Introduction to ggplot2

Introduction

Plotting in R

There are multiple plotting systems in R:

• base graphics
  • you start with an empty canvas and command-by-command draw things on it
• lattice
  • one single function that has many parrameters and does the whole plot
• ggplot2
  • the subject of this lecture

ggplot2

• R package for data visualization
• It implements the ideas from Grammar of Graphics

Grammar of Graphics

Documentation

• ggplot2 documentaton
• Cheatsheet - Data Visualization with ggplot2
• Rdocumentation
• Stackoverflow

Advantages

• lot of sensible defaults
• nice colors, labels, small margins

• powerful and flexible

• Compare the plots from base graphics:

x <- rnorm(1:100)
hist(x)

• and then with ggplot2:

ggplot(data.frame(x), aes(x)) + geom_histogram()

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

ggplot(data.frame(x), aes(x)) + 
  geom_histogram(fill="#c0392b", bins=30) + 
  labs(title="Distribution of Some Important Data",
       subtitle="An interesting insight into my data",
       caption="My plot 1",
       x="# of Entries in Listicle", 
       y="# of Listicles") +
  theme_minimal() +
  geom_hline(yintercept=0, size=0.4, color="black") +
  geom_vline(aes(xintercept=mean(x)), linetype="dashed")

Disadvantages

• only work with data.frames
• might be slower than the base graphics
• no 3-D plots
• steeper learning curve

Essentials

Load the library:

library(ggplot2)

If you do not have it, install using:

install.packages("ggplot2")

Example Dataset

• Fuel economy data 1999 and 2008 for 38 popular models of car
• A data frame with 234 rows and 11 variables
• Comes with the ggplot2 package

?mpg

str(mpg)

## Classes 'tbl_df', 'tbl' and 'data.frame':    234 obs. of  11 variables:
##  $ manufacturer: chr  "audi" "audi" "audi" "audi" ...
##  $ model       : chr  "a4" "a4" "a4" "a4" ...
##  $ displ       : num  1.8 1.8 2 2 2.8 2.8 3.1 1.8 1.8 2 ...
##  $ year        : int  1999 1999 2008 2008 1999 1999 2008 1999 1999 2008 ...
##  $ cyl         : int  4 4 4 4 6 6 6 4 4 4 ...
##  $ trans       : chr  "auto(l5)" "manual(m5)" "manual(m6)" "auto(av)" ...
##  $ drv         : chr  "f" "f" "f" "f" ...
##  $ cty         : int  18 21 20 21 16 18 18 18 16 20 ...
##  $ hwy         : int  29 29 31 30 26 26 27 26 25 28 ...
##  $ fl          : chr  "p" "p" "p" "p" ...
##  $ class       : chr  "compact" "compact" "compact" "compact" ...

summary(mpg)

##  manufacturer          model               displ            year     
##  Length:234         Length:234         Min.   :1.600   Min.   :1999  
##  Class :character   Class :character   1st Qu.:2.400   1st Qu.:1999  
##  Mode  :character   Mode  :character   Median :3.300   Median :2004  
##                                        Mean   :3.472   Mean   :2004  
##                                        3rd Qu.:4.600   3rd Qu.:2008  
##                                        Max.   :7.000   Max.   :2008  
##       cyl           trans               drv                 cty       
##  Min.   :4.000   Length:234         Length:234         Min.   : 9.00  
##  1st Qu.:4.000   Class :character   Class :character   1st Qu.:14.00  
##  Median :6.000   Mode  :character   Mode  :character   Median :17.00  
##  Mean   :5.889                                         Mean   :16.86  
##  3rd Qu.:8.000                                         3rd Qu.:19.00  
##  Max.   :8.000                                         Max.   :35.00  
##       hwy             fl               class          
##  Min.   :12.00   Length:234         Length:234        
##  1st Qu.:18.00   Class :character   Class :character  
##  Median :24.00   Mode  :character   Mode  :character  
##  Mean   :23.44                                        
##  3rd Qu.:27.00                                        
##  Max.   :44.00

Columns:

• manufacturer - car manufacturer
• model - car model
• displ - engine displacement, in litres
• year - year of manufacture
• cyl - number of cylinders
• trans - type of transmission
• drv - f = front-wheel drive, r = rear wheel drive, 4 = 4wd
• cty - city miles per gallon
• hwy - highway miles per gallon
• fl - fuel type
• class - “type” of car

