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## Example files for the title:  

# R Graphics Cookbook, by Winston Chang

[![R Graphics Cookbook, by Winston Chang](http://akamaicovers.oreilly.com/images/9781449316945/cat.gif)](https://www.safaribooksonline.com/library/view/title/9781449363086//)

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appendix: Introduction to ggplot2

==================

library(gcookbook) # For the data set

simpledat

   A1 A2 A3

B1 10  7 12

B2  9 11  6

====================================

barplot(simpledat, beside=TRUE)

====================================

t(simpledat)

   B1 B2

A1 10  9

A2  7 11

A3 12  6

====================================

barplot(t(simpledat), beside=TRUE)

====================================

plot(simpledat[1,], type="l")

lines(simpledat[2,], type="l", col="blue")

====================================

simpledat_long

 Aval Bval value

   A1   B1    10

   A1   B2     9

   A2   B1     7

   A2   B2    11

   A3   B1    12

   A3   B2     6

====================================

library(ggplot2)

ggplot(simpledat_long, aes(x=Aval, y=value, fill=Bval)) +

    geom_bar(stat="identity", position="dodge")

====================================

ggplot(simpledat_long, aes(x=Bval, y=value, fill=Aval)) +

    geom_bar(stat="identity", position="dodge")

====================================

ggplot(simpledat_long, aes(x=Aval, y=value, colour=Bval, group=Bval)) +

    geom_line()

====================================

dat <- data.frame(xval=1:4, yval=c(3,5,6,9), group=c("A","B","A","B"))

dat

 xval yval group

    1    3     A

    2    5     B

    3    6     A

    4    9     B

====================================

ggplot(dat, aes(x=xval, y=yval))

====================================

ggplot(dat, aes(x=xval, y=yval)) + geom_point()

====================================

p <- ggplot(dat, aes(x=xval, y=yval))

p + geom_point()

====================================

p + geom_point(aes(colour=group))

====================================

p + geom_point(colour="blue")

====================================

p + geom_point() + scale_x_continuous(limits=c(0,8))

====================================

p + geom_point() +

    scale_colour_manual(values=c("orange","forestgreen"))

==================

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preface: Preface

==================

install.packages("ggplot2")

install.packages("gcookbook")

====================================

library(ggplot2)

==================

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chapter: R Basics

==================

install.packages(c("ggplot2", "gcookbook"))

====================================

library(ggplot2)

library(gcookbook)

====================================

install.packages("ggplot2")

====================================

library(ggplot2)

====================================

data <- read.csv("datafile.csv")

====================================

data <- read.csv("datafile.csv", header=FALSE)

====================================

# Manually assign the header names

names(data) <- c("Column1","Column2","Column3")

====================================

data <- read.csv("datafile.csv", sep="\t")

====================================

data <- read.csv("datafile.csv", stringsAsFactors=FALSE)

# Convert to factor

data$Sex <- factor(data$Sex)

str(data)

'data.frame':   3 obs. of  4 variables:

 $ First : chr  "Currer" "Dr." ""

 $ Last  : chr  "Bell" "Seuss" "Student"

 $ Sex   : Factor w/ 2 levels "F","M": 1 2 NA

 $ Number: int  2 49 21

====================================

# Only need to install once

install.packages("xlsx")

library(xslx)

data <- read.xlsx("datafile.xlsx", 1)

====================================

# Only need to install once

install.packages("gdata")

library(gdata)

# Read first sheet

data <- read.xls("datafile.xls")

====================================

data <- read.xlsx("datafile.xls", sheetIndex=2)

data <- read.xlsx("datafile.xls", sheetName="Revenues")

====================================

data <- read.xls("datafile.xls", sheet=2)

====================================

# Only need to install the first time

install.packages("foreign")

library(foreign)

data <- read.spss("datafile.sav")

==================

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chapter: Quickly Exploring Data

==================

plot(mtcars$wt, mtcars$mpg)

====================================

library(ggplot2)

qplot(mtcars$wt, mtcars$mpg)

====================================

qplot(wt, mpg, data=mtcars)

