######################
# Importing Data
##########################

getwd()
list.files()

setwd("C:/Users/Sunthud/Desktop/")
setwd("C:/Users/Sunthud/Dropbox/Teaching/RINTRO")

#tab-delimited file: *.txt
#Comma seperated value: *.csv
data.csv <- read.table(file = "Ch2.csv", header = TRUE, sep = ",", na.strings = "-999")

data.txt <- read.table(file = "Ch2.txt", header = TRUE, sep = "\t", na.strings = "-999")

install.packages("foreign")
library(foreign)

data.spss <- read.spss(file = "Ch2.sav", to.data.frame = TRUE)

# read.csv : sep = ","
# read.csv2 : sep = ","

data.csv2 <- read.table(file = "Ch2_1.csv", header = TRUE, sep = ",", na.strings = "-999")

### Exporting Data

write.table(attitude, file = "ch2att.csv", sep = ",", col.names = TRUE, row.names = FALSE, na = "-999")

write.table(attitude, file = "ch2att.txt", sep = "\t", col.names = TRUE, row.names = FALSE, na = "-999")

obj <- lm(rating ~ complaints + privileges, data = attitude)

save(obj, file = "regression.Rdata")
rm(obj)
load(file = "regression.Rdata")

### Exploring Data

hist(airquality$Temp)
hist(airquality$Temp, breaks = 15)
hist(airquality$Temp, breaks = 15, xlab = "Temperature", ylab = "Frequency", main = "Air Temperature in New York", col = "red")

qqnorm(airquality$Temp)
qqline(airquality$Temp)

boxplot(airquality$Temp)
boxplot(airquality$Temp ~ airquality$Month, ylab = "Temperature in F", xlab = "Month")

plot(airquality$Ozone, airquality$Wind)
obj <- lm(airquality$Wind ~ airquality$Ozone)
abline(obj)


######################
# Packages
##########################

install.packages("coefplot")
require(coefplot)
coefplot(fit)

install.packages("arm")
arm::coefplot(fit)
coefplot::coefplot(fit)

sem::sem
lavaan::sem

### Factor

g <- rep(c("toyota", "honda", "mazda", "ford"), each = 5)
y <- c(9, 8, 6, 7, 5, 8, 9, 6, 5, 4, 8, 6, 4, 5, 5, 5, 6, 7, 4, 3)
summary(lm(y ~ g))

g <- rep(1:4, each = 5)
g <- factor(g, labels = c("toyota", "honda", "mazda", "ford"))

g <- rep(1:4, each = 5)
y <- c(9, 8, 6, 7, 5, 8, 9, 6, 5, 4, 8, 6, 4, 5, 5, 5, 6, 7, 4, 3)
summary(lm(y ~ g))

g <- as.factor(g)
summary(lm(y ~ g))

g <- factor(rep(4:1, each = 5), labels = c("toyota", "honda", "mazda", "ford"))
y <- c(9, 8, 6, 7, 5, 8, 9, 6, 5, 4, 8, 6, 4, 5, 5, 5, 6, 7, 4, 3)
summary(lm(y ~ g))
g <- relevel(g, ref = "ford")
summary(lm(y ~ g))

as.numeric(g)

ses <- c(3, 1, 2, 3, 1, 2, 3, 1, 2, 1, 1, 3)
ses <- factor(ses, labels = c("low", "medium", "high"), ordered = TRUE)

att <- c(52, 45, 44, 43, 56, 57, 45, 44, 50, 43, 55, 46)
summary(lm(att ~ ses))

### list

obj <- attitude$rating
obj2 <- lm(rating ~ complaints, data = attitude)

newlist <- list(obj, obj2)

list(1, 2, 3)
list(c(1, 2, 3))

aa <- list(a1 = "a", a2 = c(1, 2, 3), a3 = "new")
names(aa)

obj2 <- lm(rating ~ complaints, data = attitude)

obj2[[1]]
obj2[[2]]
aa[[1]]
aa[[3]]
obj2[["coefficients"]]
obj2$coefficients

attitude[[1]]
length(obj2)
aa[[1]] <- "b"