Sample:

knitr::kable(head(mpg, n=20), format="markdown")

manufacturer	model	displ	year	cyl	trans	drv	cty	hwy	fl	class
audi	a4	1.8	1999	4	auto(l5)	f	18	29	p	compact
audi	a4	1.8	1999	4	manual(m5)	f	21	29	p	compact
audi	a4	2.0	2008	4	manual(m6)	f	20	31	p	compact
audi	a4	2.0	2008	4	auto(av)	f	21	30	p	compact
audi	a4	2.8	1999	6	auto(l5)	f	16	26	p	compact
audi	a4	2.8	1999	6	manual(m5)	f	18	26	p	compact
audi	a4	3.1	2008	6	auto(av)	f	18	27	p	compact
audi	a4 quattro	1.8	1999	4	manual(m5)	4	18	26	p	compact
audi	a4 quattro	1.8	1999	4	auto(l5)	4	16	25	p	compact
audi	a4 quattro	2.0	2008	4	manual(m6)	4	20	28	p	compact
audi	a4 quattro	2.0	2008	4	auto(s6)	4	19	27	p	compact
audi	a4 quattro	2.8	1999	6	auto(l5)	4	15	25	p	compact
audi	a4 quattro	2.8	1999	6	manual(m5)	4	17	25	p	compact
audi	a4 quattro	3.1	2008	6	auto(s6)	4	17	25	p	compact
audi	a4 quattro	3.1	2008	6	manual(m6)	4	15	25	p	compact
audi	a6 quattro	2.8	1999	6	auto(l5)	4	15	24	p	midsize
audi	a6 quattro	3.1	2008	6	auto(s6)	4	17	25	p	midsize
audi	a6 quattro	4.2	2008	8	auto(s6)	4	16	23	p	midsize
chevrolet	c1500 suburban 2wd	5.3	2008	8	auto(l4)	r	14	20	r	suv
chevrolet	c1500 suburban 2wd	5.3	2008	8	auto(l4)	r	11	15	e	suv

Basic plotting

p <- ggplot(data=mpg, aes(displ, hwy))
p

p <- ggplot(data=mpg, aes(x=displ, y=hwy))
p + geom_point()

Essential Grammatical Elements

• data - data being plotted (R data frame)
• aesthetics - visual characteristics that represent data
• geoms - geometric objects representing the data

All Grammatical Elements

• data - data being plotted (R data frame)
• aesthetics - visual characteristics that represent data
• geoms - geometric objects representing the
• scales - how visual characteristics are converted to display values
• stats - statistical transformations summarizing data
• coordinate system - space used for projecting data
• facets - describes data splits in subsets displayed as multiple graphs
• themes - all non-data ink

Aestethics

• Basic plot

p <- ggplot(mpg, aes(x=displ, y=hwy))
p + geom_point()

• Add color aesthetics (type of a class)

p <- ggplot(mpg, aes(x=displ, y=hwy))
p + geom_point(aes(color=class))

• Add color aesthetics (number of cylinders) - continuous

p <- ggplot(mpg, aes(x=displ, y=hwy))
p + geom_point(aes(color=cyl))

• Add color aesthetics (number of cylinders) - discrete using a factor

p <- ggplot(mpg, aes(x=displ, y=hwy))
p + geom_point(aes(color=factor(cyl)))

• Add size aesthetics (number of cylinders)

p <- ggplot(mpg, aes(x=displ, y=hwy))
p + geom_point(aes(size=cyl))

• Add size aesthetics

p <- ggplot(mpg, aes(x=displ, y=hwy))
p + geom_point(aes(color=class, size=cyl))

• Add shape aesthetics

p <- ggplot(mpg, aes(x=displ, y=hwy))
p + geom_point(aes(shape=factor(cyl)), size=4)

• Add more shape aesthetics

p <- ggplot(mpg, aes(x=displ, y=hwy))
p + geom_point(colour="black", size=4.5) +
  geom_point(colour="white", size=4) +
  geom_point(aes(shape = factor(cyl)))

• Show the pimped shapes in legend

p <- ggplot(mpg, aes(x=displ, y=hwy))
p + geom_point(colour="black", size=4.5, show.legend=T) +
  geom_point(colour="white", size=4, show.legend=T) +
  geom_point(aes(shape = factor(cyl)))

• This help us to map multiple variables in a 2-D plot

List of aesthetics

• x
• y
• color
• fill
• size
• alpha
• linetype
• labels

Geoms

• Add line

p <- ggplot(mpg, aes(x=displ, y=hwy))
p + geom_point() + geom_line()

• What about color

p <- ggplot(mpg, aes(x=displ, y=hwy))
p + geom_point(aes(color=factor(cyl))) + geom_line()

