This document provides an introduction to exploring and visualizing data using the R programming language. It discusses the history and development of R, introduces key R packages like tidyverse and ggplot2 for data analysis and visualization, and provides examples of reading data, examining data structures, and creating basic plots and histograms. It also demonstrates more advanced ggplot2 concepts like faceting, mapping variables to aesthetics, using different geoms, and combining multiple geoms in a single plot.

1. Exploring data
with modern R
Winston Chang
RStudio
2016–12–21

2. https://hea-www.harvard.edu/~fine/Observatory/women.html

3. Modern R

4. A brief history of R
• In the beginning, there was S. Developed at
Bell Labs in the 1970’s.
• S was owned and licensed by AT&T
• In 1990’s, two professors from New Zealand
created a free, open source reimplementation
of S, called R
• Many of the unusual features of R exist
because they came from S
• R itself is somewhat different from S and has a
very flexible syntax

5. install.packages("tidyverse")
# Automatically installs ggplot2, dplyr, tidyr,
# and others.
library(tidyverse)
tidyverse_update()
# Update all tidyverse pacakges to the latest
# version.
The tidyverse

6. Getting started

7. faithful
head(faithful)
str(faithful)
View(faithful) # In RStudio
Looking at data with R

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50
60
70
80
90
2 3 4 5
eruptions
waiting
library(ggplot2)
ggplot(data=faithful, mapping=aes(x=eruptions, y=waiting)) +
geom_point()
# More concisely:
ggplot(faithful, aes(eruptions, waiting)) + geom_point()

9. 0
5
10
15
20
25
2 3 4 5
eruptions
count
0
10
20
30
40
2 3 4 5
eruptionscount
ggplot(faithful, aes(x=eruptions)) + geom_histogram()
ggplot(faithful, aes(x=eruptions)) + geom_histogram(binwidth=.25)

10. Your turn
Inspect the diamonds data set.
With diamonds, make a histogram of the carat
variable. Experiment with different bin sizes. What
patterns do you see?
Inspect the mpg data set.
With mpg, make a scatter plot showing the
relationship between displ and hwy.

11. 0
5000
10000
15000
0 1 2 3 4 5
carat
count
ggplot(diamonds, aes(x=carat)) +
geom_histogram()

12. ggplot(diamonds, aes(x=carat)) +
geom_histogram(binwidth=0.3)
0
5000
10000
15000
0 1 2 3 4 5
carat
count

13. 0
5000
10000
0 1 2 3 4 5
carat
count
ggplot(diamonds, aes(x=carat)) +
geom_histogram(binwidth=0.25)

14. ggplot(diamonds, aes(x=carat)) +
geom_histogram(binwidth=0.01)
0
1000
2000
0 1 2 3 4 5
carat
count

15. ggplot(mpg, aes(x=displ, y=hwy)) + geom_point()
20
30
40
2 3 4 5 6 7
displ
hwy

16. ggplot(mpg, aes(x=displ, y=hwy)) +
geom_point() +
geom_smooth(method=lm)
10
20
30
40
2 3 4 5 6 7
displ
hwy

17. head(mpg)
str(mpg)
View(mpg)

18. ggplot(mpg, aes(x=displ, y=hwy, color=drv)) +
geom_point()
20
30
40
2 3 4 5 6 7
displ
hwy
drv
4
f
r

19. 20
30
40
2 3 4 5 6 7
displ
hwy
class
2seater
compact
midsize
minivan
pickup
subcompact
suv
ggplot(mpg, aes(x=displ, y=hwy, color=class)) +
geom_point()

20. Your turn
What happens if you use shape instead of
color?
Run ?geom_smooth to see the documentation.
Then remove the confidence region from the
model line.
What happens if you add a model line and map a
variable to color?

