Scatter & Bubble chart This is the third part of our data visualization series and at this part we will explore the features of two more of the charts that googleVis provides. Read the examples below to understand the logic of what we are going to do and then test yous ... [Read more...]

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Sorry for the clickbait title. Couldn't resist. The post Counterintuitive problem: Everyone in a room keeps giving dollars to random others. You’ll never guess what happens next. appeared first on Decision Science News. [Read more...]

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Traditionally, statistics like wOBA (weighted on-base average) have been calculated using league averages. While building the baseballDBR package, I thought it would be interesting to group the American and National when making wOBA calculations. In th... [Read more...]

A year ago, I used Google’s Vision API to detect brand logos in images. Since then the DIY deep learning possibilities in R have vastly improved. With the release of Keras for R, one of the key deep learning frameworks is now available at your R fin... [Read more...]

The sparklyr package (by RStudio) provides a high-level interface between R and Apache Spark. Among many other things, it allows you to filter and aggregate data in Spark using the dplyr syntax. In Microsoft R Server 9.1, you can now connect to a a Spark session using the sparklyr package as ... [Read more...]

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[social4i size=”large” align=”float-right”] To post your R job on the next post Just visit this link and post a new R job to the R community. You [Read more...]

Bias vs Variance tradeoff is always encountered in applying supervised learning algorithms. Least squares regression provides a good fit for the training set but can suffer from high variance which lowers predictive ability. To counter this problem, we can regularize the beta coefficients by employing a penalization term. Ridge regression ... [Read more...]

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In our first and second posts on the useR! 2016 survey we discussed the responses from the demographic and programming sections. The main findings were: The useR! 2016 attendees were predominantly men, typically under 40, White/Caucasian and employed full-time. Women attendees tended to be younger, with less programming experience and lower involvement ... [Read more...]

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I recently wanted to mine data from Facebook to find out how many people are liking in different groups of interest on Facebook. Let’s say you want to know how many people give likes to different political parties or different companies. The following blog post shows how to use ... [Read more...]