Computational Statistics – Data Viz & Introduction to ggplot2

Getting help

One of the best ways to get started with ggplot is to Google what you want to do with the word ggplot. Then look through the images that come up. More often than not, the associated code is there. There are also ggplot galleries of images, one of them is here: https://plot.ly/ggplot2/
Look at the end of this presentation and the syllabus. More help options there.

What are the visual cues on this plot?

position
length
shape
area/volume
shade/color

What are the visual cues on this plot?

position
length
shape
area/volume
shade/color

What are the visual cues on this plot?

position
length
shape
area/volume
shade/color

The grammar of graphics `ggplot`

geom: the geometric “shape” used to display data

bar, point, line, ribbon, text, etc.

aesthetic: an attribute controlling how geom is displayed with respect to variables

x position, y position, color, fill, shape, size, etc.

guide: helps user convert visual data back into raw data (legends, axes)

stat: a transformation applied to data before geom gets it

example: histograms work on binned data

Set up

library(mosaic)
data(Births78)

head(Births78)

date	births	wday	year	month	day_of_year	day_of_month	day_of_week
1978-01-01	7701	Sun	1978	1	1	1	1
1978-01-02	7527	Mon	1978	1	2	2	2
1978-01-03	8825	Tue	1978	1	3	3	3
1978-01-04	8859	Wed	1978	1	4	4	4
1978-01-05	9043	Thu	1978	1	5	5	5
1978-01-06	9208	Fri	1978	1	6	6	6

How do we make this plot?

Two Questions:

What do we want R to do? (What is the goal?)
What does R need to know?

How do we make this plot?

Goal: scatterplot = a plot with points
What does R need to know?
- data source: Births78
- aesthetics:
  - date -> x
  - births -> y
  - points (!)

How do we make this plot?

ggplot(data = Births78, 
       aes(x = date, y = births)) + 
  geom_point() +
  labs(title = "US Births in 1978")

ggplot() +
  geom_point(data = Births78, 
             aes(x = date, y = births)) +
  labs(title = "US Births in 1978")

Layers: layer 0

ggplot()

Layers: layer 1

ggplot(data = Births78, 
       aes(x = date, y = births))

Layers: layer 2

ggplot(data = Births78, 
       aes(x = date, y = births)) + 
  geom_point()

Layers: layer 3

ggplot(data = Births78, 
       aes(x = date, y = births)) + 
  geom_point() +
  labs(title = "US Births in 1978")

How do we make this plot?

How do we make this plot?

What has changed?

new aesthetic: mapping color to day of week

How do we make this plot?

ggplot(data = Births78,
       aes(x = date,
           y = births, 
           color = wday)) +
  geom_point() +
  labs(title = "US Births in 1978")

How do we make this plot?

How do we make this plot?

lines instead of dots!

ggplot(data = Births78,
         aes(x = date, 
             y = births,
             color = wday)) +
  geom_line() +
  labs(title = "US Births in 1978")

How do we make this plot?

How do we make this plot?

Now there are two layers: one with points and one with lines

ggplot(data = Births78,
       aes(x = date,
           y = births,
           color = wday)) + 
  geom_point() +  
  geom_line() +
  labs(title = "US Births in 1978")

The layers are placed one on top of the other: the points are below and the lines are above.
data and aes specified in ggplot() affect all geoms

What does this code do?

ggplot(data = Births78,
       aes(x = date, y = births, color = "navy")) + 
  geom_point() +
  labs(title = "US Births in 1978")

What does this code do?

ggplot(data = Births78,
       aes(x = date, y = births, color = "navy")) + 
  geom_point()  +
  labs(title = "US Births in 1978")

This is mapping the color aesthetic to a new variable with only one value (“navy”).
So all the dots get set to the same color, but it’s not navy.

Setting vs. Mapping

If we want to set the color to be navy for all of the dots, we do it outside the aes() designation:

ggplot(data = Births78,
       aes(x = date, y = births)) +   # map variables 
  geom_point(color = "navy")      +   # set attributes
  labs(title = "US Births in 1978")

Note that color = "navy" is now outside of the aesthetics list. aes() is how ggplot2 distinguishes between mapping and setting.

How do we make this plot?

