Help for package rfordummies

Title:

Code Examples to Accompany the Book "R for Dummies"

Version:

0.1.6

Description:

Contains all the code examples in the book "R for Dummies" (2nd edition) by Andrie de Vries and Joris Meys. You can view the table of contents as well as the sample code for each chapter.

Language:

en-US

URL:

https://rfordummies.com, https://rfordummies.github.io/rfordummies/

BugReports:

https://github.com/rfordummies/rfordummies/issues

Suggests:

fortunes, stringr, sos, reshape2, ggplot2, foreign, lattice, covr, testthat, spelling

License:

GPL-2 | GPL-3

LazyData:

true

RoxygenNote:

7.1.2

Encoding:

UTF-8

Config/testthat/edition:

NeedsCompilation:

Packaged:

2022-02-25 17:00:23 UTC; apdev

Author:

Andrie de Vries [aut, cre], Joris Meys [aut]

Maintainer:

Andrie de Vries <apdevries@gmail.com>

Repository:

CRAN

Date/Publication:

2022-02-25 23:30:02 UTC

Print examples of chapter 1 of 'R for Dummies'.

Description

To print a listing of all examples of a chapter, use ch1(). To run all the examples of ch1(), use example(ch1).

Usage

ch01()

ch1()

Examples

if (interactive()) {
# Chapter 1 - Introducing R: The Big Picture

# Recognizing the Benefits of Using R

## It comes as free, open-source code

### It runs anywhere

### It supports extensions

### It provides an engaged community

### It connects with other languages


# Looking At Some of the Unique Features of R

## Performing multiple calculations with vectors

x <- 1:5
x
x + 2
x + 6:10

## Processing more than just statistics

## Running code without a compiler




}

Print examples of chapter 2 of 'R for Dummies'.

Description

To print a listing of all examples of a chapter, use ch2(). To run all the examples of ch2(), use example(ch2).

Usage

ch02()

ch2()

Examples

if (interactive()) {
# Chapter 2 - Exploring R

# Working with a Code Editor

## Exploring RGui

### Seeing the naked R console


### Issuing a simple command

24+7+11

### Closing the console

## Not run: 
quit()

## End(Not run)


## Dressing up with RStudio


# Starting Your First R Session

## Saying hello to the world

print("Hello world!")

## Doing simple math

1+2+3+4+5

## Using vectors


c(1,2,3,4,5)
1:5
sum(1:5)

## Storing and calculating values

x <- 1:5
x

y <- 10
x + y

x
y

z <- x + y
z

h <- "Hello"
h

hw <- c("Hello", "world!")
hw

paste("Hello", "world!")

## Talking back to the user

h <- "Hello"
if(interactive()){
yourname <- readline("What is your name?")
} else {
  yourname <- "Joris"
}
paste(h, yourname)

# Sourcing a Script

h <- "Hello"
yourname <- readline("What is your name?")
print(paste(h, yourname))


### Finding help on functions
?paste
help(paste)

# Navigating the Workspace
ls()

## Manipulating the content of the workspace

rm(z)
ls()

##Saving your work

getwd()

filename <- file.path(tempdir(), "yourname.rda")
## Not run: 
save(yourname, file=filename)

## End(Not run)
list.files(tempdir(), pattern = ".rda")

## Retrieving your work


rm(yourname)
## Not run: 
load("yourname.rda")

## End(Not run)

}

Print examples of chapter 3 of 'R for Dummies'.

Description

To print a listing of all examples of a chapter, use ch3(). To run all the examples of ch3(), use example(ch3).

Usage

ch03()

ch3()

Examples

if (interactive()) {
# Chapter 3 - The Fundamentals of R

# Using the Full Power of Functions

## Vectorizing your functions

baskets.of.Granny <- c(12,4,4,6,9,3)
baskets.of.Granny
sum(baskets.of.Granny)

firstnames <- c("Joris", "Carolien", "Koen")
lastname <- "Meys"
paste(firstnames,lastname)

authors <- c("Andrie","Joris")
lastnames <- c("de Vries","Meys")
paste(authors,lastnames)

## Putting the argument in a function

# print() ### This line of code leads to deliberate error for illustration
print(x = "Isn't this fun?")

print(digits=4, x = 11/7)

# Making history

filename <- file.path(tempdir(), "Chapter3.Rhistory")
## Not run: 
savehistory(file = filename)

## End(Not run)
list.files(tempdir(), pattern = ".Rhistory")
## Not run: 
loadhistory(file.path(tempdir(), "Chapter3.Rhistory"))

## End(Not run)

# Keeping Your Code Readable

## Following naming conventions

## Choosing a clear name

paste <- paste("This gets","confusing")
paste
paste("Don't","you","think?")

## Choosing a naming style

## Structuring your code

baskets.of.Geraldine <- c(5,3,2,2,12,9)
Intro <- "It is amazing! The All Star Grannies scored
a total of"

Outro <- "baskets in the last six games!"

Total.baskets <- baskets.of.Granny +
               baskets.of.Geraldine

Text <- paste(Intro,
              sum(Total.baskets),
              Outro)
cat(Text)
Text

cat('If you doubt whether it works,
+ just try it out.')

## Adding comments

# The All Star Grannies do it again!
baskets.of.Granny <- c(12,4,4,6,9,3) # Granny rules
sum(baskets.of.Granny) # total number of points


# Getting from Base R to More

## Finding packages

## Installing packages

## Not run: 
install.packages("fortunes")

## End(Not run)

library("fortunes")
fortune("This is R")
fortune(161)
detach(package:fortunes)

}

Print examples of chapter 4 of 'R for Dummies'.

Description

To print a listing of all examples of a chapter, use ch4(). To run all the examples of ch4(), use example(ch4).

Usage

ch04()

ch4()

Examples

if (interactive()) {
# Chapter 4 - Getting Started with Arithmetic

# Working with Numbers, Infinity, and Missing Values

## Doing basic arithmetic

### Using arithmetic operators


baskets.of.Granny <- c(12,4,4,6,9,3)
baskets.of.Geraldine <- c(5,3,2,2,12,9)


Granny.money <- baskets.of.Granny * 120
Geraldine.money <- baskets.of.Geraldine * 145


Granny.money + Geraldine.money


baskets.of.Granny * 120 + baskets.of.Geraldine * 145

### Controlling the order of the operations
4 + 2 * 3
(4 + 2)* 3

## Using mathematical functions

### Calculating logarithms and exponentials

log(1:3)
log(1:3,base=6)

x <- log(1:3)
exp(x)

### Putting the science in scientific notation
1.33e4

4.12e-2

1.2e6 / 2e3


### Rounding numbers

round(123.456,digits=2)
round(-123.456,digits=-2)
signif(-123.456,digits=4)

### Using trigonometric functions

cos(120)
cos(120*pi/180)

## Calculating whole vectors

`+`(2,3)


##To infinity and beyond

### Using infinity

2/0
4 - Inf
is.finite(10^(305:310))

### Dealing with undefined outcomes
Inf / Inf
NaN + 4


### Dealing with missing values

x <- NA
x + 4

log(x)

is.na(x)

### Calculating infinite, undefined, and missing values


# Organizing Data in Vectors

## Discovering the properties of vectors

### Looking at the structure of a vector

str(baskets.of.Granny)
length(baskets.of.Granny)
authors <- c("Andrie", "Joris")
str(authors)

### Testing vector types




is.numeric(baskets.of.Granny)
is.integer(baskets.of.Granny)

x <- c(4L,6L)
is.integer(x)

## Creating vectors

seq(from = 4.5, to = 2.5, by = -0.5)


seq(from = -2.7, to = 1.3, length.out = 9)


baskets.of.Granny <- c(12,4,4,6,9,3)
baskets.of.Geraldine <- c(5,3,2,2,12,9)

## Combining vectors

all.baskets <-c(baskets.of.Granny, baskets.of.Geraldine)
all.baskets

## Repeating vectors
rep(c(0, 0, 7), times = 3)
rep(c(2, 4, 2), each = 3)
rep(c(0, 7), times = c(4,2))
rep(1:3,length.out=7)

# Getting Values in and out of Vectors

## Understanding indexing in R

numbers <- 30:1
numbers

## Extracting values from a vector

numbers[5]
numbers[c(5,11,3)]

indices <- c(5,11,3)
numbers[indices]
numbers[-3]
numbers[-(1:20)]
# numbers[-1:20] # NOT RUN, gives error


## Changing values in a vector


baskets.of.Granny[3] <- 5
baskets.of.Granny

baskets.of.Geraldine[c(2,4)] <- 4
baskets.of.Geraldine

Granny.copy <- baskets.of.Granny

baskets.of.Granny[4] <- 11
baskets.of.Granny

baskets.of.Granny <- Granny.copy
baskets.of.Granny

# Working with Logical Vectors

## Comparing values

baskets.of.Granny > 5
which(baskets.of.Granny > 5)

the.best <- baskets.of.Geraldine < baskets.of.Granny
which(the.best)



