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#-*- R -*-
## Script from Fourth Edition of `Modern Applied Statistics with S'
# Chapter 3 S Language
library(MASS)
options(echo = T, width=65, digits=5, height=9999)
# from Chapter 2
powers.of.pi <- pi^(-2:2)
names(powers.of.pi) <- -2:2
mymat <- matrix(1:30, 3, 10)
myarr <- mymat
dim(myarr) <- c(3, 5, 2)
dimnames(myarr) <- list(letters[1:3], NULL, c("(i)", "(ii)"))
# 3.1 Language layout
1 - pi + exp(1.7)
a <- 6
b <- a <- 6
(z <- 1 - pi + exp(1.7))
search()
objects()
objects(2)
find("objects")
get("[<-.data.frame", pos = 2)
# hills <- hills # only needed in S-PLUS
hills$ispeed <- hills$time/hills$dist
# 3.2 More on S objects
length(letters)
Empl <- list(employee = "Anna", spouse = "Fred", children = 3,
child.ages = c(4, 7, 9))
Empl$employee
Empl$child.ages[2]
x <- "spouse"; Empl[[x]]
unlist(Empl)
unlist(Empl, use.names = F)
attributes(myarr)
attr(myarr, "dim")
Empl <- c(Empl, service = 8)
c(list(x = 1:3, a = 3:6), list(y = 8:23, b = c(3, 8, 39)))
as(powers.of.pi, "vector")
as(powers.of.pi, "numeric")
is(powers.of.pi, "numeric")
as(powers.of.pi, "character")
is(powers.of.pi, "vector")
as(powers.of.pi, "integer")
is(mymat, "array")
# 3.3 Arithmetical expressions
x <- c(10.4, 5.6, 3.1, 6.4, 21.7)
y <- c(x, x)
v <- 2 * x + y + 1
s3 <- seq(-5, 5, by = 0.2)
s4 <- seq(length = 51, from = -5, by = 0.2)
s5 <- rep(x, times = 5) # repeat whole vector
s5 <- rep(x, each = 5) # repeat element-by-element
x <- 1:4 # puts c(1,2,3,4) into x
i <- rep(2, 4) # puts c(2,2,2,2) into i
y <- rep(x, 2) # puts c(1,2,3,4,1,2,3,4) into y
z <- rep(x, i) # puts c(1,1,2,2,3,3,4,4) into z
w <- rep(x, x) # puts c(1,2,2,3,3,3,4,4,4,4) into w
( colc <- rep(1:3, each = 8) )
( rowc <- rep(rep(1:4, each = 2), 3) )
1 + (ceiling(1:24/8) - 1) %% 3 -> colc; colc
1 + (ceiling(1:24/2) - 1) %% 4 -> rowc; rowc
# or
gl(3, 8)
gl(4, 2, 24)
# 3.4 Character vector operations
paste(c("X", "Y"), 1:4)
paste(c("X", "Y"), 1:4, sep = "")
paste(c("X", "Y"), 1:4, sep = "", collapse = " + ")
substring(state.name[44:50], 1, 4)
as.vector(abbreviate(state.name[44:50]))
as.vector(abbreviate(state.name[44:50], use.classes = F))
grep("na$", state.name)
regexpr("na$", state.name)
state.name[regexpr("na$", state.name)> 0]
# 3.5 Formatting and printing
d <- date()
cat("Today's date is:", substring(d, 1, 10),
substring(d, 25, 28), "\n")
cat(1, 2, 3, 4, 5, 6, fill = 8, labels = letters)
cat(powers.of.pi, "\n")
format(powers.of.pi)
cat(format(powers.of.pi), "\n", sep=" ")
# 3.6 Calling conventions for functions
args(hist.default)
# 3.8 Control stuctures
yp <- rpois(50, lambda = 1) # full Poisson sample of size 50
table(yp)
y <- yp[yp > 0] # truncate the zeros; n = 29
ybar <- mean(y); ybar
lam <- ybar
it <- 0 # iteration count
del <- 1 # iterative adjustment
while (abs(del) > 0.0001 && (it <- it + 1) < 10) {
del <- (lam - ybar*(1 - exp(-lam)))/(1 - ybar*exp(-lam))
lam <- lam - del
cat(it, lam, "\n")}
# 3.9 Array and matrix operations
p <- dbinom(0:4, size = 4, prob = 1/3) # an example
CC <- -(p %o% p)
diag(CC) <- p + diag(CC)
structure(3^8 * CC, dimnames = list(0:4, 0:4)) # convenience
apply(iris3, c(2, 3), mean)
apply(iris3, c(2, 3), mean, trim = 0.1)
apply(iris3, 2, mean)
ir.var <- apply(iris3, 3, var)
ir.var <- array(ir.var, dim = dim(iris3)[c(2, 2, 3)],
dimnames = dimnames(iris3)[c(2, 2, 3)])
matrix(rep(1/50, 50) %*% matrix(iris3, nrow = 50), nrow = 4,
dimnames = dimnames(iris3)[-1])
ir.means <- colMeans(iris3)
sweep(iris3, c(2, 3), ir.means)
log(sweep(iris3, c(2, 3), ir.means, "/"))
# 3.10 Introduction to classes and methods
methods(summary)
# End of ch03
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