##-------------------------------##
## Script for Part 1: Basics     ##
##    John Fox                   ##
## Statistical Computing in R    ##
## McMaster University           ##
## Fall 2005                     ##
##-------------------------------##


    # Basics

# arithmetic, interacting with the interpreter

    # basic arithmetic operations

2+3
2-3
2*3
2/3
2^3

    # precedence of operators
        
4^2-3*2
(4^2)-(3*2) # use parentheses to group, clarify

-2--3
-2 - -3 # use spaces to clarify


# functions, arguments to functions, obtaining help

log(100)
log(100, base=10)
log(100, b=10)

help(log)
?log
log(100, 10)

apropos("log")
help.search("log")

RSiteSearch("loglinear", "functions")

# vectors

c(1,2,3,4)  # combine

1:4     # sequence operator
4:1
-1:2    # note precedence

seq(1,4)
seq(2, 8, by=2)
seq(0, 1, by=.1)
seq(0, 1, length=11)

    # vectorized arithmetic
    
c(1,2,3,4)/2    
c(1,2,3,4)/c(4,3,2,1)
log(c(0.1,1,10,100), 10)

c(1,2,3,4) + c(4,3)
c(1,2,3,4) + c(4,3,2)

# variables

x <- c(1,2,3,4)
x
x/2
y <- sqrt(x)
y
x <- rnorm(100)
x
summary(x)  # a "generic" function

    # basic indexing

x[21]  
x[11:20]
x[-(11:100)]    # careful here!

#comparison and logical operators

1 == 2
1 < 2
1 >= 2

TRUE && FALSE  # AND
TRUE & c(TRUE, FALSE) # vectorized AND

TRUE || FALSE  # OR
FALSE | c(TRUE, FALSE) # vectorized OR

! c(T, F)   # NOT (abbreviations of TRUE and FALSE, best avoided!)

z <- x[1:10]
z

abs(z) > 0.5
z[abs(z) > 0.5] # indexing by a logical vector


# user-defined functions

mean(x)
sum(x)/length(x)

my.mean <- function(x) sum(x)/length(x)
my.mean(x)
my.mean(y)
my.mean(1:100)
x

# cleaning up

objects()
last.warning

remove(x, y, z)
objects()

# using traceback()

letters
my.mean(letters)

traceback()


    # Duncan example
    

# creating a data frame from data stored in a file

Duncan <- read.table("D:/data/Duncan.txt", header=TRUE)
Duncan
summary(Duncan)

# attaching a data frame

prestige

attach(Duncan)
prestige

# the search path

search()

# distributions and bivariate relationships

hist(prestige)

plot(income, education)
identify(income, education, row.names(Duncan))

row.names(Duncan)[c(6,16,27)]

# fitting a regression

duncan.model <- lm(prestige ~ income + education, data=Duncan)
duncan.model
summary(duncan.model)

library(car)
Anova(duncan.model)

av.plots(duncan.model)