Plotting with ggplot2

Graphs

Graphing is an essential part of data analyses. Data with same summary statistics can look very different when plotted out.

R graphics

ggplot2 vs Base Graphics

hist(iris$Sepal.Length)

library(ggplot2)
ggplot(iris,aes(x=Sepal.Length))+
  geom_histogram()

ggplot2 vs Base Graphics

plot(iris$Petal.Length,iris$Petal.Width,
     col=c("red","green","blue")[iris$Species],
     pch=c(0,1,2)[iris$Species])
legend(x=1,y=2.5,
       legend=c("setosa","versicolor","virginica"),
       pch=c(0,1,2),col=c("red","green","blue"))

ggplot(iris,aes(Petal.Length,Sepal.Length,color=Species))+
  geom_point()

Why `ggplot2`?

Consistent code for any type of plot (almost!)
Flexible and modular (Add/remove components)
Automatic legends, colors etc
Save plot objects
Themes for reusing styles
Numerous add-ons/extensions
Nearly complete structured graphing solution
Adapted to other programming languages
- Gadfly in Julia
- gramm in MatLab
- GGPlot in Perl
- Vega in Javascript
- PlotNine , ggpy, lets-plot in Python

Grammar Of Graphics

Created by Hadley Wickham in 2005
Data: Input data
Geom: A geometry representing data. Points, Lines etc
Aesthetic: Visual characteristics of the geometry. Size, Color, Shape etc
Scale: How visual characteristics are converted to display values
Statistics: Statistical transformations. Counts, Means etc
Coordinates: Numeric system to determine position of geometry. Cartesian, Polar etc
Facets: Split data into subsets

Traditional graphing tools generally have independent set of rules for different kinds of graphs and also labelled differently such as barplots, scatterplots, boxplots etc. Each graph has it’s own function and plotting strategy.

Leland Wilkinson’s The Grammar of Graphics introduces this idea that any kind of graph can be created by following a set of rules and put forward a framework that enables this.

Grammar of graphics (GOG) tries to unify all graphs under a common umbrella. GOG brings the idea that graphs are made up of discrete elements (data, aesthetics, geometry, statistics, coordinates, facets, themes etc) which can be mixed and matched to create any plot. This creates a consistent underlying framework to graphing.

ggplot (Grammar of graphics) was built in R by Hadley Wickham in 2005 as an implementation of Leland Wilkinson’s book Grammar of Graphics.

Building A Graph: Syntax

Building A Graph

data(iris)
ggplot(iris)

Building A Graph

ggplot(iris,aes(x=Sepal.Length,
                y=Sepal.Width))

Building A Graph

ggplot(iris,aes(x=Sepal.Length,
                y=Sepal.Width))+
  geom_point()

Building A Graph

ggplot(iris,aes(x=Sepal.Length,
                y=Sepal.Width,
                colour=Species))+
  geom_point()

Data • `iris`

Input data is always an R data.frame object

Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
5.1	3.5	1.4	0.2	setosa
4.9	3.0	1.4	0.2	setosa
4.7	3.2	1.3	0.2	setosa
4.6	3.1	1.5	0.2	setosa
5.0	3.6	1.4	0.2	setosa
5.4	3.9	1.7	0.4	setosa

str(iris)

'data.frame':   150 obs. of  5 variables:
 $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

