Learning R for Data Visualization [Video]

Last year Packt asked me to develop a video course to teach various techniques of data visualization in R. Since I love the idea of video courses and tutorials, and I also enjoy plotting data, I readily agreed.
The result is this course, published last March, which I will briefly present below.

The course is available here:
https://www.packtpub.com/big-data-and-business-intelligence/learning-r-data-visualization-video

I wanted to create a course that was easy to follow, and at the same time could provide a good basis even for the most advanced forms of data visualization available today in R.
Packt was interested in presenting ggplot2, which is definitely the most advanced way of creating static plots. Since I regularly use ggplot2 and I find it a tremendous tool, I was glad to be able to present its functionalities more in details. Three chapters are dedicated to this package. Here I present all the most important types of plots: histograms, box-plots, scatterplots, bar-charts and time-series. Moreover, a whole chapter is dedicated to embellish the default plots by adding elements, such as text labels and much more.

However, I am also very interested in interactive plotting, which I believe is now rapidly becoming commonplace for lots of applications. For this reason two chapters are completely dedicated to interactive plots. In the first I present the package rCharts, which is extremely powerful but also a bit tricky to use at times. In many cases there is little documentation to work with, and for developing the course I found myself often wondering through stackoverflow searching for answers. Luckily for all of us, Prof. Ramnath Vaidyanathan, the creator of rCharts, is always available to answer all the users' questions quickly and clearly. In chapter 5 the viewer will be able to start from zero and quickly create nice interactive versions of all the plots I covered with ggplot2. 

The last chapter is dedicated to Shiny and it is aimed at the creation of a full website for importing and plotting data. Here the reader will first learn the basics of Shiny and then will write the code to create the website and add lots of interesting functionalities.

I hope this video course will help R users become familiar with data visualization.
I would also like to take this opportunity to stress that I am open to support viewers throughout the learning process, meaning that if you have any question about the material in the course you should not hesitate one second in contacting me at info@fabioveronesi.net

Plotting an Odd number of plots in single image

Sometimes I have the need to reduce the number of images for a presentation or an article. A good way of doing it is putting multiple plot on the same tif or jpg file.
R has multiple functions to achieve this objective and a nice tutorial for this topic can be reached at this link: http://www.statmethods.net/advgraphs/layout.html

The most common function is par. This function let the user create a table of plots by defining the number of rows and columns.
An example found in website above, is:

attach(mtcars)
par(mfrow=c(3,1))
hist(wt)
hist(mpg)
hist(disp)

In this case I create a table with 3 rows and 1 column and therefore each of the 3 plot will occupy a single line in the table.

The limitation of this method is that I can only create ordered tables of plots. So for example, if I need to create an image with 3 plots, my options are limited:

A plot per line, created with the code above, or a table of 2 columns and 2 rows:

attach(mtcars)
par(mfrow=c(2,2))
hist(wt)
hist(mpg)
hist(disp)

However, for my taste this is not appealing. I would rather have an image with 2 plots on top and 1 in the line below but centered.
To do this we can use the function layout. Let us see how it can be used:

First of all I created a fake dataset:

 data<-data.frame(D1=rnorm(500,mean=2,sd=0.5),  
 D2=rnorm(500,mean=2.5,sd=1),  
 D3=rnorm(500,mean=5,sd=1.3),  
 D4=rnorm(500,mean=3.5,sd=1),   
 D5=rnorm(500,mean=4.3,sd=0.8),  
 D6=rnorm(500,mean=5,sd=0.4),  
 D7=rnorm(500,mean=3.3,sd=1.3))  

I will use this data frame to create 3 identical boxplots.
The lines of code to create a single boxplot are the following:

 boxplot(data,par(mar = c(10, 5, 1, 2) + 0.1),   
 ylab="Rate of Change (%)",  
 cex.lab=1.5, names=c("24/01/2011","26/02/2011",  
 "20/03/2011","25/04/2011","23/05/2011",  
 "23/06/2011","24/07/2011"),  
 col=c("white","grey","red","blue"),  
 at=c(1,3,5,7,9,11,13),  
 yaxt="n",  
 las=2)  
   
 axis(side=2,at=seq(0,8,1),las=2)  
   
 abline(0,0)  
   
 mtext("Time (days)",1,line=8,at=7)  
   
 mtext("a)",2,line=2,at=-4,las=2,cex=2)  

This creates the following image:

I used the same options I explored in one of my previous post about box plots: BoxPlots

Notice however how the label of the y axes is bigger than the label on the x axes. This was done by using the option cex.lab = 1.5 in the boxplot function.

Also notice that the label on the x axes ("Time (days)") is two lines below the names. This was done by increasing the line parameter in the mtext call.

These two elements are crucial for producing the final image, because when we will plot the three boxplots together in a jpg file, all these elements will appear natural. Try different option to see the differences.

Now we can put the 3 plots together with the function layout.
This function uses a matrix to identify the position of each plots, in may case I use the function with the following options:

layout(matrix(c(1,1,1,1,1,0,2,2,2,2,2,0,0,0,3,3,3,3,3,0,0,0), 2, 11, byrow = TRUE))

This creates a 2x11 matrix that looks like this:

1  1  1  1  1  0  2  2  2  2  2

0  0  0  3  3  3  3  3  0  0  0

what this tells the function is:

• create a plotting window with 2 rows and 11 columns
• populate the first 5 cells of the first row with plot number 1
• create a space (that's what the 0 means)
• populate the remaining 5 spaces of the first row with plot number 2
• in the second row create 3 spaces
• add plot number 3 and use 5 spaces to do so
• finish with 3 spaces

The results is the image below:

The script is available here: Multiple_Plots_Script.r