• What about color

p <- ggplot(mpg, aes(x=displ, y=hwy))
p + geom_point(aes(color=factor(cyl))) + geom_line(aes(color=factor(cyl)))

• Inheriting aesthetics

p <- ggplot(mpg, aes(x=displ, y=hwy, color=factor(cyl)))
p + geom_point() + geom_line()

p + geom_point(color="black") + geom_line()

Jitter

• It adds a small amount of random variation to the location of each point
• It is a useful way of handling overplotting caused by discreteness in smaller datasets

p <- ggplot(mpg, aes(cyl, hwy))
p + geom_point()

p <- ggplot(mpg, aes(cyl, hwy))
p + geom_jitter()

p <- ggplot(mpg, aes(cyl, hwy))
p + geom_jitter(aes(colour = class))

Box plots

ggplot(mpg, aes(class, hwy)) + geom_boxplot()

• adding multiple data

ggplot(mpg) + 
  geom_boxplot(aes(class, hwy)) + 
  geom_boxplot(aes(class, cty))

• add colors

ggplot(mpg) + 
  geom_boxplot(aes(class, hwy), color="blue") + 
  geom_boxplot(aes(class, cty), color="red")

• using groups

ggplot(mpg, aes(class, hwy, fill = factor(year))) +
  geom_boxplot()

ggplot(mpg, aes(reorder(class, hwy, median), hwy, fill = factor(year))) +
  geom_boxplot()

reorder(mpg$class, mpg$hwy, median)

##   [1] compact    compact    compact    compact    compact    compact   
##   [7] compact    compact    compact    compact    compact    compact   
##  [13] compact    compact    compact    midsize    midsize    midsize   
##  [19] suv        suv        suv        suv        suv        2seater   
##  [25] 2seater    2seater    2seater    2seater    suv        suv       
##  [31] suv        suv        midsize    midsize    midsize    midsize   
##  [37] midsize    minivan    minivan    minivan    minivan    minivan   
##  [43] minivan    minivan    minivan    minivan    minivan    minivan   
##  [49] pickup     pickup     pickup     pickup     pickup     pickup    
##  [55] pickup     pickup     pickup     suv        suv        suv       
##  [61] suv        suv        suv        suv        pickup     pickup    
##  [67] pickup     pickup     pickup     pickup     pickup     pickup    
##  [73] pickup     pickup     suv        suv        suv        suv       
##  [79] suv        suv        suv        suv        suv        pickup    
##  [85] pickup     pickup     pickup     pickup     pickup     pickup    
##  [91] subcompact subcompact subcompact subcompact subcompact subcompact
##  [97] subcompact subcompact subcompact subcompact subcompact subcompact
## [103] subcompact subcompact subcompact subcompact subcompact subcompact
## [109] midsize    midsize    midsize    midsize    midsize    midsize   
## [115] midsize    subcompact subcompact subcompact subcompact subcompact
## [121] subcompact subcompact suv        suv        suv        suv       
## [127] suv        suv        suv        suv        suv        suv       
## [133] suv        suv        suv        suv        suv        suv       
## [139] suv        suv        suv        compact    compact    midsize   
## [145] midsize    midsize    midsize    midsize    midsize    midsize   
## [151] suv        suv        suv        suv        midsize    midsize   
## [157] midsize    midsize    midsize    suv        suv        suv       
## [163] suv        suv        suv        subcompact subcompact subcompact
## [169] subcompact compact    compact    compact    compact    suv       
## [175] suv        suv        suv        suv        suv        midsize   
## [181] midsize    midsize    midsize    midsize    midsize    midsize   
## [187] compact    compact    compact    compact    compact    compact   
## [193] compact    compact    compact    compact    compact    compact   
## [199] suv        suv        pickup     pickup     pickup     pickup    
## [205] pickup     pickup     pickup     compact    compact    compact   
## [211] compact    compact    compact    compact    compact    compact   
## [217] compact    compact    compact    compact    compact    subcompact
## [223] subcompact subcompact subcompact subcompact subcompact midsize   
## [229] midsize    midsize    midsize    midsize    midsize    midsize   
## attr(,"scores")
##    2seater    compact    midsize    minivan     pickup subcompact 
##       25.0       27.0       27.0       23.0       17.0       26.0 
##        suv 
##       17.5 
## Levels: pickup suv minivan 2seater subcompact compact midsize

with(mpg, reorder(class, hwy, median))

Bar plots

ggplot(mpg, aes(class)) + geom_bar()

ggplot(mpg, aes(class, hwy)) + geom_bar(stat="summary", fun.y=mean)

