by James Peruvankal There are plenty of options if you want to learn R and are looking for training: your college’s statistics department, massive open online courses like Coursera, Udacity, edX, Datacamp etc. SiliconANGLE recently published an article about top R-training companies. Let’s talk about how to choose ... [Read more...]

by Joseph Rickert Broadly speaking, a meta-analysis is any statistical analysis that attempts to combine the results of several individual studies. The term was apparently coined by statistician Gene V Glass in a 1976 speech he made to the American Education Research Association. Since that time, not only has meta-analysis become ... [Read more...]

by Yaniv Mor, Co-founder & CEO of Xplenty How do you get Big Data ready for R? Gigabytes or terabytes of raw data may need to be combined, cleaned, and aggregated before they can be analyzed. Processing such large amounts of data used to require installing Hadoop on a cluster of ... [Read more...]

by Joseph Rickert John Chambers opened UseR! 2014 by describing how the R language grew out of early efforts to give statisticians easier access to high quality statistical software. In 1976 computational statistics was a very active field, but most algorithms were compiled as Fortran subroutines. Building models with this software was ... [Read more...]

by Joseph Rickert UserR! 2014 got under way this past Monday with a very impressive array of tutorials delivered on the day that the conferences organizers were struggling to cope with a record breaking crowd. My guess is that conference attendance is somewhere in the 700 range. Moreover, this the first year ... [Read more...]

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by Joseph Rickert Predictive Modeling or “Predictive Analytics”, the term that appears to be gaining traction in the business world, is driving the new “Big Data” information economy. Predictably, there is no shortage of material to be found on this subject. Some discussion of predictive modeling is sure to be ... [Read more...]

by Wayne Smith, Ph.D. California State University, Northridge Editor's note: This post was abstracted from the monthly newsletter of the Southern California Chapter of the ASA. On May 13th and 14th, the Intel International Science and Engineering Fair (Intel ISEF) the world’s largest international pre-college competition, was held ... [Read more...]

by Joseph Rickert For the past year or so we have been plotting the location of R user groups around the world using code (Download RUGS) adapted from a solution that Sandy Muspratt originally posted on Stack Overflow. In last week’s post, we made a modest improvement to our ... [Read more...]

by Ilya Kipnis In this post, I will demonstrate how to obtain, stitch together, and clean data for backtesting using futures data from Quandl. Quandl was previously introduced in the Revolutions Blog. Functions I will be using can be found in my IK Trading package available on my github page. ... [Read more...]

by Joseph Rickert useR! 2014 is just about two weeks away, and I am very much looking forward to meeting R users from around the world. This is just a great time to catch up with old friends, hopefully make some new friends, and talk about R and R user groups. ... [Read more...]

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by Joseph Rickert Last week,I had the opportunity to participate in the Second Academy of Science and Engineering (ASE) Conference on Big Data Science and Computing at Stanford University. Since the conference was held simultaneously with the two other conferences, one on Social Computing and the other on Cyber ... [Read more...]

by Joseph Rickert I was very happy to have been able to attend R / Finance 2014 which wrapped up a couple of weeks ago. In general, the talks were at a very high level of play, some dealing with brand new ideas and many presented at a significant level of technical ... [Read more...]

by Joseph Rickert In last week’s post, I sketched out the history of Generalized Linear Models and their implementations. In this post I’ll attempt to outline how GLM functions evolved in R to handle large data sets. The first function to make it possible to build GLM models ... [Read more...]

by Mike Bowles In two previous posts, A Thumbnail History of Ensemble Methods and Ensemble Packages in R, Mike Bowles — a machine learning expert and serial entrepreneur — laid out a brief history of ensemble methods and described a few of the many implementations in R. In this post Mike takes ... [Read more...]

by Joseph Rickert I recently wrote about some R resources that are available for generalized linear models (GLMs). Looking over the material, I was amazed by the amount of effort that is continuing to go into GLMs, both with with respect to new theoretical developments and also in response to ... [Read more...]

By Matt Sundquist Plotly's Co-Founder Here at Plotly, we are on a mission to build a platform where data scientists can analyze data, create beautiful graphs and collaborate: like a GitHub for data, where you can share and find plots, data, and code. The benefits are: Plots (including ggplot2 plots) ... [Read more...]

by Joseph Rickert R/Finance 2014 is just about a week away. Over the past four or five years this has become my favorite conference. It is small (300 people this year), exceptionally well-run, and always offers an eclectic mix of theoretical mathematics, efficient, practical computing, industry best practices and trading “street ... [Read more...]

by Seth Mottaghinejad, Analytic Consultant for Revolution Analytics In the last article, we showed two separate R implementations of the Collatz conjecture: 'nonvec_collatz' and 'vec_collatz', with the latter being more efficient than the former because of the way it takes advantage of vectorization in R. Let's once again ... [Read more...]