x<-matrix(1:8, nrow=2)
x<-matrix(1:8, nrow=2,byrow=T)
data01<-data.frame(score = c(2,4,3,4),     
                   dose = c(rep(10,2),rep(100,2)),  
                   condition = rep(c('exp','control'),2))

dat01<-read.csv("http://www.matsuka.info/data_folder/temp_data01.txt",  
                header=T)
dat02<-read.csv("http://www.matsuka.info/data_folder/temp_data02.txt",    
                header=T, row.name=1)
dat03<-read.table("http://www.matsuka.info/data_folder/temp_data03.txt",
                  header=T, row.name=4)
dat<-read.csv("http://www.matsuka.info/data_folder/datWA01.txt",   
              header=T);
mean(dat$shoesize[dat$gender == "M"])
mean(dat$shoesize[dat$gender == "F"])
mean(dat$shoesize[dat$h > 180])

v1 = seq(-3,3,0.1)
v2 = v1^2
plot(x = v1, y = v2)
plot(v1, v2, col = 'red')

plot(v1, v2, main = "THIS IS THE TITLE", cex.lab = 1.5,
     xlab = "Label for X-axis",ylab = "Label for Y-axis")

plot(v1, v2, col = "blue", type = "o", lty = 2, pch = 19, 
     cex.lab = 1.5, lwd = 3, main = "Y=X*X", xlab = "X", 
     ylab="X*X", xlim=c(-3.5,3.5), ylim=c(-0.5, 10))

dat<- read.csv("http://www.matsuka.info/data_folder/datWA01.txt")
hist(dat$h)
hist(dat$h, breaks = 20, main = “Histogram of Height”, 
     xlab = "Height", col = 'blue', xlim = c(140, 190))

dens<-density(dat$h); 
hist(dat$h, main = "Histogram of Height", xlab = "Height",  
     xlim = c(140,190), probability = T)
lines(dens, lwd = 2, col = ‘red’, lty=2)

plot(v1, v2, col = "blue", type = "l", 
     pch = 19, cex.lab = 1.5, lwd = 3, 
     xlab = "X", ylab="f(X)", 
     xlim=c(-3.5,3.5), ylim=c(-0.5, 10))
lines(v1, v1^3, col='red',lwd = 3)
legend("bottomright", c("x^2","x^3"), col=c('blue','red'), lwd=2)

boxplot(dat$h ~ dat$gender,
        main="Distribution of Height by Gender", 
        ylab="Gender", xlab="Height", col=c('blue','cyan'),
        ylim=c(140,190), horizontal=T)

interaction.plot(dat$gender,
                 dat$affil,
                 dat$h, 
                 pch=c(20,20), 
                 col=c("skyblue","orange"), 
                 xlab="gender", ylab="height", 
                 lwd=3,type='b',cex=2,
                 trace.label="Affiliation")

hist(dat[dat$gender=='F',]$h, 
    main="Dist. of Height for Female Participants", 
    xlab="Height", xlim=c(140,190), probability=T)
dens.F = density(dat[dat$gender=='F',]$h)
lines(dens.F, col='blue',lwd=2) 

hist(dat[dat$gender==‘M’,]$h, main=“Dist. of Height for Male 
     Participants”, xlab=“Height”, xlim=c(140,190), 
     probability=T,ylim=c(0,0.08))
dens.M = density(dat[dat$gender=='M',]$h)
lines(dens.M, col='green', lwd=2)

plot(dat$shoesize, dat$h, 
     main="Relationship b/w shoesize and height",
     xlab = 'shoesize’, ylab='height’, 
     pch=19, col="red")
txt = paste("r =",round(cor(dat$shoesize,dat$h), 4))
text(22, 175, txt, cex = 1.5)

abline(h = mean(dat$h), col='blue');
abline(v = mean(dat$shoesize), col='green')

plot(dat[dat$gender=='F',]$shoesize, dat[dat$gender=='F',]$h,  
     main="Relationship b/w shoesize and height", xlab='shoesize', ylab='height', 
     cex.lab=1.5, pch=19, col='blue', xlim=c(20,29), ylim=c(140,190))
lines(dat[dat$gender=='M',]$shoesize,dat[dat$gender=='M',]$h, 
      type = 'p', pch = 15, col = 'green')
legend("topleft", c('Female','Male'), pch =c(19,15), 
       col = c('blue','green'), cex = 1.5)

dat<-data.frame(score=c(78,70,66,76,78,76,88, 76, 76,72,60,72,70,72,84,70),
                cond=c(rep('low',8), rep('high',8)))
boxplot(score~cond, col = c("skyblue",'skyblue4'),data=dat)
summary(aov(score ~ cond, data = dat))


dat <- read.csv("http://www.matsuka.info/data_folder/hwsk8-17-6.csv")
plot(ani~otouto, data=dat,pch=20,cex=3,xlab ="score of Otouto", ylab = "score of Ani")
dat.lm <- lm(ani~otouto, data=dat)
abline(dat.lm, col = 'red',lwd = 2.5)

dat<-read.csv("http://www.matsuka.info/data_folder/datWA01.txt")
dat.glm <- glm(gender~shoesize,family="binomial",data=dat)
plot(as.numeric(gender)-1~shoesize,data=dat,pch=20,cex=3,ylab="P(Male)")
cf = coef(dat.glm)
temp.x = seq(20,30,0.1)
y = 1/(1+exp(-1*(cf[1]+temp.x*cf[2])))
lines(temp.x,y,col='cyan',lwd=2)

dat <- read.table("http://www.matsuka.info/data_folder/tdkPCA01.txt")
dat.pca <- princomp(dat)
biplot(dat.pca)

dat<-read.csv("http://matsuka.info/data_folder/tdkClust.csv", header=TRUE, row.names=1)
dat.cluster=hclust(dist(dat),method="average")
plot(dat.cluster,cex=1.5)

data01<-data.frame(score = c(2,4,3,4),
                   dose = c(rep(10,2),rep(100,2)),
                   condition = rep(c('exp','control'),2))

dat01<-read.csv("http://www.matsuka.info/data_folder/temp_data01.txt",
                header=T)
dat02<-read.csv("http://www.matsuka.info/data_folder/temp_data02.txt",
                header=T, row.name=1)
dat03<-read.table("http://www.matsuka.info/data_folder/temp_data03.txt",
                  header=T, row.name=4)
dat<-read.csv("http://www.matsuka.info/data_folder/datWA01.txt",
              header=T);