Eight visualisation challenges with ggplot2

Hadley Wickham  
@hadleywickham 
Chief Scientist, RStudio
Solving 8 visualisation
challenges with ggplot2
November 2016

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http://ﬁvethirtyeight.com/features/our-47-weirdest-charts-from-2015/

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https://ﬂowingdata.com/tag/upshot/

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1Labelling plots
Solved by Bob Rudis
A problem ignored for too long

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2 3 4 5 6 7
displ
hwy
class
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2seater
compact
midsize
minivan
pickup
subcompact
suv
Two seaters (sports cars) are an exception because of their light weight
Fuel efficiency generally decreases with engine size
Data from fueleconomy.gov

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ggplot(mpg, aes(displ, hwy)) +
geom_point(aes(color = class)) +
geom_smooth(se = FALSE, method = "loess") +
labs(
title = "Fuel efficiency generally ...",
subtitle = "Two seaters (sports cars) ...",
caption = "Data from fueleconomy.gov"
)
Accessed with the labs() function

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2
Axes

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Stages of visualisation system popularity
1. Someone used it and complained about a bug
2. Someone used it in an academic paper
3. Someone used it in a newspaper
4.Someone used it to commit academic fraud
5. So many people use it that google has autocompletes
for bad graphics ideas

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Isenberg, Petra, et al. "A study on dual-scale data charts." IEEE Transactions on
Visualization and Computer Graphics 17.12 (2011): 2469-2478.
https://www.lri.fr/~isenberg/publications/papers/Isenberg_2011_ASO.pdf

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But...

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20
30
40
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16
18
20
2 3 4 5 6 7
displ
mpg
l / 100 km

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ggplot(mpg, aes(displ, hwy)) +
geom_point() +
scale_y_continuous(
"mpg",
sec.axis = sec_axis(
~ 235 / .,
name = "l / 100 km",
breaks = seq(2, 20, by = 2)
)
)
Only 1-to-1 transformations are allowed
function(x) {
235 / x
}

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2 3 4 5 6 7
displ
mpg
l / 100 km

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Labelling
3 
data

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● ● corvette
caravan 2wd
altima
forester awd
toyota tacoma 4wd
jetta
new beetle
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30
40
2 3 4 5 6 7
displ
hwy
class
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2seater
compact
midsize
minivan
pickup
subcompact
suv
geom_text()

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● ● corvette
caravan 2wd
altima
forester awd
toyota tacoma 4wd
jetta
new beetle
20
30
40
2 3 4 5 6 7
displ
hwy
class
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2seater
compact
midsize
minivan
pickup
subcompact
suv
geom_label()

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corvette
caravan 2wd
altima
forester awd
toyota tacoma 4wd
jetta
new beetle
20
30
40
2 3 4 5 6 7
displ
hwy
class
●
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2seater
compact
midsize
minivan
pickup
subcompact
suv
https://github.com/slowkow/ggrepel
geom_label_repel()

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corvette
caravan 2wd
altima
forester awd
toyota tacoma 4wd
jetta
new beetle
20
30
40
2 3 4 5 6 7
displ
hwy
class
●
●
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●
●
●
2seater
compact
midsize
minivan
pickup
subcompact
suv
dev version

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Two diﬀerence between a factor and a string:
1.Fixed set of possible values
2.Arbitrary order

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relig <- gss_cat %>%
group_by(relig) %>%
summarise(
tvhours = mean(tvhours, na.rm = TRUE),
n = n()
)
Some data from the general social survey

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No answer
Don't know
Inter−nondenominational
Native american
Christian
Orthodox−christian
Moslem/islam
Other eastern
Hinduism
Buddhism
Other
None
Jewish
Catholic
Protestant
2 3 4
tvhours
relig

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Other eastern
Hinduism
Buddhism
Orthodox−christian
Moslem/islam
Jewish
None
No answer
Other
Christian
Inter−nondenominational
Catholic
Protestant
Native american
Don't know
2 3 4
tvhours
fct_reorder(relig, tvhours)