• plot both hwy an cty per groups

hwy <- mpg[, c("class", "hwy")]
hwy <- cbind(hwy, "hwy")
names(hwy) <- c("class", "val", "type")

cty <- mpg[, c("class", "cty")]
cty <- cbind(cty, "cty")
names(cty) <- c("class", "val", "type")

ds <- rbind(hwy, cty)
ds[sample(1:nrow(ds), 10),]

##          class val type
## 146    midsize  27  hwy
## 410        suv  15  cty
## 344    midsize  18  cty
## 58         suv  17  hwy
## 381    midsize  19  cty
## 316        suv  13  cty
## 290     pickup  11  cty
## 355 subcompact  16  cty
## 165        suv  23  hwy
## 382    midsize  18  cty

• Plot the by grupping them using fill color

ggplot(ds, aes(class, val, fill=type)) + geom_bar(stat="summary", fun.y=mean)

• better position

ggplot(ds, aes(class, val, fill=type)) + geom_bar(stat="summary", fun.y=mean, position="dodge")

Positions

• position_identity - default of most geoms
• position_jitter - default of geom_jitter
• position_dodge - default of geom_boxplot
• position_stack - default of geom_bar==geom_histogram and geom_area
• position_fill - useful for geom_bar==geom_histogram and geom_area

Error bars

ds2 <- lapply(unique(ds$class), function(x) {
  v <- ds[ds$class==x, ]
  
  cty <- mean_cl_boot(v[v$type == "cty", "val"])
  cty["type"] <- "cty"
  
  hwy <- mean_cl_boot(v[v$type == "hwy", "val"])
  hwy["type"] <- "hwy"
  
  df <- rbind(cty, hwy)
  df["class"]<-x
  df
})

ds2 <- do.call(rbind, ds2)
ds2

##           y     ymin     ymax type      class
## 1  20.12766 19.25479 21.10691  cty    compact
## 2  28.29787 27.27606 29.51117  hwy    compact
## 3  18.75610 18.17073 19.31707  cty    midsize
## 4  27.29268 26.68293 27.90244  hwy    midsize
## 5  13.50000 12.90323 14.09718  cty        suv
## 6  18.12903 17.40323 18.88750  hwy        suv
## 7  15.40000 15.00000 15.80000  cty    2seater
## 8  24.80000 23.80000 25.80000  hwy    2seater
## 9  15.81818 14.72500 16.63636  cty    minivan
## 10 22.36364 21.00000 23.36591  hwy    minivan
## 11 13.00000 12.36364 13.69697  cty     pickup
## 12 16.87879 16.06061 17.60606  hwy     pickup
## 13 20.37143 18.85643 21.91500  cty subcompact
## 14 28.14286 26.42786 30.05714  hwy subcompact

ggplot(ds2, aes(class, y, fill=type)) + 
  geom_bar(stat="identity", position="dodge") +
  geom_errorbar(aes(ymin=ymin, ymax=ymax), position=position_dodge(.9), width=.2)

ggplot(ds, aes(class, val, fill=type)) + 
  geom_bar(stat="summary", fun.y=mean, position="dodge") +
  stat_summary(fun.data=mean_cl_boot, color="black", geom="errorbar", position=position_dodge(.9), width=.2)

Text

ggplot(mtcars, aes(mpg, wt)) + 
  geom_point() +
  geom_text(aes(label=rownames(mtcars)))

ggplot(mtcars, aes(mpg, wt)) + 
  geom_point() +
  geom_text(aes(label=rownames(mtcars)), size=2)

ggplot(mtcars, aes(mpg, wt)) + 
  geom_point() +
  geom_text(aes(label=rownames(mtcars), size=wt))

ggplot(mtcars, aes(mpg, wt)) + 
  geom_point() +
  annotate("text", label = "plot mpg vs. wt", x = 2, y = 5, size = 8, colour = "red")

Adding text to bars

ggplot(mpg, aes(class, cty)) + geom_bar(stat="identity") +
  geom_text(aes(label=cty), color="white")

• What are the values of cty for 2seater

mpg[mpg$class=="2seater", "cty"]

## # A tibble: 5 × 1
##     cty
##   <int>
## 1    16
## 2    15
## 3    16
## 4    15
## 5    15

ds2 <- lapply(unique(mpg$class), function(x) {
  v <- ds[mpg$class==x, ]
  cty <- mean(v[v$type == "cty", "val"])
  data.frame(class=x, cty)
})

ds2 <- do.call(rbind, ds2)
ds2

##        class      cty
## 1    compact 20.12766
## 2    midsize 18.75610
## 3        suv 13.50000
## 4    2seater 15.40000
## 5    minivan 15.81818
## 6     pickup 13.00000
## 7 subcompact 20.37143

ggplot(ds2, aes(class, cty)) + geom_bar(stat="identity") +
  geom_text(aes(label=round(cty, digits=2)), color="white", vjust=2)

