Image courtesy Upshot: How the non employed spend their weekdays

The initial focus of this blog post was going to be on how you can create interactive visualizations with R. But then I thought, that it would be great to give you an example of a real-time application of how such visualizations are being used. So what I have done is broken this post into two parts. Part 1- will focus on an example and a quick guide to the popular interactive data visualization tools. Part 2- will outline how you can actually use one of the tools, while also highlighting other alternatives to creating interactive visualizations with R.

Part 1: How Upshot put data-based reporting at the core of how reporters tell stories, using visualizations and interactive features.

“The Upshot” is the NY Times online news and data visualization portal focused on sharing news around politics, polity and economic analysis in US. “The Upshot” project is a team of 17 cross-disciplinary journalists at NY Times whose objective is to make news more approachable and even conversational. And this team started storytelling with the use of interactive visualizations as a tool to better enable readers understand the news and the world better.

In less than a year, The Upshot has made its mark on the NY Times by bringing in 5% of overall traffic to the publication and also generated two of the 20 most-viewed stories on nytimes.com in 2014. Much of this success can be attributed to the use of interactive visualizations which would make the whole experience of reading online news more fun and lively.

Surely, interactive visualizations have also reaped huge benefits in business scenarios and many commercial tools exist in the market providing these capabilities. One of the popular tools is Tableau, an interactive data visualization product focused on business intelligence. Other tools like QlikView, TIBCO Spotfire, SAP Business Objects, Microstrategy and IBM Cognos also offer similar interactive visualization capabilities. These tools are very straight forward to use and often support drag and drop functionality which makes it easier for any non-programmer to build visualizations. In terms of open source offerings in this space, the best ones include D3 (Data-Driven Documents), Gephi, Vega, Processing and DYGraphs. Definitely having a hands-on exposure on these open source options would help you create some very appealing visualizations.

Data

However one major drawback in using these tools is the requirement of javascript coding. You also need a deep knowledge of web development tools, making it harder to use for data scientists. More often data scientists work with few lines of code to create plots using languages like R/SAS, and don’t build from scratch, as is with the case of open source interactive plotting libraries.

However  thanks to Ramnath Vaidyanthan, the creator of “rCharts”, data scientists can now develop interactive visualizations without any need for javascript coding. This R package can be used to create, customize and share interactive visualizations straight from R by leveraging several existing javascript visualization libraries. In the words of Ramnath, “The main motivation behind rCharts is to provide data scientists a seamless workflow that allows them to execute all steps of the data visualization process, from acquiring data to exploring it, visualizing it, and sharing the results as an interactive presentation, without having to leave the comfort of their primary language for data analysis (in this case, its R).”

rCharts supports multiple charting javascript libraries such as Highcharts, Morris, NVD3, Timeline, Polychart, Rickshaw, xCharts, Leaflet and few others.

Hope you found this interesting. Look out for part 2 – where I will explain how you can create Polychart using rCharts package and also highlight other alternatives to create interactive visualizations with R.

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Related reads:

5 Popular Tools for Data Visualization

Data Visualization in Analytics

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