Data • `diamonds`

carat	cut	color	clarity	depth	table	price	x	y	z
0.23	Ideal	E	SI2	61.5	55	326	3.95	3.98	2.43
0.21	Premium	E	SI1	59.8	61	326	3.89	3.84	2.31
0.23	Good	E	VS1	56.9	65	327	4.05	4.07	2.31
0.29	Premium	I	VS2	62.4	58	334	4.20	4.23	2.63
0.31	Good	J	SI2	63.3	58	335	4.34	4.35	2.75
0.24	Very Good	J	VVS2	62.8	57	336	3.94	3.96	2.48

str(diamonds)

tibble [53,940 × 10] (S3: tbl_df/tbl/data.frame)
 $ carat  : num [1:53940] 0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...
 $ cut    : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
 $ color  : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...
 $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...
 $ depth  : num [1:53940] 61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...
 $ table  : num [1:53940] 55 61 65 58 58 57 57 55 61 61 ...
 $ price  : int [1:53940] 326 326 327 334 335 336 336 337 337 338 ...
 $ x      : num [1:53940] 3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...
 $ y      : num [1:53940] 3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...
 $ z      : num [1:53940] 2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...

Data • Format

Wide

Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
5.1	3.5	1.4	0.2	setosa
4.9	3.0	1.4	0.2	setosa
4.7	3.2	1.3	0.2	setosa

Long

Species	variable	value
setosa	Sepal.Length	5.1
setosa	Sepal.Length	4.9
setosa	Sepal.Length	4.7

Geoms

geoms

p <- ggplot(iris)
# scatterplot
p+geom_point(aes(x=Sepal.Length,y=Sepal.Width))
# barplot
p+geom_bar(aes(x=Sepal.Length))
# boxplot
p+geom_boxplot(aes(x=Species,y=Sepal.Width))
# search
help.search("^geom_",package="ggplot2")

Stats

Stats compute new variables from input data.

Geoms have default stats.

x <- ggplot(iris) + geom_bar(aes(x=Sepal.Length),stat="bin")
y <- ggplot(iris) + geom_bar(aes(x=Species),stat="count")
z <- ggplot(iris) + geom_bar(aes(x=Species,y=Sepal.Length),stat="identity")
wrap_plots(x,y,z,nrow=1)

Stats

Plots can be built with stats.

x <- ggplot(iris) + stat_bin(aes(x=Sepal.Length),geom="bar")
y <- ggplot(iris) + stat_count(aes(x=Species),geom="bar")
z <- ggplot(iris) + stat_identity(aes(x=Species,y=Sepal.Length),geom="bar")
wrap_plots(x,y,z,nrow=1)

Stats

Stats have default geoms.

plot	stat	geom
histogram	bin	bar
smooth	smooth	line
boxplot	boxplot	boxplot
density	density	line
freqpoly	freqpoly	line

Use args(geom_bar) to check arguments.

Aesthetics

Aesthetic mapping

ggplot(iris)+
  geom_point(aes(x=Sepal.Length,
                 y=Sepal.Width,
                 size=Petal.Length,
                 alpha=Petal.Width,
                 shape=Species,
                 color=Species))

Aesthetic parameter

ggplot(iris)+
  geom_point(aes(x=Sepal.Length,
                 y=Sepal.Width),
                 size=2,
                 alpha=0.8,
                 shape=15,
                 color="steelblue")

Aesthetics are used to assign values to geometries. For example, a set of points can be a fixed size or can be different colors or sizes denoting a variable.

This would be an incorrect way to do it.

ggplot(iris)+
geom_point(aes(x=Sepal.Length,y=Sepal.Width,size=2)

Aesthetics

x1 <- ggplot(iris) +
  geom_point(aes(x=Sepal.Length,y=Sepal.Width))+
  stat_smooth(aes(x=Sepal.Length,y=Sepal.Width))

x2 <- ggplot(iris,aes(x=Sepal.Length,y=Sepal.Width))+
                      geom_point() + geom_smooth()

wrap_plots(x1,x2,nrow=1,ncol=2)

Multiple Geoms

ggplot(iris,aes(x=Sepal.Length,y=Sepal.Width))+
      geom_point()+
      geom_line()+
      geom_smooth()+
      geom_rug()+
      geom_step()+
      geom_text(data=subset(iris,iris$Species=="setosa"),aes(label=Species))

Multiple geoms can be plotted one after the other. The order in which items are specified in the command dictates the plotting order on the actual plot.

