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Thursday, February 18
SC1 Communicating Data Clearly
Thu, Feb 18, 8:00 AM - 5:30 PM
Diamond I
Instructor(s): Naomi B Robbins, NBR

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Communicating Data Clearly describes how to draw clear, concise, accurate graphs that are easier to understand than many of the graphs one sees today. The course emphasizes how to avoid common mistakes that produce confusing, or even misleading, graphs. Graphs for one, two, three, and many variables are covered, as well as general principles for creating effective graphs.

The course emphasizes principles of effective graphs, rather than how to create graphs with specific software. These principles apply to whatever software the attendees use. Participants will learn to:

• Present data more effectively in all media

• Display data so their structure is more apparent

• Understand principles of effective simple graphs to build upon when creating interactive or dynamic displays

• Become more critical and analytical when viewing graphs

Outline & Objectives

This course begins by reviewing human perception and our ability to decode graphical information. It continues by:

• Ranking elementary graphical perception tasks to identify those that we do the best.

• Showing the limitations of many common graphical constructions.

• Demonstrating newer, more effective graphical forms developed on the basis of the ranking.

• Providing general principles for creating effective graphs, as well as metrics on the quality of graphs.

• Commenting on software packages that produce graphs.

• Comparing the same data using different graph forms so the audience can see how understanding depends on the graphical construction used.

• Discussing Trellis Display (a framework for the visualization of multivariate data) and other innovative methods for presenting more than two variables.

• Presenting some graphical methods for categorical data.

• Discussing some examples of embedded graphs for complex data.

Since scales (the rulers along which we graph the data) have a profound effect on our interpretation of graphs, the section on general principles contains a detailed discussion of scales.

About the Instructor

• Chair of Statistical Graphics Section of ASA

• Author of Creating More Effective Graphs (Chart House, 2013; originally Wiley, 2005).

• Invited to blog for Forbes on Effective Graphs (see

• Speaker on graphs to numerous universities and professional societies

• Presenter to government, corporate and non-profit organizations

• Delivered tutorials and/or presentations at most O’Reilly Strata Conferences

• Speaker at the 61st Deming Conference on December 5, 2005

• Short course instructor at the Joint Statistical Meetings in Seattle on August 7, 2006

• A.B. degree in mathematics from Bryn Mawr College

• M.A. in mathematics from Cornell University

• Ph.D. in mathematical statistics from Columbia University

• Founding member of the New Jersey Chapter of ASA and served as President, Vice-President, Secretary, Treasurer, and Chair of the Advisory Committee

• Member of ASA, the Society for Technical Communication, Chapters of ASTD, IEEE and other professional societies

• Associate Fellow of the Society of Technical Communication

• Formerly a Member of Technical Staff at Bell Laboratories

Relevance to Conference Goals

Attendees will be exposed to graphical techniques, some of which may be new to them. Ideas covered are immediately applicable.

The entire emphasis of the course is to use best graphical practices to communicate quantitative information better.

Effective charts and graphs and understanding data better lead to better decisions which have a positive impact on the company. Communicating data better saves time at meetings.

Better communication of data enhances one’s career and avoids the loss of credibility that comes with using confusing, misleading or deceptive figures.

SC2 Interactive Graphics and Reports with R Markdown and Shiny
Thu, Feb 18, 8:00 AM - 5:30 PM
Diamond II
Instructor(s): Garrett Grolemund, RStudio, Inc.
This course will teach you how to use the Shiny and R Markdown packages to create interactive data products straight from R. The packages create an ideal workflow for sharing data and results with clients and colleagues. This is a beginner's course for intermediate R users.

The R Markdown package builds reports, documents, and presentations straight from your R code. You write your reports in markdown (an easy-to-write, plain text format) and embed R code chunks that create output to be included in the final document. R Markdown documents are fully reproducible (they can be automatically regenerated whenever underlying R code or data changes) and completely dynamic (you can export an R Markdown document as a slide show or an html, pdf, or MS Word file).

The Shiny package uses R to build interactive web apps, an ideal data product to share with consulting clients. Shiny apps reduce iteration and allow clients to explore their own data without relying on the consultant's resources. Clients can use a shiny app to explore data and run analyses---without needing to write or understand R code (you write the code for them when you create the app).

Outline & Objectives

You will learn to write reports with R Markdown and will create your own Shiny web apps. I will alternate brief lectures with hands-on practice: you’ll get plenty of experience actually using R Markdown and Shiny (not just hearing about them!) Outline:

1. Reports - Write reports in markdown. Embed R code chunks and customize how code output appears in the report. Render reports as html, pdf, MS Word docs and slideshows.

2. Interactivity - Add self-contained interactive data visualizations to your reports with htmlwidgets. Learn to use Shiny's reactive programming framework.

3. Web Apps - Layout interactive components into a web app with a customizable user interface. Learn how to share apps on the internet (with a password), or a private intranet.

This class will be a good fit for you if you already use R, but are new to R Markdown and Shiny.

Come ready to learn. You'll need your laptop and the latest versions of R and RStudio. Install the shiny and reportsWS packages ahead of time with install.packages(c("shiny", "devtools")) and devtools::install_github("rstudio/reportsWS"). R Markdown comes with RStudio. Each of these is free and open source, as is R and RStudio.

About the Instructor

The course will be led by RStudio Master Instructor and author Garrett Grolemund, Ph.D. Garrett is a teacher and statistician who has used R to analyze data for over 10 years. He works closely with the developers of Shiny and R Markdown, and is Editor in Chief of the Shiny Development Center web page. Garrett created and maintains the popular lubridate R package and is the author of Hands-On Programming with R as well as Data Science with R, an upcoming book by O'Reilly Media.

Garrett gave a very well-received talk at the 2015 Conference on Statistical Practice titled "Reporting results with R, R Markdown, and Shiny."

Relevance to Conference Goals

This course teaches students two ways to better communicate with customers. Embedded in the methods are benefits that will have a positive impact on the student's organization. The two methods that improve communication are:

1. An easy and quick way to create reports from the code that statisticians use to generate results. This method has the benefits that it

* Creates reproducible reports, which can quickly be regenerated on new data to update an analysis or to prove the scientific validity of the results.

* Lets students export the same report in multiple formats, which saves time and makes content easier to share through different channels.

2. A way to build interactive data products that clients can use to explore their own data and explore the results of the student's analysis. This method has the benefits that it

* Conveys large amounts of information efficiently, and provides a navigation system for accessing the information.

* Shortens cycles of iteration. Clients can use the app to fine tune analyses without the aid of the consultant.

* Lets clients recreate analysis and explore data without needing to know how to program in R.

* Looks impressive.