## Using logical vectors as indices

baskets.of.Granny[the.best]
x <- c(3, 6, 1, NA, 2)
x[x > 2]
x > 2

## Combining logical statements

min.baskets <- baskets.of.Granny == min(baskets.of.Granny)
max.baskets <- baskets.of.Granny == max(baskets.of.Granny)
min.baskets | max.baskets

x[!is.na(x)]

## Summarizing logical vectors


sum(the.best)
any(the.best)
all(the.best)




# Powering Up Your Math with Vector Functions


## Using arithmetic vector operations

### Summarizing a vector
min(baskets.of.Granny)

max(baskets.of.Granny)
sum(baskets.of.Granny,baskets.of.Geraldine)



x <- c(3,6,2,NA,1)
sum(x)
sum(x,na.rm=TRUE)

### Cumulating operations

cumsum(baskets.of.Granny)
cummax(baskets.of.Geraldine)
cummin(x)

### Calculating differences

diff(baskets.of.Granny)
diff(x)

## Recycling arguments

Granny.pointers <- c(10,2,4,0,4,1,4,2,7,2,1,2)
points <- Granny.pointers * c(2,3)
points
sum(points)

sum(Granny.pointers * c(2,3))

round(diff(baskets.of.Granny) / baskets.of.Granny[1:5] * 100)
}

Print examples of chapter 5 of 'R for Dummies'.

Description

To print a listing of all examples of a chapter, use ch5(). To run all the examples of ch5(), use example(ch5).

Usage

ch05()

ch5()

Examples

if (interactive()) {
# Chapter 5 - Getting Started with Reading and Writing

# Using Character Vectors for Text Data

## Assigning a value to a character vector

x <- "Hello world!"
is.character(x)
length(x)
nchar(x)

## Creating a character vector with more than one element

x <- c("Hello", "world!")
length(x)
nchar(x)

## Extracting a subset of a vector

letters
LETTERS
letters[10]
LETTERS[24:26]
tail(LETTERS, 5)
head(letters, 10)

## Naming the values in your vectors

### Looking at how named vectors work

str(islands)
islands[c("Asia", "Africa", "Antarctica")]
names(islands)[1:9]
names(sort(islands, decreasing=TRUE)[1:6])

## Creating and assigning named vectors

month.days <- c(31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31)
names(month.days) <- month.name
month.days
names(month.days[month.days==31])

# Manipulating Text

## String theory: Combining and splitting strings

### Splitting text

pangram <- "The quick brown fox jumps over the lazy dog"
pangram
strsplit(pangram, " ")

words <- strsplit(pangram, " ")[[1]]
words

### Changing text case

unique(tolower(words))
toupper(words[c(4, 9)])
tolower("Some TEXT in Mixed CASE")

### Concatenating text

paste("The", "quick", "brown", "fox")
paste(c("The", "quick", "brown", "fox"))
paste(words, collapse=" ")
paste(words, collapse="_")
paste(LETTERS[1:5], 1:5, sep="_", collapse="---")
paste("Sample", 1:5)
paste(c("A", "B"), c(1, 2, 3, 4), sep="-")
paste(c("A"), c(1, 2, 3, 4, 5), sep="-")

## Sorting text

sort(letters, decreasing=TRUE)
sort(words)

## Finding text inside text

### Searching for individual words

head(state.name)

### Searching by position

head(substr(state.name, start=3, stop=6))

### Searching by pattern

grep("New", state.name)
state.name[29]
state.name[grep("New", state.name)]
state.name[grep("new", state.name)]

### Searching for multiple words

state.name[grep(" ", state.name)]
state.name[grep("East", state.name)]

## Substituting text


gsub("cheap", "sheep's", "A wolf in cheap clothing")
x <- c("file_a.csv", "file_b.csv", "file_c.csv")
y <- gsub("file_", "", x)
y
gsub(".csv", "", y)


#### Extending text functionality with stringr

## Not run: 
install.packages("stringr")

## End(Not run)
library(stringr)


## Revving up with regular expressions

rwords <- c("bach", "back", "beech", "beach", "black")
grep("beach|beech", rwords)
rwords[grep("beach|beech", rwords)]
rwords[grep("be(a|e)ch", rwords)]
rwords[grep("b(e*|a*)ch", rwords)]


# Factoring in Factors

## Creating a factor

directions <- c("North", "East", "South", "South")
factor(directions)
factor(directions, levels= c("North", "East", "South", "West"))
factor(directions, levels= c("North", "East", "South", "West"), labels=c("N", "E", "S", "W"))

## Converting a factor

directions <- c("North", "East", "South", "South")
directions.factor <- factor(directions)
directions.factor
as.character(directions.factor)
as.numeric(directions.factor)

numbers <- factor(c(9, 8, 10, 8, 9))
as.character(numbers)
as.numeric(numbers)
as.numeric(as.character(numbers))

## Looking at levels

str(state.region)
levels(state.region)
levels(state.region) <- c("NE", "S", "NC", "W")
head(state.region)
nlevels(state.region)
length(levels(state.region))
levels(state.region)[2:3]

## Distinguishing data types

head(state.region)
table(state.region)
state.region

## Working with ordered factors

status <- c("Lo", "Hi", "Med", "Med", "Hi")
ordered.status <- factor(status, levels=c("Lo", "Med", "Hi"), ordered=TRUE)
ordered.status
table(status)
table(ordered.status)


}

Print examples of chapter 6 of 'R for Dummies'.

Description

To print a listing of all examples of a chapter, use ch6(). To run all the examples of ch6(), use example(ch6).

Usage

ch06()

ch6()

Examples

if (interactive()) {
# Chapter 6 - Going on a Date with R

# Working with Dates

xd <- as.Date("2012-07-27")
xd
str(xd)
weekdays(xd)
xd + 7
xd + 0:6
weekdays(xd + 0:6)

startDate <- as.Date("2012-01-01")
xm <- seq(startDate, by="2 months", length.out=6)
xm
 months(xm)
quarters(xm)

Sys.localeconv()

as.Date("27 July 2012", format="%d %B %Y")

as.Date("27/7/12", format="%d/%m/%y")

# Adding Time Information to Dates

apollo <- "July 20, 1969, 20:17:39"
apollo.fmt <- "%B %d, %Y, %H:%M:%S"
xct <- as.POSIXct(apollo, format=apollo.fmt, tz="UTC")
xct <- as.POSIXct(apollo, format=apollo.fmt)
xct

format(xct, "%d/%m/%y")
format(xct, "%S minutes past %I %p, on %d %B %Y")

# Performing Operations on Dates and Times

## Addition and subtraction

24*60*60
xct + 7*86400
xct + 3*60*60
xct - 7*86400
as.Date(xct) - 7

## Comparison of dates

Sys.time()
Sys.time() < xct

dec.start <- as.POSIXct("1950-01-01")
dec <- seq(dec.start, by="10 years", length.out=4)
dec
dec > xct

## Extraction

xlt <- as.POSIXlt(xct)
xlt
xlt$year
xlt$mon
unclass(xlt)









}

Print examples of chapter 7 of 'R for Dummies'.

Description

To print a listing of all examples of a chapter, use ch7(). To run all the examples of ch7(), use example(ch7).

Usage

ch07()

ch7()

Examples

if (interactive()) {
# Chapter 7
# Working in More Dimensions

# Adding a Second Dimension

## Discovering a new dimension

### Creating your first matrix

first.matrix <- matrix(1:12, ncol=4)
first.matrix
matrix(1:12, ncol=4, byrow=TRUE)

### Looking at the properties

str(first.matrix)
dim(first.matrix)
length(first.matrix)
my.array <- array(1:24, dim=c(3,4,2))
baskets.of.Granny <- c(12,4,5,6,9,3)
baskets.of.Geraldine <- c(5,4,2,4,12,9)
baskets.team <- rbind(baskets.of.Granny, baskets.of.Geraldine)

attributes(my.array)
attr(baskets.team,'season') <- '2010-2011'
attr(baskets.team,'season')
attr(baskets.team,'season') <- NULL

## Combining vectors into a matrix

baskets.of.Granny <- c(12,4,5,6,9,3)
baskets.of.Geraldine <- c(5,4,2,4,12,9)
baskets.team <- rbind(baskets.of.Granny, baskets.of.Geraldine)

baskets.team

cbind(1:3, 4:6, matrix(7:12, ncol=2))

# Using the Indices

## Extracting values from a matrix

### Using numeric indices

first.matrix[1:2, 2:3]
first.matrix[2:3,]

### Dropping values using negative indices

first.matrix[-2,-3]

nr <- nrow(first.matrix)
id <- nr*2+2
first.matrix[-id]

first.matrix[-(2 * nrow(first.matrix) + 2)]


