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# Remove most objects from the working environment
rm(list = ls())
options(stringsAsFactors = F)
# 20.1. A review of the language
# 20.1.1. Data types
## Atomic Vectors
passed <- c(TRUE, TRUE, FALSE, TRUE)
passed
# [1] TRUE TRUE FALSE TRUE
ages <- c(15, 18, 25, 14, 19)
ages
# [1] 15 18 25 14 19
cmplxNums <- c(1+2i, 0+1i, 39+3i, 12+2i)
cmplxNums
# [1] 1+2i 0+1i 39+3i 12+2i
names <- c("Bob", "Ted", "Carol", "Alice")
names
# [1] "Bob" "Ted" "Carol" "Alice"
x <- c(1,2,3,4,5,6,7,8)
class(x)
# [1] "numeric"
x
# [1] 1 2 3 4 5 6 7 8
attr(x, "dim") <- c(2, 4)
x
# [,1] [,2] [,3] [,4]
# [1,] 1 3 5 7
# [2,] 2 4 6 8
class(x)
# [1] "matrix" "array"
attributes(x)
# $dim
# [1] 2 4
attr(x, "dimnames") <- list(c("A1", "A2"),
c("B1", "B2", "B3", "B4"))
x
# B1 B2 B3 B4
# A1 1 3 5 7
# A2 2 4 6 8
attr(x, "dim") <- NULL
class(x)
# [1] "numeric"
x
# [1] 1 2 3 4 5 6 7 8
## Generic Vectors or Lists
head(iris)
# Sepal.Length Sepal.Width Petal.Length Petal.Width Species
# 1 5.1 3.5 1.4 0.2 setosa
# 2 4.9 3.0 1.4 0.2 setosa
# 3 4.7 3.2 1.3 0.2 setosa
# 4 4.6 3.1 1.5 0.2 setosa
# 5 5.0 3.6 1.4 0.2 setosa
# 6 5.4 3.9 1.7 0.4 setosa
unclass(iris)
attributes(iris)
str(iris)
set.seed(1234)
fit <- kmeans(iris[1:4], 3)
names(fit)
# [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
# [6] "betweenss" "size" "iter" "ifault"
unclass(fit)
# $cluster
# [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
# [44] 1 1 1 1 1 1 1 2 2 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 2
# [87] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 3 3 3 3 2 3 3 3 3 3 3 2 2 3 3 3 3 2 3 2 3 2 3 3 2 2 3
# [130] 3 3 3 3 2 3 3 3 3 2 3 3 3 2 3 3 3 2 3 3 2
#
# $centers
# Sepal.Length Sepal.Width Petal.Length Petal.Width
# 1 5.006000 3.428000 1.462000 0.246000
# 2 5.901613 2.748387 4.393548 1.433871
# 3 6.850000 3.073684 5.742105 2.071053
#
# $totss
# [1] 681.3706
#
# $withinss
# [1] 15.15100 39.82097 23.87947
#
# $tot.withinss
# [1] 78.85144
#
# $betweenss
# [1] 602.5192
#
# $size
# [1] 50 62 38
#
# $iter
# [1] 2
#
# $ifault
# [1] 0
sapply(fit, class)
# $cluster
# [1] "integer"
#
# $centers
# [1] "matrix" "array"
#
# $totss
# [1] "numeric"
#
# $withinss
# [1] "numeric"
#
# $tot.withinss
# [1] "numeric"
#
# $betweenss
# [1] "numeric"
#
# $size
# [1] "integer"
#
# $iter
# [1] "integer"
#
# $ifault
# [1] "integer"
#===========================================
## indexing
x <- c(20, 30, 40)
x[3]
# [1] 40
x[c(2, 3)]
# [1] 30 40
x <- c(A=20, B=30, C=40)
x
# A B C
# 20 30 40
x[c(2,3)]
# B C
# 30 40
x[c("B", "C")]
# B C
# 30 40
str(fit)
