrame$late + abs(outputFrame$missing) + outputFrame$outBounds + outputFrame$errenousDates)
outputFrame$fix <- outputFrame$fix / sumFix * 100
outputFrame$noFix <- outputFrame$noFix / sumFix * 100
outputFrame$early <- outputFrame$early / sumFix * 100
outputFrame$late <- outputFrame$late / sumFix * 100
outputFrame$missing <- outputFrame$missing / sumFix * 100
outputFrame$outBounds <- outputFrame$outBounds / sumFix * 100
outputFrame$errenousDates <- outputFrame$errenousDates / sumFix * 100
return(outputFrame)
}
#GPS Summary Raw ----
gpsSummaryRaw <- function(results, badDates, zeros, out)
{
outputFrame <- data.frame(ID = character(0), expFix = numeric(0), Fix = numeric(0), early = numeric(0), late = numeric(0), missing = numeric(0), errenousDates = numeric(0), zeros = numeric(0), outBounds = numeric(0))
for(j in 1:length(results))
{
copyFrame <- results[[j]]
for(i in 1:length(levels(copyFrame$BullID)))
{
CollarID <- levels(copyFrame$CollarID)
CollarID <- CollarID[1]
prefix <- "ID"
varname <- paste(prefix, CollarID, sep="_")
BullID <- levels(copyFrame$BullID)
BullID <- BullID[i]
copyFrameDates <- badDates[[varname]]
copyFrameDates <- copyFrameDates[copyFrameDates$BullID == BullID, ]
copyFrameZeros <- zeros[[varname]]
copyFrameZeros <- copyFrameZeros[copyFrameZeros$BullID == BullID, ]
copyFrameOut <- out[[varname]]
copyFrameOut <- copyFrameOut[copyFrameOut$BullID == BullID, ]
copyFrameResults <- copyFrame[copyFrame$BullID == BullID, ]
if(is.null(copyFrameResults) | nrow(copyFrameResults) == 0)
{
expFix <- 0
fix <- 0
early <- 0
late <- 0
missing <- 0
}
else
{
expFix <- sum(copyFrameResults$expFix)
fix <- sum(copyFrameResults$fix)
early <- sum(copyFrameResults$early)
late <- sum(copyFrameResults$late)
# missing <- sum(copyFrameResults$missing)
}
if(is.null(copyFrameDates))
{
errenousDates <- 0
}
else
{
errenousDates <- nrow(copyFrameDates)
}
if(is.null(copyFrameZeros))
{
noFix <- 0
}
else
{
noFix <- nrow(copyFrameZeros)
}
if(is.null(copyFrameOut))
{
outBounds <- 0
}
else
{
outBounds <- nrow(copyFrameOut)
}
missing <- expFix - (fix + early + late + errenousDates + noFix + outBounds)
ID <- paste("CollarID", CollarID, "BullID", BullID, sep=" ")
holdFrame <- data.frame(ID, expFix, fix, early, late, missing, errenousDates, noFix, outBounds)
outputFrame <- rbind(outputFrame, holdFrame)
}
}
#
# sumFix <- (outputFrame$fix + outputFrame$early + outputFrame$late + abs(outputFrame$missing) + outputFrame$errenousDates + outputFrame$noFix + outputFrame$outBounds)
#
# outputFrame$fix <- outputFrame$fix / sumFix * 100
# outputFrame$early <- outputFrame$early / sumFix * 100
# outputFrame$late <- outputFrame$late / sumFix * 100
# outputFrame$missing <- outputFrame$missing / sumFix * 100
# outputFrame$errenousDates <- outputFrame$errenousDates / sumFix * 100
# outputFrame$noFix <- outputFrame$noFix / sumFix * 100
# outputFrame$outBounds <- outputFrame$outBounds / sumFix * 100
return(outputFrame)
}
gpsSummaryCombine <- function(data1, data2)
{
ID <- data1$ID
expFix <- data1$expFix + data2$expFix
fix <- data1$fix + data2$fix
early <- data1$early + data2$early
late <- data1$late + data2$late
missing <- data1$missing + data2$missing
errenousDates <- data1$errenousDates + data2$errenousDates
noFix <- data1$noFix + data2$noFix
outBounds <- data1$outBounds + data2$outBounds
#
outputFrame <- data.frame(ID, expFix, fix, early, late, missing, errenousDates, noFix, outBounds)
#
sumFix <- (outputFrame$fix + outputFrame$early + outputFrame$late + abs(outputFrame$missing) + outputFrame$errenousDates + outputFrame$noFix + outputFrame$outBounds)
