library(ggplot2)
repro2<-read.csv("Repro2.csv")
# reorganized reproduction table to work with subsets
fidalgo<-subset(repro2, Site=="Fidalgo")
# Subset for just Repro Numbers from Fidalgo
ggplot(data=fidalgo, aes(x=Date, y=Brooders, group=Pop, colour=Pop))+geom_line()+geom_point()+ylim(c(0,15))+scale_colour_manual(values=c("#3366CC","#CC66CC","#FF9900"))+theme(axis.text.x=element_text(angle=90, size=10, vjust=0.5))
# Plots Brooders at Fidalgo for all three populations
manch<-subset(repro2, Site == "Manchester")
# Subset for just Manchester
ggplot(data=manch, aes(x=Date, y=Brooders, group=Pop, colour=Pop))+geom_line()+geom_point()+ylim(c(0,15))+scale_colour_manual(values=c("#3366CC","#CC66CC","#FF9900"))+theme(axis.text.x=element_text(angle=90, size=10, vjust=0.5))
# Plots Brooders at Manchester for all pops
oysbay<-subset(repro2, Site=="Oyster Bay")
# Subset for just Oyster Bay data
ggplot(data=oysbay, aes(x=Date, y=Brooders, group=Pop, colour=Pop))+geom_line()+geom_point()+ylim(c(0,15))+scale_colour_manual(values=c("#3366CC","#CC66CC","#FF9900"))+theme(axis.text.x=element_text(angle=90, size=10, vjust=0.5))
# Plots brooders at Oyster Bay for all Pops
ggplot(repro2, aes(x=Date, y=Brooders, group=Pop, colour=Pop))+geom_line()+geom_point()+facet_wrap(~Site, ncol=1)+scale_colour_manual(values=c("#3366CC","#CC66CC","#FF9900"))+theme(axis.text.x=element_text(angle=90, size=10, vjust=0.5))
# Creates a multi-level plot of all Brooders at All Sites
ggplot(repro2, aes(x=Date, y=Brooders, group=Site, colour=Site))+geom_line()+geom_point()+facet_wrap(~Pop, ncol=1)+theme(axis.text.x=element_text(angle=90, size=10, vjust=0.5))
# Creates a multi-level plot of all sites comparing different populations
repro3<-read.csv('reproperc.csv')
# CSV with percent brooders
ggplot(repro3, aes(x=Date, y=Percent, group=Site, colour=Site))+geom_line()+geom_point()+facet_wrap(~Pop, ncol=1)+theme(axis.text.x=element_text(angle=90, size=10, vjust=0.5))
# Plots percent brooders for each pop comparing sites
ggplot(repro3, aes(x=Date, y=Percent, group=Pop, colour=Pop))+geom_line()+geom_point()+facet_wrap(~Site, ncol=1)+theme(axis.text.x=element_text(angle=90, size=10, vjust=0.5))+scale_colour_manual(values=c("#3366CC","#CC66CC","#FF9900"))
# Plots percent brooders for each site comparing pops
