require(plyr)
require(doBy)
require(ggplot2)
reproanalysis<-read.csv('ReproAnalysis.csv', header=T)
print(reproanalysis)
#First we will find the time in days between the threshold temp and the peak brooding
peakbrood<-ddply(reproanalysis,.(Site,Pop),subset,Brooders==max(Brooders, na.rm=T))
print(peakbrood)
#find the dates with maximum number of brooders
oysthresh<-ddply(oysmintemp,.(Date),subset,min_temp>=12.5)
#create a list of temps to find those above threshold temp for minimum daily temps at each site
print(oysthresh)
fidthresh<-ddply(fidmintemp,.(Date),subset,min_temp>=12.5)
View(fidthresh)
manthresh<-ddply(manmintemp,.(Date),subset,min_temp>=12.5)
View(manthresh)
peakbrood$Date<-as.Date(peakbrood$Date)
oysthresh$Date<-as.Date(oysthresh$Date)
fidthresh$Date<-as.Date(fidthresh$Date)
manthresh$Date<-as.Date(manthresh$Date)
#make sure everything works as a Date in R after producing all the threshold temp data
d<-c("2014-06-03","2014-06-08","2014-05-14")
#dates visually confirmed for threshold temps
p<-c("Fidalgo","Manchester","Oyster Bay")
thresholddate<-data.frame(p,d)
thresholddate$d<-as.Date(thresholddate$d)
peakthresh<-merge(peakbrood,thresholddate,by.x="Site",by.y="p",all=F)
print(peakthresh)
#create a data frame that compares dates for threshold and peak spawning dates
peakthresh$time_to_peak<-difftime(peakthresh$Date,peakthresh$d,units="days")
#finds the difference in days between threshold temp and peak spawning
print(peakthresh)
peakthresh2<-peakthresh[c("Date","Site","Pop","Brooders","d","time_to_peak")]
#subsets df to only relevant information
peakthresh2<-rename(peakthresh2,c('Date'='Peak_Date',"Site"="Site","Pop"="Pop","Brooders"="Brooders","d"="Threshold Date","time_to_peak"="Days_to_Peak"))
#renames df columns to more meaningful names
print(peakthresh2)
#next we want to find the degree days from minimum winter temp to spawning peak
#looking at previously generated temp graphs we decided that 8 was minimum winter temp
#we have to visually confirm when the temps continually increase from 8 to spawning
oysdd<-ddply(oysmintemp,.(Date),subset,min_temp>=8)
#subsets minimum temp data to find dates with temps above 8 C.
oysdd<-oysmintemp[c(oysmintemp$Date>="2014-03-06"),]
#after visually confirming the initial temp date we then subset the data from this point on
print(oysdd)
#we have to subset temp data to just the time frame between 8C beginning and peak spawn for each pop at each site
#luckily two pops at each site had the same spawn time data so we use that
oyshndd<-oysdd[c(oysdd$Date<="2014-07-10"),]
oyssdd<-oysdd[c(oysdd$Date<="2014-06-19"),]
print(oyshndd)
print(oyssdd)
#once these subsets are created we need to create a column of the difference between the 8 C minimum
#and the daily minimum temp for each subsets
oyshndd$tempdiff<-oyshndd$min_temp-8
oyssdd$tempdiff<-oyssdd$min_temp-8
#use this temp diff column to create the degree days between 8C minimum and the peak threshold
colSums(oyshndd[,-1])
colSums(oyssdd[,-1])
#we generate this same info for all pops at all sites
fiddd<-ddply(fidmintemp,.(Date),subset,min_temp>=8)
fiddd<-fidmintemp[c(fidmintemp$Date>="2014-03-06"),]
View(fiddd)
fidhndd<-fiddd[c(fiddd$Date<="2014-08-08"),]
fidsdd<-fiddd[c(fiddd$Date<="2014-07-11"),]
View(fidhndd)
View(fidsdd)
fidhndd$tempdiff<-fidhndd$min_temp-8
fidsdd$tempdiff<-fidsdd$min_temp-8
colSums(fidhndd[,-1])
colSums(fidsdd[,-1])
mandd<-ddply(manmintemp,.(Date),subset,min_temp>=8)
mandd<-manmintemp[c(manmintemp$Date>="2014-03-06"),]
View(mandd)
manhsdd<-mandd[c(mandd$Date<="2014-08-06"),]
manndd<-mandd[c(mandd$Date<="2014-06-25"),]
View(manhsdd)
View(manndd)
manhsdd$tempdiff<-manhsdd$min_temp-8