### Juggling dimensions

first.matrix[-c(1, 3), ]
first.matrix[2, , drop=FALSE]

## Replacing values in a matrix

first.matrix[3, 2] <- 4
first.matrix

first.matrix[2, ] <- c(1,3)
first.matrix

first.matrix[1:2, 3:4] <- c(8,4,2,1)
first.matrix

# Naming Matrix Rows and Columns

## Changing the row and column names

rownames(baskets.team) <- c('Granny','Geraldine')
rownames(baskets.team)
colnames(baskets.team) <- c('1st','2nd','3th','4th','5th','6th')
baskets.team

colnames(baskets.team)[3] <- '3rd'

baskets.copy <- baskets.team
colnames(baskets.copy) <- NULL
baskets.copy

## Using names as indices

baskets.team[, c("2nd","5th")]

baskets.team["Granny",]

# Calculating with Matrices

## Using standard operations with matrices
first.matrix + 4

second.matrix <- matrix(1:3, nrow=3, ncol=4)

first.matrix + second.matrix

# first.matrix + second.matrix[,1:3] # gives error for illustration
# Error in first.matrix + second.matrix[, 1:3] : non-conformable arrays

first.matrix + 1:3

## Calculating row and column summaries

rowSums(baskets.team)

## Doing matrix arithmetic

### Transposing a matrix

t(first.matrix)

t(1:10)

t(first.matrix[2,])

### Inverting a matrix

square.matrix <- matrix(c(1,0,3,2,2,4,3,2,1),ncol=3)
solve(square.matrix)

### Multiplying two matrices

first.matrix %*% t(second.matrix)

first.matrix %*% 1:4
1:3 %*% first.matrix

# Adding More Dimensions

## Creating an array

### Using the creator functions

my.array <- array(1:24, dim=c(3,4,2))
my.array

### Changing the dimensions of a vector


my.vector <- 1:24
dim(my.vector) <- c(3,4,2)
identical(my.array, my.vector)

## Using dimensions to extract values

my.array[2,3,1]

my.array[, 3, 2, drop=FALSE]


my.array[2, , ]


# Combining Different Types of Values in a Data Frame

## Creating a data frame from a matrix

### Using the function as.data.frame

baskets.df <- as.data.frame(t(baskets.team))

### Looking at the structure of a data frame

baskets.df
str(baskets.df)

### Counting values and variables

nrow(baskets.df)
length(baskets.df)

## Creating a data frame from scratch

### Making a data frame from vectors

employee <- c('John Doe','Peter Gynn','Jolie Hope')
salary <- c(21000, 23400, 26800)
startdate <- as.Date(c('2010-11-1','2008-3-25','2007-3-14'))

employ.data <- data.frame(employee, salary, startdate)

str(employ.data)

### Keeping characters as characters

employ.data <- data.frame(employee, salary, startdate, stringsAsFactors=FALSE)
str(employ.data)

## Naming variables and observations

### Working with variable names

colnames(employ.data)
names(employ.data)

names(employ.data)[3] <- 'firstday'
names(employ.data)

### Naming observations

rownames(employ.data)
rownames(employ.data) <- c('Chef','BigChef','BiggerChef')
employ.data

# Manipulating Values in a Data Frame

## Extracting variables, observations, and values

### Pretending it's a matrix

baskets.df['3rd', 'Geraldine']
baskets.df[, 1]

str(baskets.df[, 1, drop=FALSE])

### Putting your dollar where your data is

baskets.df$Granny

## Adding observations to a data frame

### Adding a single observation

result <- rbind(baskets.df, c(7,4))
result

baskets.df <- rbind(baskets.df,'7th' = c(7,4))
baskets.df

### Adding a series of new observations using rbind

new.baskets <- data.frame(Granny=c(3,8),Geraldine=c(9,4))
rownames(new.baskets) <- c('8th','9th')
baskets.df <- rbind(baskets.df, new.baskets)

### Adding a series of values using indices

baskets.df[c('8th','9th'), ] <- matrix(c(3,8,9,4), ncol=2)
baskets.df[c('8th','9th'), ] <- c(3,8,9,4)

## Adding variables to a data frame

### Adding a single variable

baskets.of.Gabrielle <- c(11,5,6,7,3,12,4,5,9)
baskets.df$Gabrielle <- baskets.of.Gabrielle

head(baskets.df, 4)

### Adding multiple variables using cbind

new.df <- data.frame(
   Gertrude  =  c(3,5,2,1,NA,3,1,1,4),
   Guinevere =  c(6,9,7,3,3,6,2,10,6)
)

head(cbind(baskets.df, new.df), 4)

# Combining Different Objects in a List

## Creating a list

### Creating an unnamed list

baskets.list <- list(baskets.team, '2010-2011')
baskets.list

### Creating a named list

baskets.nlist <- list(scores=baskets.team, season='2010-2011')
baskets.nlist


### Playing with the names of elements

names(baskets.nlist)

### Getting the number of elements

length(baskets.list)

## Extracting elements from lists

### Using [[]]

baskets.list[[1]]
baskets.nlist[['scores']]

### Using []

baskets.list[-1]
baskets.nlist[names(baskets.nlist)=='season']

## Changing the elements in lists

### Changing the value of elements

baskets.nlist[[1]] <- baskets.df
baskets.nlist[['scores']] <- baskets.df
baskets.nlist$scores <- baskets.df

baskets.nlist[1] <- list(baskets.df)

baskets.list[1:2] <- list(baskets.df, '2009-2010')

### Removing elements

baskets.nlist[[1]] <- NULL
baskets.nlist$scores <- NULL
baskets.nlist['scores'] <- NULL

baskets.nlist <- list(scores=baskets.df, season='2010-2011')
baskets.nlist['scores'] <- list(NULL)
baskets.nlist

### Adding extra elements using indices

baskets.nlist$players <- c('Granny','Geraldine')
baskets.nlist[['players']] <- c('Granny','Geraldine')
baskets.nlist['players'] <- list(c('Granny','Geraldine'))

baskets.list[[3]] <- c('Granny','Geraldine')
baskets.list[3] <- list(c('Granny','Geraldine'))

### Combining lists



baskets.list <- list(baskets.team,'2010-2011')
players <- list(rownames(baskets.team))

c(baskets.list, players)

## Reading the output of str() for lists

str(baskets.list)

## Seeing the forest through the trees
}

Print examples of chapter 8 of 'R for Dummies'.

Description

To print a listing of all examples of a chapter, use ch8(). To run all the examples of ch8(), use example(ch8).

Usage

ch08()

ch8()

Examples

if (interactive()) {
# Chapter 8
# Putting the Fun in Functions

# Moving from Scripts to Functions

## Making the script

x <- c(0.458, 1.6653, 0.83112)
percent <- round(x * 100, digits = 1)
result <- paste(percent, "%", sep = "")
print(result)

## Not run: 
# source('pastePercent.R') # Only after saving

## End(Not run)

## Transforming the script

addPercent <- function(x){
  percent <- round(x * 100, digits = 1)
  result <- paste(percent, "%", sep = "")
  return(result)
}

## Using the function

ls()

### Formatting the numbers

new.numbers <- c(0.8223, 0.02487, 1.62, 0.4)
addPercent(new.numbers)

### Playing with function objects

ppaste <- addPercent
ppaste

## Reducing the number of lines

### Returning values by default

# AddPercent function without last return - not written in book
addPercent <- function(x){
  percent <- round(x * 100, digits = 1)
  result <- paste(percent, "%", sep = "")
}

print( addPercent(new.numbers) )

addPercent <- function(x){
  percent <- round(x * 100, digits = 1)
  paste(percent, "%", sep = "")
}

addPercent <- function(x){
  if( !is.numeric(x) ) return(NULL)
  percent <- round(x * 100, digits = 1)
  paste(percent, "%", sep = "")
}

### Breaking the walls

odds <- function(x) x / (1-x)

odds(0.8)

addPercent <- function(x) paste(round(x * 100, digits = 1), "%", sep = "")

# Using Arguments the Smart Way

## Adding more arguments

percentages <- c(58.23, 120.4, 33)
addPercent(percentages/100)

### Adding the mult argument

addPercent <- function(x, mult){
  percent <- round(x * mult, digits = 1)
  paste(percent, "%", sep = "")
}

addPercent(percentages, mult = 1)

### Adding a default value

# addPercent(new.numbers) # Gives error for illustrative purposes
# Error in x * mult : 'mult' is missing

addPercent <- function(x, mult = 100){
  percent <- round(x * mult, digits = 1)
  paste(percent, "%", sep = "")
}

addPercent(new.numbers)

addPercent(percentages, 1)

## Conjuring tricks with dots

addPercent <- function(x, mult = 100, ...){
  percent <- round(x * mult, ...)
  paste(percent, "%", sep = "")
}

addPercent(new.numbers, digits = 2)
addPercent(new.numbers)


addPercent <- function(x, mult = 100, digits = 1){
  percent <- round(x * mult, digits = digits)
  paste(percent, "%", sep = "")
}

