# 6
1+1
2*7/8-9
pi
log(e)
log(exp(1))
log10(1000)
sin(2*pi)
2^10
sqrt(81)

# 7
1:10
seq(from=1,to=10,by=1)
x<-1:10
y<-11:20
x+y
x*y
x^2
x<-rnorn(10)
sort(x)

# 8
matrix(1:16,4,4)
x<-matrix(1:16,4,4)
y<-0.5
x+y
1:4 %*% x

#page 9
x<-1:10
plot(x,x^2)
hist(rnorm(3000))
image(matrix(1:16,4,4))
#install.packages("fields")
library(fields)
image.plot(matrix(1:16,4,4))

#14
rm(list=ls())

# 21
myData<-read.csv("compensation.csv")
dim(myData)
names(myData)
str(myData)
summary(myData)

#26
myData$Root
myData$Fruit
myData$Grazing

#27
myData[1,1]
myData[5,1]
myData[1,2]

#28
myData$Root
myData$root
myData[,1]
myData[,"Root"]

#---------------Tables---------------------------

#32
?aggregate

#35
aggregate(myData$Fruit,myData$Grazing,mean)

#37
aggregate(myData$Fruit,list(myData$Grazing),mean)

#38
aggregate(myData$Fruit,list(Grazing=myData$Grazing),mean)

#40
aggregate(x=myData$Fruit,by=list(Grazing=myData$Grazing),FUN=mean)


aggregate(x=myData$Fruit,
          by=list(Grazing=myData$Grazing),
          FUN=mean)

#41

aggregate(x=myData$Fruit,
          by=list(Grazing=myData$Grazing),
          FUN=length)

aggregate(x=myData$Fruit,
          by=list(Grazing=myData$Grazing),
          FUN=mean)

aggregate(x=myData$Fruit,
          by=list(Grazing=myData$Grazing),
          FUN=sd)

#42
n.fruit<-aggregate(x=myData$Fruit,
          by=list(Grazing=myData$Grazing),
          FUN=length)

mean.fruit<-aggregate(x=myData$Fruit,
          by=list(Grazing=myData$Grazing),
          FUN=mean)

sd.fruit<-aggregate(x=myData$Fruit,
          by=list(Grazing=myData$Grazing),
          FUN=sd)

summary.table<-cbind(n.fruit[,2],mean.fruit[,2],sd.fruit[,2])
summary.table

#43
dimnames(summary.table)<-list(n.fruit[,1],c("n","mean","SD"))
summary.table

#44

rm(list=ls())
#setwd("")
#myData<-read.csv("growth.csv)
myData<-read.csv("growth.csv")
head(myData)
str(myData)

#45
mean.gain<-aggregate(x=myData$gain,
          by=list(myData$supplement,myData$diet),
          FUN=mean)
mean.gain

#46
mean.gain<-tapply(myData$gain,
           list(myData$supplement,myData$diet),
           mean)
mean.gain
class(mean.gain)


#------------Graph-----------------

#49
myData<-read.csv("compensation.csv")
n.fruit<-tapply(myData$Fruit, list(myData$Grazing), length)
mean.fruit<-tapply(myData$Fruit, list(Grazing=myData$Grazing), mean)
sd.fruit<-tapply(myData$Fruit, list(Grazing=myData$Grazing), sd)

#50
barplot(mean.fruit)

#52
barplot(mean.fruit,
       xlab="Treatment",
       ylab="Fruit production")

#53
barplot(mean.fruit,
       xlab="Treatment",
       ylab="Fruit production",
       ylim=c(0,100))

#54
mids<- barplot(mean.fruit,
       xlab="Treatment",
       ylab="Fruit production",
       ylim=c(0,100))

arrows(mids,
       mean.fruit+sd.fruit,
       mids, 
       mean.fruit-sd.fruit)

#56
mids<- barplot(mean.fruit,
       xlab="Treatment",
       ylab="Fruit production",
       ylim=c(0,100))

arrows(mids,
       mean.fruit+sd.fruit,
       mids, 
       mean.fruit-sd.fruit,
       angle=90)

#57
#help(arrows)
mids<- barplot(mean.fruit,
       xlab="Treatment",
       ylab="Fruit production",
       ylim=c(0,100))

arrows(mids,
       mean.fruit+sd.fruit,
       mids,
       mean.fruit-sd.fruit,
       angle=90,
       code=3)

#57
mids<- barplot(mean.fruit,
       xlab="Treatment",
       ylab="Fruit production",
       ylim=c(0,100))

arrows(mids,
       mean.fruit+sd.fruit,
       mids,
       mean.fruit-sd.fruit,
       angle=90,
       code=3)
text(mids,5,paste("N= ",n.fruit))

#59

myData<-read.csv("growth.csv")
mean.gain<-tapply(myData$gain,
           list(myData$supplement,myData$diet),
           mean)
mean.gain
class(mean.gain)

sd.gain<-tapply(myData$gain,
           list(myData$supplement,myData$diet),
           sd)
n.gain<-tapply(myData$gain,
           list(myData$supplement,myData$diet),
           length)



#60
barplot(mean.gain)
#help(barplot)
barplot(mean.gain,beside=T)

#61
rownames(mean.gain)
colnames(mean.gain)

#62
mids<- barplot(mean.gain,beside=T,
       xlab="Feed type",
       ylab="Weight gain",
       ylim=c(0,35),
       col=grey(c(0,0.3,0.6,1)))

arrows(mids,
       mean.gain+sd.gain,
       mids,
       mean.gain-sd.gain,
       angle=90,
       code=3)

mids<- barplot(mean.gain,beside=T,
       xlab="Feed type",
       ylab="Weight gain",
       ylim=c(0,35),
       col=grey(c(0,0.3,0.6,1)))

arrows(mids,
       mean.gain+sd.gain,
       mids,
       mean.gain-sd.gain,
       angle=90,
       code=3,
       length=0.1)

text(mids,2,paste(n.gain))

text(mids,2,paste(n.gain),col=c("white",rep("black",3)))

legend("topright",legend=rownames(mean.gain),fill=grey(c(0,0.3,0.6,1)))

pdf(filename.pdf)
#code
dev.off()

#64
myData<-read.csv("compensation.csv")

plot(myData$Root,myData$Fruit)
plot(myData$Fruit~myData$Root)

#65
plot(myData$Root,myData$Fruit,
    xlab="Root stock width",
    ylab="Fruit production")

#66
plot(myData$Root,myData$Fruit,
    xlab="Root stock width",
    ylab="Fruit production",
    pch=21,bg="grey",cex=2)

plot(1:25,pch=1:25)
plot(1:25,col=1:25)

#67
culr <- ifelse(myData$Grazing == "Grazed",
               "Green",
               "Blue")

plot(myData$Root,myData$Fruit,
    xlab="Root stock width",
    ylab="Fruit production",
    pch=21,bg=culr,cex=2)

#68
culr <- ifelse(myData$Grazing == "Grazed",
               "Green",
               "Blue")

plot(myData$Root,myData$Fruit,
    xlab="Root stock width",
    ylab="Fruit production",
    pch=21,bg=culr,cex=2)

legend("topleft",
       legend=c("Grazed","Ungrazed"),
       pch=21,
       pt.bg=c("Green","Blue"),
       pt.cex=2)


# 69
plot(myData$Root,myData$Fruit)
plot(myData$Grazing,myData$Fruit)

str(myData$Root)
str(myData$Grazing)