The rpy2 is an interface that allows you to run R in Python processes. You can use the best of one language while working in the other.
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The rpy2 is an interface that allows you to run R in Python processes. You can use the best of one language while working in the other.
Even when filtering by the relatively sober #rstats hashtag, I find twitter to be the stream of consciousness of an undisciplined collective mind: disjoint and ephemeral. Nevertheless, on any given day some useful R resources float by, and it is frequently the case that interesting items disappear downstream before I can record them. Here are a few I did manage to fish out recently.
Two hundred and six new packages stuck to CRAN in March. Here are my “Top 40” selections in thirteen categories: Computational Methods, Data, Finance, Game Theory, Genomics, Machine Learning, Medicine, Networks, Science, Statistics, Time Series, Utilities, and Visualization.
This post introduces the MLDataR package which contains real-world examples of clinical and hospital systems datasets that are suitable for exploring supervised machine learning classification and regression models. In a sort of a call to arms, I am working with the NHS-R community to equip the package with even more examples of excellent datasets that can be used for machine learning.
Inflation is a hot topic for both globally and in Turkey nowadays. Inspired by Rami Krispin, I reverse engineered his U.S. Electricity dashboard and created a dashboard to explore a forecast for inflation in Turkey. In this post, I would like to begin to describe some of the technology that went into creating the dashboard.
February was a good month for new R packages on CRAN. Here are my “Top 40” selections from the two hundred packages that arrived in thirteen categories: Computational Methods, Data, Genomics, Linguistics, Machine Learning, Mathematics, Medicine, Networks, Science, Sports, Statistics, Utilities, Visualization.
The 2022 R Conference season is already underway. Here is a list of upcoming conferences that we know about. If we have missed your conference, please write to us with the details. We will update our list as we receive more information.
Data challenges provide opportunities for R users to practice their programming skills and test their perseverance.
Two Hundred and two new packages made it to CRAN in January. Several were in applications areas that don’t show up very often, and a few of these made it into my January “Top 40” picks. My selections are listed below in thirteen categories: Agriculture, Computational Methods, Data, Engineering, Finance, Genomics, Machine Learning, Mathematics, Medicine, Science, Statistics, Utilities, and Visualization. Slightly expanding the number of categories helps to emphasize the various application areas, but makes it more difficult to classify packages which could fit in multiple categories.
Arthur Holtz is a Senior FP&A Manager at Shipt, but don’t let the title mislead you — he spends most of his days buried in databases and R, analyzing data. This post is neither affiliated with nor endorsed by his employer.
Intro By now, I’m sure most people are familiar with the viral game Wordle. If you’ve been living under a rock and have no idea what I’m talking about, I recommend playing it for yourself.