How do we make this plot?

ggplot(data = Births78,
       aes(x = date,
           y = births)) + 
  geom_line(aes(color = wday)) +      
  geom_point(color = "navy")  +         
  labs(title = "US Births in 1978")

ggplot() establishes the default data and aesthetics for the geoms, but each geom may change these defaults.
good practice: put into ggplot() the things that affect all (or most) of the layers; put the rest in geom_*()

Setting vs. Mapping (again)

Information gets passed to the plot via:

map the variable information inside the aes() (aesthetic) function
set the non-variable information outside the aes() (aesthetic) function

Other geoms

apropos("^geom_")

 [1] "geom_abline"            "geom_area"              "geom_ash"              
 [4] "geom_bar"               "geom_bin_2d"            "geom_bin2d"            
 [7] "geom_blank"             "geom_boxplot"           "geom_col"              
[10] "geom_contour"           "geom_contour_filled"    "geom_count"            
[13] "geom_crossbar"          "geom_curve"             "geom_density"          
[16] "geom_density_2d"        "geom_density_2d_filled" "geom_density2d"        
[19] "geom_density2d_filled"  "geom_dotplot"           "geom_errorbar"         
[22] "geom_errorbarh"         "geom_freqpoly"          "geom_function"         
[25] "geom_hex"               "geom_histogram"         "geom_hline"            
[28] "geom_jitter"            "geom_label"             "geom_line"             
[31] "geom_linerange"         "geom_lm"                "geom_map"              
[34] "geom_path"              "geom_point"             "geom_pointrange"       
[37] "geom_polygon"           "geom_qq"                "geom_qq_line"          
[40] "geom_quantile"          "geom_rangeframe"        "geom_raster"           
[43] "geom_rect"              "geom_ribbon"            "geom_rug"              
[46] "geom_segment"           "geom_sf"                "geom_sf_label"         
[49] "geom_sf_text"           "geom_smooth"            "geom_spline"           
[52] "geom_spoke"             "geom_step"              "geom_text"             
[55] "geom_tile"              "geom_tufteboxplot"      "geom_violin"           
[58] "geom_vline"

Other geoms

help pages will tell you their aesthetics, default stats, etc.

?geom_area             # for example

Let’s try `geom_area`

ggplot(data = Births78,
       aes(x = date,
           y = births, 
           fill = wday)) + 
  geom_area() +
  labs(title = "US Births in 1978")

Let’s try `geom_area`

ggplot(data = Births78,
       aes(x = date, y = births, fill = wday)) + 
  geom_area() +
  labs(title = "US Births in 1978")

… not a good plot

overplotting is hiding much of the data
extending y-axis to 0 may or may not be desirable.

Side note: what makes a plot good?

Most (all?) graphics are intended to help us make comparisons

How does something change over time?
Do my treatments matter? How much?
Do treatment and control respond the same way?

Key plot metric

Does my plot make the comparisons I am interested in:

easily, and
accurately?

Time for some different data

HELPrct: Health Evaluation and Linkage to Primary care randomized clinical trial. Subjects admitted for treatment for addiction to one of three substances.

head(HELPrct)

age	anysubstatus	anysub	cesd	d1	daysanysub	dayslink	drugrisk	e2b	female	sex	g1b	homeless	i1	i2	id	indtot	linkstatus	link	mcs	pcs	pss_fr	racegrp	satreat	sexrisk	substance	treat	avg_drinks	max_drinks	hospitalizations
37	1	yes	49	3	177	225	0	NA	0	male	yes	housed	13	26	1	39	1	yes	25.111990	58.41369	0	black	no	4	cocaine	yes	13	26	3
37	1	yes	30	22	2	NA	0	NA	0	male	yes	homeless	56	62	2	43	NA	NA	26.670307	36.03694	1	white	no	7	alcohol	yes	56	62	22
26	1	yes	39	0	3	365	20	NA	0	male	no	housed	0	0	3	41	0	no	6.762923	74.80633	13	black	no	2	heroin	no	0	0	0
39	1	yes	15	2	189	343	0	1	1	female	no	housed	5	5	4	28	0	no	43.967880	61.93168	11	white	yes	4	heroin	no	5	5	2
32	1	yes	39	12	2	57	0	1	0	male	no	homeless	10	13	5	38	1	yes	21.675755	37.34558	10	black	no	6	cocaine	no	10	13	12
47	1	yes	6	1	31	365	0	NA	1	female	no	housed	4	4	6	29	0	no	55.508991	46.47521	5	black	no	5	cocaine	yes	4	4	1

Who are the people in the study?

ggplot(data = HELP_data,
       aes(x = substance)) + 
  geom_bar() +
  labs(title = "HELP trial")

Hmm. What’s up with y?
- stat_bin() is being applied to the data before the geom_bar() gets to do its thing. Binning creates the y values.

