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.

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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.

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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.

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Strava athletes upload millions of activities every day. R enthusiasts can explore their exercise routines with this vast amount of information — once they have access to the data. Julian During walks you through how to use Strava data in R, first by configuring the Strava API to import the data and then wrangling it into useable data frames. Then, he showcase a visualization of the geospatial information gathered from his Strava account. - Note: This post by Julian During is the third place winner in the Call for Documentation contest.

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This post will guide you step-by-step through the process of creating an HTML xaringan slide deck and deploying it to the web for easy sharing with others. We will be using the xaringan package to build the slide deck, GitHub to help us host our slides for free with GitHub Pages, and the usethis package to help us out along the way. - Note: this post took second place in the recent R Views Call for Documentation Contest.

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If you haven’t recently visited bookdown.org, RStudio’s free site for publishing books written with the bookdown R package, you many be amazed at what is available. Currently, there are over one hundred fifty titles listed under the Books tab. These are written in a panoply of languages including Bulgarian, Chinese, English, French, German, Hindi, Italian, Japanese, Korean, Lithuanian (maybe), Norwegian, Portuguese, Russian, Slovenian (I think) and Vietnamese. The breadth of topics is extraordinary!

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Two hundred twenty new packages stuck to CRAN in September. Here are my “Top 40” picks in fourteen categories: Art, Computational Methods, Data, Econometrics, Finance and Insurance, Genomics, Machine Learning, Medicine, Networks and Graphs, Science, Statistics, Time Series, Utilities, and Visualization.

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Shiny modules are often taught as an advanced topic, but they can also be a great way for novice Shiny developers to start building more complex applications. If you already are an R user who likes to think and write functions and understand Shiny basics (i.e. reactivity), then modules for certain types of tasks (discussed at the end of this post) are an excellent way to up your game. In this post, I walk through a toy example of building a reporting app from the flights data in the nycflights13 package to demonstrate how modules help scale basic Shiny skills. - Note: this post by Emily Riederer is the winning entry in our recent Call for Documentation contest.

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An elegant application of Functional Data Analysis is to model longitudinal data as a curve and then study the curve’s dynamics. For example, in pharmacokinetics and other medical studies analyzing multiple measurements of drug or protein concentrations in blood samples, it may be interest to determine if the concentrations in subjects undergoing one type of treatment rise quicker than those undergoing an alternative treatment. In this post, I will generate some plausible fake data for measurements taken over time for two groups of subjects, use the techniques of Functional Data Analysis to represent these data as a continuous curve for each subject, and look at some of the dynamic properties of the curves, in particular their velocities and accelerations.

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One hundred sixty new packages covering a wide array of topics made it to CRAN in August. I thought I would emphasize the breadth of topics by expanding the number of categories organizing my “Top 40” selections beyond core categories that appear month after month. Here are my picks in fourteen categories: Archaeology, Computational Methods, Data, Education, Finance, Forestry, Genomics, Machine Learning, Medicine, Science, Statistics, Time Series, Utilities, and Visualization.

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