People learning R for work have to imagine how they might apply what they are learning to the kinds of problems the see on their jobs. This post explains the concept and shows how to take the first steps.
This post highlights three outstanding COVID-19 resources: an illuminating dashboard, the COVID-19 Data Hub, and the four talks given at the recent COVID-19 Data Forum webinar.
One hundred forty-eight new packages made it to CRAN in April. Here are my Top 40 picks in nine categories: Computational Methods, Data, Machine Learning, Medicine, Science, Statistics, Time Series, Utilities, and Visualization.
In this introductory post, I preview a series of four blog posts to accompany a book my co-authors and I wrote, Data Science in Education Using R. In the coming weeks, we’ll be sharing about the people and tools in the R community that inspired us to write DSIEUR.
Machine learning models come in many shapes and sizes. While deep learning models currently have the lion’s share of coverage, there are many other classes of models that are effective across many different problem domains. This post gives a short summary of several rule-based models that are closely related to tree-based models (but are less widely known).
If you been been trying to make sense of the various published COVID-19 models but are not an epidemiologist, you may want to acquire some background knowledge. One good way to do this is to find a handful of relevant R packages with documentation and pointers to source material. This post provides a guided search and turns up some R packages that may be of interest to novices and experts alike.
A short address to the BSc and MSc Statistics Graduates of the Cal State East Bay Class of 2020
A short list of some upcoming R related, virtual conferences and events
An interview with RStudio’s Greg Wilson who won this year’s ACM’s Influential Educator Award
Two hundred ninety-six new packages made it to CRAN in March. Here are my “Top 40” picks in ten categories: Computational Methods, Data, Machine Learning, Mathematics, Medicine, Science, Statistics, Time Series, Utilities, and Visualization.
Computational Methods celltrackR v0.3.1: Provides a methodology to analyze cells that move in a two- or three-dimensional space. While the methodology has been developed for cell trajectory analysis, it is applicable to anything that moves including animals, people, or vehicles.