R / Medicine is a big deal. This year, the conference grew by 13% with 665 people from over 60 countries signing up for the virtual event which was held last month. 34% percent of the registrants were from outside of the United States and 17% identified as physicians. The conference is now an established international event where experts report on the advanced use of the R language, Machine Learning, and statistical analysis, and discuss the successes and challenges associated with bringing these technologies to day-to-day medical practice.

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One hundred eighty-three new packages stuck to CRAN in July. Here are my “Top 40” picks in eleven categories: Data, Ecology, Finance, Genomics, Machine Learning, Medicine, Science, Statistics, Time Series, Utilities, and Visualization. Although I don’t have any formal specification for these categories, I do my best to main my subjective sense of consistency from month to month. Nevertheless, watching the monthly ebb and flow of the number of packages that fit into the various categories is interesting.

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R/Medicine 2021, the premier conference for the use of R in clinical applications is less than two weeks away! This conference reflects the increasing importance of data science, computational statistics and machine learning to clinical applications, and emphasizes the effectiveness of the R language as a vehicle for making data driven medicine accessible to clinicians with diverse backgrounds. The two conference keynote talks Bringing Machine Learning Models to the Bedside by Karandeep Singh and Dissecting Algorithmic Bias by Ziad Obermeyer directly address important technical and ethical issues confronting modern data driven medicine.

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We at R Views believe that the R ecosystem, centered around CRAN and Bioconductor, is the world’s largest open repository of statistical knowledge. R packages provide implementations and examples for a tremendous number of statistical methods, procedures, and algorithms. Yet, the virtual library of the R ecosystem is far from being complete. There is plenty of room for examples that explore some little travelled path of computational statistics or illuminate a familiar field with clarity.

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One hundred ninety-seven new packages made it to CRAN in June. Here are my selections for the “Top 40” in ten categories: Computational Methods, Data, Finance, Genomics, Machine Learning, Medicine, Statistics, Time Series, Utilities, and Visualization. The Medicine category includes multiple packages for medical reporting and table building. Note that eight new packages were removed from CRAN by the time I began my research for this post on July 16th, so they were not considered for the “Top 40”.

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In this post, I pick up where my June 10th post left off and look at how one might explore a sparse, longitudinal data set with the FPCA tools provided in the fdapace package. I begin by highligting some of the really nice tools available in the brolgar package for doing exploratory longitudinal data analysis, and then explore what FPCA might contrbute to an exploratory analysis. Instead of using artifical data as I did in my previous three posts, I take advantage of the work done by the brolgar authors and use the wages data set that they feature in several of their vignettes.

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Two hundred five packages made it to CRAN in May, but at least seven were removed before this post went to print. Here are my “Top40” picks in ten categories: Computational Methods, Data, Genomics, Machine Learning, Medicine, Science, Statistics, Time Series, Utilities, and Visualization.

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Summer is here, but it is not too late sign up for some summer conferences. The following short list promises interesting speakers, a wide range of topics and plenty of R content.

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In two previous posts, Introduction to Functional Data Analysis with R and Basic FDA Descriptive Statistics with R, I began looking into FDA from a beginners perspective. In this post, I would like to continue where I left off and investigate Functional Principal Components Analysis (FPCA), the analog of ordinary Principal Components Analysis in multivariate statistics. I begin with the math, and then show how to compute FPCs with R.

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The COVID19 pandemic has raised the profile of public health workers at all levels from the nurses and doctors working on the front lines at our hospitals, to high level state and federal public health officials. I think its a good bet that eighteen months ago few of us had any clear idea about how the public health care system works, or thought much about the people charged with the awesome responsibility to keep us safe.

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