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by_age <- gss_cat %>%
filter(!is.na(age)) %>%
group_by(age, marital) %>%
count() %>%
mutate(prop = n / sum(n))
You have the same problem with more dimensions

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0.00
0.25
0.50
0.75
1.00
20 40 60 80
age
prop
marital
No answer
Never married
Separated
Divorced
Widowed
Married

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0.00
0.25
0.50
0.75
1.00
20 40 60 80
age
prop
marital
Widowed
Married
Divorced
Never married
No answer
Separated

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5
Missing
values

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An explicit missing value (NA)
is the presence of an absence;  
an implicit missing value is the
absence of a presence.

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Demo

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6
Histograms

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hist(1:4)

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df <- tibble(x = 1:4)
df %>%
ggplot(aes(x)) +
geom_histogram(binwidth = 1)
Equivalent ggplot2 code is a little longer

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0.00
0.25
0.50
0.75
1.00
1 2 3 4
x
count
(0.5, 1.5] (1.5, 2.5] (2.5, 3.5] (3.5, 4.5]
Thanks to Randall Pruim

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df %>%
ggplot(aes(x)) +
geom_histogram(
binwidth = 1,
boundary = 0
)
df %>%
ggplot(aes(x)) +
geom_histogram(
binwidth = 1,
boundary = 0,
closed = "left"
)

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0.0
0.5
1.0
1.5
2.0
1 2 3 4
x
count
[1, 2] (2, 3] (3, 4]

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0.0
0.5
1.0
1.5
2.0
1 2 3 4
x
count
(0.5, 1.5]
[1, 2) [2, 3) [3, 4]

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0.0
0.5
1.0
1.5
2.0
1 2 3 4
x
count
(0.5, 1.5]
[0.99999, 1.99999) [1.99999, 2.99999) [2.99999, 4.00001]

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7
Bar
charts

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0
20
40
60
2seater compact midsize minivan pickup subcompact suv
class
count
ggplot(mpg, aes(class)) +
geom_bar(colour = "white")

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0
20
40
60
class
count
ggplot(mpg, aes(class, group = id)) +
geom_bar(col = "white")

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0
20
40
60
class
count
drv
4
f
r
ggplot(mpg, aes(class, group = id, fill = drv)) +

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0
20
40
60
class
count
drv
4
f
r
ggplot(mpg, aes(class, fill = drv)) +

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class_mpg <- mpg %>%
group_by(class) %>%
summarise(
mean = mean(hwy),
se = 1.96 * sd(hwy) / sqrt(n())
)
Another type of bar chart displays summaries

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0
10
20
class
mean
ggplot(class_mpg, aes(class, mean)) +
geom_bar(stat = "identity")

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0
10
20
class
mean
ggplot(class_mpg, aes(class, mean)) +
geom_col() # Thanks to Bob Rudis

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20
24
28
class
mean

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15
20
25
30
class
mean

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8
ggplot2
extension
9 10
11

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2.1.0 introduced a formal extension mechanism
https://www.ggplot2-exts.org, by Daniel Emaasit

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ggraph, by Thomas Lin Pedersen
https://github.com/thomasp85/ggraph

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ggseas by Peter Ellis
https://github.com/ellisp/ggseas
Uses X13-SEATS-ARIMA  
in seasonal package

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gganimate by David Robinson
https://github.com/dgrtwo/gganimate

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Conclusion

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1Labelling plots
Solved by Bob Rudis
A problem ignored for too long

View Slide

2
Axes

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Labelling
3 
data

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View Slide

5
Missing
values

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6
Histograms

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7
Bar
charts

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8
ggplot2
extension
9 10
11

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Many of the features I
discussed here have
been added in recent
versions of ggplot2.  
 
See the release notes
for more detail.

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http://ggplot2.tidyverse.org

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Eight visualisation challenges with ggplot2

Eight visualisation challenges with ggplot2

Hadley Wickham

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