Who are the people in the study?

ggplot(data = HELP_data,
       aes(x = substance,
           fill = children)) + 
  geom_bar() +
  labs(title = "HELP trial")

Who are the people in the study?

ggplot(HELP_data,
       aes(x = substance,
           fill = children)) + 
  geom_bar(position = "fill") +
  labs(title = "HELP trial",
       y = "actually, percent")

How old are people in the HELP study?

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

ggplot(data = HELP_data,
       aes(x = age)) + 
  geom_histogram() +
  labs(title = "HELP trial")

Notice the messages

stat_bin: Histograms are not mapping the raw data but binned data.
stat_bin() performs the data transformation.
binwidth: a default binwidth has been selected, but we should really choose our own.

Setting the binwidth manually

ggplot(data = HELP_data,
       aes(x = age)) + 
  geom_histogram(binwidth=2) +
  labs(title = "HELP trial")

How old are people in the HELP study? – Other geoms

ggplot(data = HELP_data,
       aes(x = age)) + 
  geom_freqpoly(binwidth=2) +
  labs(title = "HELP clinical trial at detoxification unit")

ggplot(data = HELP_data,
       aes(x = age)) + 
  geom_density() +
  labs(title = "HELP clinical trial at detoxification unit")

Selecting stat and geom manually

Every geom comes with a default stat

for simple cases, the stat is stat_identity() which does nothing
we can mix and match geoms and stats however we like

ggplot(data = HELP_data,
       aes(x = age)) + 
  geom_line(stat = "density") +
  labs(title = "HELP clinical trial at detoxification unit")

Selecting stat and geom manually

Every stat comes with a default geom, every geom with a default stat

we can specify stats instead of geom, if we prefer
we can mix and match geoms and stats however we like

ggplot(data = HELP_data,
       aes(x = age)) + 
  stat_density(geom = "line") +
  labs(title = "HELP clinical trial at detoxification unit")

More combinations

ggplot(data = HELP_data,
       aes(x = age)) + 
  geom_point(stat = "bin", binwidth=3) + 
  geom_line(stat = "bin", binwidth=3)  +
  labs(title = "HELP clinical trial at detoxification unit")

More combinations

ggplot(data = HELP_data,
       aes(x = age)) + 
  geom_area(stat = "bin", binwidth=3)  +
  labs(title = "HELP clinical trial at detoxification unit")

More combinations

ggplot(data = HELP_data,
       aes(x = age)) + 
  geom_point(stat = "bin", 
             binwidth=3, 
             aes(size = ..count..)) +
  geom_line(stat = "bin", binwidth=3) +
  labs(title = "HELP clinical trial at detoxification unit")

How much drinking? (i1)

ggplot(data = HELP_data,
       aes(x = i1)) + geom_histogram() +
  labs(title = "HELP clinical trial at detoxification unit")

How much drinking? (i1)

ggplot(data = HELP_data,
       aes(x = i1)) + 
  geom_density() +
  labs(title = "HELP clinical trial at detoxification unit")

How much drinking? (i1)

ggplot(data = HELP_data, 
       aes(x = i1)) + 
  geom_area(stat = "density") +
  labs(title = "HELP clinical trial at detoxification unit")

Covariates: Adding in more variables

Using color and linetype:

ggplot(data = HELP_data,
       aes(x = i1,
           color = substance,
           linetype = children)) + 
  geom_line(stat = "density") +
  labs(title = "HELP clinical trial at detoxification unit")

Using color and facets

ggplot(data = HELP_data,
       aes(x = i1, color = substance)) + 
  geom_line(stat = "density") + 
  facet_grid( . ~ children ) +
  labs(title = "HELP clinical trial at detoxification unit")

ggplot(data = HELP_data,
       aes(x = i1, color = substance)) + 
  geom_line(stat = "density") + 
  facet_grid( children ~ . ) +
  labs(title = "HELP clinical trial at detoxification unit")

Boxplots

Boxplots use stat_quantile() (five number summary).

