How to create stunning visualisations using R
What is data visualisation?
Put simply, data visualisation is
the pictorial representation of data. Any data set can be understood at a
glance by representing it on interactive charts and graphs. This presentation
method is employed everywhere around you, right from statistical graphs in
textbooks and academic papers to colourful infographics in newspapers. As
technology has progressed, we’ve been able to add increasingly more specifics
and data, and with more data arises the need for accurate and appropriate
visualisation techniques. That’s where ‘R’ comes in handy.
What is R?
Programming to create visualisation?
Is R user friendly for students?
R, since its introduction, has
become quite popular not only among the PhD holders in Data Science but also
amongst mainstream users in need of a common platform for computing and
visualisation. In this workshop, we’ll get you started on some simple data
visualisation techniques using R, which will serve as an adequate platform to
develop visualisations that are even more advanced. For first time users of
programming languages: Do not be afraid. The idea of coding from scratch can be
intimidating, especially if you’ve never used programming languages before.
Remember that every line of code is simply a written equivalent of a keyboard
shortcut in a sense. It performs a task and these commands can mostly be looked
up in a book or online.
What are the advantages of using R
over MATLAB?
R has some distinct advantages.
Since it’s a free software service released under the GNU license it’s free!
This gives it a huge advantage over commercial software such as MATLAB, which
are really expensive especially for students and small enterprises. Because of
its Open Source nature R attracts a host of talented developers which means awesome
support, documentation and user defined packages for diverse needs. R has some
amazing packages to unlock a variety of complex and useful data visualisation
techniques.
What else can R do?
Besides creating stunning
visualisations you can also easily perform run-of-the mill data analysis.
There’s good support for computations regarding statistics and probability, and
support for machine learning and data mining is getting better.
A
screenshot from the data sheet where Sachin Tendulkar’s records are stored.
Source: stats.espncricinfo.com
Installing the IDE
One of the best and most robust IDEs
for R is RStudio. (An IDE or integrated development environment is a
facilitator that helps you communicate effectively and intuitively with the programming
language). It comes bundled with R software and basic packages, and can be
installed on Linux, Windows or Mac with the single click of a button.
The installation in Windows/Mac is a
routine procedure as with any other software. Download RStudio Desktop, run the
.exe file and follow the procedural steps. While in Linux one has to install R
via the terminal (type ‘sudo apt-get install r-base’) and then install RStudio
separately by selecting it from the software centre.
Basic layout – as user-friendly as
it gets!
The home screen is an arrangement of
four panes, each of which can be expanded to fill the screen whenever required.
The top left window is where the codes are written and run en masse, however
this part is not important to our tutorial. The top right window maintains a
history of all the commands written. The bottom left window is the ‘console’
where you enter commands and see the results (if any) in the bottom right
window.
Select and read data
Let’s now create a visualisation.
Here’s some data from Sachin Tendulkar’s batting record each year (Source:
stats.espncricinfo.com) pasted in an Excel sheet as shown here:
Click on File > Save as > Save
it in the CSV (Comma Separated values) format in My Documents (the default
location from which RStudio inputs files)
In the RStudio console (bottom
left), type the following:
sachin<-read.csv(‘sachin.csv’,header
= TRUE);
This will ensure that sachin is now
a data set with rows and columns. Type ‘sachin’ and press [Enter] to view the
table with its headers.
The default packages allow some
plotting in R. For example, type the following:
qplot(sachin$Grouping,sachin$Average);
Average
annual score plotted versus every year - heavily scaled up graph. While there
seems to be remarkable consistency, it does appear to push the average score
closer to zero
You’ll find a plot of the average
scores by Sachin in each year, with the ‘Grouping’ column showing along X-axis
and ‘Average’ column showing along the Y-axis. It might seem a little haphazard
so change the scales until you find a graph that works for you. For example:
qplot(sachin$Grouping,
sachin$Average, ylim = c(0,200));
Average
annual score plotted versus every year - the default setting
Average
annual score plotted versus every year - slightly scaled up graph. Notice how
everything seems to appear a lot more ‘averaged’ and ‘flattish’
qplot(sachin$Grouping,
sachin$Average, ylim = c(0,2000));
These produce radically different
visualisations compared to the first data set!
To unleash the full potential of R’s
graphing capabilities, employ the ggplot2 package. Ensure you have a working
internet connection and type:
install.packages(“ggplot2”);
library(ggplot2);
You can now use all the functionalities
of ggplot2.
ggplot2 has two basic components:
aesthetics, which is all the data and elements, which is the kind of
visualisations you want – viz bars, lines, heat maps, etc.
In the console, let’s try the
following:
ggplot(sachin, aes(Grouping,
Average)) geom_point();
This yields the same graph as the
one shown above with the difference being the ability to customise. Type the
following:
ggplot(sachin,
aes(x=Grouping,y=Average,colour = X100s)) geom_point(shape=15, size = 6, alpha
= 0.8)
This gives a plot of averages versus
grouping, except each point’s shade is determined by the hundreds – lighter the
shade, more the number of centuries that year. The shape and size can be
changed accordingly, and the parameter ‘alpha’ determines the intensity of
colour. Replace ‘X100s’ with ‘X50s’ and you’ll get the point shaded according
to the 50s scored.
Tendulkar’s
average annual runs tally shaded according to number of 50s scored
ggplot(sachin,
aes(x=Grouping,y=Average,colour = X50s)) geom_point(shape=15, size = 6, alpha =
0.8)
This brings some new insight into
the dataset – for example, it shows that the more 100s he scores, the higher is
his average. However, his highest average runs in one year need not have the
maximum number of centuries – indicated by the lighter points occupying a band
of points just below the maximum score. Even with the 50s (replacing X100s with
X50s), you see a similar pattern although maximum runs in a year is also very
close to the year with maximum number of fifties.
Tendulkar’s
average annual runs tally shaded according to number of 100s scored
This sort of an analysis, called
‘correlation’ is central to every aspect of study – from cricket stats to the
population of tigers in a jungle.
There are several built-in functions
and dozens of sources from which you can get interesting plots and
analyses.
Interface and Interfacing
Each of these plots can be saved in
PDF or JPEG formats with the simple click of a button. Just click on the
‘Plots’ tab in the bottom right window, select ‘export’ and choose the required
option. You can even adjust the size and aspect ratios before saving.
There is extensive help available
for R. You can search online forums for commands and codes, or if the command
is known, a ‘?’ followed by the name on the console will give all the
information on the respective command.
R has one of the largest numbers of
packages and these are still growing. Several languages and GIS applications
can be integrated with R, apart from visualisation. Basic R charts and graphs
are the basis for several infographics, which you can then polish up with some
art to improve visually appeal.You can even make interactive graphs
with given data, similar to the one just described.
EU27
trading data from the BEC group since 1999 - a beautifully coloured graph for a
seemigly drab data set!
http://www.rstudio.com/shiny/showcase/ has several such examples. RStudio’s package
‘Shiny’ makes it super simple for you to turn analyses into interactive web
applications that anyone can use. It lets you incorporate parameters such as
sliders, dropdowns and text fields, and gives you control over the number of
outputs you want to add such as plots, tables and summaries. Shiny also
provides a guide to creating such interesting apps.
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