Posts

Summary Diabetes is growing at an epidemic rate in the United States. In North Carolina alone, diabetes and prediabetes cost an estimated $10.9 billion each year (American Diabetes Association, 2015). This post introduces the exploration of the Diabetes epidemic in North Carolina. Through a series of posts this project will examine various public data available on diabetes and explore possible solutions to address the rise of diabetes in North Carolina.

Demonstrating how to 1) build interactive visualizationsusing plotly::ggplotly(), 2) compute relative timelines for each country and 3) plot sequence of key events for cross-country comparison.

Graphing the trends of suicides in Florida from 2006 to 2017.

optimizeAPA is an R package which allows for multi-parameter optimization. That means you can use it to find the maximum (or the minimum) value of a function with many input values. What makes optimizeAPA unique? It works with arbitrary precision arithmetic.

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Out in the real world you may often find yourself working with data from multiple sources. It will probably be stored in separate files and you’ll need to combine them before you can attempt to answer any of your research questions. Click here to learn how you can combine data frames using a set of dplyr functions called joins.

This blogposts shows how to extract population estimates data reported by the Florida Department of Health and prepare them for analysis, specifically, for exploring the trends in demograph growth between 2006 and 2020

Welcome to the world of manifold regression! In part 2 we will apply manifold regression to a case study involving fMRI brain imaging data using R and MGLMRiem. See part 1 for an introduction to these models.

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Welcome to the world of manifold regression! In part 1 we will introduce the basic concepts, overview the theory behind regression on manifolds, develop an intuition for these models, and discuss their applications.

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The 4th post in the Scientist’s Guide to R series introduces data transformation techniques useful for wrangling/tidying/cleaning data. Specifically, we will be learning how to use the 6 core functions (and a few others) from the popular dplyr package, perform similar operations in base R, and chain operations with the pipe operator (%>%) to streamline the process. Click here for the full post.

The workshop introduces R and RStudio and makes the case for project-oriented workflows for applied data analysis. Using logistic regression on Titanic data as an example, the participants will learn to communicate statistical findings more effectively, and will evaluate the advantages of using computational notebooks in RStudio to disseminate the results