# List of 9
# $ cluster : int [1:150] 1 1 1 1 1 1 1 1 1 1 ...
# $ centers : num [1:3, 1:4] 5.01 5.9 6.85 3.43 2.75 ...
# ..- attr(*, "dimnames")=List of 2
# .. ..$ : chr [1:3] "1" "2" "3"
# .. ..$ : chr [1:4] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width"
# $ totss : num 681
# $ withinss : num [1:3] 15.2 39.8 23.9
# $ tot.withinss: num 78.9
# $ betweenss : num 603
# $ size : int [1:3] 50 62 38
# $ iter : int 2
# $ ifault : int 0
# - attr(*, "class")= chr "kmeans"
fit[7]
# $size
# [1] 50 62 38
fit[c(2,7)]
# $centers
# Sepal.Length Sepal.Width Petal.Length Petal.Width
# 1 5.006000 3.428000 1.462000 0.246000
# 2 5.901613 2.748387 4.393548 1.433871
# 3 6.850000 3.073684 5.742105 2.071053
#
# $size
# [1] 50 62 38
fit[2]
# $centers
# Sepal.Length Sepal.Width Petal.Length Petal.Width
# 1 5.006000 3.428000 1.462000 0.246000
# 2 5.901613 2.748387 4.393548 1.433871
# 3 6.850000 3.073684 5.742105 2.071053
fit[[2]]
# Sepal.Length Sepal.Width Petal.Length Petal.Width
# 1 5.006000 3.428000 1.462000 0.246000
# 2 5.901613 2.748387 4.393548 1.433871
# 3 6.850000 3.073684 5.742105 2.071053
fit[[2]][1]
# [1] 5.006
fit[[2]][1,]
# Sepal.Length Sepal.Width Petal.Length Petal.Width
# 5.006 3.428 1.462 0.246
fit$centers
# Sepal.Length Sepal.Width Petal.Length Petal.Width
# 1 5.006000 3.428000 1.462000 0.246000
# 2 5.901613 2.748387 4.393548 1.433871
# 3 6.850000 3.073684 5.742105 2.071053
# code listing 20.1. Plotting the centroids from a k-means cluster analysis
set.seed(1234)
fit <- kmeans(iris[1:4], 3)
means <- fit$centers
library(reshape2)
dfm <- melt(means)
names(dfm) <- c("Cluster", "Measurement", "Centimeters")
dfm$Cluster <- factor(dfm$Cluster)
head(dfm)
# Cluster Measurement Centimeters
# 1 1 Sepal.Length 5.006000
# 2 2 Sepal.Length 5.901613
# 3 3 Sepal.Length 6.850000
# 4 1 Sepal.Width 3.428000
# 5 2 Sepal.Width 2.748387
# 6 3 Sepal.Width 3.073684
library(ggplot2)
ggplot(data = dfm,
aes(x=Measurement, y=Centimeters, group=Cluster)) +
geom_point(size=3, aes(shape=Cluster, color=Cluster)) +
geom_line(size=1, aes(color=Cluster)) +
ggtitle("Profiles for Iris Clusters")
# 20.1.2. Control structures
## for()
1:5
# [1] 1 2 3 4 5
for (i in 1:5) print(1:i)
# [1] 1
# [1] 1 2
# [1] 1 2 3
# [1] 1 2 3 4
# [1] 1 2 3 4 5
5:1
# [1] 5 4 3 2 1
for (i in 5:1) {
print(1:i)
}
# [1] 1 2 3 4 5
# [1] 1 2 3 4
# [1] 1 2 3
# [1] 1 2
# [1] 1
## if() and else
## ifelse()
pvalues <- c(.0867, .0018, .0054, .1572, .0183, .5386)
results <- ifelse(pvalues < 0.05, "Significant", "Not Significant")
results
# [1] "Not Significant" "Significant" "Significant" "Not Significant"
# [5] "Significant" "Not Significant"
# The same result can be accomplished with explicit loops using
pvalues <- c(.0867, .0018, .0054, .1572, .0183, .5386)
results <- vector(mode = "character", length = length(pvalues))
for (i in 1:length(pvalues)) {
if (pvalues[i] < 0.05) results[i] <- "Significant"
else results[i] <- "Not Significant"
}
results
# [1] "Not Significant" "Significant" "Significant" "Not Significant"
# [5] "Significant" "Not Significant"
results[2]
