Data analysis has recently emerged as a very important focus for a huge range of organizations and businesses. R makes detailed data analysis easier, by making advanced data exploration and insight accessible to anyone interested in learning it. This course's hands-on approach will help you perform data analysis. You will learn to perform social network analysis, to uncover hidden insights and trends from data. Later you will perform geospatial analysis to bring data into action with the easy-to-follow examples featured in the video course. By the end of this course, you will mastered quickly adapting the example code for your own needs, thus saving yourself the time-consuming task of constructing code from scratch.
About the Author
Viswa Viswanathan is an associate professor of Computing and Decision Sciences at the Stillman School of Business in Seton Hall University. After completing his PhD in Artificial Intelligence, Viswa spent a decade in Academia and then switched to a leadership position in the software industry for a decade. During this period, he worked for Infosys, Igate, and Starbase. He embraced Academia once again in 2001.
Viswa has taught extensively in diverse fields, including operations research, computer science, software engineering, management information systems, and enterprise systems. In addition to teaching at the university, Viswa has conducted training programs for industry professionals. He has written several peer-reviewed research publications in journals such as Operations Research, IEEE Software, Computers and Industrial Engineering, and International Journal of Artificial Intelligence in Education. He authored a book entitled Data Analytics with R: A Hands-on Approach.
Viswa thoroughly enjoys hands-on software development, and has single-handedly conceived, architected, developed, and deployed several web-based applications.
Apart from his deep interest in technical fields such as data analytics, Artificial Intelligence, computer science, and software engineering, Viswa harbors a deep interest in education, with a special emphasis on the roots of learning and methods to foster deeper learning. He has done research in this area and hopes to pursue the subject further.
Viswa would like to express deep gratitude to professors Amitava Bagchi and Anup Sen, who were inspirational during his early research career. He is also grateful to several extremely intelligent colleagues, notably Rajesh Venkatesh, Dan Richner, and Sriram Bala, who significantly shaped his thinking. His aunt, Analdavalli; his sister, Sankari; and his wife, Shanthi, taught him much about hard work, and even the little he has absorbed has helped him immensely. His sons, Nitin and Siddarth, have helped with numerous insightful comments on various topics.
Shanthi Viswanathan is an experienced technologist who has delivered technology management and enterprise architecture consulting to many enterprise customers. She has worked for Infosys Technologies, Oracle Corporation, and Accenture. As a consultant, Shanthi has helped several large organizations, such as Canon, Cisco, Celgene, Amway, Time Warner Cable, and GE among others, in areas such as data architecture and analytics, master data management, service-oriented architecture, business process management, and modeling. When she is not in front of her Mac, Shanthi spends time hiking in the suburbs of NY/NJ, working in the garden, and teaching yoga.
Shanthi would like to thank her husband, Viswa, for all the great discussions on numerous topics during their hikes together and for exposing her to R and Java. She would also like to thank her sons, Nitin and Siddarth, for getting her into the data analytics world.
The base R package provides date functionality. This video will show you several date-related operations in R.
R supports many useful manipulations with date objects, such as date addition and subtraction, and the creation of date sequences.
Before creating proper time series objects, we will learn some preliminary analyses.
In this video, we will look at various features to create and plot time-series objects. We will consider data with single and multiple time series.
This video covers the use of the decompose and stl functions to extract the seasonal, trend, and random components of time series.
This video shows how we can use the filter function from the stats package to compute moving averages.
The stats package contains functionality for applying the HoltWinters method for exponential smoothing in the presence of trends and seasonality.
The forecast package provides the auto.arima function to fit the best ARIMA models for a univariate time series.
In this video, we’ll cover the process of downloading data from Meetup.com using their public API.
In this application, the nodes represent the users of Meetup.com and an edge connects two nodes if they are members of at least one common group.
This video covers the features in the igraph package to create graph objects, plot them, and extract network information from graph objects.
This video covers the methods used to compute some of the common metrics used on social networks.
R Markdown provides a simple syntax to define analysis reports. Based on such a report definition, knitr can generate reports in HTML, PDF, Microsoft Word format, and several presentation formats.
This video illustrates the main components of a shiny application through some examples.
Rpres, built into RStudio, enables you to create PDF slide presentations of your data analysis. In this video, we’ll develop a small application that showcases the important Rpres features.
The function can either be a built-in R function or a custom function. In your own code, before you resort to a loop to process all
the elements of a vector
The apply function can apply a user-specified function to all the rows or columns of a matrix and return an appropriate collection with the results.
The lapply function works on objects of type vector, list, or data frame. It applies a user-specified function to each element of the
passed-in object and returns a list of the results.
The tapply function applies a function to each partition of the dataset. Hence, when we need to evaluate a function over subsets of a vector defined by a factor, tapply comes in handy.
Many data analysis tasks involve first splitting the data into subsets, applying some operation on each subset, and then combining the results suitably. The plyr package provides simple functions to apply this pattern while simplifying the specification of the object types through systematic naming of the functions.
In this video, we will cover data.table, which processes large amounts of data very efficiently without us having to write detailed
We will use the RgoogleMaps package to get and plot Google maps of specific areas based on latitude and longitude.
RgoogleMaps also allows you to overlay your own data points on static maps. In this video, we will use a data file to plot a Google map of the general area covered by the data points
Using RgoogleMaps is easy, and we have seen that it offers very little control over map elements and plotting. Importing shape files, on the other hand, gives us total control.
The sp package has the necessary features to store and plot geographic data. In this recipe, we will use the sp package to plot
imported shape files.
The maps package has several pre-built maps that we can download and adapt. This video demonstrates the capabilities of these maps.
When you have a regular data frame that has spatial attributes in addition to other attributes, processing them becomes easier if you convert them to full-fledged spatial objects.
In order to display geographical aspect information on a map representation, we need to embellish the basic data with enough
geographic coordinate information for plotting.
This video shows how we can add variables to spatial data frame objects. We can also add nonspatial variables to an existing spatial data frame object.
Sometimes, we may develop parts of an application in Java and may need to access them from R. The rJava package allows us to access Java objects directly from within R.
The JRI allows us to execute R commands inside Java applications as a single thread. JRI loads R libraries into Java and thus provides a Java API to R functions.
In this video, we’ll execute an R script from Java and read the results from R into Java for further processing.
There are multiple packages to connect to Excel with R; in this recipe, we’ll discuss the xlsx package. Other commonly used packages are RExcel and XLConnect.
The fluid state of NoSQL databases means that no such standard approach has yet evolved. We illustrate this with MongoDB using the rmongodb package.
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