21. Faceting

22. ggplot(mpg, aes(x=displ, y=hwy)) + geom_point() +
facet_wrap(~class)
suv
minivan pickup subcompact
2seater compact midsize
2 3 4 5 6 7
2 3 4 5 6 7 2 3 4 5 6 7
20
30
40
20
30
40
20
30
40
displ
hwy

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4 5 6 8
2 3 4 5 6 7 2 3 4 5 6 7 2 3 4 5 6 7 2 3 4 5 6 7
20
30
40
displ
hwy
ggplot(mpg, aes(x=displ, y=hwy)) +
geom_point() +
facet_grid(. ~ cyl)
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4fr
2 3 4 5 6 7
20
30
40
20
30
40
20
30
40
displ
hwy
ggplot(mpg, aes(x=displ, y=hwy)) +
geom_point() +
facet_grid(drv ~ .)

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4 5 6 8
4fr
2 3 4 5 6 7 2 3 4 5 6 7 2 3 4 5 6 7 2 3 4 5 6 7
20
30
40
20
30
40
20
30
40
displ
hwy
ggplot(mpg, aes(x=displ, y=hwy)) + geom_point() +
facet_grid(drv ~ cyl)
cyl
drv

25. Your turn
Try faceting with a histogram

26. ggplot2
concepts

27. Geoms
Points
Lines
Bars
Error bars
Box plot

28. Aesthetics
Y position
X position
Color
Size

29. Aesthetics

30. Mapping data values to aesthetics
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4
6
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2 3 4 5 6 7
var1
var2
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4
6
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2 3 4 5 6 7
var1
var2
0
1
2
3
4
5
var3
var1 var2 var3
2 2 5
3 4 0
5 8 4
7 5 1
ggplot(dat, aes(x=var1, y=var2)) +
geom_point()
ggplot(dat, aes(x=var1, y=var2, color=var3)) +
geom_point()

31. ●
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2
4
6
8
2 3 4 5 6 7
var1
var2
Setting data values to aesthetics
var1 var2 var3
2 2 5
3 4 0
5 8 4
7 5 1
ggplot(dat, aes(x=var1, y=var2)) +
geom_point(color="red")
ggplot(dat, aes(x=var1, y=var2)) +
geom_point(color="red", size=6)
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2
4
6
8
2 3 4 5 6 7
var1
var2

32. Different geoms
●
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2
4
6
8
2 3 4 5 6 7
var1
var2
ggplot(dat, aes(x=var1, y=var2)) +
geom_point()
2
4
6
8
2 3 4 5 6 7
var1
var2
ggplot(dat, aes(x=var1, y=var2)) +
geom_line()
0
2
4
6
8
2 4 6
var1
var2
ggplot(dat, aes(x=var1, y=var2)) +
geom_bar(stat="identity")

33. Using multiple geoms
●
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2
4
6
8
2 3 4 5 6 7
var1
var2
ggplot(dat, aes(x=var1, y=var2)) +
geom_point() + geom_line()
# Equivalent to
ggplot(dat) +
geom_point(aes(x=var1, y=var2)) +
geom_line(aes(x=var1, y=var2))
ggplot() +
geom_point(aes(x=var1, y=var2), data=dat) +
geom_line(aes(x=var1, y=var2), data=dat)
Default
data
Default
mapping
Override
defaults in each
geom

34. Discrete Continuous
Color Rainbow of colors
Gradient from light
blue to
dark blue
Size Discrete size steps
Linear mapping
between radius and
value
Shape
Different shape for
each
Shouldn’t work

35. ●
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0
2
4
6
A B
var1
var3
var2
●
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G0
G1
G2
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0
2
4
6
A B
var1
var3
ggplot(dat2, aes(x=var1, y=var3)) +
geom_point()
Mapping discrete variables
ggplot(dat2, aes(x=var1, y=var3, color=var2)) +
geom_point()
var1 var2 var3
A G1 5
B G0 0
A G2 4
B G1 1
A G0 6
B G2 3