# [1] "Significant"
# 20.1.3. Creating functions
## Function syntax
f <- function(x ,y, z=1) {
result <- x + (2*y) + (3*z)
return(result)
}
f(2, 3, 4)
# [1] 20
f(2,3) # equal f(2, 3, 1)
# [1] 11
f(x=2, y=3) # equal f(x=2, y=3, z=1)
# [1] 11
f(z=4, y=2, 3) # equal f(x=3, y=2, z=4)
# [1] 19
f(z=4, y=2, x=3)
args(f)
# function (x, y, z = 1)
# NULL
## Object scope
x <- 2
y <- 3
z <- 4
f <- function(w) {
z <- 2
x <- w*y*z
return(x)
}
f(x)
# [1] 12
x
# [1] 2
y
# [1] 3
z
# [1] 4
#============================================
# 20.2. Working with environments
# Remove most objects from the working environment
rm(list = ls())
options(stringsAsFactors = F)
x <- 5
myenv <- new.env()
assign("x", "Homer", env=myenv)
ls()
# [1] "myenv" "x"
ls(myenv)
# [1] "x"
x
# [1] 5
get("x", env=myenv)
# [1] "Homer"
myenv$x
# [1] "Homer"
parent.env(myenv)
# <environment: R_GlobalEnv>
help("environment")
trim <- function(p){
trimit <- function(x){
n <- length(x)
lo <- floor(n*p) + 1
hi <- n + 1 - lo
x <- sort.int(x, partial = unique(c(lo, hi)))[lo:hi]
}
trimit
}
x <- 1:10
x
# [1] 1 2 3 4 5 6 7 8 9 10
trim10pct <- trim(.1)
y <- trim10pct(x)
y
# [1] 2 3 4 5 6 7 8 9
trim20pct <- trim(.2)
y <- trim20pct(x)
y
# [1] 3 4 5 6 7 8
ls(environment(trim10pct))
# [1] "p" "trimit"
get("p", env=environment(trim10pct))
# [1] 0.1
get("p", env=environment(trim20pct))
# [1] 0.2
makeFunction <- function(k) {
f <- function(x) {
print(x + k)
}
}
g <- makeFunction(10)
g(4)
# [1] 14
k <- 2
g(5)
# [1] 15
ls(environment(g))
# [1] "f" "k"
environment(g)$k
# [1] 10
# 20.3.1 Generic functions
summary(women)
fit <- lm(weight ~ height, data = women)
summary(fit)
class(women)
class(fit)
# [1] "lm"
methods(summary)
# get code
summary.data.frame
# get code labeled with '*'
getAnywhere(summary.ggplot)
# code lisitng 20.2. An example of an arbitrary generic function
mymethod <- function(x, ...) UseMethod("mymethod")
mymethod.a <- function(x) print("Using A")
mymethod.b <- function(x) print("Using B")
mymethod.default <- function(x) print("Using Default")
x <- 1:5
y <- 6:10
z <- 10:15
class(x) <- "a"
class(y) <- "b"
mymethod(x)
# [1] "Using A"
mymethod(y)
# "Using B"
mymethod(z)
# "Using Default"
class(z) <- c("a", "b")
mymethod(z)
# [1] "Using A"
class(z) <- c("c", "a", "b")
mymethod(z)
# [1] "Using A"
#=======================================
# 20.3.2. Limitations of the S3 model
class(women) <- "lm"
summary(women)
# Error in if (p == 0) { : argument is of length zero
# 20.4. Writing efficient code
set.seed(1234)
mymatrix <- matrix(rnorm(10000000), ncol = 10)
accum <- function(x) {
sums <- numeric(ncol(x))
for (i in 1:ncol(x)) {
for (j in 1:nrow(x)) {
sums[i] <- sums[i] + x[j,i]
}
}
}
system.time(accum(mymatrix))
# user system elapsed
# 0.732 0.002 0.736
system.time(colSums(mymatrix))
# user system elapsed
# 0.011 0.000 0.011
0.736/0.011
set.seed(1234)
k <- 100000
x <- rnorm(k)
y <- 0
system.time(for (i in 1:length(x)) y[i] <- x[i]^2)
# user system elapsed
# 0.027 0.004 0.031
y <- numeric(length=k)
