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R Tutorial

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Plotting with ggplot
Umesh P., SilpaBhaskaran Dept. of Computational Biology and Bioinformatics, University of Kerala
R package is nowadays used widely for generating dynamic and interactive graphics because of its capability to produce wide variety of graphs out of the input data. Several packages are available with
R to create such high resolution, publication ready images. ‘
ggplot2
’ is one of
the most popular packages in R that helps to generate beautiful graphics. Apart from the R's
plot()
function
, ggplot2
allows you to create more complex graphs and plots.
ggplot2
allows composing a set of independent components- such as scales and layers, in different ways to get variety of graphs.
ggplot2
helps us to create hassle-free, quality plots in seconds without compromising the accuracy. It also takes care of the formatting requirements of the plot and provides a comprehensive theming system that complements the plots in appearance. In this issue we are discussing some basic commands used in ggplot2 for producing interactive graphics. To make
ggplot2
available for our application, first install the package and load it into R environment:
install.packages( ggplot2 ) library(ggplot2)
Now, let us look into
qplot( )
which is one of the basic plotting function in the
ggplot2
package. Consider the following data of accident cases occurred in a particular place during five years(from 2010- 2014): Now try the given R statement:
>qplot(Accident, data = new, geom= density , fill=YearRange, xlab= Accidents , ylab= Density , main= Density of Accidents per 5 years , alpha=I(.4))
This statement will plot the density of accidents of each year, from its beginning to end, as each layer as given in Fig. 1. Fig. 1: Data and correspoding density plot The attribute
alpha
allows transparency of overlapping items, as indicated in the figure, with a value indicating the transparency strength.
geom
specifies the geometric objects that define the
graph type. It can be either point , smooth , boxplot , line , histogram , density , bar , or histogram . Now let us have a look at the
ggplot()
function in the ggplot2 library which is more advanced than
qplot().
A typical
ggplot()
function takes two primary arguments
–
data and aes.
“
data
”
indicates the data frame contacting the data that is to be plotted and
aes
itself is like a function to pass arguments to the plot. Now let us try
ggplot ( )
with an example.
myplot<- ggplot(heightweight, aes(Height, Weight))
This statement will give us an error and no graph is plotted, as we have to specify the geometrical object we would like to use too. If we wish to get scatter plot, add
geom_point( ).
i.e.
myplot + geom_point()<- ggplot(heightweight, aes(Height, Weight))
For getting lines that join data points, we can use
myplot + geom_point()+ geom_line()<- ggplot(heightweight, aes(Height, Weight))
We can colour either the point or the line by adding
aes(color = factor( ))
into the argument of
geom
. That is if we want to add colour to the points use the below statement:
myplot+geom_point(aes(color = factor(Height)))+ geom_line()
See the resultant plot (Fig. 2).
Fig. 2: Sample data and the obtained plot
Now let us see how bubble plot is used to represent the literacy of people in different states of India. We used the sample data from
http://en.wikipedia.org/wiki/Demographics_of_India.
Let us try the following code:
ggplot(data, aes(x = no, y = Population)) + geom_point(aes(size = Literacy), alpha = 0.5, position = jitter , color = red ) + geom_text(data=data, mapping=aes(x=no, y=Population, label=State), size=4, vjust=3, hjust=0.5) + scale_size(range = c(10, 50)) + theme_classic()
Fig. 3: Bubble plot
In this article, we have indicated some of the usages of
ggplot2
package. You can explore a lot more capabilities of
ggplot2
and generate wonderful graphs to represent your data.

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