36. Data wrangling
with modern R

37. Tidyverse
=
Tidy + universe
Source: https://www.flickr.com/photos/rubbermaid/7203340384 Source: http://hubblesite.org/newscenter/archive/releases/2014/27/image/a/

38. faithful
as.tbl(faithful)
Tibbles

39. Tidy data
A B C D A B C D
Each variable is
in a column
Each observation
is in a row

40. Example of non-tidy data
subject sex cond1 cond2 cond3
1 M 7.9 12.3 10.7
2 F 6.3 10.6 11.1
3 F 9.5 13.1 13.8
4 M 11.5 13.4 12.9
Each row has 3
observations
Not Tidy

41. Converting to tidy data
subject sex cond1 cond2 cond3
1 M 7.9 12.3 10.7
2 F 6.3 10.6 11.1
3 F 9.5 13.1 13.8
4 M 11.5 13.4 12.9
subject sex condition value
1 M cond1 7.9
1 M cond2 12.3
1 M cond3 10.7
2 F cond1 6.3
2 F cond2 10.6
2 F cond3 11.1
3 F cond1 9.5
3 F cond2 13.1
3 F cond3 13.8
4 M cond1 11.5
4 M cond2 13.4
4 M cond3 12.9
Not Tidy
Tidy

42. • filter: Keep rows
• select: Keep columns
• mutate: Add new columns
• arrange: Sort rows
• summarise: Reduce variables

43. # Traditional R
mpg[mpg$hwy > 30, ]
# dplyr
filter(mpg, hwy > 30)
Filter: get a subset of rows

44. # AND
filter(mpg, hwy > 30, class == "compact")
filter(mpg, hwy > 30 & class == "compact")
# OR
filter(mpg, hwy > 30 | class == "compact")
Filter: get a subset of rows

45. %>%

46. filter(mpg, hwy > 30)
mpg %>% filter(hwy > 30)
select(filter(mpg, hwy > 30), model, hwy, class)
mpg %>%
filter(hwy > 30) %>%
select(model, hwy, class)
mpg %>%
filter(hwy > 30) %>%
select(model, hwy, class) %>%
View()
Piping with %>%

47. # Traditional R
mpg[, c("model", "displ", "cyl", "drv", "class",
"hwy")]
# dplyr
select(mpg, model, displ, cyl, drv, class, hwy)
select(mpg, -manufacturer, -fl)
Select: get a subset of columns

48. # Traditional R
mpg$avg <- (mpg$cty + mpg$hwy)/2
# dplyr
mpg %>% mutate(avg = (cty+hwy)/2)
mpg %>% mutate(
avg = (cty+hwy)/2,
ratio = hwy/cty
)
Mutate: add new columns

49. # Traditional R
mpg[order(mpg$hwy), ]
# dplyr
arrange(mpg, hwy)
Arrange: sort rows

50. # Traditional R
mean(mpg$hwy)
sd(mpg$hwy)
# dplyr
summarise(mpg, hwy_m = mean(hwy))
summarise(mpg,
hwy_m = mean(hwy),
hwy_sd = sd(hwy),
cty_m = mean(cty),
cty_sd = sd(cty)
)
Summarise: reduce variables
summarise ≠ summarize

51. Group
operations

52. Why is this important?

53. Summarise
subject sex condition value
1 M cond1 7.9
1 M cond2 12.3
1 M cond3 10.7
2 F cond1 6.3
2 F cond2 10.6
2 F cond3 11.1
3 F cond1 9.5
3 F cond2 13.1
3 F cond3 13.8
4 M cond1 11.5
4 M cond2 13.4
4 M cond3 12.9
value
11.1
data %>%
summarise(value = mean(value))

54. Group-wise summarise
subject sex condition value
1 M cond1 7.9
1 M cond2 12.3
1 M cond3 10.7
2 F cond1 6.3
2 F cond2 10.6
2 F cond3 11.1
3 F cond1 9.5
3 F cond2 13.1
3 F cond3 13.8
4 M cond1 11.5
4 M cond2 13.4
4 M cond3 12.9
subject value
1 10.3
2 9.3
3 12.1
4 12.6
data %>%
group_by(subject) %>%
summarise(value = mean(value))

