02 Large

Stat405 Graphics for large data

Hadley Wickham
Thursday, 26 August 2010

Majoring in Stat

• Declare early (even if you’re not sure)
• Weekly lunches
• Summer opportunities
(research & internships)


1. Leftovers from last lecture
2. The diamonds data
3. Histograms and bar charts
4. More boxplots and scatterplots
5. Homework


# Remember: start with ● ●

library(ggplot2) ●

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pickup suv minivan 2seater midsize subcompact compact
qplot(reorder(class, hwy),reorder(class, hwy) = mpg, geom = "jitter")
hwy, data

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pickup suv minivan 2seater midsize subcompact compact
qplot(reorder(class, hwy), hwy, data hwy)mpg, geom = "boxplot")
reorder(class, =

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qplot(reorder(class,minivan
pickup suv
hwy), 2seater data = subcompact
hwy, midsize mpg, compact
geom = c("jitter", "boxplot"))
reorder(class, hwy)

Your turn

Read the help for reorder. Redraw the
previous plots with class ordered by
median hwy.
How would you put the jittered points on
top of the boxplots?


Diamonds


Diamonds data
~54,000 round diamonds from
http://www.diamondse.info/
Carat, colour, clarity, cut
Total depth, table, depth,
width, height
Price


x
table width

z

depth = z / diameter
table = table width / x * 100


Recall

Write down ﬁve ways to inspect the
diamonds dataset.
You have one minute!


Your turn

Inspect the data and familiarise yourself
with the variables. If you don’t know what
they mean, look them up on wikipedia.


Histogram &
bar charts


Histograms and
barcharts

Used to display the distribution of a
variable
Categorical variable → bar chart
Continuous variable → histogram


Always
experiment with
the bin width!

Examples
# With only one variable, qplot guesses that
# you want a bar chart or histogram
qplot(cut, data = diamonds)

qplot(carat, data = diamonds)
qplot(carat, data = diamonds, binwidth = 1)
qplot(carat, data = diamonds, binwidth = 0.1)
resolution(diamonds$carat)

last_plot() + xlim(0, 3)


Examples
# With only one variable, qplot guesses that
# you want a bar chart or histogram
qplot(cut, data = diamonds)

qplot(carat, data = diamonds)
qplot(carat, data = diamonds, binwidth = 1)
Common ggplot2
technique: adding
together plot
resolution(diamonds$carat)
components

last_plot() + xlim(0, 3)


qplot(table, data = diamonds, binwidth = 1)

# To zoom in on a plot region use xlim() and ylim()
qplot(table, data = diamonds, binwidth = 1) +
xlim(50, 70)
qplot(table, data = diamonds, binwidth = 0.1) +
xlim(50, 70)
qplot(table, data = diamonds, binwidth = 0.1) +
xlim(50, 70) + ylim(0, 50)

# Note that this type of zooming discards data
outside of the plot regions
# See coord_cartesian() for an alternative


Additional variables

As with scatterplots can use aesthetics
or faceting. Using aesthetics creates
pretty, but ineffective, plots.
The following examples show the
difference, when investigation the
relationship between cut and depth.


4000

3000
count

2000

1000

0

56 58 60 62 64 66 68 70
qplot(depth, data = diamonds, binwidth = 0.2)
depth

4000

3000

cut
Fair
Good
count

2000 Very Good
Premium
Ideal

1000

0

qplot(depth, data = diamonds, binwidth = 0.2,
56 58 60 62 64 66 68 70
fill = cut) + xlim(55, 70)
depth

4000

3000

cut
Fair
Good
count

2000 Very Good
Premium
Ideal

1000

Fill is the aesthetic
0
for ﬁll colour
qplot(depth, data = diamonds, binwidth = 0.2,
56 58 60 62 64 66 68 70
fill = cut) + xlim(55, 70)
depth

Fair Good Very Good

2500

2000

1500

1000

500

0
count

Premium Ideal

2500

2000

1500

1000

500

0
qplot(depth, 62 64 66= 68 70 56 58 60 binwidth = 0.2) +
56 58 60 data diamonds, 62 64 66 68 70 56 58 60 62 64 66 68 70

xlim(55, 70) + facet_wrap(~depth cut)

Your turn

Explore the distribution of price.
How does it vary with colour, or cut, and
clarity?
Practice zooming in on regions of interest.


Box and
whisker plots


Boxplots

Less information than a histogram, but
take up much less space.
Already seen them used with discrete x
values. Can also use with continuous x
values, by specifying how we want the
data grouped.


qplot(table, price, data = diamonds)

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10000
price

5000

50 60 70 80 90
qplot(table, price, data = diamonds, geom = "boxplot")
table

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5000

qplot(table, price, data = diamonds, geom 80 "boxplot",
50 60 70 = 90
group = round(table)) table

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One boxplot for
each unique value
of this aesthetic
qplot(table, price, data = diamonds, geom 80 "boxplot",
50 60 70 = 90
group = round(table)) table

Scatterplots


Interpreting a
scatterplot

• Global patterns
• Local patterns
• Deviations


Strong linear relationship.
A number of outliers.


Unusual striations. Two
groups? Little relationship
between table and price?


Curved (exponential?)
relationship. Outliers mostly
cheaper than expected.


But what’s the
problem with
all these plots?

qplot(carat, price, data = diamonds)

But what’s the
problem with
all these plots?
In pairs, brainstorm
solutions for 2 minutes.

qplot(carat, price, data = diamonds)

Idea ggplot
Small points shape = I(".")

Transparency alpha = I(1/50)

Jittering geom = "jitter"

Smooth curve geom = "smooth"
geom = "bin2d" or
2d bins geom = "hex"

Density contours geom = "density2d"

Your turn

Practice doing these plots yourself.
Read the online documentation for each
plot type: http://had.co.nz/ggplot2


Homework

Practice your graphics/data exploration
skills with the diamonds or mpg data.
Due in one week.
Make sure to read the grading rubric, and
ﬁnd a colour printer.


Asking questions

You have two minutes to write down as
many questions as you can come up with
that you might want to answer about the
diamonds data.
Write your best question on a piece of
paper and turn it in.


02 Large

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