## Using functions as arguments

### Applying different ways of rounding

addPercent <- function(x, mult = 100, FUN = round, ...){
  percent <- FUN(x * mult, ...)
  paste(percent, "%", sep = "")
}

addPercent(new.numbers, FUN = signif, digits = 3)

### Using anonymous functions

profits <- c(2100, 1430, 3580, 5230)
rel.profit <- function(x) round(x / sum(x) * 100)
addPercent(profits,
                FUN = function(x) round(x / sum(x) * 100) )

addPercent(profits / sum(profits))

# Coping with Scoping



## Crossing the borders

### Creating a test case

x <- 1:5
test <- function(x){
  cat("This is x:", x, "\n")
  rm(x)
  cat("This is x after removing it:",x,"\n")
}

test(5:1)

### Searching the path

## Using internal functions

calculate.eff <- function(x, y, control){
  min.base <- function(z) z - mean(control)
  min.base(x) / min.base(y)
}


half <- c(2.23, 3.23, 1.48)
full <- c(4.85, 4.95, 4.12)
nothing <- c(0.14, 0.18, 0.56, 0.23)
calculate.eff(half, full, nothing)

# Dispatching to a Method

## Finding the methods behind the function

print

### Using methods with UseMethod

small.one <- data.frame(a = 1:2, b = 2:1)
print.data.frame(small.one)

### Using default methods

print.default(small.one)

## Doing it yourself

### Adapting the addPercent function

addPercent.character <- function(x){
  paste(x,"%",sep="")
}

# Not written out in the book - needed for rest code # 
addPercent.numeric <- function(x, mult = 100, FUN = round, ...){
  percent <- FUN(x * mult, ...)
  paste(percent, "%", sep = "")
}

addPercent <- function(x,...){
  UseMethod("addPercent")
}

addPercent(new.numbers, FUN = floor)

addPercent(letters[1:6])

# Adding a default function

# addPercent(small.one) # Gives error on purpose
# Error in UseMethod("addPercent") :
#  no applicable method for 'addPercent' applied to an object of class "data.frame"

addPercent.default <- function(x){
  cat('You should try a numeric or character vector.\n')
}
}

Print examples of chapter 9 of 'R for Dummies'.

Description

To print a listing of all examples of a chapter, use ch9(). To run all the examples of ch9(), use example(ch9).

Usage

ch09()

ch9()

Examples

if (interactive()) {
# Chapter 9
# Controlling the Logical Flow

#Making Choices with if Statements

priceCalculator <- function(hours, pph=40){
    net.price <- hours * pph
    round(net.price)
}

priceCalculator <- function(hours, pph=40){
    net.price <- hours * pph
    if (hours > 100) {
      net.price <- net.price * 0.9
    }
    round(net.price)
}

priceCalculator(hours = 55)
priceCalculator(hours = 110)

priceCalculator <- function(hours, pph=40){
    net.price <- hours * pph
    if(hours > 100) net.price <- net.price * 0.9
    round(net.price)
}

?'if'
?"if"
?`if`

## Doing Something Else with an if...else Statement

priceCalculator <- function(hours, pph=40, public=TRUE){
    net.price <- hours * pph
    if(hours > 100) net.price <- net.price * 0.9
    if(public) {
      tot.price <- net.price * 1.06
    } else {
      tot.price <- net.price * 1.12
    }
    round(tot.price)
}

priceCalculator(25,public=TRUE)
priceCalculator(25,public=FALSE)

priceCalculator <- function(hours, pph=40, public=TRUE){
    net.price <- hours * pph
    if(hours > 100) net.price <- net.price * 0.9
    if(public) tot.price <- net.price * 1.06 else
               tot.price <- net.price * 1.12
    round(tot.price)
}

priceCalculator <- function(hours, pph=40, public=TRUE){
    net.price <- hours * pph
    if(hours > 100) net.price <- net.price * 0.9
    tot.price <- net.price * if(public) 1.06 else 1.12
    round(tot.price)
}

# Vectorizing Choices

## Looking at the problem

#priceCalculator(c(25,110))
priceCalculator(110)
c(25, 110) > 100

## Choosing based on a logical vector

### Understanding how it works

ifelse(c(1,3) < 2.5 , 1:2 , 3:4)

### Trying it out

my.hours <- c(25,110)
my.hours * 40 * ifelse(my.hours > 100, 0.9, 1)

### Adapting the function

priceCalculator <- function(hours,pph=40,public){
    net.price <- hours * pph
    net.price <- net.price * ifelse(hours > 100 , 0.9, 1)
    tot.price <- net.price * ifelse(public, 1.06, 1.12)
    round(tot.price)
}

clients <- data.frame(
  hours = c(25, 110, 125, 40),
  public = c(TRUE,TRUE,FALSE,FALSE)
)

with(clients, priceCalculator(hours, public = public))

# Making Multiple Choices

## Chaining if...else statements


# Code example # NOT run
#if(client=='private'){
#  tot.price <- net.price * 1.12      # 12% VAT
#} else {
#  if(client=='public'){
#    tot.price <- net.price * 1.06    # 6% VAT
#  } else {
#    tot.price <- net.price * 1    # 0% VAT
#  }
#}

# Code example # NOT run
#if(client=='private'){
#    tot.price <- net.price * 1.12
#} else if(client=='public'){
#    tot.price <- net.price * 1.06
#} else {
#    tot.price <- net.price
#}

# Code example # NOT run
#VAT <- ifelse(client=='private', 1.12,
#          ifelse(client == 'public', 1.06, 1)
#       )
#tot.price <- net.price * VAT
#

## Switching between possibilities

### Making choices with switch

# Code example # NOT run
# VAT <- switch(client, private=1.12, public=1.06, abroad=1)



### Using default values in switch

# Code example # NOT run
# VAT <- switch(client, private=1.12, public=1.06, 1)

client <- 'other'
switch(client, private=1.12, public=1.06, 1)


# Looping Through Values

## Constructing a for loop

## Calculating values in a for loop

### Using the values of the vector

priceCalculator <- function(hours, pph=40, client){
    net.price <- hours * pph *
                   ifelse(hours > 100, 0.9, 1)

    VAT <- numeric(0)
    for(i in client){
      VAT <- c(VAT,switch(i, private=1.12, public=1.06, 1))
    }

    tot.price <- net.price * VAT
    round(tot.price)
}


clients$type <- c('public','abroad','private','abroad')
priceCalculator(clients$hours, client=clients$type)

### Using loops and indices

nclient <- length(client)
VAT <- numeric(nclient)
for(i in seq_along(client)){
  VAT[i] <- switch(client[i], private=1.12, public=1.06, 1)
}
VAT

# Looping without Loops: Meeting the Apply Family

songline <- 'Get out of my dreams...'
for(songline in 1:5) print('...Get into my car!')

songline

## Looking at the family features

## Meeting three of the members


## Applying functions on rows and columns

### Counting birds

counts <- matrix(c(3,2,4,6,5,1,8,6,1), ncol=3)
colnames(counts) <- c('sparrow','dove','crow')
counts

apply(counts, 2, max)

### Adding extra arguments

counts[2, 2] <- NA
apply(counts,2,max)
apply(counts, 2, max, na.rm=TRUE)

## Applying functions to listlike objects

### Applying a function to a vector

#### Using switch on vectors

sapply(c('a','b'), switch, a='Hello', b='Goodbye')

#### Replacing a complete for loop with a single statement

priceCalculator <- function(hours, pph=40, client){
  net.price <- hours * pph * ifelse(hours > 100, 0.9, 1)

  VAT <- sapply(client, switch, private=1.12, public=1.06, 1)

  tot.price <- net.price * VAT
  round(tot.price)
}

### Applying a function to a data frame

sapply(clients,class)

### Simplifying results (or not) with sapply

sapply(clients, unique)

### Getting lists using lapply

sapply(clients[c(1,3), ], unique)

lapply(clients[c(1,3), ], unique)
}

Print examples of chapter 10 of 'R for Dummies'.

Description

To print a listing of all examples of a chapter, use ch10(). To run all the examples of ch10(), use example(ch10).