system.time(for (i in 1:k) y[i] <- x[i]^2)
# user system elapsed
# 0.008 0.001 0.008
## Parallelization
# code listing 20.3. Parallelization with foreach and doParallel
library(foreach)
# install.packages("doParallel")
library(doParallel)
registerDoParallel(cores = 2) # Total Number of Cores: 2 from about this mac
eig <- function(n, p){
x <- matrix(rnorm(100000), ncol = 100)
r <- cor(x)
eigen(r)$values
}
n <- 1000000
p <- 100
k <- 500
system.time(
x <- foreach(i=1:k, .combine=rbind) %do% eig(n, p)
)
# user system elapsed
# 7.612 0.416 8.064
system.time(
x <- foreach(i=1:k, .combine = rbind) %dopar% eig(n, p)
)
# user system elapsed
# 7.867 0.559 4.260
#======================================================
# 20.5. Debugging
# 20.5.1. Common sources of errors
mtcars$Transmission <- factor(mtcars$a,
levels = c(1, 2),
labels = c("Automatic", "Manual"))
aov(mpg ~ Transmission, data = mtcars)
# Error in `contrasts<-`(`*tmp*`, value = contr.funs[1 + isOF[nn]]) :
# contrasts can be applied only to factors with 2 or more levels
head(mtcars[c("mpg", "Transmission")])
# mpg Transmission
# Mazda RX4 21.0 Automatic
# Mazda RX4 Wag 21.0 Automatic
# Datsun 710 22.8 Automatic
# Hornet 4 Drive 21.4 <NA>
# Hornet Sportabout 18.7 <NA>
# Valiant 18.1 <NA>
table(mtcars$Transmission)
# Automatic Manual
# 13 0
# 20.5.2. Debugging tools
# code listing 20.4. A sample debugging session
args(mad)
# function (x, center = median(x), constant = 1.4826, na.rm = FALSE,
# low = FALSE, high = FALSE)
# NULL
debug(mad)
mad(1:10)
# 20.5.3. Session options that support debugging
# code listing 20.5. Sample debugging session with recover()
f <- function(x, y) {
z <- x + y
g(z)
}
g <- function(x) {
z <- round(x)
h(z)
}
h <- function(x) {
set.seed(1234)
z <- rnorm(x)
print(z)
}
options(error=recover)
f(2, 3)
# [1] -1.2070657 0.2774292 1.0844412 -2.3456977 0.4291247
f(2, -3)
# Error in rnorm(x) : invalid arguments
#
# Enter a frame number, or 0 to exit
#
# 1: f(2, -3)
# 2: #3: g(z)
# 3: #3: h(z)
# 4: #3: rnorm(x)
# Selection: 4
# Called from: rnorm(x)
# Browse[1]> ls()
# [1] "mean" "n" "sd"
# Browse[1]> mean
# [1] 0
# Browse[1]> print(n)
# [1] -1
# Browse[1]> c
# Enter a frame number, or 0 to exit
#
# 1: f(2, -3)
# 2: #3: g(z)
# 3: #3: h(z)
# 4: #3: rnorm(x)
#
# Selection: 3
# Called from: h(z)
# Browse[1]> ls()
# [1] "x"
# Browse[1]> x
# [1] -1
# Browse[1]> c
#
# Enter a frame number, or 0 to exit
#
# 1: f(2, -3)
# 2: #3: g(z)
# 3: #3: h(z)
# 4: #3: rnorm(x)
#
# Selection: 2
# Called from: g(z)
# Browse[1]> ls()
# [1] "x" "z"
# Browse[1]> x
# [1] -1
# Browse[1]> z
# [1] -1
# Browse[1]> c
#
# Enter a frame number, or 0 to exit
#
# 1: f(2, -3)
# 2: #3: g(z)
# 3: #3: h(z)
# 4: #3: rnorm(x)
#
# Selection: 1
# Called from: f(2, -3)
# Browse[1]> ls()
# [1] "x" "y" "z"
# Browse[1]> x
# [1] 2
# Browse[1]> y
# [1] -3
# Browse[1]> z
# [1] -1
# Browse[1]> c
#
# Enter a frame number, or 0 to exit
#
# 1: f(2, -3)
# 2: #3: g(z)
# 3: #3: h(z)
# 4: #3: rnorm(x)
#
# Selection: 0
options(error=NULL)
|
