• What is ggplot2?
    • High-level graphics system developed by Hadley Wickham
    • Implements grammar of graphics from Leland Wilkinson
    • Streamlines many graphics workflows for complex plots
    • Syntax centered around main ggplot function
    • Simpler qplot function provides many shortcuts
  • Documentation and Help

ggplot2 Usage

  • ggplot function accepts two arguments
    • Data set to be plotted
    • Aesthetic mappings provided by aes function
  • Additional parameters such as geometric objects (e.g. points, lines, bars) are passed on by appending them with + as separator.
  • List of available geom_* functions see here
  • Settings of plotting theme can be accessed with the command theme_get() and its settings can be changed with theme().
  • Preferred input data object
    • qplot: data.frame or tibble (support for vector, matrix, ...)
    • ggplot: data.frame or tibble
  • Packages with convenience utilities to create expected inputs
    • plyr
    • reshape

qplot Function

The syntax of qplot is similar as R’s basic plot function

  • Arguments
    • x: x-coordinates (e.g. col1)
    • y: y-coordinates (e.g. col2)
    • data: data.frame or tibble with corresponding column names
    • xlim, ylim: e.g. xlim=c(0,10)
    • log: e.g. log="x" or log="xy"
    • main: main title; see ?plotmath for mathematical formula
    • xlab, ylab: labels for the x- and y-axes
    • color, shape, size
    • ...: many arguments accepted by plot function

qplot: scatter plot basics

Create sample data

library(ggplot2)
x <- sample(1:10, 10); y <- sample(1:10, 10); cat <- rep(c("A", "B"), 5)

Simple scatter plot

qplot(x, y, geom="point")

Prints dots with different sizes and colors

qplot(x, y, geom="point", size=x, color=cat, 
      main="Dot Size and Color Relative to Some Values")

Drops legend

qplot(x, y, geom="point", size=x, color=cat) + 
      theme(legend.position = "none")

Plot different shapes

qplot(x, y, geom="point", size=5, shape=cat)

Colored groups

p <- qplot(x, y, geom="point", size=x, color=cat, 
            main="Dot Size and Color Relative to Some Values") + 
     theme(legend.position = "none")
print(p)

Regression line

set.seed(1410)
dsmall <- diamonds[sample(nrow(diamonds), 1000), ]
p <- qplot(carat, price, data = dsmall) +
           geom_smooth(method="lm")
print(p)

Local regression curve (loess)

p <- qplot(carat, price, data=dsmall, geom=c("point", "smooth")) 
print(p) # Setting se=FALSE removes error shade

## `geom_smooth()` using method = 'gam'

ggplot Function

  • More important than qplot to access full functionality of ggplot2
  • Main arguments
    • data set, usually a data.frame or tibble
    • aesthetic mappings provided by aes function
  • General ggplot syntax
    • ggplot(data, aes(...)) + geom() + ... + stat() + ...
  • Layer specifications
    • geom(mapping, data, ..., geom, position)
    • stat(mapping, data, ..., stat, position)
  • Additional components
    • scales
    • coordinates
    • facet
  • aes() mappings can be passed on to all components (ggplot, geom, etc.). Effects are global when passed on to ggplot() and local for other components.
    • x, y
    • color: grouping vector (factor)
    • group: grouping vector (factor)

Changing Plotting Themes in ggplot

  • Theme settings can be accessed with theme_get()
  • Their settings can be changed with theme()

Example how to change background color to white

... + theme(panel.background=element_rect(fill = "white", colour = "black"))

Storing ggplot Specifications

Plots and layers can be stored in variables

p <- ggplot(dsmall, aes(carat, price)) + geom_point() 
p # or print(p)

Returns information about data and aesthetic mappings followed by each layer

summary(p)

Print dots with different sizes and colors

bestfit <- geom_smooth(method = "lm", se = F, color = alpha("steelblue", 0.5), size = 2)
p + bestfit # Plot with custom regression line

Syntax to pass on other data sets

p %+% diamonds[sample(nrow(diamonds), 100),]

Saves plot stored in variable p to file

ggsave(p, file="myplot.pdf")

ggplot: scatter plots

Basic example

set.seed(1410)  
dsmall <- as.data.frame(diamonds[sample(nrow(diamonds), 1000), ])
p <- ggplot(dsmall, aes(carat, price, color=color)) + 
            geom_point(size=4)
print(p)

Regression line

p <- ggplot(dsmall, aes(carat, price)) + geom_point() + 
            geom_smooth(method="lm", se=FALSE) +
    	    theme(panel.background=element_rect(fill = "white", colour = "black"))
print(p)

