##--------------------------------------##
##      Script for Basic R Review       ##
##              John Fox                ##
##          Programming in R            ##
##      ICPSR Summer Program 2009       ##
##--------------------------------------##

# Basics

    # arithmetic, interacting with the interpreter

        # basic arithmetic operations

2+3
2-3
2*3
2/3
2^3  #exponentiation
2**3 #exponentiation

        # precedence of operators

4^2-3*2
(4^2) - (3*2) # use parentheses group, spaces to clarify

1 - 6 + 4

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

4 - 2 ^2
(4 - 2)^2  # parentheses to group


    # 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")

"+"(2, 3) # even operators are functions
`+`(2, 3) # "backtick" preferred in this context (to mean name of object)

    # 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)
seq_along(seq(2, 8, by=2)) # to get indices for vector of unknown length

        # 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)  # "recycling" rule
c(1,2,3,4) + c(4,3,2)

    # variables

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

`a vector` <- 1:5  # a non-standard name
`a vector`

z = 1:10  # alternative assignment operator
z
1:10 -> w # also works
w
assign("u", sqrt(w)) # as does this
u


    # basic indexing

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

    #comparison and logical operators

1 == 2
1 < 2
"b" < c("a", "c")

        # full set: < <= == >= > !=

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!)
T <- FALSE
T
T || F
remove(T)

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) # function definition
my.mean(x)
my.mean(y)
my.mean(1:100)
x # "global" value of x unaffected

    # cleaning up

objects()

remove(`a vector`, u, w, x, y, z)
objects()

    # using traceback()

letters

my.var <- function(x) sum((x - my.mean(x))^2)/(length(x) - 1)
my.var(rnorm(1e6))
my.var(letters)

traceback()


# Duncan example


    # creating a data frame from data stored in a file

Duncan <- read.table(file.choose(), header=TRUE)
Duncan
View(Duncan)
head(Duncan)
summary(Duncan)

    # attaching a data frame (not best practice!)

prestige

attach(Duncan)
prestige

    # the search path

search()

    # distributions and bivariate relationships

hist(prestige)

pairs(cbind(prestige,income,education),
    panel=function(x,y){
        points(x,y)
        abline(lm(y~x), lty=2)
        lines(lowess(x,y))
        },
    diag.panel=function(x){
        par(new=TRUE)
        hist(x, main="", axes=FALSE)
        }
    )


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)
duncan.model
summary(duncan.model)

# regression diagnostics

library(car)
search()

plot(cookd(duncan.model))
abline(h = 4/(45-2-1))
identify(1:45, cookd(duncan.model), row.names(Duncan))

influencePlot(duncan.model, labels=row.names(Duncan))

av.plots(duncan.model, labels=row.names(Duncan))

# refitting the model

remove <- which.names(c("minister", "conductor"), Duncan)
remove

duncan.model.2 <- update(duncan.model, subset=-remove)
summary(duncan.model.2)