# -- 10 ---------------
1+2
(3 * 4) ^ 2

a <- 3 * 4
A <- 5
a
A

sales <- c(1,2,3,4,5)
sales
sales <- 1:5
sales
sales2 <- sales	
sales2

idx <- "sales"
idx
idx2 <- c("stock", "sales")
idx2

# -- 11 ---------------
sales[2]
sales * 1.1
sqrt(2)
sqrt(sales)
mean(sales)

# -- 14 ---------------
dat <- read.csv("c:/R_work/grade1.csv")
dat <- read.csv("grade1.csv")
dat <- read.table("c:\R_work/\grade1.csv", header=T, sep=",")

dat        # すべてが表示される
head(dat)  # 最初の6行が表示される
head(dat,6)

# -- 15 ---------------
apply(dat[,2:5], 2, mean)
plot(dat[,2],dat[,4])

# -- 19 ---------------
cst <- read.csv("customer.csv")
cst
head(cst)

summary(cst)

# -- 20 ---------------
tapply(cst$Purchase, cst$Sex, mean)
tapply(cst$Purchase, cst$Branch, mean)
tapply(cst$Purchase, list(cst$Sex, cst$Branch), mean)

# -- 21 ---------------
hist(cst$Purchase)
boxplot(cst$Purchase)
boxplot(cst$Purchase~cst$Branch)
stripchart(cst$Purchase~cst$Branch, vertical = TRUE, pch = 21, col = "maroon", bg ="orange",method = "jitter", add = TRUE)

# -- 22 ---------------
plot(cst$Visit, cst$Purchase)
plot(cst$Time, cst$Purchase)
plot(cst$Visit, cst$Time)

cor(cst[,c(3,4,6)])

# -- 23 ---------------
ttn <- read.csv("Titanic.csv")
head(ttn)

summary(ttn)

# -- 24 ---------------
table(ttn$Class, ttn$Survive)
table(ttn$Age, ttn$Survive)
table(ttn$Sex, ttn$Survive)

mosaicplot(table(ttn$Class, ttn$Survive))

# -- 26 ---------------
mcn <- read.csv("machine5.csv")

a <- mcn[mcn$MachineID=="A",]
b <- mcn[mcn$MachineID=="B",]
d <- mcn[mcn$Period=="Day",]
n <- mcn[mcn$Period=="Night",]

hist(a$Weight, breaks=seq(98.5,100.5,0.1), border="#990000", col="#99343550", main="Histogram", xlab="weight", ylim=c(0,40))
hist(b$Weight, breaks=seq(98.5,100.5,0.1), border="#999900", col="#edae0050", add=T)
hist(d$Weight, breaks=seq(98.5,100.5,0.1), border="#009900", col="#53995250", main="Histogram", xlab="weight", ylim=c(0,40))
hist(n$Weight, breaks=seq(98.5,100.5,0.1), border="#000099", col="#5399ff50", add=T)

# -- 27 ---------------
library(ggplot2)

ggplot(iris,aes(x=Sepal.Length,y=Sepal.Width))+geom_point()
ggplot(iris,aes(x=Sepal.Length,y=Sepal.Width,colour=Species,size=Petal.Width))+geom_point()

# -- 30 ---------------
ice <- read.csv("IceCream.csv")

cor(ice)

pairs(ice)

# -- 31 ---------------
library(lattice)
splom(~ice[3:7], groups = ice$Month, pch=16, col=c(1,2,3,4,5,6,7,8,9,10,11,12))

# -- 32 ---------------
lm.ice <- lm(Paid~Temp, data=ice)
summary(lm.ice)

# -- 33 ---------------
plot(ice$Temp, ice$Paid)
abline(lm.ice$coef)
text(ice$Temp, ice$Paid, ice$Month, pos=2, col=3)

x <- c(1, 17)
t(lm.ice$coef) %*% x

# -- 33 ---------------
sak <- read.csv("sake.csv", row.names="Pref")

pca.sak<-prcomp(sak, scale=T)
pca.sak

# -- 34 ---------------
plot(pca.sak$x[,1], pca.sak$x[,2])
text(pca.sak$x[,1], pca.sak$x[,2], rownames(sak), pos=2, col=3)

plot(pca.sak$rotation[,1], pca.sak$rotation[,2])
text(pca.sak$rotation[,1], pca.sak$rotation[,2], colnames(sak), pos=2, col=3)

biplot(pca.sak)

# -- 35 ---------------
nds <- read.csv("ndsoup.csv", row.names="Brand")

library(MASS)
ca.nds <- corresp(nds, nf=2)
biplot(ca.nds, col=c(3, 4))

# -- 36 ---------------
cty <- read.csv("cityecon2.csv", row.names="City")

x=scale(cty)
hc <- hclust(dist(x, method = "euclidean"), "complete")
plot(hc, hang = -1)

# -- 42 ---------------
rec <- function(x, y){
  x * y
  }

rec(3, 4)