In this case, the points appear over the lines.

ggplot(iris,aes(x=Sepal.Length,y=Sepal.Width))+
      geom_point()+
      geom_line()+

while here the lines appear above the points.

ggplot(iris,aes(x=Sepal.Length,y=Sepal.Width))+
      geom_line()+
      geom_point()+

Each geom takes input from ggplot() inputs. If extra input is required to a geom, it can be specified additionally inside aes().

data can be changed if needed for specific geoms.

Just because you can doesn’t mean you should!

Scales • Discrete Colors

scales: position, color, fill, size, shape, alpha, linetype
syntax: scale_<aesthetic>_<type>

p <- ggplot(iris)+geom_point(aes(x=Sepal.Length,
                     y=Sepal.Width,color=Species))
p

p + scale_color_manual(
     name="Manual",
     values=c("#5BC0EB","#FDE74C","#9BC53D"))

Scales • Continuous Colors

In RStudio, type scale_, then press TAB

p <- ggplot(iris)+
      geom_point(aes(x=Sepal.Length,
                     y=Sepal.Width,
      shape=Species,color=Petal.Length))
p

p +
scale_color_gradient(name="Pet Len",
  breaks=range(iris$Petal.Length),
  labels=c("Min","Max"),
  low="black",high="red")

Scales • Shape

p <- ggplot(iris)+
      geom_point(aes(x=Sepal.Length,
                     y=Sepal.Width,
      shape=Species,color=Species))
p

p +
scale_color_manual(name="New",
   values=c("blue","green","red"))+
scale_shape_manual(name="Bla",values=c(0,1,2))

Scales • Axes

scales: x, y
syntax: scale_<axis>_<type>
arguments: name, limits, breaks, labels

p <- ggplot(iris)+geom_point(
  aes(x=Sepal.Length,y=Sepal.Width))
p

p + scale_x_continuous(name="Sepal Length",
        breaks=seq(1,8),limits=c(3,5))

Facets • `facet_wrap`

Split to subplots based on variable(s), Faceting in one dimension

p <- ggplot(iris)+
      geom_point(aes(x=Sepal.Length,
                     y=Sepal.Width,
                     color=Species))
p

p + facet_wrap(~Species)

p + facet_wrap(~Species,nrow=3)

Facets • `facet_grid`

Faceting in two dimensions

p <- diamonds %>%
     ggplot(aes(carat,price))+
     geom_point()
p + facet_grid(~cut+clarity)

p + facet_grid(cut~clarity)

Coordinate Systems

coord_cartesian(xlim=c(2,8)) for zooming in
coord_map for controlling limits on maps
coord_polar for polar ccordinates

p <- ggplot(iris,aes(x="",y=Petal.Length,fill=Species))+
  geom_bar(stat="identity")
p

p + coord_polar("y", start = 0)

Theming

Modify non-data plot elements/appearance
Axis labels, panel colors, legend appearance etc

ggplot(iris, aes(Petal.Length)) +
    geom_histogram() +
    facet_wrap(~Species, nrow = 2) +
    theme_grey()

Save a particular appearance for reuse
?theme

ggplot(iris, aes(Petal.Length)) +
    geom_histogram() +
    facet_wrap(~Species, nrow = 2) +
    theme_bw()

Theme • Legend

p <- ggplot(iris)+
      geom_point(aes(x=Sepal.Length,
                     y=Sepal.Width,
                     color=Species))

p + theme(legend.position="top")

p + theme(legend.position="bottom")

Theme • Text

element_text(family=NULL,face=NULL,color=NULL,size=NULL,hjust=NULL,
             vjust=NULL, angle=NULL,lineheight=NULL,margin = NULL)

p <- p + theme(
    axis.title=element_text(color="#e41a1c"),
    axis.text=element_text(color="#377eb8"),
    plot.title=element_text(color="#4daf4a"),
    plot.subtitle=element_text(color="#984ea3"),
    legend.text=element_text(color="#ff7f00"),
    legend.title=element_text(color="#ffff33"),
    strip.text=element_text(color="#a65628")
)

