Lovell — Jun 5, 2014, 3:53 PM
library(ggplot2)
library(GGally)
rm(list=ls())
############
#Part I: Processing and organization of data
#1.1: Read in the data
setwd("~/Desktop/Adaptomics_Project/Drydown_Analysis")
rawD13<-read.csv("mr2014_d13Craw_edit1.csv", header=T, na.strings="#VALUE!")
rawD13<-rawD13[1:526,]
dd_info<-read.csv("Drydown1-2_ConsolidatedWeights_December13.csv", header=T, na.strings="#VALUE!")
#1.2: Combine relevant information
#spatifolia is in the 1st 192 rows
spatD13<-rawD13[1:192,]
head(spatD13)
Tray_Name Well_Id Sample_ID d13C C_Amount_.ug. sample_wt X
1 spatifolia_1 A1 147 -32.46 585.7 1760
2 spatifolia_1 A2 18 -32.99 762.4 2083
3 spatifolia_1 A3 148 -33.04 736.2 1932
4 spatifolia_1 A4 81 -32.29 501.6 1719
5 spatifolia_1 A5 115 -32.65 690.5 2020
6 spatifolia_1 A6 51 -33.03 703.5 1945
info<-dd_info[1:257,c(8,2:4,1)]
names(info)[5]<-"Sample_ID"
head(info)
Genotype Position Treatment Flat Sample_ID
1 Tiesiding#2 1 Dry 3 105
2 Cripple#6 2 Dry 3 43
3 Cripple#6 3 Dry 3 44
4 Royal#1 4 Dry 3 12
5 Cripple#7 5 Dry 3 26
6 Tiesiding#7 6 Dry 3 124
alldata<-merge(info,spatD13,by="Sample_ID")
alldata<-alldata[,-11]
head(alldata)
Sample_ID Genotype Position Treatment Flat Tray_Name Well_Id
1 1 Royal#1 18 Wet 1 spatifolia_1 B3
2 10 Royal#1 20 Dry 3 spatifolia_1 G11
3 100 Tiesiding#2 28 Wet 1 spatifolia_1 B12
4 101 Tiesiding#2 19 Wet 2 spatifolia_1 D5
5 102 Tiesiding#2 25 Wet 2 spatifolia_1 D10
6 104 Tiesiding#2 29 Wet 2 spatifolia_1 E1
d13C C_Amount_.ug. sample_wt
1 -32.45 847.8 2184
2 -30.73 828.1 2067
3 -33.19 736.2 1809
4 -32.90 723.1 1815
5 -33.02 729.7 1830
6 -31.98 631.2 1581
#1.3 Process info, bring apomixis location etc. info into the fold
genotype_info<-alldata$Genotype
ids<-as.data.frame(do.call(rbind, strsplit(as.character(genotype_info),"#")))
colnames(ids)<-c("population","genotype")
test<-cbind(ids,alldata[,-2])
apodata<-data.frame(c("Alvarado", "Alvarado","Chicago","Chicago","Chiquito" ,"Chiquito" ,"Cripple","Cripple" ,"Rosita","Rosita","Royal","Royal","Tiesiding","Tiesiding"),
c(2,3,2,4,4,7,6,7,3,4,1,2,2,7),c("apo","sex",'sex',"apo","apo","sex","sex","apo","sex","apo","apo","sex","sex","apo"))
names(apodata)<-c("population","genotype","mating_system")
all<-merge(test,apodata,by=c("population","genotype"),all.x=T,all.y=T)
ggpairs(all[,c(9,10,5)], color="Treatment",alpha=0.4)
aov1<-aov(d13C~population*Treatment*mating_system, data=all)
summary(aov1)
Df Sum Sq Mean Sq F value Pr(>F)
population 6 43.7 7.3 22.17 < 2e-16 ***
Treatment 1 50.1 50.1 152.53 < 2e-16 ***
mating_system 1 0.1 0.1 0.28 0.60
population:Treatment 6 14.7 2.4 7.43 5.2e-07 ***
population:mating_system 6 21.5 3.6 10.92 3.9e-10 ***
Treatment:mating_system 1 0.0 0.0 0.09 0.77
population:Treatment:mating_system 6 1.6 0.3 0.79 0.58
Residuals 156 51.3 0.3
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ggplot(all,aes(x=Treatment,y=d13C,col=mating_system, group=mating_system))+
geom_point()+
facet_wrap(~population)
