## Chapter 2 R code.

## install.packages("ggplot2", repos = "http://cran.stat.ucla.edu/")

summary (c(10 , 5, 2))

(2 + 3) + (2 * 3) - (6 / 3) - 3 ^ 2

myresult <- 2 + 3
myresult

myresult * 2

mydat <- c(2, 3, 1, 0, 3, 4)
mydat 

mydat * 2

mynames <- c(" Carlos ", " Marta ", " Salome ", " Philip ")
mynames

mynames * 2

paste (" grade. ", as.character (5:8), sep = "")

mydat
mean(mydat)
sd(mydat)
summary(mydat)
min(mydat); max(mydat)

myfac0 <- c(0, 1, 1, 0)
myfac  <- factor (myfac0, labels = c(" Male ", " Female "))
myfac

str(myfac)

mynum <- c(1.5, 10, 8.467, 8)
str(mynum)

mychar <- c("Carlos", "Marta", "Salome", "Philip")
str(mychar)

mynum2 <- c(1.5, NA, 8.467, 8)
mynum2

mynum2
summary(mynum2)
mean(mynum2)
mean(mynum2, na.rm = TRUE)

mynum <- c(1.5, 10, 8.467, 8)
mychar <- c("Carlos", "Marta", "Salome", "Philip")

mymat <- matrix(c(mynum, mychar), ncol = 2) # Matrix with 2 columns.
mymat
str(mymat)

mydat <- data.frame(Char = mychar, Num = mynum) # Data frame.
mydat
str(mydat)

mylist <- list(Char = mychar, Num4 = mynum, Num3 = c(1, 2, 3)) # List.
mylist
str(mylist)

colnames(mydat)
colnames(mydat) <- c("Name", "Score") # Assigns new column names.
mydat # Implicit print.
mydat$Name
mydat$Score

attach(mydat)
mean(Score)
sd(Score)
detach(mydat)

with(mydat, mean(Score))
with(mydat, c(mean(Score), sd(Score)))

mymat
mymat[1,2]
mydat
mydat[2,2]
mymat[1:2,2]
mydat[1:2,1:2]
mymat[c(1,3),1]
mydat[ ,1]
mydat[1, ]

mynum <- c(1.5, 10, 8.467, 8)
mynum[1]
mynum[3]

mylist #Print all list elements.
mylist[[2]]
mylist[[2]][2]
mylist[[3]]
mylist[[3]][1]

mydat
mydat2 <- mydat[order(mydat$Score), ]
mydat2

mydat2[2,2] <- NA
mydat2

##save(mynum, mydat, file = "C:/Mine/myfile.Rdata")

rm(list = ls()) # Delete all objects from memory.
ls() # No objects available.
load(file = "C:/Mine/myfile.Rdata")
objects()
rm(mynum)
objects()

mynum <- c(1.5, 10, 8.467, 8)
mymean <- function() {sum(mynum) / length(mynum)}
mymean()
mean(mynum)

mymean <- function(x) {sum(x) / length(x)}
mymean(x = mynum)
mynum2 <- c(10, 8, 2, 0)
mymean(mynum2)

mymean2 <- function(){
 mysamp <- rnorm(n = 10, mean = 100, sd = 15)
 mean(mysamp)
}
mymean2()

require(ggplot2)
set.seed(111)
myresult <- rdply(.n = 15, .expr = mymean2())
colnames(myresult) <- c("rep", "mean")
myresult

myresult <- rdply(.n = 15000, .expr = mymean2())
colnames(myresult) <- c("rep", "mean")
(ggplot(data = myresult, aes(x = mean)) + geom_density())

myresult <- as.data.frame(matrix(NA, ncol = 2, nrow = 15)) # Define storage data frame.
for(i in 1:15){ # Repetition details.
 set.seed(i) # Allows replication.
 mysamp <- rnorm(n = 10, mean = 100, sd = 15) # Generate sample data.
 myresult[i ,1] <- i # Store repetition number.
 myresult[i ,2] <- mean(mysamp) # Store sample mean.
}
colnames(myresult) <- c("rep", "mean")
myresult

set.seed(123) # Allows reproduction of results.
y <- rnorm(n = 20, mean = 100, sd = 15) # Generate variables.
x1 <- rnorm(n = 20, mean = 100, sd = 15)
x2 <- rnorm(n = 20, mean = 100, sd = 15)
lm.out <- lm(y ~ x1 + x2) # Estimate model and save output.
summary(lm.out)           # Display output.

##require(RSiteSearch)
##RSiteSearch("random effects")