manndd$tempdiff<-manndd$min_temp-8
colSums(manhsdd[,-1])
colSums(manndd[,-1])
#due to how R works its easier to just copy these numbers and create a data frame to merge with the peak threshold info
DegreeDays<-c("512.999","512.999","354.156","453.021","453.021","307.894","377.561","175.322","377.561")
Pop<-c("H","N","S")
Site<-c("Oyster Bay","Oyster Bay","Oyster Bay","Fidalgo","Fidalgo","Fidalgo","Manchester","Manchester","Manchester")
Degree<-data.frame(Site,Pop,DegreeDays)
#onces the Degree data frame is created it can be merged with the peakthresh2 data frame to show degree days and time to peak in the same table
peakthresh3<-merge(peakthresh2,Degree,by.x=c("Site","Pop"),by.y=c("Site","Pop"),all=T)
print(peakthresh3)
#now we need to make a graph because nothing is good unless its a graph
#first we merge the three longest time frame tempdiff to create a data frame that works with ggplot2
odd<-oysdd[c(oysdd$Date<="2014-08-15"),]
odd$tempdiff<-odd$min_temp-8
fdd<-fiddd[c(fiddd$Date<="2014-08-15"),]
fdd$tempdiff<-fdd$min_temp-8
mdd<-mandd[c(mandd$Date<="2014-08-15"),]
mdd$tempdiff<-mdd$min_temp-8
of<-merge(odd,fdd,by="Date",all=T,incomparables="0")
dddf<-merge(of,mdd,by="Date",all=T,incomparables="0")
#we need to clean up the NAs produced so that these can be graphed in ggplot2
dddf[is.na(dddf)]<-0
#Now we rename the columns to meaningful titles
dddf<-rename(dddf,c('Date'='Date','min_temp.x'='oysmin','tempdiff.x'='oystempdiff','min_temp.y'='fidmin','tempdiff.y'='fidtempdiff','min_temp'='manmin','tempdiff'='mantempdiff'))
#check the data frame to make sure that everything aligns to the X axis dates of interest with the right tempdiff numbers
View(dddf)
#using ggplot and cumsum(cumulativesum) we can create cumulative lines of the tempdiffs
#we have to manually add points to the line through annotate to show the threshold temps and peak brooding for each pop
ggplot(dddf)+
  geom_line(aes(x=Date,y=cumsum(dddf$oystempdiff)),color="orange",size=2)+
  geom_line(aes(x=Date,y=cumsum(dddf$fidtempdiff)),color="purple",size=2)+
  geom_line(aes(x=Date,y=cumsum(dddf$mantempdif)),color="red",size=2)+
  annotate("point",x=as.Date("2014-06-03",'%Y-%m-%d'),y=133,size=5,color='red',pch=15)+
  annotate("point",x=as.Date("2014-05-14",'%Y-%m-%d'),y=143,size=5,color='red',pch=15)+
  annotate("point",x=as.Date("2014-06-08",'%Y-%m-%d'),y=113,size=5,color='red',pch=15)+
  annotate("point",x=as.Date("2014-08-08",'%Y-%m-%d'),y=460,size=10,color='blue',pch=13)+
  annotate("point",x=as.Date("2014-08-06",'%Y-%m-%d'),y=383,size=10,color='blue',pch=13)+
  annotate("point",x=as.Date("2014-07-10",'%Y-%m-%d'),y=520,size=10,color='blue',pch=13)+
  annotate("point",x=as.Date("2014-08-08",'%Y-%m-%d'),y=453.021,size=10,color='purple',pch=13)+
  annotate("point",x=as.Date("2014-06-25",'%Y-%m-%d'),y=175.322,size=10,color='purple',pch=13)+
  annotate("point",x=as.Date("2014-07-10",'%Y-%m-%d'),y=512.999,size=10,color='purple',pch=13)+
  annotate("point",x=as.Date("2014-07-11",'%Y-%m-%d'),y=307.894,size=10,color='orange',pch=13)+
  annotate("point",x=as.Date("2014-08-06",'%Y-%m-%d'),y=377.561,size=10,color='orange',pch=13)+
  annotate("point",x=as.Date("2014-06-19",'%Y-%m-%d'),y=354.156,size=10,color='orange',pch=13)+
  theme_bw()+
  labs(title="Degree Days compared between Sites and Populations",x="Date",y="Cumulative Degrees over 8 C Minimum")
#each red square represents the date when the threshold 12.5 C spawning temp was reached
#the orange, purple, and red lines are Oyster Bay, Fidalgo, and Manchester Sites respectfull
#the orange, blue, and purple crosshairs are peak brooding for Oyster Bay, Dabob, and Fidalgo pops at each site respectfully
#