The third annual R / Medicine Conference was held online this year from August 27th to August 29th and was an unqualified success. The last minute pivot from small, in-person conference, which was to be held onsite at the Children’s Hospital of Philadelphia, CHOP, to a virtual event turned out to be a catalyst for positive change. Under the imaginative, and tireless leadership of conference chair Dr.
In this post, I provide some reasons for why a statistician or data scientist might want to simulate synthetic or ‘fake’ data, and briefly examine several R packages that make this task a little easier.
This post spotlights one of the most comprehensive open-source event data sets on protests, demonstrations, and other political gatherings in the contemporary U.S., along with a new GitHub repository and Shiny dashboard that make the data much easier to use and explore.
Here are my picks for the ‘Top 40’ new CRAN packages for July 2020 in seven categories: Computational Methods, Data, Genomics, Machine Learning, Science, Statistics, and Utilities.
We continue the tutorial on developing Rcpp implementations that keep both reusable and standard C++ code separate from the R interface. In Part 4, we went through the build processes for a simple package containing a single C++ .cpp file. In this post, we work through a more realistic case which involves multiple reusable C++ files and the C++ interface files that export functions to R.
This post is a step by step guide for using the RStudio IDE to build an R package with integrated C++ code, and shows how to use exported functions from within an R session.
If you are looking for a place to monitor expert forecasts for United States weekly and cumulative COVID-19 deaths, you can’t do any better than the Reich Lab (University of Massachusetts) COVID-19 Forecast Hub. The same is true for data scientists who may be seeking to publish their own forecasts and compare them with the work of their peers.
The R community offers so many opportunities to get feedback, advice, and information from a wide variety of users. Early on in the book development, we had a lot of questions to nail down. We listened to the stories of many data scientists who work in education, then found common experiences we could describe in our writing.
This post shows how to configure code that allows the use of standard reusable C++ in an R package, without having to modify it with any R or Rcpp-specific syntax.
Here are my picks for the Top 40 new packages selected from the two hundred ninety new packages that made it to CRAN in June.