Usage

ch10()

Examples

if (interactive()) {
# Chapter 10
# Debugging Your Code

# NOTE : Much code is commented out, as they generate
# errors on purpose. Uncomment the code and run the
# line to see the error and try the debugging out

# Knowing What to Look For

# Reading Errors and Warnings

## Reading error messages

# "a" + 1
# Error in "a" + 1 : non-numeric argument to binary operator

# data.frame(1:10,10:1,)
# Error in data.frame(1:10, 10:1, ) : argument is missing, with no default

## Caring about warnings (or not)

x <- 1:10
#y <- if (x < 5 ) 0 else 1

x <- 4
#sqrt(x - 5)

plot(1:10, 10:1, col='green')


# Going Bug Hunting

## Calculating the logit

# checks input and does logit calculation
logit <- function(x){
  x <- ifelse(x < 0 | x > 1, "NA", x)
  log(x / (1 - x) )
}
# transforms percentage to number and calls logit
logitpercent <- function(x){
  x <- gsub("%", "", x)
  logit(as.numeric(x))
}



## Knowing where an error comes from

# logitpercent('50%')
# Error in 1 - x : non-numeric argument to binary operator

# traceback()

## Looking inside a function


### Telling R which function to debug

# debug(logit)
# logitpercent('50%')

### Stepping through the function

### Start browsing from within the function

logit <- function(x){
  x <- ifelse(x < 0 | x > 1, "NA", x)
  browser()
  log(x / (1 - x) )
}

# logit(50)

# Generating Your Own Messages

## Creating errors

logit <- function(x){
  if( any(x < 0 | x > 1) ) stop('x not between 0 and 1')
  log(x / (1 - x) )
}


# logitpercent(c('50%','150%'))
# Error in logit(as.numeric(x)/100) : x not between 0 and 1

## Creating warnings
# Function wrapped around for illustrative purposes
# In book only body is given
logit <- function(x){
  x <- ifelse(x < 0 | x > 1, NA, x )
  if( any(is.na(x)) ) warning('x not between 0 and 1')
  log(x / (1 - x) )
}

# logitpercent(c('50%','150%')) # throws error

# Recognizing the Mistakes You're Sure to Make

## Starting with the wrong data


## Having your data in the wrong format



### Dropping dimensions when you don't expect it

rowsum.df <- function(x){
  id <- sapply(x,is.numeric)
  rowSums(x[, id])
}

# rowsum.df(sleep)

### Messing up with lists
strsplit('this is a sentence',' ')[2]

strsplit('this is a sentence',' ')

strsplit('this is a sentence',' ')[[1]][2]

customer <- c('Johan Delong','Marie Petit')
namesplit <- strsplit(customer,' ')

paste(namesplit[2],collapse='.')

paste(namesplit[[2]],collapse='.')



### Mixing up factors and numeric vectors

cyl.factor <- as.factor(mtcars$cyl)

median(as.numeric(cyl.factor))

as.numeric(levels(cyl.factor))[cyl.factor]
}

Print examples of chapter 11 of 'R for Dummies'.

Description

To print a listing of all examples of a chapter, use ch11(). To run all the examples of ch11(), use example(ch11).

Usage

ch11()

Examples

if (interactive()) {
# Chapter 11 - Getting Help


# Finding Information in the R Help Files

## When you know exactly what you're looking for

#?date

## When you don't know exactly what you're looking for

#??date


# Searching the Web for Help with R

## Not run: 
RSiteSearch("cluster analysis")

## End(Not run)

## Not run: 
install.packages("sos")

## End(Not run)
library("sos")
## Not run: 
findFn("cluster")

## End(Not run)

# Getting Involved in the R Community

## Using the R mailing lists

## Discussing R on Stack Overflow and Stack Exchange

## Tweeting about R

# Making a Minimal Reproducible Example

dput(cars[1:4, ])

## Creating sample data with random values

set.seed(1)
x <- rnorm(5)
x

cards <- c(1:9, "J", "Q", "K", "A")
suits <- c("Spades", "Diamonds", "Hearts", "Clubs")
deck <- paste(rep(suits, each=13), cards)
set.seed(123)
sample(deck, 7)

set.seed(5)
sample(LETTERS[1:3], 12, replace=TRUE)

set.seed(42)
dat <- data.frame(
   x = sample(1:5),
   y = sample(c("yes", "no"), 5, replace = TRUE)
)
dat

dput(cars[1:4, ])

## Producing minimal code

## Providing the necessary information

sessionInfo()
}

Print examples of chapter 12 of 'R for Dummies'.

Description

To print a listing of all examples of a chapter, use ch12(). To run all the examples of ch12(), use example(ch12).

Usage

ch12()

Examples

if (interactive()) {
# Chapter 12
# Getting Data into and out of R

# NOTE : Most of the code depends on actions, directories
# and the presence of files. Code that isn't runnable is
# commented out.

# Getting Data into R

## Entering data in the R text editor

elements <- data.frame()
# elements <- edit(elements)

# print(elements)

## Using the Clipboard to copy and paste
# Reminder : This only works on Windows

## Not run: 
# x <- readClipboard()

## End(Not run)
# x
## Not run: 
# x <- readClipboard()

## End(Not run)
# x
# x <- read.table(file = "clipboard", sep = "\t", header=TRUE)
# x

## Reading data in CSV files

### Using read.csv() to import data

# elements <- read.csv(file.path("f:", "elements.csv"))
# str(elements)
# elements <- read.csv(file.path("f:", "elements.csv"), stringsAsFactors=FALSE)
# str(elements)

### Using read.table() to import tabular data in text files

## Reading data from Excel
## Not run: 
# install.packages("XLConnect")

## End(Not run)
# library("XLConnect")
# excel.file <- file.path("~/Elements.xlsx")

# elements <- readWorksheetFromFile(excel.file, sheet=1)
# elements <- readWorksheetFromFile(excel.file, sheet="Sheet1")

## Working with other data types

# library(foreign)
# read.spss(file="location/of/myfile")

# Getting Your Data out of R


# writeClipboard(names(iris))

# write.table(head(iris), file="clipboard", sep="\t", row.names=FALSE)

# Working with Files and Folders

## Understanding the working directory
getwd()

# setwd("F:/git/roxygen2")
# getwd()
# setwd("F:\git\stringr")
# getwd()

file.path("f:", "git", "surveyor")

# setwd(file.path("F:", "git", "roxygen2"))
# getwd()

file.path("F:", "git", "roxygen2", "roxygen2", "README.md" )

## Manipulating files

# list.files(file.path("F:", "git", "roxygen2"))
my.file <- tempfile()
my.file
write.csv(iris, file=my.file)
list.files(tempdir())

file.iris <- read.csv(my.file)

file.remove(my.file)
list.files(tempdir())


}

Print examples of chapter 13 of 'R for Dummies'.

Description

To print a listing of all examples of a chapter, use ch13(). To run all the examples of ch13(), use example(ch13).

Usage

ch13()

Examples

if (interactive()) {
# C hapter 13 - Manipulating and Processing Data

# Deciding on the Most Appropriate Data Structure

# Creating Subsets of Your Data

## Understanding the three subset operators
## Understanding the five ways of specifying the subset

str(islands)
islands[]
islands[c(8, 1, 1, 42)]
islands[-(3:46)]
islands[islands < 20]
islands[c("Madagascar", "Cuba")]

## Subsetting data frames

str(iris)
iris[1:5, ]
iris[, c("Sepal.Length", "Sepal.Width")]
iris[, 'Sepal.Length']
iris[, 'Sepal.Length', drop=FALSE]
iris['Sepal.Length']
iris[1:5, c("Sepal.Length", "Sepal.Width")]

### Taking samples from data

sample(1:6, 10, replace=TRUE)

set.seed(1)
sample(1:6, 10, replace=TRUE)
sample(1:6, 10, replace=TRUE)

set.seed(1)
sample(1:6, 10, replace=TRUE)

set.seed(123)
index <- sample(1:nrow(iris), 5)
index
iris[index, ]

### Removing duplicate data

duplicated(c(1,2,1,3,1,4))
duplicated(iris)
which(duplicated(iris))
iris[!duplicated(iris), ]

index <- which(duplicated(iris))
iris[-index, ]

### Removing rows with missing data

str(airquality)
complete.cases(airquality)

x <- airquality[complete.cases(airquality), ]
str(x)
x <- na.omit(airquality)



# Adding Calculated Fields to Data

## Doing arithmetic on columns of a data frame

x <- iris$Sepal.Length / iris$Sepal.Width
head(x)

## Using with and within to improve code readability

y <- with(iris, Sepal.Length / Sepal.Width)
head(y)
identical(x, y)

iris$ratio <- iris$Sepal.Length / iris$Sepal.Width
iris <- within(iris, ratio <- Sepal.Length / Sepal.Width)
head(iris$ratio)

## Creating subgroups or bins of data

### Using cut to create a fixed number of subgroups

head(state.x77)
frost <- state.x77[, "Frost"]
head(frost, 5)
cut(frost, 3, include.lowest=TRUE)

### Adding labels to cut

cut(frost, 3, include.lowest=TRUE, labels=c("Low", "Med", "High"))

### Using table to count the number of observations

x <- cut(frost, 3, include.lowest=TRUE, labels=c("Low", "Med", "High"))
table(x)
x