#
outputFrame$fix <- outputFrame$fix / sumFix * 100
outputFrame$early <- outputFrame$early / sumFix * 100
outputFrame$late <- outputFrame$late / sumFix * 100
outputFrame$missing <- outputFrame$missing / sumFix * 100
outputFrame$errenousDates <- outputFrame$errenousDates / sumFix * 100
outputFrame$noFix <- outputFrame$noFix / sumFix * 100
outputFrame$outBounds <- outputFrame$outBounds / sumFix * 100
#
#
return(outputFrame)
#
}
test <- gpsSummaryCombine(gpsSummaryPre, gpsSummaryPre)
#GPS Out of Bounds ----
filterOutBounds <- function(dataIn)
{
dataList <- list()
movementCalendar <- loadMovement()
shapes <- loadShapeFiles("raw/bounds")
sharedCRS <- proj4string(shapes[[1]])
gpsCoords <- loadGPSCoordsList(dataIn, sharedCRS)
for(j in 1:nrow(movementCalendar))
{
copyCalendar <- movementCalendar[j, ]
CollarID <- copyCalendar$Collar
BullID <- copyCalendar$Bull
pen <- copyCalendar$arcGISShapeFileName
penList <- strsplit(pen, ", ")
prefix <- "ID"
varname <- paste(prefix, CollarID, sep="_")
copyFrame <- dataIn[[varname]]
copyCoords <- gpsCoords[[varname]]
if(is.null(copyFrame))
{}
else if(nrow(copyFrame) > 0)
{
copyFrame <- copyFrame[copyFrame$BullID == BullID, ]
copyFrame <- copyFrame[copyCalendar$StartDate <= as.Date(copyFrame$DateTime) & copyCalendar$EndDate >= as.Date(copyFrame$DateTime), ]
for(i in 1:length(penList[[1]]))
{
penName <- penList[[1]][i]
copyShape <- shapes[[penName]]
if(i == 1)
{
outputFrame <- over(copyCoords, copyShape)
}
else if(i > 1)
{
if("CN_SE" != penName)
{
holdFrame <- over(copyCoords, copyShape)
outputFrame <- rbind(outputFrame, holdFrame)
}
}
}
outBounds <- outputFrame[!is.na(outputFrame$Shape_Leng) | !is.na(outputFrame$Shape_Area), ]
index <- rownames(outBounds)
outputFrame <- copyFrame[rownames(copyFrame) %in% index,]
logNames <- names(dataList) == varname
listSum <- sum(logNames)
if(listSum > 0)
{
dataList[[varname]] <- rbind(dataList[[varname]], outputFrame)
}
else
{
dataList[[varname]] <- outputFrame
}
}
}
return(dataList)
}
extractOutBounds <- function(dataIn)
{
dataList <- list()
movementCalendar <- loadMovement()
shapes <- loadShapeFiles("raw/bounds")
sharedCRS <- proj4string(shapes[[1]])
gpsCoords <- loadGPSCoordsList(dataIn, sharedCRS)
for(j in 1:nrow(movementCalendar))
{
copyCalendar <- movementCalendar[j, ]
CollarID <- copyCalendar$Collar
BullID <- copyCalendar$Bull
pen <- copyCalendar$arcGISShapeFileName
penList <- strsplit(pen, ", ")
prefix <- "ID"
varname <- paste(prefix, CollarID, sep="_")
copyFrame <- dataIn[[varname]]
copyCoords <- gpsCoords[[varname]]
if(is.null(copyFrame))
{}
else if(nrow(copyFrame) > 0)
{
copyFrame <- copyFrame[copyFrame$BullID == BullID, ]
copyFrame <- copyFrame[copyCalendar$StartDate <= as.Date(copyFrame$DateTime) & copyCalendar$EndDate >= as.Date(copyFrame$DateTime), ]
for(i in 1:length(penList[[1]]))
{
penName <- penList[[1]][i]
copyShape <- shapes[[penName]]
if(i == 1)
{
outputFrame <- over(copyCoords, copyShape)
}
else if(i > 1)
{
if("CN_SE" != penName)
{
holdFrame <- over(copyCoords, copyShape)
outputFrame <- rbind(outputFrame, holdFrame)
}
}
}
outBounds <- outputFrame[is.na(outputFrame$Shape_Leng) | is.na(outputFrame$Shape_Area),]
index <- rownames(outBounds)
outputFrame <- copyFrame[rownames(copyFrame) %in% index,]
logNames <- names(dataList) == varname
listSum <- sum(logNames)
if(listSum > 0)
{
dataList[[varname]] <- rbind(dataList[[varname]], outputFrame)
}
else
{
dataList[[varname]] <- outputFrame
}
}
}