One hundred eighty-nine new packages made it to CRAN in June. Here are my “Top 40” selections in eleven categories: Computational Methods, Data, Ecology, Genomics, Machine Learning, Mathematics, Medicine, Statistics, Time Series, Utilities, and Visualizations.
Beginning with the invitation-only 2016 Shiny Developer Conference, Shiny has played a prominent part in all RStudio conferences, and rstudio::conf(2022) is no exception. Two workshops and eighteen talks showcase Shiny’s multiple, ever-increasing capabilities. What started out as a way to introduce R’s interactive statistical computations to the web has grown into a production-grade tool that supports serious data science workflows and facilitates the communication of data-generated insights throughout large organizations in both industry and government.
Actuarial data science lies at the intersection of math and business studies, combining statistical knowledge and methods from insurance and finance areas. Compared to data scientists, actuaries focus more on finance and business knowledge, while still collecting and analyzing data.
The profession is in high demand, and according to the Bureau of Labor Statistics (BLS), it is expected that actuary jobs will a enjoy 24% increase from 2020-30. This is much faster than the average for all occupations.
One hundred seventy-nine new packages made it to CRAN in May. Here are my “Top 40” picks in twelve categories: Computational Methods, Data, Ecology, Epidemiology, Finance, Machine Learning, Networks, Science, Statistics, Time Series, Utilities, and Visualization.
Frank Harrell`s new eBook, R Workflow, which aims to: “to foster best practices in reproducible data documentation and manipulation, statistical analysis, graphics, and reporting” is an ambitious document that is notable on multiple levels.
One hundred seventeen new packages stuck to CRAN in April. Here are my “Top 40” selections in twelve categories: Computational Methods, Data, Ecology, Finance, Machine Learning, Mathematics, Medicine, Networks, Statistics, Time Series, Utilities, and Visualization.
The rpy2 package 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.