55. Group-wise summarise
subject sex condition value
1 M cond1 7.9
1 M cond2 12.3
1 M cond3 10.7
2 F cond1 6.3
2 F cond2 10.6
2 F cond3 11.1
3 F cond1 9.5
3 F cond2 13.1
3 F cond3 13.8
4 M cond1 11.5
4 M cond2 13.4
4 M cond3 12.9
sex condition value
F cond1 11.9
F cond2 12.5
F cond3 7.9
M cond1 12.9
M cond2 11.8
M cond3 9.7
data %>%
group_by(sex, condition) %>%
summarise(value = mean(value))

56. Mutate
subject sex condition value
1 M cond1 7.9
1 M cond2 12.3
1 M cond3 10.7
2 F cond1 6.3
2 F cond2 10.6
2 F cond3 11.1
3 F cond1 9.5
3 F cond2 13.1
3 F cond3 13.8
4 M cond1 11.5
4 M cond2 13.4
4 M cond3 12.9
data %>%
mutate(norm = value - mean(value))
subject sex condition value norm
1 M cond1 7.9 -3.2
1 M cond2 12.3 1.2
1 M cond3 10.7 -0.4
2 F cond1 6.3 -4.8
2 F cond2 10.6 -0.5
2 F cond3 11.1 0
3 F cond1 9.5 -1.6
3 F cond2 13.1 2
3 F cond3 13.8 2.7
4 M cond1 11.5 0.4
4 M cond2 13.4 2.3
4 M cond3 12.9 1.8

57. Group-wise mutate
subject sex condition value
1 M cond1 7.9
1 M cond2 12.3
1 M cond3 10.7
2 F cond1 6.3
2 F cond2 10.6
2 F cond3 11.1
3 F cond1 9.5
3 F cond2 13.1
3 F cond3 13.8
4 M cond1 11.5
4 M cond2 13.4
4 M cond3 12.9
data %>%
group_by(subject) %>%
mutate(norm = value - mean(value))
subject sex condition value norm
1 M cond1 7.9 -2.4
1 M cond2 12.3 2
1 M cond3 10.7 0.4
2 F cond1 6.3 -3
2 F cond2 10.6 1.3
2 F cond3 11.1 1.8
3 F cond1 9.5 -2.6
3 F cond2 13.1 1
3 F cond3 13.8 1.7
4 M cond1 11.5 -1.1
4 M cond2 13.4 0.8
4 M cond3 12.9 0.3

58. Tidying data with
tidyr

59. Converting to tidy data
subject sex cond1 cond2 cond3
1 M 7.9 12.3 10.7
2 F 6.3 10.6 11.1
3 F 9.5 13.1 13.8
4 M 11.5 13.4 12.9
subject sex condition value
1 M cond1 7.9
1 M cond2 12.3
1 M cond3 10.7
2 F cond1 6.3
2 F cond2 10.6
2 F cond3 11.1
3 F cond1 9.5
3 F cond2 13.1
3 F cond3 13.8
4 M cond1 11.5
4 M cond2 13.4
4 M cond3 12.9
Not Tidy
Tidy

60. Converting to tidy data
subject sex cond1 cond2 cond3
1 M 7.9 12.3 10.7
2 F 6.3 10.6 11.1
3 F 9.5 13.1 13.8
4 M 11.5 13.4 12.9
gather(data, condition, value, cond1:cond3)
subject sex condition value
1 M cond1 7.9
1 M cond2 12.3
1 M cond3 10.7
2 F cond1 6.3
2 F cond2 10.6
2 F cond3 11.1
3 F cond1 9.5
3 F cond2 13.1
3 F cond3 13.8
4 M cond1 11.5
4 M cond2 13.4
4 M cond3 12.9
data

61. Thank you!