Several regression lines

p <- ggplot(dsmall, aes(carat, price, group=color)) + 
            geom_point(aes(color=color), size=2) + 
            geom_smooth(aes(color=color), method = "lm", se=FALSE) 
print(p)

Local regression curve (loess)

p <- ggplot(dsmall, aes(carat, price)) + geom_point() + geom_smooth() 
print(p) # Setting se=FALSE removes error shade

## `geom_smooth()` using method = 'gam'

ggplot: line plot

p <- ggplot(iris, aes(Petal.Length, Petal.Width, group=Species, 
            color=Species)) + geom_line() 
print(p)

Faceting

p <- ggplot(iris, aes(Sepal.Length, Sepal.Width)) + 
    	    geom_line(aes(color=Species), size=1) + 
            facet_wrap(~Species, ncol=1)
print(p)

Exercise 3

Scatter plots with ggplot2

  • Task 1: Generate scatter plot for first two columns in iris data frame and color dots by its Species column.
  • Task 2: Use the xlim and ylim arguments to set limits on the x- and y-axes so that all data points are restricted to the left bottom quadrant of the plot.
  • Task 3: Generate corresponding line plot with faceting show individual data sets in saparate plots.

Structure of iris data set

class(iris)

## [1] "data.frame"

iris[1:4,]

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa

table(iris$Species)

## 
##     setosa versicolor  virginica 
##         50         50         50

Bar Plots

Sample Set: the following transforms the iris data set into a ggplot2-friendly format.

Calculate mean values for aggregates given by Species column in iris data set

iris_mean <- aggregate(iris[,1:4], by=list(Species=iris$Species), FUN=mean)

Calculate standard deviations for aggregates given by Species column in iris data set

iris_sd <- aggregate(iris[,1:4], by=list(Species=iris$Species), FUN=sd)

Reformat iris_mean with melt

library(reshape2) # Defines melt function
df_mean <- melt(iris_mean, id.vars=c("Species"), variable.name = "Samples", value.name="Values")

Reformat iris_sd with melt

df_sd <- melt(iris_sd, id.vars=c("Species"), variable.name = "Samples", value.name="Values")

Define standard deviation limits

limits <- aes(ymax = df_mean[,"Values"] + df_sd[,"Values"], ymin=df_mean[,"Values"] - df_sd[,"Values"])

Verical orientation

p <- ggplot(df_mean, aes(Samples, Values, fill = Species)) + 
	    geom_bar(position="dodge", stat="identity")
print(p)

To enforce that the bars are plotted in the order specified in the input data, one can instruct ggplot to do so by turning the corresponding column (here Species) into an ordered factor as follows.

df_mean$Species <- factor(df_mean$Species, levels=unique(df_mean$Species), ordered=TRUE)

In the above example this is not necessary since ggplot uses this order already.

Horizontal orientation

p <- ggplot(df_mean, aes(Samples, Values, fill = Species)) + 
            geom_bar(position="dodge", stat="identity") + coord_flip() + 
            theme(axis.text.y=element_text(angle=0, hjust=1))
print(p)

Faceting

p <- ggplot(df_mean, aes(Samples, Values)) + geom_bar(aes(fill = Species), stat="identity") +
            facet_wrap(~Species, ncol=1)
print(p)

Error bars

p <- ggplot(df_mean, aes(Samples, Values, fill = Species)) + 
	    geom_bar(position="dodge", stat="identity") + geom_errorbar(limits, position="dodge") 
print(p)

Mirrored

df <- data.frame(group = rep(c("Above", "Below"), each=10), x = rep(1:10, 2), y = c(runif(10, 0, 1), runif(10, -1, 0)))
p <- ggplot(df, aes(x=x, y=y, fill=group)) + 
	    geom_bar(stat="identity", position="identity")
print(p)

Changing Color Settings

library(RColorBrewer)
# display.brewer.all() 
p <- ggplot(df_mean, aes(Samples, Values, fill=Species, color=Species)) +
            geom_bar(position="dodge", stat="identity") + geom_errorbar(limits, position="dodge") + 
            scale_fill_brewer(palette="Blues") + scale_color_brewer(palette = "Greys") 
print(p)

Using standard colors

p <- ggplot(df_mean, aes(Samples, Values, fill=Species, color=Species)) + 
            geom_bar(position="dodge", stat="identity") + geom_errorbar(limits, position="dodge") + 
            scale_fill_manual(values=c("red", "green3", "blue")) + 
            scale_color_manual(values=c("red", "green3", "blue")) 
print(p)

Exercise 4

Bar plots

  • Task 1: Calculate the mean values for the Species components of the first four columns in the iris data set. Use the melt function from the reshape2 package to bring the data into the expected format for ggplot.
  • Task 2: Generate two bar plots: one with stacked bars and one with horizontally arranged bars.

