ggplot2 Exercises - Solutions

Recreate the following plots shown below. Don't worry if your plots don't match exactly what is shown below, as long as you have a general understanding of ggplot2 and the grammar of graphics

---

Important Note!

Some of the images may be distorted from the conversion to a web format

---

For the first few plots, use the mpg dataset

library(ggplot2)
library(ggthemes)
head(mpg)

	manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class
1	audi	a4	1.8	1999	4	auto(l5)	f	18	29	p	compact
2	audi	a4	1.8	1999	4	manual(m5)	f	21	29	p	compact
3	audi	a4	2	2008	4	manual(m6)	f	20	31	p	compact
4	audi	a4	2	2008	4	auto(av)	f	21	30	p	compact
5	audi	a4	2.8	1999	6	auto(l5)	f	16	26	p	compact
6	audi	a4	2.8	1999	6	manual(m5)	f	18	26	p	compact

Histogram of hwy mpg values:

ggplot(mpg,aes(x=hwy)) + geom_histogram(bins=20,fill='red',alpha=0.5)

Barplot of car counts per manufacturer with color fill defined by cyl count

ggplot(mpg,aes(x=manufacturer)) + geom_bar(aes(fill=factor(cyl))) + theme_gdocs()

---

Switch now to use the txhousing dataset that comes with ggplot2

head(txhousing)

	city	year	month	sales	volume	median	listings	inventory	date
1	Abilene	2000	1	72	5380000	71400	701	6.3	2000
2	Abilene	2000	2	98	6505000	58700	746	6.6	2000.083
3	Abilene	2000	3	130	9285000	58100	784	6.8	2000.167
4	Abilene	2000	4	98	9730000	68600	785	6.9	2000.25
5	Abilene	2000	5	141	10590000	67300	794	6.8	2000.333
6	Abilene	2000	6	156	13910000	66900	780	6.6	2000.417

Create a scatterplot of volume versus sales. Afterwards play around with alpha and color arguments to clarify information.

pl <- ggplot(txhousing,aes(x=sales,y=volume)) + geom_point(color='blue',alpha=0.5)
print(pl)

Warning message:
: Removed 568 rows containing missing values (geom_point).

Add a smooth fit line to the scatterplot from above. Hint: You may need to look up geom_smooth()

pl + geom_smooth(color='red')

Warning message:
: Removed 568 rows containing non-finite values (stat_smooth).Warning message:
: Removed 568 rows containing missing values (geom_point).

Great Job!

Up next you'll have a data visualization project in which you will build up a real data visualization used in The Economist.