1. Stat405 Graphic tips & tricks
Hadley Wickham
Wednesday, 9 September 2009

2. 1. Homework
2. Reading a scatterplot
3. Scatterplot techniques for large data
4. Iteration & story telling
5. Project & homework
Wednesday, 9 September 2009

3. Homework
Great start!
Remember the grading scheme:
4.5–5 = A+, 4–4.5 = A, 3.5–4 = A-
Shorter is better than longer.
Check aspect ratios.
Read the comments!
Wednesday, 9 September 2009

4. Revision:
reading a scatterplot
• Big patterns
• Small patterns
• Deviations from the pattern
• Strange patterns
Wednesday, 9 September 2009

5. Wednesday, 9 September 2009

6. Strong linear relationship.
A number of outliers.
Wednesday, 9 September 2009

7. Wednesday, 9 September 2009

8. Unusual striations. Two
groups? Little relationship
between table and price?
Wednesday, 9 September 2009

9. Wednesday, 9 September 2009

10. Curved (exponential?)
relationship. Outliers mostly
cheaper than expected.
Wednesday, 9 September 2009

11. But what’s the
problem with
all these plots?
qplot(carat, price, data = diamonds)
Wednesday, 9 September 2009

12. But what’s the
problem with
all these plots?
In pairs, brainstorm
solutions for 2 minutes.
qplot(carat, price, data = diamonds)
Wednesday, 9 September 2009

13. Ideas
If x discrete, use boxplots.
Use semi-transparent points.
Divide into bins and count number of
points in each bin (2d histogram).
Display statistical summary.
Wednesday, 9 September 2009

14. Box and
whisker plots
Wednesday, 9 September 2009

15. 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.
Wednesday, 9 September 2009

16. qplot(table, price, data = diamonds)
Wednesday, 9 September 2009

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price
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qplot(table, price, data = diamonds, geom = "boxplot")
table
Wednesday, 9 September 2009

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qplot(table, price, data = diamonds, geom 80 "boxplot",
50 60 70 = 90
group = round(table)) table
Wednesday, 9 September 2009

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5000
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
Wednesday, 9 September 2009

20. Alpha blending
Wednesday, 9 September 2009

21. qplot(carat, price, data = diamonds, alpha = I(1/10))
Wednesday, 9 September 2009

22. qplot(carat, price, data = diamonds, alpha = I(1/50))
Wednesday, 9 September 2009

23. qplot(carat, price, data = diamonds, alpha = I(1/250))
Wednesday, 9 September 2009

24. Statistical summary
Wednesday, 9 September 2009

25. qplot(carat, price, data = diamonds) + geom_smooth()
Wednesday, 9 September 2009

26. qplot(log10(carat), log10(price), data = diamonds) + geom_smooth()
Wednesday, 9 September 2009

27. qplot(log10(carat), log10(price), data = diamonds) +
geom_smooth(method = "lm")
Wednesday, 9 September 2009

28. 2d bins
Wednesday, 9 September 2009

29. # Very basic cleaning
diamonds$x[diamonds$x == 0] <- NA
diamonds$y[diamonds$y == 0] <- NA
diamonds$y[diamonds$y > 12] <- NA
qplot(x, y, data = diamonds)
qplot(x, y, data = diamonds, geom = "bin2d")
qplot(x, y, data = diamonds, geom = "hex")
qplot(x, y, data = diamonds, geom = "bin2d", bins = 100)
qplot(x, y, data = diamonds, geom = "hex", bins = 100)
# Zoom in
qplot(x, y, data = diamonds, geom = "bin2d", bins = 100) +
xlim(4,7) + ylim(4,7)
qplot(x, y, data = diamonds, geom = "bin2d", bins = 100) +
xlim(4,5) + ylim(4,5)
Wednesday, 9 September 2009

30. qplot(x, x / y, data = diamonds,
geom = "bin2d")
qplot(x, log(x / y), data = diamonds,
geom = "bin2d")
clean <- subset(diamonds, abs(log(x / y)) < 0.1)
qplot(x, log(x / y), data = clean, geom = "bin2d")
qplot(x, log(x / y), data = clean, geom = "bin2d",
bins = 80)
Wednesday, 9 September 2009

31. qplot(x, x / y, data = diamonds,
geom = "bin2d")
qplot(x, log(x / y), data = diamonds,
geom = "bin2d")
clean <- subset(diamonds, abs(log(x / y)) < 0.1)
qplot(x, log(x / y), data = clean, geom = "bin2d")
qplot(x, log(x / y), data = clean, geom = "bin2d",
bins = 80)
What would be a good name for
log(x / y)? What other variable
might you create to go with it?
Wednesday, 9 September 2009

32. Your turn
Continue to explore the relationship
between x, y, z and depth. Create new
variables as necessary.
(Hint: rerun the cleaning code from last
week, and create more as necessary)
Some good ideas here: http://
www.diamondhelpers.com/fivesteps/4-
certified-diamonds.shtml
Wednesday, 9 September 2009

33. x
table width
z
depth = z / diameter
table = table width / x * 100
Wednesday, 9 September 2009

34. y_big <- diamonds$y > 10
z_big <- diamonds$z > 6
x_zero <- diamonds$x == 0
y_zero <- diamonds$y == 0
z_zero <- diamonds$z == 0
diamonds$x[x_zero] <- NA
diamonds$y[y_zero | y_big] <- NA
diamonds$z[z_zero | z_big] <- NA
Wednesday, 9 September 2009

35. qplot(z/y * 100, depth, data = diamonds)
last_plot() + xlim(50, 100)
last_plot() + xlim(50, 80) + ylim(50, 80)
qplot(z/x * 100, depth, data = diamonds) +
xlim(50, 80) + ylim(50, 80)
qplot(z/x * 100, depth / (z/x), data = diamonds)
last_plot() + xlim(50, 80) + ylim(80, 120)
last_plot() + ylim(95, 105)
# ...
Wednesday, 9 September 2009

36. Iteration & stories
Wednesday, 9 September 2009

37. Stories
Best data analyses tell a story, with a
natural flow from beginning to end.
For homeworks, try and come up with
three plots that tell a story.
Stories about a small sample of the data
can work well.
Wednesday, 9 September 2009

38. qplot(cty, hwy, data = mpg)
qplot(cty, hwy, data = mpg, geom = "jitter")
qplot(cty, hwy, data = mpg, geom = "jitter", colour =
class)
qplot(cty, cty / hwy, data = mpg, geom = "jitter",
colour = class)
qplot(cty, cty / hwy, data = mpg, colour = class)
qplot(displ, cty / hwy, data = mpg, colour = class)
qplot(displ, cty / hwy, data = mpg) + facet_wrap(~
class)
qplot(displ, cty / hwy, data = mpg) + facet_wrap(~
class) + geom_smooth(se = F)
qplot(displ, cty / hwy, data = mpg) + facet_wrap(~
class) + geom_smooth(method = "lm", se = F)
qplot(displ, cty, data = mpg) + facet_wrap(~ class)
Wednesday, 9 September 2009

39. Project
Due in 3.5 weeks.
Bigger group data analysis project. (Will
be discussing group dynamics on
Monday)
Homework is to get you started working
with the data.
Wednesday, 9 September 2009

40. Next week
Checking on a slot machine.
Learning how to write functions.
Basics of simulation.
Wednesday, 9 September 2009

41. Feedback
http://hadley.wufoo.com/forms/stat405-feedback/
Wednesday, 9 September 2009