Directions

---

Speaker:

Robert Kabacoff, Ph.D.

Dr. Kabacoff is a seasoned researcher, with 30 years of experience in data analysis and data visualization.

As Vice President of Research for Management Research Group (1997-present), he consults widely with academic, government, and corporate organizations throughout North America, Western Europe, and the Pacific Rim.  

As a Professor in the Center for Psychological Studies at Nova Southeastern University (1987-1997), he taught numerous graduate courses on multivariate statistics, statistical consulting, and research computing.

Dr. Kabacoff created and maintains the popular tutorial website Quick-R.

R in Action: Data Analysis and Graphics with R is the first book to present both the R system and the use cases that make it such a compelling package for business developers. The book begins by introducing the R language, including the development environment.

Focusing on practical solutions, the book also offers a crash course in practical statistics and covers elegant methods for dealing with messy and incomplete data using features of R.

The newly released second edition adds 200 pages of new material, making it a "must have" for R developers and users.

---

Printable Flier

Why Use R?

If you currently use another statistical package, why learn R?

1. It's free! If you are a teacher or a student, the benefits are obvious.
2. It runs on a variety of platforms including Windows, Unix and MacOS.
3. It provides an unparalleled platform for programming new statistical methods in an easy and straightforward manner.
4. It contains advanced statistical routines not yet available in other packages.
5. It has state-of-the-art graphics capabilities.

---

Obtaining R

R is available for Linux, MacOS X, and Windows (95 or later) platforms. Software can be downloaded from one of the Comprehensive R Archive Network (CRAN) mirror sites.

---

Why R has a Steep Learning Curve

(Or, why you should attend this seminar!)

Robert Kabacoff notes:

"I have been a hardcore SAS and SPSS programmer for more than 25 years, a Systat programmer for 15 years and a Stata programmer for 2 years. But when I started learning R recently, I found it frustratingly difficult. Why?

I think that there are two reasons why R can be challenging to learn quickly."

First, while there are many introductory tutorials (covering data types, basic commands, the interface), none alone are comprehensive. In part, this is because much of the advanced functionality of R comes from hundreds of user contributed packages. Hunting for what you want can be time consuming, and it can be hard to get a clear overview of what procedures are available.

The second reason is more ephemeral. As users of statistical packages, we tend to run one prescribed procedure for each type of analysis. Think of PROC GLM in SAS. We can carefully set up the run with all the parameters and options that we need. When we run the procedure, the resulting output may be a hundred pages long. We then sift through this output pulling out what we need and discarding the rest.

The paradigm in R is different. Rather than setting up a complete analysis at once, the process is highly interactive. You run a command (say fit a model), take the results and process it through another command (say a set of diagnostic plots), take those results and process it through another command (say cross-validation), etc. The cycle may include transforming the data, and looping back through the whole process again. You stop when you feel that you have fully analyzed the data. It may sound trite, but this reminds me of the paradigm shift from top-down procedural programming to object oriented programming we saw a few years ago. It is not an easy mental shift for many of us to make.

In that in the end, however, I believe that you will feel much more intimately in touch with your data and in control of your work. And it's fun!

---

---

 

Update: Seminar Cancelled

We regret that we have to cancel the Practical Data Visualization with R seminar scheduled for October 3, 2015.We hope to reschedule this seminar at a later date.

---

---

Back by Popular Demand!

In 2014 Robert Kabacoff delivered a GBC/ACM seminar on R. That seminar was a great introduction to R, focusing on learning R and applying it to Big Data Analytics. The attendees wanted more - specifically more on using R for data visualization, graphics, and presentations. Based on this demand, Dr. Kabacoff has agreed to deliver a follow-on workshop dedicated to visualization.

Visualization in R

Data visualization has become a central feature of modern data analysis. The R platform provides one of the most flexible and powerful platform for graphing data, understanding and evaluating statistical models, and communicating results to others.

This day-long workshop will provide practical review of R’s major graphing capabilities, including its base functionality and exciting new capabilities provided by add on packages (including ggplot2, ggvis, and rCharts).

If you are looking at this workshop, you probably have some data that you need to collect, summarize, transform, explore, model, visualize, or present. If so, then R is for you! R has become the world-wide language for statistics, predictive analytics, and data visualization. It offers the widest range of methodologies for understanding data, from the most basic to the most complex and bleeding edge.

R is a complete system. The first challenge with any analysis project is getting the data. R allows you to import data from a variety of sources and then clean, recode and restructure it. Note that in the real world the biggest challenge is making data usable – there are always issues with the data you have to work with!
After importing data, R has many functions for summarizing, modeling, analyzing and graphing data.

Statistical analysis tools include linear and nonlinear modeling, classical statistical test, time series analysis, classification, and clustering as well as other capabilities. Further, R can readily be extended through functions and extensions; the R community is well known for active contributions of many packages.

Finally, R has powerful visualization tools. These range from simple charts to publication quality graphs, through dynamic visualization, to interactive graphics. Visualization is key to successful data analytics – it helps the person doing the analysis to better understand the results and is an invaluable tool for explaining the results to others. This may well be the most important aspect of data analytics – providing information that can be used to make decisions.

Join us in this full day seminar and learn from one of the leading authorities on R.

---

In This Workshop

This workshop will provide a practical introduction to data visualization using R. The workshop will be highly practical and interactive. Participants are encouraged to bring a laptop and work through each of the examples as they are described.

Course Outline

I. Introduction – R basics; importing data; the base graphics system; creating univariate and multivariate plots; customizing plots (fonts, axes, grids, titles, labels, colors, symbols, legends); saving plots in various formats (e.g., pdf, svg, png, emf, jpeg).

II. Using ggplot2 – Creating sophisticated charts with the ggplot2 package; exploring complex data, customizing ggplot2 graphs.

III. Interactive Graphics – Visualizing data interactively; using the advanced capabilities of rCharts, googleVis and other packages. Creating visualizations for reports and websites.

 

A production of the Greater Boston Chapter of the ACM