# Combining and Merging Data Sets

## Creating sample data to illustrate merging

all.states <- as.data.frame(state.x77)
all.states$Name <- rownames(state.x77)
rownames(all.states) <- NULL
str(all.states)

### Creating a subset of cold states

cold.states <- all.states[all.states$Frost>150, c("Name", "Frost")]
cold.states

### Creating a subset of large states

large.states <- all.states[all.states$Area>=100000, c("Name", "Area")]
large.states

## Using the merge() function

### Using merge to find the intersection of data

merge(cold.states, large.states)

### Understanding the different types of merge

merge(cold.states, large.states, all=TRUE)


## Working with lookup tables

### Finding a match

index <- match(cold.states$Name, large.states$Name)
index

large.states[na.omit(index), ]

### Making sense of %in%

index <- cold.states$Name %in% large.states$Name
index
!is.na(match(cold.states$Name,large.states$Name))
cold.states[index, ]

# Sorting and Ordering Data

some.states <- data.frame(
     Region = state.region,
     state.x77)

some.states <- some.states[1:10, 1:3]
some.states

## Sorting vectors

### Sorting a vector in ascending order

sort(some.states$Population)

### Sorting a vector in decreasing order

sort(some.states$Population, decreasing=TRUE)

## Sorting data frames

### Getting the order

order.pop <- order(some.states$Population)
order.pop

some.states$Population[order.pop]

## Sorting a data frame in ascending order

some.states[order.pop, ]
order(some.states$Population)
order(some.states$Population, decreasing=TRUE)

some.states[order(some.states$Population, decreasing=TRUE), ]

### Sorting on more than one column

index <- with(some.states, order(Region, Population))
some.states[index, ]

### Sorting multiple columns in mixed order
index <- order(-xtfrm(some.states$Region), some.states$Population)
some.states[index, ]

# Traversing Your Data with the Apply Functions

## Using the apply() function to summarize arrays

str(Titanic)
apply(Titanic, 1, sum)
apply(Titanic, 3, sum)
apply(Titanic, c(3, 4), sum)

## Using lapply() and sapply() to traverse a list or data frame

lapply(iris, class)
sapply(iris, class)
sapply(iris, function(x) ifelse(is.numeric(x), mean(x), NA))

## Using tapply() to create tabular summaries

tapply(iris$Sepal.Length, iris$Species, mean)
with(iris, tapply(Sepal.Length, Species, mean))

### Using tapply() to create higher-dimensional tables

str(mtcars)
cars <- within(mtcars,
    am <- factor(am, levels=0:1, labels=c("Automatic", "Manual"))
)

with(cars, tapply(mpg, am, mean))
with(cars, tapply(mpg, list(gear, am), mean))

### Using aggregate()

with(cars, aggregate(mpg, list(gear=gear, am=am), mean))

# Getting to Know the Formula Interface


aggregate(mpg ~ gear + am, data=cars, mean)

aov(mpg ~ gear + am, data=cars)

library(lattice)
xyplot(mpg ~ gear + am, data=cars)


# Whipping Your Data into Shape


## Understanding data in long and wide format


## Getting started with the reshape2 package

## Not run: 
install.packages("reshape2")

## End(Not run)
library("reshape2")

goals <- data.frame(
    Game = c("1st", "2nd", "3rd", "4th"),
    Venue = c("Bruges", "Ghent", "Ghent", "Bruges"),
    Granny = c(12, 4, 5, 6),
    Geraldine = c(5, 4, 2, 4),
    Gertrude = c(11, 5, 6, 7)
)

## Melting data to long format

mgoals <- melt(goals)
mgoals <- melt(goals, id.vars=c("Game", "Venue"))
mgoals

## Casting data to wide format

dcast(mgoals,  Venue + Game ~ variable, sum)
dcast(mgoals, variable ~ Venue , sum)
dcast(mgoals,  Venue ~ variable , sum)

dcast(mgoals,  Venue + variable ~ Game , sum)

library(ggplot2)
ggplot(mgoals, aes(x=variable, y=value, fill=Game)) + geom_bar(stat="identity")
}

Print examples of chapter 14 of 'R for Dummies'.

Description

To print a listing of all examples of a chapter, use ch14(). To run all the examples of ch14(), use example(ch14).

Usage

ch14()

Examples

if (interactive()) {
# Chapter 14
# Summarizing Data

# Starting with the Right Data

## Using factors or numeric data

## Counting unique values

sapply(mtcars, function(x) length(unique(x)))

## Preparing the data

cars <- mtcars[c(1,2,9,10)]
cars$gear <- ordered(cars$gear)
cars$am <- factor(cars$am, labels=c('auto', 'manual'))
str(cars)

# Describing Continuous Variables

## Talking about the center of your data

mean(cars$mpg)
median(cars$cyl)

## Describing the variation
sd(cars$mpg)

## Checking the quantiles

### Calculating the range
range(cars$mpg)

### Calculating the quantiles
quantile(cars$mpg)

### Getting on speed with the quantile function
quantile(cars$mpg, probs=c(0.05, 0.95))

# Describing Categories

## Counting appearances

### Creating a table
amtable <- table(cars$am)
amtable

### Working with tables

## Calculating proportions
amtable/sum(amtable)
prop.table(amtable)

## Finding the center
id <- amtable == max(amtable)
names(amtable)[id]

# Describing Distributions

## Plotting histograms

### Making the plot
hist(cars$mpg, col='grey')

### Playing with breaks
hist(cars$mpg, breaks=c(5,15,25,35))

## Using frequencies or densities

### Creating a density plot

mpgdens <- density(cars$mpg)
plot(mpgdens)

### Plotting densities in a histogram
hist(cars$mpg, col='grey', freq=FALSE)
lines(mpgdens)

# Describing Multiple Variables

## Summarizing a complete dataset

### Getting the output
summary(cars)

### Fixing a problem

cars$cyl <- as.factor(cars$cyl)

## Plotting quantiles for subgroups

boxplot(mpg ~ cyl, data=cars)

## Tracking correlations

names(iris)

### Looking at relations
plot(iris[-5])

### Getting the numbers

with(iris, cor(Petal.Width, Petal.Length))

### Calculating correlations for multiple variables

iris.cor <- cor(iris[-5])
str(iris.cor)

iris.cor['Petal.Width', 'Petal.Length']

### Dealing with missing values

# Working with Tables

## Creating a two-way table

### Creating a table from two variables

with(cars, table(am, gear))

### Creating tables from a matrix

trial <- matrix(c(34,11,9,32), ncol=2)
colnames(trial) <- c('sick', 'healthy')
rownames(trial) <- c('risk', 'no_risk')
trial.table <- as.table(trial)
trial.table

### Extracting the numbers

trial.table['risk', 'sick']

##Converting tables to a data frame

trial.df <- as.data.frame(trial)
str(trial.df)

trial.table.df <- as.data.frame(trial.table)
str(trial.table.df)

## Looking at margins and proportions

### Adding margins to the table

addmargins(trial.table)
addmargins(trial.table,margin=2)

### Calculating proportions

prop.table(trial.table)

### Calculating proportions over columns and rows
prop.table(trial.table, margin=1)
}

Print examples of chapter 15 of 'R for Dummies'.

Description

To print a listing of all examples of a chapter, use ch15(). To run all the examples of ch15(), use example(ch15).

Usage

ch15()

Examples

if (interactive()) {
# Chapter 15
# Testing Differences and Relations

# Taking a Closer Look at Distributions

## Observing beavers
str(beaver2)

## Testing normality graphically
library(lattice)
histogram(~temp | factor(activ), data=beaver2)

## Using quantile plots

### Comparing two samples

qqplot(beaver2$temp[beaver2$activ==1],
       beaver2$temp[beaver2$activ==0])

### Using a QQ plot to check for normality

qqnorm( beaver2$temp[beaver2$activ==0], main='Inactive')
qqline( beaver2$temp[beaver2$activ==0] )

## Testing normality in a formal way

shapiro.test(beaver2$temp)
result <- shapiro.test(beaver2$temp)
result$p.value

with(beaver2, tapply(temp, activ, shapiro.test))

# Comparing Two Samples

## Testing differences

### Carrying out a t-test

t.test(temp ~ activ, data=beaver2)


activetemp <- beaver2$temp[beaver2$activ==1]
inactivetemp <- beaver2$temp[beaver2$activ==0]
t.test(activetemp, inactivetemp)

### Dropping assumptions

wilcox.test(temp ~ activ, data=beaver2)

### Testing direction

## Comparing paired data

t.test(extra ~ group, data=sleep, paired=TRUE)

# Testing Counts and Proportions

## Checking out proportions
survivors <- matrix(c(1781,1443,135,47), ncol=2)
colnames(survivors) <- c('survived','died')
rownames(survivors) <- c('no seat belt','seat belt')
survivors

result.prop <- prop.test(survivors)
result.prop

## Analyzing tables

### Testing contingency of tables
chisq.test(survivors)

### Testing tables with more than two columns
str(HairEyeColor)
HairEyeMargin <- margin.table(HairEyeColor, margin=c(1,2))
HairEyeMargin

chisq.test(HairEyeMargin)

