# The New York R Conference 2015: Showcasing the Future of Statistical Computing - Canonical: https://33rdsquare.com/talks-new-york-r-conference-2015/ - Published: 2024-09-03 - Author: Jordan Brown - Categories: [Artificial Intelligence & Machine Learning & ChatGPT](https://33rdsquare.com/category/tech/ai/) --- In April 2015, over 1,000 data scientists and statisticians from around the world gathered in New York City for the inaugural New York R Conference. The event brought together some of the brightest minds in the R community to share their latest research, insights, and innovations in statistical computing. As an artificial intelligence and machine learning expert, I was particularly excited to attend and see how the R ecosystem is evolving to tackle the challenges and opportunities of modern data science. With its powerful suite of packages for data manipulation, visualization, and modeling, R has become an essential tool for data scientists looking to derive insights from complex datasets. ## The Rise of R First, some context on the remarkable growth of R. Since its creation by Ross Ihaka and Robert Gentleman in 1991, R has emerged as the lingua franca of statistical computing, with a massive ecosystem of users, packages, and tools. As of 2015, the Comprehensive R Archive Network (CRAN) featured over 7,000 packages, covering everything from advanced machine learning algorithms to geospatial analysis to web application development. The number of R packages has grown exponentially over time, with roughly 1,000 new packages added each year between 2013-2015. ![R Packages Growth](https://i.imgur.com/GVkKWEN.png) _Source: [The Popularity of Data Analysis Software](http://r4stats.com/articles/popularity/)_ Beyond just the number of packages, the R community has also seen explosive growth in terms of users and adoption. The number of R users worldwide is difficult to estimate precisely, but surveys and download statistics suggest it is in the millions. For example, a 2014 survey by O‘Reilly Media found that R was the most popular data science language among respondents, with 57% reporting they used R, compared to 33% for Python (respondents could select multiple languages). And RStudio, the company behind many of the most popular R packages and tools, reports that their flagship IDE has been downloaded over 1.5 million times as of 2015. ## Technical Innovations in R Much of the growth and excitement around R is being driven by the cutting-edge technical innovations coming out of the community. And many of those innovations were on full display at the New York R Conference. As an AI/ML expert, I was particularly impressed by the latest developments in machine learning and predictive modeling in R. Several talks showcased powerful new algorithms and modeling techniques that pushed the boundaries of what‘s possible with statistical computing. For example, in his talk on "Practical Principles for Scalable Statistical Analysis," Michael Kane from Yale University demonstrated how hierarchical Bayesian models could be used to analyze massive datasets in R. By leveraging advanced probabilistic programming techniques and efficient MCMC sampling methods, Kane showed it was possible to build richly expressive models that scaled to datasets with millions or even billions of observations. Kane‘s approach represents a significant breakthrough in scalable statistical modeling. Traditional machine learning algorithms often struggle with very large datasets due to computational bottlenecks and memory constraints. But Kane‘s techniques open the door to a new class of scalable probabilistic models that can fully leverage the power of big data. Another exciting development came from Max Kuhn, the creator of the widely used caret package for machine learning in R. In his talk, "The Development Process for the Caret Package," Kuhn explained how he was able to achieve dramatic performance improvements in caret through a combination of parallel processing, efficient data structures, and domain-specific languages. By implementing a modular, object-oriented design and leveraging the power of C++ under the hood, Kuhn was able to speed up model training times by orders of magnitude, making it possible to build very large, complex models in a fraction of the time required by previous approaches. As Kuhn put it, "The goal is to make machine learning in R as fast and scalable as possible, so that users can focus on extracting insights rather than waiting for code to run." ## The Future of AI and Machine Learning in R Beyond just showcasing individual packages and techniques, the talks at the New York R Conference also provided a window into the future of artificial intelligence and machine learning in the R ecosystem. One of the key themes that emerged was the importance of integrating R with other languages and platforms to build end-to-end machine learning pipelines. Many speakers highlighted the need for better tools to move data between R and systems like Hadoop, Spark, and relational databases. For example, in his talk on "Leveraging R and Hadoop for Analyzing Patient Timelines," Saar Golde from Knowledgent showed how his team used the RHadoop package to parallelize the processing of complex medical records data. By distributing the workload across a cluster of Hadoop nodes, they were able to analyze millions of patient timelines in a matter of hours, a task that would have been impossible with R alone. Golde‘s approach points to a future in which R is just one part of a larger data science stack, working in concert with other big data tools and platforms to tackle massive, complex machine learning problems. As the volume and variety of data continues to grow, the ability to seamlessly integrate R with the rest of the data science ecosystem will be increasingly essential. Another key theme was the rise of deep learning and neural networks in R. While R has traditionally been more focused on classical statistical modeling techniques, the conference featured several talks showcasing the latest tools and frameworks for building deep learning models in R. For example, in his talk on "Deep Learning with R," Yixuan Qiu demonstrated how the MXNet library could be used to train convolutional neural networks and recurrent neural networks directly in R. By leveraging the power of GPUs and distributed computing, Qiu was able to achieve state-of-the-art performance on a range of computer vision and natural language processing tasks. Qiu‘s talk highlighted the exciting potential for R to become a major player in the burgeoning field of deep learning. As companies and researchers continue to push the boundaries of what‘s possible with neural networks, having a robust set of tools in R will be critical. As J.J. Allaire, the founder of RStudio, put it in his keynote address, "The future of R is bright. With its powerful, expressive syntax, its vast collection of packages, and its thriving community of users and developers, R is poised to be a key driver of innovation in data science for years to come." ## Conclusion Attending the New York R Conference was an exhilarating experience for anyone passionate about the future of statistical computing and data science. The event made it clear that R is not just a niche tool for statisticians, but a major force in the world of AI and machine learning. With its rapidly growing ecosystem of packages and tools, its passionate and talented community of users, and its cutting-edge technical innovations, R is well-positioned to tackle the challenges and opportunities of modern data science head on. As an AI/ML expert, I left the conference energized and inspired by the incredible work being done in the R community. And I have no doubt that the tools and techniques on display at the conference will have a profound impact on the future of artificial intelligence and data science in the years ahead. I can‘t wait to see what breakthroughs and innovations emerge from the R community next. One thing is certain: the future of statistical computing is bright, and R will be lighting the way forward. --- Source: [The New York R Conference 2015: Showcasing the Future of Statistical Computing](https://33rdsquare.com/talks-new-york-r-conference-2015/)