Structure of iris data set

class(iris)

## [1] "data.frame"

iris[1:4,]

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa

table(iris$Species)

## 
##     setosa versicolor  virginica 
##         50         50         50

Data reformatting example

Here for line plot

y <- matrix(rnorm(500), 100, 5, dimnames=list(paste("g", 1:100, sep=""), paste("Sample", 1:5, sep="")))
y <- data.frame(Position=1:length(y[,1]), y)
y[1:4, ] # First rows of input format expected by melt()

##    Position    Sample1    Sample2     Sample3     Sample4    Sample5
## g1        1 -1.2024596 -1.5004962 -0.01111579  0.07584497 -0.7100662
## g2        2  0.1023678 -0.5153367  0.28564390  1.41522878  1.1084695
## g3        3  1.3294248 -1.2084007 -0.19581898 -0.42361768  1.7139697
## g4        4  0.9219004 -0.3471160  3.32380305 -1.23402917 -0.3985408

df <- melt(y, id.vars=c("Position"), variable.name = "Samples", value.name="Values")
p <- ggplot(df, aes(Position, Values)) + geom_line(aes(color=Samples)) + facet_wrap(~Samples, ncol=1)
print(p)

Same data can be represented in box plot as follows

ggplot(df, aes(Samples, Values, fill=Samples)) + geom_boxplot()

Jitter Plots

p <- ggplot(dsmall, aes(color, price/carat)) + 
            geom_jitter(alpha = I(1 / 2), aes(color=color))
print(p)

Box plots

p <- ggplot(dsmall, aes(color, price/carat, fill=color)) + geom_boxplot()
print(p)

Violin plots

p <- ggplot(dsmall, aes(color, price/carat, fill=color)) + geom_violin()                                                                                                      
print(p)

Density plots

Line coloring

p <- ggplot(dsmall, aes(carat)) + geom_density(aes(color = color))
print(p)

Area coloring

p <- ggplot(dsmall, aes(carat)) + geom_density(aes(fill = color))
print(p)

Histograms

p <- ggplot(iris, aes(x=Sepal.Width)) + geom_histogram(aes(y = ..density.., 
            fill = ..count..), binwidth=0.2) + geom_density()  
print(p)

Pie Chart

df <- data.frame(variable=rep(c("cat", "mouse", "dog", "bird", "fly")), 
                 value=c(1,3,3,4,2)) 
p <- ggplot(df, aes(x = "", y = value, fill = variable)) + 
            geom_bar(width = 1, stat="identity") + 
            coord_polar("y", start=pi / 3) + ggtitle("Pie Chart") 
print(p)

Wind Rose Pie Chart

p <- ggplot(df, aes(x = variable, y = value, fill = variable)) + 
       geom_bar(width = 1, stat="identity") + coord_polar("y", start=pi / 3) + 
       ggtitle("Pie Chart") 
print(p)

Arranging Graphics on Page

Using grid package

library(grid)
a <- ggplot(dsmall, aes(color, price/carat)) + geom_jitter(size=4, alpha = I(1 / 1.5), aes(color=color))
b <- ggplot(dsmall, aes(color, price/carat, color=color)) + geom_boxplot()
c <- ggplot(dsmall, aes(color, price/carat, fill=color)) + geom_boxplot() + theme(legend.position = "none")
grid.newpage() # Open a new page on grid device
pushViewport(viewport(layout = grid.layout(2, 2))) # Assign to device viewport with 2 by 2 grid layout 
print(a, vp = viewport(layout.pos.row = 1, layout.pos.col = 1:2))
print(b, vp = viewport(layout.pos.row = 2, layout.pos.col = 1))
print(c, vp = viewport(layout.pos.row = 2, layout.pos.col = 2, width=0.3, height=0.3, x=0.8, y=0.8))

Using gridExtra package

library(gridExtra)                                                                                                                                                            
grid.arrange(a, b, c, nrow = 2, ncol=2)

Inserting Graphics into Plots

library(grid)
print(a)
print(b, vp=viewport(width=0.3, height=0.3, x=0.8, y=0.8))



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