Call for Presentations – EARL Conference, London

March 3, 2014
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(This article was first published on R-users: let's work together! » Jobs, and kindly contributed to R-bloggers)

The EARL (Effective Applications of the R Language) Conference takes place in London on the 16th and 17th September 2014. It will provide an opportunity for those using and developing the open source statistical programming language R to share and discuss their innovative practices of the R Language.

The Conference will create opportunities for:
• Information and knowledge of business applications through addresses from leading commercial practitioners and developers of the R language
• Discussion of cross industry methodologies for the implementation, management and validation of open source software
• Examination of complementary technologies to R

IMPORTANT DATES:
Call for Presentations 3 March 2014
Deadline for Submission of Presentations 1 May 2014
Notification of Acceptance 8 May 2014
Registration opens 24 March 2014

Conference Session Themes:

Effective Visualisation of Data
As the saying goes, “a picture speaks a thousand words”. As the need to effectively communicate complex ideas to business users increases, the use of graphical presentation is certainly increasing. How can R facilitate this need? Do you have examples where R-driven visualisations have succeeded in providing a graphical language that enables end users to quickly make decisions?
Keywords: graphics, interactive, information, decisions, business

Business Applications of R
Examples of effective business applications involving R, from a business and technical perspective. In particular, how best to put the power of R into the hands of end-users who don’t want to program?
Keywords: interface, data, business, reports, visual, results

R as a Development Technology
As analytics are used more strategically, the use of R as part of a development stack is increasing. What are the pros and cons of using R in this manner? How can R be effectively integrated within an existing development environment?
Keywords: development, IDE, integration, API, interface

Managing R in a Commercial Environment
There can be challenges in introducing new technologies into a commercial organisation. The introduction of R can be even more challenging given its open source nature, the package management system and the lack of IT awareness around R’s capabilities and management. What challenges and solutions have people seen around introducing and managing R within a commercial environment?
Keywords: introduction, teaching, validation, acceptance, control, IT

Alternatives to R
R’s growth over recent years has been well documented, and it is fast becoming the de-facto statistical language of choice. However, there are many alternatives to R such as relatively new technologies (e.g. Julia), more general technologies that are adding analytic capabilities (e.g. Python) and existing traditional technologies (SAS, Stata, SPSS). What are the alternatives to using R? How does R compare to these technologies? Why would you choose R over one of these technologies (and vice versa)?
Keywords: comparison, Julia, Python, SAS, SPSS, Stats

Performance
As an in-memory technology, R has often been criticised (e.g. on the public mailing lists) for its poor performance. This is exacerbated by the need to apply analytics to larger and larger data sources. What approaches and technologies exist that allow R to achieve higher levels of performance in order to answer business questions quickly so that decisions can be swiftly made?
Keywords: efficient, speed, big data, structure, code

Social Media and the R Community
R’s growth in popularity has coincided with major advancements in how people share ideas and communicate, chiefly though social media. How has the advent of social media allowed R users to interact more effectively in order to progress their knowledge and share ideas?
Keywords: Social, blogs, online, courses, meetups, share, collaborate

Big Data Technologies
Increasingly large amounts of data are being analysed in order to identify trends, spot anomalies and product effective models. How can R be used to analyse large amounts of data?
Keywords: hadoop, in-database, memory

Commercial R
As the R market matures, many commercial R-based offerings have arisen. This session will provide an overview of some of the R-based technologies available in the market today.

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