19 March 2022
If you’re looking for a new way to discover books from the collection, follow the Big Book of R Twitter bot. Every couple of hours it tweets out a book from the collection.
Before we begin – if you’re looking for a real-world, messy dataset to practice data cleaning and analysis on, check out this post for links to a salary survey. I’ve updated the post to include a few starter questions for you :).
by Nick Ulle
An introduction to using the R programming language for reproducible data analysis and scientific computing. Topics include programming basics, how to work with tabular data, how to break down programming problems, and how to organize code for clarity and reproducibility.
BrailleR in Action
by A. Jonathan R. Godfrey
Showing how tools that support blind R users were developed with examples. Suggestions of how blind R users should work are provided.
Stats in sports
by Zachary Binney
Materials for the Statistics in Sports class for first-year undergrads at Oxford College of Emory University. This course is unique in that it assumes no background. It covers an introduction to sports analytics and R for Baseball, Basketball, Football, Soccer and Sports business analytics.
Reproducible Finance with R: Code Flows and Shiny Apps for Portfolio Analysis
by Jonathan K. Regenstein Jr.
A unique introduction to data science for investment management that explores the three major R/finance coding paradigms, emphasizes data visualization, and explains how to build a cohesive suite of functioning Shiny applications. The full source code, asset price data and live Shiny applications are available at reproduciblefinance.com. The ideal reader works in finance or wants to work in finance and has a desire to learn R code and Shiny through simple, yet practical real-world examples.
R for Geographic Data Science
by Stefano De Sabbata
The materials aim to cover the necessary skills in basic programming, data wrangling and reproducible research to tackle sophisticated but non-spatial data analyses. The first part of the module will focus on core programming techniques, data wrangling and practices for reproducible research. The second part of the module will focus on non-spatial data analysis approaches, including statistical analysis and machine learning.
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