The quantitative variable must be y, and there must be an additional x variable.

ggplot(data = HELP_data,
       aes(x = substance, y = age, color = children)) + 
  geom_boxplot() +
  labs(title = "HELP clinical trial at detoxification unit")

Horizontal boxplots

coord_flip() may be used with other plots as well to reverse the roles f x and y on the plot.

ggplot(data = HELP_data,
       aes(x = substance, 
           y = age, 
           color = children)) + 
  geom_boxplot() +
  coord_flip() +
  labs(title = "HELP clinical trial at detoxification unit")

Axes scaling with boxplots

We can scale the continuous axis

ggplot(data = HELP_data,
       aes(x = substance, 
           y = age, 
           color = children)) + 
  geom_boxplot() +
  coord_trans(y = "exp") +
  labs(title = "HELP clinical trial at detoxification unit")

Give me some space

We’ve triggered a new feature: dodge (for dodging things left/right). We can control how much if we set the dodge manually.

ggplot(data = HELP_data,
       aes(x = substance, 
           y = age, 
           color = children)) + 
  geom_boxplot(position = position_dodge(width=1)) +
  labs(title = "HELP clinical trial at detoxification unit")

Issues with bigger data

Although we can see a generally positive association (as we would expect), the overplotting may be hiding information.

library(NHANES)
dim(NHANES)

[1] 10000    76

head(NHANES)

ID	SurveyYr	Gender	Age	AgeDecade	AgeMonths	Race1	Race3	Education	MaritalStatus	HHIncome	HHIncomeMid	Poverty	HomeRooms	HomeOwn	Work	Weight	Length	HeadCirc	Height	BMI	BMICatUnder20yrs	BMI_WHO	Pulse	BPSysAve	BPDiaAve	BPSys1	BPDia1	BPSys2	BPDia2	BPSys3	BPDia3	Testosterone	DirectChol	TotChol	UrineVol1	UrineFlow1	UrineVol2	UrineFlow2	Diabetes	DiabetesAge	HealthGen	DaysPhysHlthBad	DaysMentHlthBad	LittleInterest	Depressed	nPregnancies	nBabies	Age1stBaby	SleepHrsNight	SleepTrouble	PhysActive	PhysActiveDays	TVHrsDay	CompHrsDay	TVHrsDayChild	CompHrsDayChild	Alcohol12PlusYr	AlcoholDay	AlcoholYear	SmokeNow	Smoke100	Smoke100n	SmokeAge	Marijuana	AgeFirstMarij	RegularMarij	AgeRegMarij	HardDrugs	SexEver	SexAge	SexNumPartnLife	SexNumPartYear	SameSex	SexOrientation	PregnantNow
51624	2009_10	male	34	30-39	409	White	NA	High School	Married	25000-34999	30000	1.36	6	Own	NotWorking	87.4	NA	NA	164.7	32.22	NA	30.0_plus	70	113	85	114	88	114	88	112	82	NA	1.29	3.49	352	NA	NA	NA	No	NA	Good	0	15	Most	Several	NA	NA	NA	4	Yes	No	NA	NA	NA	NA	NA	Yes	NA	0	No	Yes	Smoker	18	Yes	17	No	NA	Yes	Yes	16	8	1	No	Heterosexual	NA
51624	2009_10	male	34	30-39	409	White	NA	High School	Married	25000-34999	30000	1.36	6	Own	NotWorking	87.4	NA	NA	164.7	32.22	NA	30.0_plus	70	113	85	114	88	114	88	112	82	NA	1.29	3.49	352	NA	NA	NA	No	NA	Good	0	15	Most	Several	NA	NA	NA	4	Yes	No	NA	NA	NA	NA	NA	Yes	NA	0	No	Yes	Smoker	18	Yes	17	No	NA	Yes	Yes	16	8	1	No	Heterosexual	NA
51624	2009_10	male	34	30-39	409	White	NA	High School	Married	25000-34999	30000	1.36	6	Own	NotWorking	87.4	NA	NA	164.7	32.22	NA	30.0_plus	70	113	85	114	88	114	88	112	82	NA	1.29	3.49	352	NA	NA	NA	No	NA	Good	0	15	Most	Several	NA	NA	NA	4	Yes	No	NA	NA	NA	NA	NA	Yes	NA	0	No	Yes	Smoker	18	Yes	17	No	NA	Yes	Yes	16	8	1	No	Heterosexual	NA
51625	2009_10	male	4	0-9	49	Other	NA	NA	NA	20000-24999	22500	1.07	9	Own	NA	17.0	NA	NA	105.4	15.30	NA	12.0_18.5	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	No	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	4	1	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
51630	2009_10	female	49	40-49	596	White	NA	Some College	LivePartner	35000-44999	40000	1.91	5	Rent	NotWorking	86.7	NA	NA	168.4	30.57	NA	30.0_plus	86	112	75	118	82	108	74	116	76	NA	1.16	6.70	77	0.094	NA	NA	No	NA	Good	0	10	Several	Several	2	2	27	8	Yes	No	NA	NA	NA	NA	NA	Yes	2	20	Yes	Yes	Smoker	38	Yes	18	No	NA	Yes	Yes	12	10	1	Yes	Heterosexual	NA
51638	2009_10	male	9	0-9	115	White	NA	NA	NA	75000-99999	87500	1.84	6	Rent	NA	29.8	NA	NA	133.1	16.82	NA	12.0_18.5	82	86	47	84	50	84	50	88	44	NA	1.34	4.86	123	1.538	NA	NA	No	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	5	0	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA

