require(ggplot2)
require(plyr)
#Loads required plyr package and a ggplot2 package for plotting
reproaov<-aov(repro4$Brooders~repro4$Site+repro4$Pop+repro4$Site:repro4$Pop,repro4)
summary(reproaov)
SitevPopTukey<-TukeyHSD(reproaov)
print(SitevPopTukey)
#Standard ANOVA and TukeyHSD on non transformed data. Finds huge difference between pops and sites but heavily skewed and non normaly distributed.
repro4$arcsinbrooders<-asin(sign(repro4$Brooders)*sqrt(abs(repro4$Brooders)))
repro4$arcsinbrooders<-replace(repro4$arcsinbrooders,is.na(repro4$arcsinbrooders),0)
#For fun, arc sine transformed brooder data. This is essentially meaningless in the long run. 
reproaov2<-aov(repro4$arcsinbrooders~repro4$Site+repro4$Pop+repro4$Site:repro4$Pop,repro4)
summary(reproaov2)
arcsinbroodertukey<-TukeyHSD(reproaov2)
print(arcsinbroodertukey)
#Makes ANOVA based on arcsine transform of brooders. Finds no differences which is also skewed.
repro4$prop<-repro4$Brooders/repro4$Gaping
#creates individual proportion for each sample date
repro4$arcsinprop<-asin(sign(repro4$prop)*sqrt(abs(repro4$prop)))
repro4$arcsinprop<-replace(repro4$arcsinprop,is.na(repro4$arcsinprop),0)
#arcsine transforms individual proportion data to normalize for use in ANOVA and TukeyHSD.
reproaov3<-aov(repro4$arcsinprop~repro4$Site+repro4$Pop+repro4$Site:repro4$Pop,repro4)
summary(reproaov3)
summary.aov(reproaov3)
arcsinproptukey<-TukeyHSD(reproaov3)
print(arcsinproptukey)
#ANOVA and TukeyHSD on Arcsine transformed proportions conservatively finds significant differences between pops and sites. More trust worthy due to normality.
Gapingsum<-ddply(repro4,.(Site,Pop),summarise,sum=sum(Gaping,na.rm=T))
#Summarises Gaping data for each pop at each site
Broodsum<-ddply(repro4,.(Site,Pop),summarise,sum=sum(Brooders,na.rm=T))
#Summarises Brooder data for each pop at each site
BroodGapingSum<-merge(Gapingsum,Broodsum,by=c("Site","Pop"))
BroodGapingSum<-rename(BroodGapingSum, c("Site"="Site","Pop"="Pop","sum.x"="Gaping","sum.y"="Brooder"))
BroodGapingSum$prop<-BroodGapingSum$Brooder/BroodGapingSum$Gaping
print(BroodGapingSum)
#Creates clean Data frame for Summarized data as well as creating raw proportion data from summaries
BroodGapingSum$ArcSine<-asin(sign(BroodGapingSum$prop)*sqrt(abs(BroodGapingSum$prop)))
#Arcsine transforms raw proportions but is not useful.
arcaov<-aov(BroodGapingSum$ArcSine~BroodGapingSum$Site+BroodGapingSum$Pop+BroodGapingSum$Site:BroodGapingSum$Pop,BroodGapingSum)
summary(arcaov)
arcTukey<-TukeyHSD(arcaov)
print(arcTukey)
#Arcsine transform on raw proportions fails due to lack of replicates
Proptest<-prop.test(BroodGapingSum$Brooder,BroodGapingSum$Gaping, conf.level=0.95)
print(Proptest)
#Proportion test with confidence levels. Doesn't have meaning.
ggplot(data=BroodGapingSum,aes(x=Site, y=prop, group=Pop, col=Pop))+
  geom_line(size=2)+geom_point(size=6)+
  scale_color_manual(name="Population",
                     labels=c("Dabob","Fidalgo","Oyster Bay"),
                     values=c("blue","purple","orange"))+
  labs(title="Raw Proportion of Brooders\nSite by Population",
       x="Site",y="Raw Proportion of Brooders")+
  theme_bw()+
  theme(axis.text.x=element_text(color=c("purple","red","orange"), size=20),
        axis.text.y=element_text(color="black",size=15),
        axis.title.x=element_text(color="black",size=25),
        axis.title.y=element_text(color="black",size=25),
        plot.title=element_text(color="forestgreen",size=35),
        legend.justification=c(0,1),
        legend.position=c(0,1))
#Graph uses raw proportions of brooders to gaping animals produced at each site.
#ANOVA and Tukey test on proportions uses arcsin transformed data for normality
#ANOVA/Tukey Find that the Oyster Bay site is significantly Different from the other two sites
#ANOVA/Tukey Find that the Oyster Bay Pop is significantly Different from the other two pops.
#Individual interactions find that the Oyster Bay pop at Oyster Bay site is significantly
#different than all almost all other populations at all other sites including itself at Manchester 
#but not significantly different than the other two populations at Oyster Bay site