require(ggplot2)
## Loading required package: ggplot2
require(plyr)
## Loading required package: plyr
require(splitstackshape)
## Loading required package: splitstackshape
## Loading required package: data.table
require(nparcomp)
## Loading required package: nparcomp
## Loading required package: multcomp
## Loading required package: mvtnorm
## Loading required package: survival
## Loading required package: splines
## Loading required package: TH.data
y1size=read.csv('Y1size.csv')
#creates dataframe and reads in the CSV file for sizes
View(y1size)
#check data
y1size$Date<-as.Date(y1size$Date, "%m/%d/%Y")
#make R understand dates
y1meansize<-ddply(y1size,.(Date,Site,Pop),summarise, mean_size=mean(Length.mm,na.rm=T))
#create table of ave size for outplant and year one for each pop at each site
#print it out
print(y1meansize)
## Date Site Pop mean_size
## 1 2013-08-16 Fidalgo 2H 10.67
## 2 2013-08-16 Fidalgo 2N 11.60
## 3 2013-08-16 Fidalgo 2S 11.25
## 4 2013-08-16 Manchester 4H 10.53
## 5 2013-08-16 Manchester 4N 13.40
## 6 2013-08-16 Manchester 4S 11.30
## 7 2013-08-16 Oyster Bay 1H 10.49
## 8 2013-08-16 Oyster Bay 1N 10.90
## 9 2013-08-16 Oyster Bay 1S 12.15
## 10 2014-09-19 Oyster Bay 1H 27.96
## 11 2014-09-19 Oyster Bay 1N 34.65
## 12 2014-09-19 Oyster Bay 1S 27.98
## 13 2014-10-17 Fidalgo 2H 24.40
## 14 2014-10-17 Fidalgo 2N 29.10
## 15 2014-10-17 Fidalgo 2S 28.91
## 16 2014-10-24 Manchester 4H 21.49
## 17 2014-10-24 Manchester 4N 24.37
## 18 2014-10-24 Manchester 4S 23.99
#now we need to create subsets for each site for out plant and end of year 1
outmany1<-ddply(y1size,.(Length.mm,Pop,Tray,Sample,Area),subset,Date=="2013-08-16"&Site=="Manchester")
outfidy1<-ddply(y1size,.(Length.mm,Pop,Tray,Sample,Area),subset,Date=="2013-08-16"&Site=="Fidalgo")
outoysy1<-ddply(y1size,.(Length.mm,Pop,Tray,Sample,Area),subset,Date=="2013-08-16"&Site=="Oyster Bay")
endmany1<-ddply(y1size,.(Length.mm,Pop,Tray,Sample,Area),subset,Date=="2014-10-24"&Site=="Manchester")
endfidy1<-ddply(y1size,.(Length.mm,Pop,Tray,Sample,Area),subset,Date=="2014-10-17"&Site=="Fidalgo")
endoysy1<-ddply(y1size,.(Length.mm,Pop,Tray,Sample,Area),subset,Date=="2014-09-19"&Site=="Oyster Bay")
ggplot()+
geom_boxplot(data=outmany1,aes(x=Pop,y=Length.mm,fill=Pop))+
scale_colour_manual(values=c("blue","purple","orange"))+
scale_fill_manual(values=c("blue","purple","orange"))
ggplot()+
geom_boxplot(data=endmany1,aes(x=Pop,y=Length.mm,fill=Pop))+
scale_colour_manual(values=c("blue","purple","orange"),guide=F)+
scale_fill_manual(values=c("blue","purple","orange"), guide=F)+
ylim(c(0,50))+
labs(x="Population",y="Average Length (mm)")+
scale_x_discrete(labels=c("Dabob","Fidalgo","Oyster Bay"))+
annotate("text", x=c("4N","4H","4S"),y=50, label=c("A","B","A"),size=10)+
theme_bw()+
theme(axis.text.x=element_text(size=20),
axis.title.x=element_text(size=25),
axis.title.y=element_text(size=25, vjust=2),
axis.text.y=element_text(size=20))
ggplot()+
geom_boxplot(data=outfidy1,aes(x=Pop,y=Length.mm,fill=Pop))+
scale_colour_manual(values=c("blue","purple","orange"))+
scale_fill_manual(values=c("blue","purple","orange"))
ggplot()+
geom_boxplot(data=endfidy1,aes(x=Pop,y=Length.mm,fill=Pop))+
scale_colour_manual(values=c("blue","purple","orange"),guide=F)+
scale_fill_manual(values=c("blue","purple","orange"),guide=F)+
ylim(c(0,50))+
labs(x="Population",y="Average Length (mm)")+
scale_x_discrete(labels=c("Dabob","Fidalgo","Oyster Bay"))+
annotate("text", x=c("2N","2H","2S"),y=50, label=c("A","B","A"),size=10)+
theme_bw()+
theme(axis.text.x=element_text(size=20),
axis.title.x=element_text(size=25),
axis.title.y=element_text(size=25, vjust=2),
axis.text.y=element_text(size=20))
## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
ggplot()+
geom_boxplot(data=outoysy1,aes(x=Pop,y=Length.mm,fill=Pop))+
scale_colour_manual(values=c("blue","purple","orange"))+
