Data challenges provide opportunities for R users to practice their programming skills and test their perseverance.
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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.
If you are a regular R Views reader, then you know that every month I post my Top 40 picks for new CRAN packages. In this post, I’ll borrow some additional terminology from the Top 40 AM radio of my youth, and talk about two new categories of CRAN packages: Golden Oldies and One Hit Wonders.
Golden Oldies On Top 40 radio, Golden Oldies are tunes that continue to get a lot of air play over decades, and are generally thought to be classics of 50’s, 60’s, and 70’s pop culture.
Forecast errors in Structural Equation Models can be analyzed by using a stochastic simulation in which the structural disturbances are given values with specified stochastic properties. Moreover, a performance measure (i.e. objective-function) is assigned to an econometric model, depending on the value of forecasted endogenous variables; thus, analysts try to enhance this measure by fine-tuning exogenous variables (i.e. the instruments) of the model (e.g. policy analysis). In this post, I show how to complete these analyses in R.
One hundred thirty-four new packages made it to CRAN last December. Here are my “Top 40” picks in eight categories: Data, Genomics, Machine Learning, Medicine, Science, Statistics, Utilities, and Visualization.
In this blog post, we interview Oscar Baruffa, the creator of the Big Book of R. The Big Book of R contains over 200 R-related books on a variety of subjects and has garnered over 73,000 unique visitors since its release in August 2020.
Two hundred eleven new packages made it to CRAN in November. Here are my “Top 40” picks in thirteen categories: Computational Methods, Data, Ecology, Finance, Genomics, Humanities, Machine Learning, Medicine, Networks, Statistics, Time Series, Utilities, and Visualization. It was gratifying to see multiple packages developed for applications in the Computational Humanities. R is helping to extend the reach of data literacy.
The COVID-19 Data Hub provides a daily summary of COVID-19 cases, deaths, recovered, tests, vaccinations, and hospitalizations for 230+ countries, 760+ regions, and 12000+ administrative divisions of lower level. It includes policy measures, mobility, and geospatial data. This post presents version 3.0.0 of the COVID19 package to seamlessly import the data in R.
One hundred forty-one new packages made it to CRAN in October. Here are my “Top 40” picks in twelve categories: Computational Methods, Data, Genomics, Machine Learning, Medicine, Networks, Science, Social Science, Statistics, Time Series, Utilities, and Visualization.