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In order to provide an option to compare graphs produced by basic internal plot function and ggplot2, I recreated the figures in the book, 25 Recipes for Getting Started with R, with ggplot2.

The code used to create the images is in separate paragraphs, allowing easy comparison.

1.16 Creating a Scatter Plot

```plot(cars)
```

```ggplot(cars,aes(speed,dist))+geom_point()
```

1.17 Creating a Bar Chart

```heights <- tapply(airquality\$Temp, airquality\$Month, mean)
par(mfrow=c(1,2))
barplot(heights)
barplot(heights,
main="Mean Temp. by Month",
names.arg=c("May", "Jun", "Jul", "Aug", "Sep"),
ylab="Temp (deg. F)")
```

```require(gridExtra)
heights=ddply(airquality,.(Month), mean)
heights\$Month=as.character(heights\$Month)
p1 <- ggplot(heights, aes(x=Month,weight=Temp))+
geom_bar()
p2 <- ggplot(heights, aes(x=factor(heights\$Month,
labels=c("May", "Jun", "Jul", "Aug", "Sep")),
weight=Temp))+
geom_bar()+
opts(title="Mean Temp. By Month") +
xlab("") +
ylab("Temp (deg. F)")

grid.arrange(p1,p2, ncol=2)
```

1.18 Creating a Box Plot

```y <- c(-5, rnorm(100), 5)
boxplot(y)
```

```ggplot()+geom_boxplot(aes(x=factor(1),y=y))+xlab("")+ylab("")
```

1.19 Creating a Histogram

```data(Cars93, package="MASS")
par(mfrow=c(1,2))
hist(Cars93\$MPG.city)
hist(Cars93\$MPG.city, 20)
```

```p <- ggplot(Cars93, aes(MPG.city))
p1 <- p + geom_histogram(binwidth=diff(range(Cars93\$MPG.city))/5)
p2 <- p + geom_histogram(binwidth=diff(range(Cars93\$MPG.city))/20)
grid.arrange(p1,p2, ncol=2)
```

1.23 Diagnosing a Linear Regression

```data(iris)
m = lm( Sepal.Length ~ Sepal.Width, data=iris)
par(mfrow=c(2,2))
plot(m)
```

```r <- residuals(m)
yh <- predict(m)
scatterplot <- function(x,y, title="", xlab="", ylab="") {
d <- data.frame(x=x,y=y)
p <- ggplot(d, aes(x=x,y=y)) + geom_point() + opts(title=title) + xlab(xlab) + ylab(ylab)
return(p)
}

p1 <- scatterplot(yh,r,
title="Residuals vs Fitted",
xlab="Fitted values",
ylab="Residuals")
p1 <- p1 +geom_hline(yintercept=0)+geom_smooth()

s <- sqrt(deviance(m)/df.residual(m))
rs <- r/s

qqplot <- function(y,
distribution=qnorm,
title="Normal Q-Q",
xlab="Theretical Quantiles",
ylab="Sample Quantiles") {
require(ggplot2)
x <- distribution(ppoints(y))
d <- data.frame(x=x, y=sort(y))
p <- ggplot(d, aes(x=x, y=y)) +
geom_point() +
geom_line(aes(x=x, y=x)) +
opts(title=title) +
xlab(xlab) +
ylab(ylab)
return(p)
}

p2 <- qqplot(rs, ylab="Standardized residuals")

sqrt.rs <- sqrt(abs(rs))
p3 <- scatterplot(yh,sqrt.rs,
title="Scale-Location",
xlab="Fitted values",
ylab=expression(sqrt("Standardized residuals")))
p3 <- p3 + geom_smooth()

hii <- lm.influence(m, do.coef = FALSE)\$hat
p4 <- scatterplot(hii,rs)
p4 <- p4+
geom_hline(yintercept=0)+
geom_smooth() +
geom_text(aes(x=min(hii)+diff(range(hii))*0.3,
y=min(rs)+diff(range(rs))*0.04,
label="--   Cook's distance", size=3))+
opts(legend.position="none")

grid.arrange(p1,p2,p3,p4, ncol=2)
```