scale_fill_manual(values=c("blue","purple","orange"))
ggplot()+
geom_boxplot(data=endoysy1,aes(x=Pop,y=Length.mm,fill=Pop))+
scale_colour_manual(values=c("blue","purple","orange"),guide=F)+
scale_fill_manual(values=c("blue","purple","orange"),guide=F)+
ylim(c(0,50))+
labs(x="Population",y="Average Length (mm)")+
scale_x_discrete(labels=c("Dabob","Fidalgo","Oyster Bay"))+
annotate("text", x=c("1N","1H","1S"),y=50, label=c("B","A","A"),size=10)+
theme_bw()+
theme(axis.text.x=element_text(size=20),
axis.title.x=element_text(size=25),
axis.title.y=element_text(size=25, vjust=2),
axis.text.y=element_text(size=20))
## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
normality<-ddply(y1size,.(Date,Site,Pop),summarize,n=length(Length.mm),sw=shapiro.test(as.numeric(Length.mm))[2])
y1size$Pop2<-y1size$Pop
y1size$Pop2<-revalue(y1size$Pop2,c("1H"="H","2H"="H","4H"="H","1N"="N","2N"="N","4N"="N","1S"="S","2S"="S","4S"="S"))
#Here we subset the data set to only include data from the end of year 1
endy1<-ddply(y1size,.(Length.mm,Site,Pop,Tray,Sample,Area,Pop2),subset,Date>="2014-09-19")
normality<-ddply(endy1,.(Date,Site,Pop),summarize,n=length(Length.mm),sw=shapiro.test(as.numeric(Length.mm))[2])
endy1$log<-log2(endy1$Length.mm)
endy1$asin<-asin(sign(endy1$Length.mm)*sqrt(abs(endy1$Length.mm)))
## Warning: NaNs produced
normality<-ddply(endy1,.(Date,Site,Pop),summarize,n=length(log),sw=shapiro.test(as.numeric(log))[2])
sizekw<-kruskal.test(endy1$Length.mm~endy1$Site,endy1)
print(sizekw)
##
## Kruskal-Wallis rank sum test
##
## data: endy1$Length.mm by endy1$Site
## Kruskal-Wallis chi-squared = 426.2, df = 2, p-value < 2.2e-16
sizekwpop<-kruskal.test(endy1$Length.mm~endy1$Pop2,endy1)
print(sizekwpop)
##
## Kruskal-Wallis rank sum test
##
## data: endy1$Length.mm by endy1$Pop2
## Kruskal-Wallis chi-squared = 230, df = 2, p-value < 2.2e-16
require(PMCMR)
## Loading required package: PMCMR
sizenemenyi1<-posthoc.kruskal.nemenyi.test(x=endy1$Length.mm,g=endy1$Site, method="Tukey")
## Warning: Ties are present, p-values are not corrected.
sizenemenyi1
##
## Pairwise comparisons using Tukey and Kramer (Nemenyi) test
## with Tukey-Dist approximation for independent samples
##
## data: endy1$Length.mm and endy1$Site
##
## Fidalgo Manchester
## Manchester < 2e-16 -
## Oyster Bay 1.1e-12 < 2e-16
##
## P value adjustment method: none
sizenemenyi2<-posthoc.kruskal.nemenyi.test(x=endy1$Length.mm,g=endy1$Pop2, method="Tukey")
## Warning: Ties are present, p-values are not corrected.
sizenemenyi2
##
## Pairwise comparisons using Tukey and Kramer (Nemenyi) test
## with Tukey-Dist approximation for independent samples
##
## data: endy1$Length.mm and endy1$Pop2
##
## H N
## N < 2e-16 -
## S 3.4e-14 4.9e-08
##
## P value adjustment method: none
sizenemenyi3<-posthoc.kruskal.nemenyi.test(x=endy1$Length.mm,g=endy1$Site:endy1$Pop2, method="Tukey")
## Warning: Ties are present, p-values are not corrected.
sizenemenyi3
##
## Pairwise comparisons using Tukey and Kramer (Nemenyi) test
## with Tukey-Dist approximation for independent samples
##
## data: endy1$Length.mm and endy1$Site:endy1$Pop2
##
## Fidalgo:H Fidalgo:N Fidalgo:S Manchester:H Manchester:N
## Fidalgo:N < 2e-16 - - - -
## Fidalgo:S 7.8e-14 0.9995 - - -
## Manchester:H 2.7e-07 < 2e-16 < 2e-16 - -
## Manchester:N 1.0000 9.2e-14 1.1e-13 7.3e-06 -
## Manchester:S 0.9786 < 2e-16 8.5e-14 0.0004 0.9880
## Oyster Bay:H 3.1e-10 0.2781 0.6352 1.2e-14 2.4e-08
## Oyster Bay:N < 2e-16 1.5e-11 7.2e-13 < 2e-16 < 2e-16
## Oyster Bay:S 1.8e-09 0.6813 0.9255 9.1e-14 5.7e-08
## Manchester:S Oyster Bay:H Oyster Bay:N
## Fidalgo:N - - -
## Fidalgo:S - - -
## Manchester:H - - -
## Manchester:N - - -
## Manchester:S - - -
## Oyster Bay:H 9.5e-12 - -
## Oyster Bay:N < 2e-16 1.0e-13 -
## Oyster Bay:S 6.0e-11 1.0000 6.9e-12
##
## P value adjustment method: none