Plots properties

Titles

p <- ggplot(mpg, aes(x=displ, y=hwy))
p + geom_point(aes(color=class, size=cyl)) +
  labs(title="Fuel economy in relation to engine dislacement",
       subtitle="Less is more",
       caption="A caption",
       x="engine displacement", 
       y="highway miles per gallon") 

• Update the labels of legends

p <- ggplot(mpg, aes(x=displ, y=hwy))
p + geom_point(aes(color=class, size=cyl)) +
  labs(title="Fuel economy in relation to engine dislacement",
       subtitle="Less is more",
       caption="A caption",
       x="engine displacement", 
       y="highway miles per gallon") +
  guides(color=guide_legend(title="Car class"), size=guide_legend(title="Number of cylinders"))

Axises

p <- ggplot(mpg, aes(x=model, y=hwy))
p + geom_bar(stat="identity")

p <- ggplot(mpg, aes(x=model, y=hwy))
p + geom_bar(stat="identity") + 
  theme(axis.text.x = element_text(angle = 90))

• better justification

p <- ggplot(mpg, aes(x=model, y=hwy))
p + geom_bar(stat="identity") + 
  theme(axis.text.x = element_text(angle = 90, hjust = 1))

Themes

• https://cran.r-project.org/web/packages/ggthemes/vignettes/ggthemes.html

library(ggthemes)

• Default

p <- ggplot(mpg, aes(x=displ, y=hwy))
p + geom_point(aes(color=class, size=cyl))

• The economist

p <- ggplot(mpg, aes(x=displ, y=hwy))
p + geom_point(aes(color=class, size=cyl)) +
  theme_economist() + 
  scale_colour_economist() 

• Minimal

p <- ggplot(mpg, aes(x=displ, y=hwy))
p + geom_point(aes(color=class, size=cyl)) +
theme_minimal()

• Excel 2003

p <- ggplot(mpg, aes(x=displ, y=hwy))
p + geom_point(aes(color=class, size=cyl)) +
theme_excel() + scale_colour_excel()

Displaying statistics

• Add fit using a liner model

p <- ggplot(mpg, aes(displ, hwy))
p + geom_point() + geom_smooth(method="lm")

• Add fit using a liner model with groups

p <- ggplot(mpg, aes(displ, hwy, color=factor(cyl)))
p + geom_point() + geom_smooth(method="lm")

• Using a better fit: local polynomial regression

p <- ggplot(mpg, aes(displ, hwy))
p + geom_point() + geom_smooth(method="loess")

ggplot() vs qplot()

• qplot - quick plot
  • designed to be close to the base plot()
  • easy to produce basic graphs
  • may deplay understanding of the concepts behind ggplot2
• `ggplot - grammar of graphics plot
  • steeper learning curve
  • more flexibility
  • implements The Grammar of Graphics

Facets

A formula with the rows (of the tabular display) on the LHS and the columns (of the tabular display) on the RHS; the dot in the formula is used to indicate there should be no faceting on this dimension (either row or column).

Formulae

y ~ x 

• x is the explanatory variable
• y is the response variable

y ~ x + z 

Examples

• Vertical

p <- ggplot(mpg, aes(x=displ, y=hwy)) + geom_point()
p + facet_grid(. ~ cyl)

• Horizontal

p <- ggplot(mpg, aes(x=displ, y=hwy)) + geom_point() 
p + facet_grid(cyl ~ .)

• Both

p <- ggplot(mpg, aes(x=displ, y=hwy)) + geom_point() 
p + facet_grid(drv ~ cyl)

• Free axis

p <- ggplot(mtcars, aes(mpg, wt, colour = factor(cyl))) + geom_point()
p + facet_grid(. ~ cyl, scales = "free")

• Free axis and space

ggplot(mpg, aes(drv, model)) +
  geom_point() +
  facet_grid(manufacturer ~ ., scales = "free", space = "free") +
  theme(strip.text.y = element_text(angle = 0))

Plotly

https://plot.ly/ggplot2/

library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:Hmisc':
## 
##     subplot

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

p <- ggplot(mpg, aes(x=displ, y=hwy))
p <- p + geom_point(aes(color=class, size=cyl))

(gg <- ggplotly(p))

Other resources

• Examples of histograms