If you're an Excel user (or any other spreadsheet, really), adapting to learn R can be hard. As this blog post by Gordon Shotwell explains, one of the reasons is that simple things can be harder to do in R than Excel. But it's worth perservering, because complex things can ... [Read more...]

Radiohead is known for having some fairly maudlin songs, but of all of their tracks, which is the most depressing? Data scientist and R enthusiast Charlie Thompson ranked all of their tracks according to a "gloom index", and created the following chart of gloominess for each of the band's nine ... [Read more...]

As a devotee of Tufte, I'm generally against chartjunk. Graphical elements that obscure interpretation of the data occasionally have a useful role to play, but more often than not that role is to entertain the expense of enlightenment, or worse, to actively mislead. So it's with mixed feelings that I ... [Read more...]

Tomaž Kaštrun is developer and data analyst working for the IT group at SPAR (the ubiquitous European chain of convenience stores) in Austria. He blogs regularly about using Microsoft R and SQL Server for data analyis, and recently published a roundup of his articles about R and SQL Server. ... [Read more...]

R 3.3.3 (codename: "Another Canoe") is scheduled for release on March 6. This is the "wrap-up" release of the R 3.3 series, which means it will include minor bug fixes and improvements, but eschew major new features. Major changes are coming though, with the subsequent release of R 3.4.0. While the NEWS file announcing ... [Read more...]

One of the major "wow!" moments in the keynote where SQL Server 2016 was first introduced was a demo that automated the process classifying images of galaxies in a huge database of astronomical images. The SQL Server Blog has since published a step-by-step tutorial on implementing the galaxy classifier in SQL ... [Read more...]

Oksana Kutina and Stefan Feuerriegel fom University of Freiburg recently published an in-depth comparison of four R packages for deep learning. The packages reviewed were: MXNet: The R interface to the MXNet deep learning library. (The blog post refers to an older name for the package, MXNetR.) darch: An R ... [Read more...]

On January 31, the R Consortium presented a webinar with updates on various projects that have been funded (thanks to the R Consortium member dues) and are underway. Each project was presented by the project leader, a member of the R community. You can watch the recording of the webinar here, ... [Read more...]

When we last looked at job trends from indeed.com, job listings for "R statistics" were on the rise but were still around half the volume of listings for "SAS statistics". Three-and-a-half years later, R has overtaken SAS in job listings for "statistics". I added Python to the search this ... [Read more...]

If you're thinking about teaching a course on statistics and data science using R, Chester Ismay and Albert Kim have created an online, open-source textbook for just that purpose. ModernDive is a textbook for that instructs students how to: use R to explore and visualize data; use randomization and simulation ... [Read more...]

In the hyper-competitive retail industry, intelligence about your customers is key. You need to be able to find the right customers, understand what types of customers you have, and know how to keep the best ones. Three solutions based around R and SQL Server R Services will help you do ... [Read more...]

In case you missed them, here are some articles from January of particular interest to R users. The Data Science Virtual Machine on Azure has been updated with the latest Microsoft R Server, and adds RStudio and JuliaPro. A crowdsourced list of local R user groups and community events, maintained ... [Read more...]

The heatmap is a useful graphical tool in any data scientist's arsenal. It's a useful way of representing data that naturally aligns to numeric data in a 2-dimensional grid, where the value of each cell in the grid is represented by a color. It's a natural fit for data that's ... [Read more...]

If you want to get data out of R and into another application or system, simply copying the data as it resides in memory generally isn't an option. Instead you have to serialize the data (into a file, usually), which the other application can then deserialize to recreate the original ... [Read more...]

Thomas Dinsmore's ML/DL blog recently concluded a look back on significant advancements in data science, machine learning and deep learning — many of which involved R and/or Microsoft. Here are those highlights (reproduced with permission): The R Project R and Python maintained their leadership as primary tools for open ... [Read more...]

The Windows edition of the Data Science Virtual Machine (DSVM) was recently updated on the Azure Marketplace. This update upgrades some existing components and adds some new ones as well. You now have your choice of integrated development environment to use with R. RStudio Desktop is now included in the ... [Read more...]

For 8 years now, we've maintained a list of local R user groups here at the Revolutions blog. This is a list that began with a single group (the Bay Area RUG, the first and still one of the largest groups), and now includes 360 user groups worldwide (including 27 specifically for women). ... [Read more...]

CRAN, the global repository of open-source packages that extend the capabiltiies of R, reached a milestone today. There are now more than 10,000 R packages available for download*. (Incidentally, that count doesn't even include all the R packages out there. There are also another 1294 packages for genomic analysis in the BioConductor ... [Read more...]

A great way to hone your skills as a data scientist is to pick a topic you're passionate about, find some data related to it, and analyze the heck out of it. Jim Vallandingham is clearly passionate about old Kung Fu movies — particularly those from the Shaw Brothers Studio — and ... [Read more...]

Heartland Bank, a rapidly growing bank in New Zealand, has adopted a data-driven approach to analyzing risk, evaluating credit lines, and understanding cash flows. But they found their legacy SAS system to be labor-intensive and time consuming when it came to updating financial models, and it was expensive to boot. (... [Read more...]