Theme • Rect

element_rect(fill=NULL,color=NULL,size=NULL,linetype=NULL)

p <- p + theme(
    plot.background=element_rect(fill="#b3e2cd"),
    panel.background=element_rect(fill="#fdcdac"),
    panel.border=element_rect(fill=NA,color="#cbd5e8",size=3),
    legend.background=element_rect(fill="#f4cae4"),
    legend.box.background=element_rect(fill="#e6f5c9"),
    strip.background=element_rect(fill="#fff2ae")
)

Theme • Reuse

newtheme <- theme_bw() + theme(
  axis.ticks=element_blank(), panel.background=element_rect(fill="white"),
  panel.grid.minor=element_blank(), panel.grid.major.x=element_blank(),
  panel.grid.major.y=element_line(size=0.3,color="grey90"), panel.border=element_blank(),
  legend.position="top", legend.justification="right"
)

p + newtheme

Professional themes

How BBC works with R graphics

Position

us <- USArrests %>% mutate(state=rownames(.)) %>% slice(1:4) %>%
                    gather(key=type,value=value,-state)
p <- ggplot(us,aes(x=state,y=value,fill=type))

p + geom_bar(stat="identity",position="stack")

p + geom_bar(stat="identity",position="dodge")

Saving plots

p <- ggplot(iris,aes(Petal.Length,Sepal.Length,color=Species))+
  geom_point()

ggplot2 plots can be saved just like base plots

png("plot.png",height=5,width=7,units="cm",res=200)
print(p)
dev.off()

ggplot2 package offers a convenient function

ggsave("plot.png",p,height=5,width=7,units="cm",dpi=200,type="cairo")

Note that default units in png is pixels while in ggsave it’s inches

Combining Plots

p <- ggplot(us,aes(x=state,y=value,color=type))+geom_point()
q <- ggplot(us,aes(x=state,y=value,fill=type))+geom_bar(stat="identity")

patchwork::wrap_plots(p,q)

Refer to patchwork documentation.

Combining two or more ggplot2 plots is often required and several packages exist to help with this situation. Some functions allow plots to be placed adjacently, also allowing varying heights or widths of each plot. Some functions allow one plot to be plotted on another plot like a subset plot. Here are alternative options.

gridExtra::grid.arrange(p,q,ncol=2)
ggpubr::ggarrange(p,q,ncol=2,widths=c(1.5,1),common.legend=T)
cowplot::plot_grid()

Interactive

Convert ggplot2 object to interactive HTML

p <- ggplot(iris,aes(x=Sepal.Length,y=Sepal.Width,col=Species))

p1 <- p+geom_point()
plotly::ggplotly(p1,width=500,height=400)

p2 <- p+ggiraph::geom_point_interactive(
  aes(tooltip=paste0("<b>Species: </b>",Species)))+
  theme_bw(base_size=12)
ggiraph::ggiraph(code=print(p2))

Extensions

patchwork: Combining plots
ggrepel: Text labels including overlap control
ggforce: Circles, splines, hulls, voronoi etc
ggpmisc: Miscellaneaous features
ggthemes: Set of extra themes
ggthemr: More themes
ggsci: Color palettes for scales
ggmap: Dedicated to mapping
ggraph: Network graphs
ggiraph: Converting ggplot2 to interactive graphics

A collection of ggplot extension packages: https://exts.ggplot2.tidyverse.org/.
Curated list of ggplot2 links: https://github.com/erikgahner/awesome-ggplot2.

Help

Thank you! Questions?

         _                  
platform x86_64-pc-linux-gnu
os       linux-gnu          
major    4                  
minor    2.3

2023 • SciLifeLab • NBIS • RaukR