## Extracting test results
str(result)
t.test(temp ~ activ, data=beaver2)$p.value

# Working with Models

## Analyzing variances
str(InsectSprays)

### Building the model
AOVModel <- aov(count ~ spray, data=InsectSprays)

### Looking at the object
AOVModel

## Evaluating the differences
summary(AOVModel)

### Checking the model tables
model.tables(AOVModel, type='effects')

### Looking at the individual differences
Comparisons <- TukeyHSD(AOVModel)
Comparisons$spray['D-C',]

### Plotting the differences
plot(Comparisons, las=1)

## Modeling linear relations

### Building a linear model
Model <- lm(mpg ~ wt, data=mtcars)

### Extracting information from the model

coef.Model <- coef(Model)
coef.Model

plot(mpg ~ wt, data = mtcars)
abline(a=coef.Model[1], b=coef.Model[2])

## Evaluating linear models

### Summarizing the model
Model.summary <- summary(Model)
Model.summary

coef(Model.summary)

### Testing the impact of model terms
Model.anova <- anova(Model)
Model.anova

Model.anova['wt','Pr(>F)']

## Predicting new values

### Getting the values
new.cars <- data.frame(wt=c(1.7, 2.4, 3.6))
predict(Model, newdata=new.cars)

### Having confidence in your predictions
predict(Model, newdata=new.cars, interval='confidence')
predict(Model,newdata=new.cars, interval='prediction')


}

Print examples of chapter 16 of 'R for Dummies'.

Description

To print a listing of all examples of a chapter, use ch16(). To run all the examples of ch16(), use example(ch16).

Usage

ch16()

Examples

if (interactive()) {
# Chapter 16 - Using Base Graphics

# Creating Different Types of Plots


## Getting an overview of plot

large.islands <- head(sort(islands, decreasing=TRUE), 10)

plot(large.islands, main="Land area of continents and islands",
   ylab="Land area in square miles")
text(large.islands, labels=names(large.islands), adj=c(0.5, 1))


## Adding points and lines to a plot

plot(faithful)

## Adding points

short.eruptions <- with(faithful, faithful[eruptions < 3, ])

plot(faithful)
points(short.eruptions, col="red", pch=19)

## Changing the shape of points

## Changing the color

head(colors(), 10)

## Adding lines to a plot

fit <- lm(waiting~eruptions, data=faithful)

plot(faithful)
lines(faithful$eruptions, fitted(fit), col="blue")
abline(v=3, col="purple")

abline(h=mean(faithful$waiting))
abline(a=coef(fit)[1], b=coef(fit)[2])
abline(fit, col = "red")

# Different plot types

plot(LakeHuron, type="l", main='type="l"')
plot(LakeHuron, type="p", main='type=p"')
plot(LakeHuron, type="b", main='type="b"')


x <- seq(0.5, 1.5, 0.25)
y <- rep(1, length(x))
plot(x, y, type="n")
points(x, y)

with(mtcars, plot(mpg, disp))
with(mtcars, boxplot(disp, mpg))
with(mtcars, hist(mpg))

# Controlling Plot Options and Arguments

## Adding titles and axis labels

plot(faithful,
   main = "Eruptions of Old Faithful",
   xlab = "Eruption time (min)",
   ylab = "Waiting time to next eruption (min)")


## Changing plot options

### The axes label style

plot(faithful, las=1)

### The box type

plot(faithful, bty="n")

### More than one option

plot(faithful, las=1, bty="l", col="red", pch=19)

### Font size of text and axes

x <- seq(0.5, 1.5, 0.25)
y <- rep(1, length(x))
plot(x, y, main="Effect of cex on text size")
text(x, y+0.1, labels=x, cex=x)

plot(x, y, main="Effect of cex.main, cex.lab and cex.axis",
  cex.main=1.25, cex.lab=1.5, cex.axis=0.75)

## Putting multiple plots on a single page

old.par <- par(mfrow=c(1, 2))
plot(faithful, main="Faithful eruptions")
plot(large.islands, main="Islands", ylab="Area")
par(old.par)


# Saving Graphics to Image Files

filename <- file.path(tempdir(), "faithful.png")
## Not run: 
png(filename=filename)

## End(Not run)
plot(faithful)
## Not run: 
dev.off()

## End(Not run)

}

Print examples of chapter 17 of 'R for Dummies'.

Description

To print a listing of all examples of a chapter, use ch17(). To run all the examples of ch17(), use example(ch17).

Usage

ch17()

Examples

if (interactive()) {
# Chapter 17 - Creating Faceted Graphics with Lattice


# Creating a Lattice Plot

str(mtcars)

## Loading the lattice package

library("lattice")

## Making a lattice scatterplot

xyplot(mpg ~ hp | factor(cyl), data=mtcars)

## Adding trend lines

xyplot(mpg ~ hp | factor(cyl), data=mtcars,
   type=c("p", "r"))


# Changing Plot Options

## Adding titles and labels

xyplot(mpg ~ hp | factor(cyl), data=mtcars,
   type=c("p", "r"),
   main="Fuel economy vs. Performance",
   xlab="Performance (horse power)",
   ylab="Fuel economy (miles per gallon)",
)

xyplot(mpg ~ hp | factor(cyl), data=mtcars,
   type=c("p", "r"),
   main=list(
       label="Fuel economy vs. Performance given Number of Cylinders",
       cex=0.75)
)

## Changing the font size of titles and labels

xyplot(mpg ~ hp | factor(cyl), data=mtcars,
   type=c("p", "r"),
   main=list(
       label="Fuel economy vs. Performance given Number of Cylinders",
       cex=0.75),
   xlab=list(
       label="Performance (horse power)",
       cex=0.75),
   ylab=list(
       label="Fuel economy (miles per gallon)",
       cex=0.75),
   scales=list(cex=0.5)
)


## Using themes to modify plot options

xyplot(mpg ~ hp | factor(cyl), data=mtcars,
   type=c("p", "r"),
   par.settings=simpleTheme(col="red", col.line="blue")
)


# Plotting Different Types

## Making a bar chart

mtcars$cars <- rownames(mtcars)

barchart(cars ~ mpg | factor(cyl), data=mtcars,
   main="barchart",
   scales=list(cex=0.5),
   layout=c(3, 1)
)

## Making a box-and-whisker plot

bwplot(~ hp | factor(cyl), data=mtcars, main="bwplot")


# Plotting Data in Groups

## Using data in tall format

str(longley)
library("reshape2")
mlongley <- melt(longley, id.vars="Year")

str(mlongley)

xyplot(value ~ Year | variable, data=mlongley,
   layout=c(6, 1),
   par.strip.text=list(cex=0.7),
   scales=list(cex=0.7)
)

## Creating a chart with groups

mtcars$cars <- rownames(mtcars)
mtcars$am <- with(mtcars, ifelse(am==0, "Automatic", "Manual"))

barchart(cars ~ mpg | factor(cyl), data=mtcars,
   group=am,
   scales=list(cex=0.5),
   layout=c(3, 1),
)

## Adding a key

barchart(cars ~ mpg | factor(cyl), data=mtcars,
   main="barchart with groups",
   group=am,
   auto.key=TRUE,
   par.settings = simpleTheme(col=c("grey80", "grey20")),
   scales=list(cex=0.5),
   layout=c(3, 1)
)


# Printing and Saving a Lattice Plot

## Assigning a lattice plot to an object

my.plot <- xyplot(mpg ~ hp | cyl, data=mtcars)
class(my.plot)

## Printing a lattice plot in a script

xyplot(mpg ~ hp | cyl, data=mtcars)

my.plot <- xyplot(mpg ~ hp | cyl, data=mtcars)
print(my.plot)


## Saving a lattice plot to file

filename <- file.path(tempdir(), "xyplot")
## Not run: 
trellis.device(device="png", filename=filename)

## End(Not run)
print(my.plot)
## Not run: 
dev.off()

## End(Not run)

}

Print examples of chapter 18 of 'R for Dummies'.

Description

To print a listing of all examples of a chapter, use ch18(). To run all the examples of ch18(), use example(ch18).