# This is equivalent to:

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

====================================

plot(pressure$temperature, pressure$pressure, type="l")

====================================

plot(pressure$temperature, pressure$pressure, type="l")

points(pressure$temperature, pressure$pressure)

lines(pressure$temperature, pressure$pressure/2, col="red")

points(pressure$temperature, pressure$pressure/2, col="red")

====================================

library(ggplot2)

qplot(pressure$temperature, pressure$pressure, geom="line")

====================================

qplot(temperature, pressure, data=pressure, geom="line")

# This is equivalent to:

ggplot(pressure, aes(x=temperature, y=pressure)) + geom_line()

# Lines and points together

qplot(temperature, pressure, data=pressure, geom=c("line", "point"))

# Equivalent to:

ggplot(pressure, aes(x=temperature, y=pressure)) + geom_line() + geom_point()

====================================

barplot(BOD$demand, names.arg=BOD$Time)

====================================

table(mtcars$cyl)

 4  6  8

11  7 14

# There are 11 cases of the value 4, 7 cases of 6, and 14 cases of 8

====================================

# Generate a table of counts

barplot(table(mtcars$cyl))

====================================

library(ggplot2)

qplot(BOD$Time, BOD$demand, geom="bar", stat="identity")

# Convert the x variable to a factor, so that it is treated as discrete

qplot(factor(BOD$Time), BOD$demand, geom="bar", stat="identity")

====================================

# cyl is continuous here

qplot(mtcars$cyl)

# Treat cyl as discrete

qplot(factor(mtcars$cyl))

====================================

# Bar graph of values. This uses the BOD data frame, with the 

#"Time" column for x values and the "demand" column for y values.

qplot(Time, demand, data=BOD, geom="bar", stat="identity")

# This is equivalent to:

ggplot(BOD, aes(x=Time, y=demand)) + geom_bar(stat="identity")

# Bar graph of counts

qplot(factor(cyl), data=mtcars)

# This is equivalent to:

ggplot(mtcars, aes(x=factor(cyl))) + geom_bar()

====================================

hist(mtcars$mpg)

# Specify approximate number of bins with breaks

hist(mtcars$mpg, breaks=10)

====================================

qplot(mtcars$mpg)

====================================

library(ggplot2)

qplot(mpg, data=mtcars, binwidth=4)

# This is equivalent to:

ggplot(mtcars, aes(x=mpg)) + geom_histogram(binwidth=4)

====================================

plot(ToothGrowth$supp, ToothGrowth$len)

====================================

# Formula syntax

boxplot(len ~ supp, data = ToothGrowth)

# Put interaction of two variables on x-axis

boxplot(len ~ supp + dose, data = ToothGrowth)

====================================

library(ggplot2)

qplot(ToothGrowth$supp, ToothGrowth$len, geom="boxplot")

====================================

qplot(supp, len, data=ToothGrowth, geom="boxplot")

# This is equivalent to:

ggplot(ToothGrowth, aes(x=supp, y=len)) + geom_boxplot()

====================================

# Using three separate vectors

qplot(interaction(ToothGrowth$supp, ToothGrowth$dose), ToothGrowth$len,

      geom="boxplot")

# Alternatively, get the columns from the data frame

qplot(interaction(supp, dose), len, data=ToothGrowth, geom="boxplot")

# This is equivalent to:

ggplot(ToothGrowth, aes(x=interaction(supp, dose), y=len)) + geom_boxplot()

====================================

curve(x^3 - 5*x, from=-4, to=4)

====================================

# Plot a user-defined function

myfun <- function(xvar) {

    1/(1 + exp(-xvar + 10))

}

curve(myfun(x), from=0, to=20)

# Add a line:

curve(1-myfun(x), add = TRUE, col = "red")

====================================

library(ggplot2)

# This sets the x range from 0 to 20

qplot(c(0,20), fun=myfun, stat="function", geom="line")

# This is equivalent to:

ggplot(data.frame(x=c(0, 20)), aes(x=x)) + stat_function(fun=myfun, geom="line")

==================

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