Ch05 Advanced data management.R

#-----------------------------------#
# R in Action (2nd ed): Chapter 5   #
# Advanced data management          #
# requires that the reshape2        #
# package has been installed        #
# install.packages("reshape2")      #
#-----------------------------------#

# Class Roster Dataset
Student <- c("John Davis","Angela Williams","Bullwinkle Moose",
             "David Jones","Janice Markhammer",
             "Cheryl Cushing","Reuven Ytzrhak",
             "Greg Knox","Joel England","Mary Rayburn")
math <- c(502, 600, 412, 358, 495, 512, 410, 625, 573, 522)
science <- c(95, 99, 80, 82, 75, 85, 80, 95, 89, 86)
english <- c(25, 22, 18, 15, 20, 28, 15, 30, 27, 18)
roster <- data.frame(Student, math, science, english, 
                     stringsAsFactors=FALSE)


# Listing 5.1 - Calculating the mean and standard deviation
x <- c(1, 2, 3, 4, 5, 6, 7, 8)
mean(x)
sd(x)
n <- length(x)
meanx <- sum(x)/n
css <- sum((x - meanx)**2)            
sdx <- sqrt(css / (n-1))
meanx
sdx


# Listing 5.2 - Generating pseudo-random numbers from 
# a uniform distribution
runif(5)
runif(5)
set.seed(1234)                                                     
runif(5)
set.seed(1234)                                                      
runif(5)


# Listing 5.3 - Generating data from a multivariate
# normal distribution
library(MASS)
mean <- c(230.7, 146.7, 3.6)                                           
sigma <- matrix( c(15360.8, 6721.2, -47.1,                              
                   6721.2, 4700.9, -16.5,
                   -47.1,  -16.5,   0.3), nrow=3, ncol=3)
set.seed(1234)
mydata <- mvrnorm(500, mean, sigma)                                     
mydata <- as.data.frame(mydata)                                         
names(mydata) <- c("y", "x1", "x2")                                       
dim(mydata)                                                             
head(mydata, n=10)   


# Listing 5.4 - Applying functions to data objects
a <- 5
sqrt(a)
b <- c(1.243, 5.654, 2.99)
round(b)
c <- matrix(runif(12), nrow=3)
c
log(c)
mean(c)


#  Listing 5.5 - Applying a function to the rows (columns) of a matrix
mydata <- matrix(rnorm(30), nrow=6)
mydata
apply(mydata, 1, mean)     
apply(mydata, 2, mean) 
apply(mydata, 2, mean, trim=.4)   


# Listing 5.6 - A solution to the learning example
options(digits=2)
Student <- c("John Davis", "Angela Williams", "Bullwinkle Moose",
             "David Jones", "Janice Markhammer", "Cheryl Cushing",
             "Reuven Ytzrhak", "Greg Knox", "Joel England",
             "Mary Rayburn")
Math <- c(502, 600, 412, 358, 495, 512, 410, 625, 573, 522)
Science <- c(95, 99, 80, 82, 75, 85, 80, 95, 89, 86)
English <- c(25, 22, 18, 15, 20, 28, 15, 30, 27, 18)

roster <- data.frame(Student, Math, Science, English,
                     stringsAsFactors=FALSE)

z <- scale(roster[,2:4])
score <- apply(z, 1, mean)
roster <- cbind(roster, score)

y <- quantile(score, c(.8,.6,.4,.2))
roster$grade[score >= y[1]] <- "A"
roster$grade[score < y[1] & score >= y[2]] <- "B"
roster$grade[score < y[2] & score >= y[3]] <- "C"
roster$grade[score < y[3] & score >= y[4]] <- "D"
roster$grade[score < y[4]] <- "F"

name <- strsplit((roster$Student), " ")
Lastname <- sapply(name, "[", 2)
Firstname <- sapply(name, "[", 1)
roster <- cbind(Firstname,Lastname, roster[,-1])
roster <- roster[order(Lastname,Firstname),]

roster


# Listing 5.4 - A switch example
feelings <- c("sad", "afraid")
for (i in feelings)
  print(
    switch(i,
           happy  = "I am glad you are happy",
           afraid = "There is nothing to fear",
           sad    = "Cheer up",
           angry  = "Calm down now"
    )
  )


# Listing 5.5 - mystats(): a user-written function for 
# summary statistics
mystats <- function(x, parametric=TRUE, print=FALSE) {
  if (parametric) {
    center <- mean(x); spread <- sd(x)
  } else {
    center <- median(x); spread <- mad(x)
  }
  if (print & parametric) {
    cat("Mean=", center, "\n", "SD=", spread, "\n")
  } else if (print & !parametric) {
    cat("Median=", center, "\n", "MAD=", spread, "\n")
  }
  result <- list(center=center, spread=spread)
  return(result)
}


# trying it out
set.seed(1234)
x <- rnorm(500) 
y <- mystats(x)
y <- mystats(x, parametric=FALSE, print=TRUE)


# mydate: a user-written function using switch
mydate <- function(type="long") {
  switch(type,
         long =  format(Sys.time(), "%A %B %d %Y"), 
         short = format(Sys.time(), "%m-%d-%y"),
         cat(type, "is not a recognized type\n"))
}
mydate("long")
mydate("short")
mydate()
mydate("medium")


# Listing 5.9 - Transposing a dataset
cars <- mtcars[1:5, 1:4]      
cars
t(cars)


# Listing 5.10 - Aggregating data
options(digits=3)
attach(mtcars)
aggdata <-aggregate(mtcars, by=list(cyl,gear), 
                    FUN=mean, na.rm=TRUE)
aggdata


# Using the reshape2 package
library(reshape2)

# input data
mydata <- read.table(header=TRUE, sep=" ", text="
ID Time X1 X2
1 1 5 6
1 2 3 5
2 1 6 1
2 2 2 4
")

# melt data
md <- melt(mydata, id=c("ID", "Time"))

# reshaping with aggregation
dcast(md, ID~variable, mean)
dcast(md, Time~variable, mean)
dcast(md, ID~Time, mean)

# reshaping without aggregation
dcast(md, ID+Time~variable)
dcast(md, ID+variable~Time)
dcast(md, ID~variable+Time)