Usage

ch18()

Examples

if (interactive()) {
# Chapter 18 - Looking At ggplot2 Graphics

# Installing and Loading ggplot2

## Not run: 
install.packages("ggplot2")

## End(Not run)
library("ggplot2")

# Looking At Layers

ggplot(faithful, aes(x=eruptions, y=waiting)) + geom_point() + stat_smooth()

# Using Geoms and Stats

## Defining what data to use

## Mapping data to plot aesthetics

ggplot(faithful, aes(x=eruptions, y=waiting)) + geom_point() + stat_smooth()

## Getting geoms

### Creating a bar chart

ggplot(quakes, aes(x=depth)) + geom_bar()
ggplot(quakes, aes(x=depth)) + geom_histogram(binwidth=50)

quakes.agg <- aggregate(mag ~ round(depth, -1), data=quakes, FUN=length)
names(quakes.agg) <- c("depth", "mag")

ggplot(quakes.agg, aes(x=depth, y=mag)) +
   geom_bar(stat="identity")


### Making a scatterplot

ggplot(quakes, aes(x=long, y=lat)) + geom_point()


### Creating line charts

ggplot(longley, aes(x=Year, y=Unemployed)) + geom_line()


# Sussing Stats

## Binning data

ggplot(quakes, aes(x=depth)) + geom_bar()
ggplot(quakes, aes(x=depth)) + stat_bin(binwidth = 50)

## Smoothing data

ggplot(longley, aes(x=Year, y=Employed)) + geom_point()

ggplot(longley, aes(x=Year, y=Employed)) +
   geom_point() + stat_smooth()

ggplot(longley, aes(x=Year, y=Employed)) +
   geom_point() + stat_smooth(method="lm")


# Adding Facets, Scales, and Options

## Adding facets

ggplot(mtcars, aes(x=hp, y=mpg)) + geom_point()

ggplot(mtcars, aes(x=hp, y=mpg)) + geom_point() +
   stat_smooth(method="lm") + facet_grid(~cyl)


ggplot(mtcars, aes(x=hp, y=mpg)) +
   geom_point(aes(shape=factor(cyl), colour=factor(cyl)))

ggplot(mtcars, aes(x=hp, y=mpg)) +
   geom_point(aes(shape=factor(cyl), colour=factor(cyl))) +
   scale_shape_discrete(name="Cylinders") +
   scale_colour_discrete(name="Cylinders")

## Changing options

ggplot(mtcars, aes(x=hp, y=mpg)) + geom_point(color="red") +
   xlab("Performance (horse power") +
   ylab("Fuel consumption (mpg)") +
   ggtitle("Motor car comparison")

}

Print examples of chapter 19 of 'R for Dummies'.

Description

To print a listing of all examples of a chapter, use ch19(). To run all the examples of ch19(), use example(ch19).

Usage

ch19()

Examples

if (interactive()) {
# Chapter 19 - Ten Things You Can Do in R That You Would've Done in Microsoft Excel

# Adding Row and Column Totals

iris.num <- iris[, -5]

colSums(iris.num)
colMeans(iris.num)

apply(iris.num, 2, min)
apply(iris.num, 2, max)

sapply(iris.num, min)
sapply(iris.num, max)


# Formatting Numbers

format(12345.6789, digits=9, decimal.mark=",",
   big.mark=" ",small.mark=".", , small.interval=3)

x <- colMeans(mtcars[, 1:4])
format(x, digits=2, nsmall=2)

x <- seq(0.5, 0.55, 0.01)
sprintf("%.1f %%", 100*x)

set.seed(1)
x <- 1000*runif(5)
sprintf("$ %3.2f", x)

stuff <- c("bread", "cookies")
price <- c(2.1, 4)
sprintf("%s costed $ %3.2f ", stuff, price)


# Sorting Data

with(mtcars, mtcars[order(hp), ])
with(mtcars, mtcars[order(hp, decreasing=TRUE), ])

# Making Choices with If

mtcars <- within(mtcars,
   mpgClass <- ifelse(mpg < mean(mpg), "Low", "High"))

mtcars[mtcars$mpgClass == "High", ]


# Calculating Conditional Totals

with(mtcars, mean(mpg))
with(mtcars, mean(mpg[hp < 150]))
with(mtcars, mean(mpg[hp >= 150]))
with(mtcars, length(mpg[hp > 150]))


# Transposing Columns or Rows

x <- matrix(1:12, ncol=3)
x
t(x)

t(mtcars[1:4, ])


# Finding Unique or Duplicated Values

unique(mtcars$cyl)
dupes <- duplicated(iris)
head(dupes)
which(dupes)
iris[dupes, ]
iris[!dupes, ]
nrow(iris[!dupes, ])


# Working with Lookup Tables

index <- match("Toyota Corolla", rownames(mtcars))
index
mtcars[index, 1:4]


# Working with Pivot Tables

with(mtcars, tapply(hp, list(cyl, gear), mean))
aggregate(hp~cyl+gear+am, mtcars, mean)


# Using the Goal Seek and Solver

sales <- function(price) { 100 - 0.5 * price }
revenue <- function(price) { price * sales(price) }


par(mfrow=c(1, 2))
curve(sales, from=50, to=150, xname="price", ylab="Sales", main="Sales")
curve(revenue, from=50, to=150, xname="price", ylab="Revenue", main="Revenue")
par(mfrow=c(1, 1))

optimize(revenue, interval=c(50, 150), maximum=TRUE)
}

Print examples of chapter 20 of 'R for Dummies'.

Description

To print a listing of all examples of a chapter, use ch20(). To run all the examples of ch20(), use example(ch20).

Usage

ch20()

Examples

if (interactive()) {
# Chapter 20 - Ten Tips on Working with Packages

## Poking Around the Nooks and Crannies of CRAN

options("repos" = c(CRAN = "https://cran.ma.imperial.ac.uk/"))

## Finding Interesting Packages

## Installing Packages

## Not run: 
install.packages("fortunes")

## End(Not run)

## Loading Packages

library("fortunes")

## Reading the Package Manual and Vignette

library(help=fortunes)
## Not run: 
vignette("fortunes")

## End(Not run)

## Updating Packages

## Not run: 
update.packages()

## End(Not run)

## Unloading Packages

search()
detach(package:fortunes, unload=TRUE)

## Forging Ahead with R-Forge

## Not run: 
install.packages("data.table", repos="https://R-Forge.R-project.org")

## End(Not run)

## Conducting Installations from BioConductor

## Not run: 
source("https://bioconductor.org/biocLite.R")

## End(Not run)

## Reading the R Manual

}

Periodic table of elements.

Description

A data set containing properties of the periodic table of elements.

Format

A data frame with 118 rows and 9 variables

Details

Atomic.no
Name
Symbol
Group
Period
Block
State.at.STP
Occurrence
Description

Source

https://en.wikipedia.org/wiki/Periodic_table

A package to accompany the book "R for Dummies".

Description

This package contains all the code examples in the book 'R for Dummies' (2nd edition) by Andrie de Vries and Joris Meys.

Details

To print the sample code for every chapter:

ch1()
ch2()
etc.

To print the table of contents:

toc()

To save the elements data frame to either excel or csv format, use:

saveElements()

References

de Vries, A. , & Meys, J. (2012). R for dummies. Chichester: Wiley. https://rfordummies.com/, ISBN-13: 978-1119962847.

Saves a copy of the periodic table of elements as excel or csv file.

Description

Saves a copy of the periodic table of elements as excel or csv file.

Usage

saveElements(outfile, type = c("csv"))

Arguments

outfile

File name

type

Either excel or csv

Examples

saveElements(file.path(tempdir(), "elements.xlsx"))
saveElements(file.path(tempdir(), "elements.csv"), type = "csv")
list.files(tempdir(), pattern = "xlsx|csv", full.names = TRUE)

Print table of contents.

Description

Print table of contents.

Usage

toc()

Examples

toc()

Print examples of chapter 1 of 'R for Dummies'.

Description

Usage

See Also

Examples

Print examples of chapter 2 of 'R for Dummies'.

Description

Usage

See Also

Examples

Print examples of chapter 3 of 'R for Dummies'.

Description

Usage

See Also

Examples

Print examples of chapter 4 of 'R for Dummies'.

Description

Usage

See Also

Examples

Print examples of chapter 5 of 'R for Dummies'.

Description

Usage

See Also

Examples

Print examples of chapter 6 of 'R for Dummies'.

Description

Usage

See Also

Examples

Print examples of chapter 7 of 'R for Dummies'.

Description

Usage

See Also

Examples

Print examples of chapter 8 of 'R for Dummies'.

Description

Usage

See Also

Examples

Print examples of chapter 9 of 'R for Dummies'.

Description

Usage

See Also

Examples

Print examples of chapter 10 of 'R for Dummies'.

Description

Usage

See Also

Examples

Print examples of chapter 11 of 'R for Dummies'.

Description

Usage

See Also

Examples

Print examples of chapter 12 of 'R for Dummies'.

Description

Usage

See Also

Examples

Print examples of chapter 13 of 'R for Dummies'.

Description

Usage

See Also

Examples

Print examples of chapter 14 of 'R for Dummies'.

Description

Usage

See Also

Examples

Print examples of chapter 15 of 'R for Dummies'.

Description

Usage

See Also

Examples

Print examples of chapter 16 of 'R for Dummies'.

Description

Usage

See Also

Examples