Overplotting

ggplot(data = NHANES,
       aes(x = Height, y = Weight)) +
  geom_point() + 
  facet_grid( Gender ~ PregnantNow )

Using alpha (opacity)

One way to deal with overplotting is to set the opacity low.

ggplot(data = NHANES,
       aes(x = Height, y = Weight)) +
  geom_point(alpha = 0.01) + 
  facet_grid( Gender ~ PregnantNow )

geom_density2d

Alternatively (or simultaneously) we might prefer a different geom altogether.

ggplot(data = NHANES,
       aes(x = Height, y = Weight)) +
  geom_density2d() + 
  facet_grid( Gender ~ PregnantNow )

Multiple layers

ggplot(data = HELP_data, 
       aes(x = children, y = age)) +
  geom_boxplot(outlier.size = 0) +
  geom_point(alpha = .6) +
  coord_flip() +
  labs(title = "HELP clinical trial at detoxification unit")

ggplot(data = HELP_data,
       aes(x = children, y = age)) +
  geom_boxplot(outlier.size = 0) +
  geom_jitter(alpha = .6, width=0.1) +
  coord_flip() +
  labs(title = "HELP clinical trial at detoxification unit")

Multiple layers

ggplot(data = HELP_data,
       aes(x = children, y = age)) +
  geom_boxplot(outlier.size = 0) +
  geom_point(alpha = .6, 
             position = position_jitter(width = .1, height = 0)) +
  coord_flip() +
  labs(title = "HELP clinical trial at detoxification unit")

Things I haven’t mentioned (much)

coords (coord_flip() is good to know about)
themes (for customizing appearance)
position (position_dodge(), position_jitterdodge() (for use with points on top of dodged boxplots), position_stack(), etc.)
transforming axes

themes

library(ggthemes)
ggplot(Births78, aes(x = date, y = births)) + 
  geom_point() + 
  theme_wsj()

`jitterdodge()`

ggplot(data = HELP_data, 
       aes(x = substance, y = age, color = children)) +
  geom_boxplot(position = position_dodge(width = 1)) +
  geom_point(aes(color = children, 
                 fill = children), 
             position = position_jitterdodge(dodge.width = 1, jitter.width = 0.1), 
             size = 0.5) +
  labs(title = "HELP clinical trial at detoxification unit")

A little bit of everything

ggplot(data = HELP_data, aes(x = substance, y = age, color = children)) +
  geom_boxplot(position = position_dodge(width=1)) +
  geom_point(aes(fill = children), 
             alpha = .5, 
             position = position_jitterdodge(dodge.width = 1, jitter.width = 0.2)) + 
  facet_wrap(~homeless) +
  labs(title = "HELP clinical trial at detoxification unit")

Want to learn more?

docs.ggplot2.org/
R for Data Science by Hadley Wickham and Garrett Grolemund

What’s around the corner?

shiny

interactive graphics / modeling
https://shiny.rstudio.com/

plotly

Plotly is an R package for creating interactive web-based graphs via plotly’s JavaScript graphing library, plotly.js. The plotly R libary contains the ggplotly function , which will convert ggplot2 figures into a Plotly object. Furthermore, you have the option of manipulating the Plotly object with the style function.

https://plot.ly/ggplot2/getting-started/

gganimate

gganimate tutorial