Resources

Hadley Wickham @ Posit | Giving benefit to people using what you build | Data Science Hangout - YouTube

A Data Science Hangout interview with Hadley Wickham, discussing the philosophy of giving benefit to people using the tools he builds.

Hands-on demonstration of {golem}!

Happy Git with R

Happy Git and GitHub for the useR provides instructions on how to install and use Git and GitHub with R and R Markdown. It covers key workflows and demonstrates the synergy between R/R Markdown/RStudio and GitHub. The target audience includes those who use R for data analysis or work on R packages.

haven

Import and Export SPSS, Stata, and SAS files using the haven package

Helpers for Automatic Translation of Markdown-based Content • babeldown

Helpers for Automatic Translation of Markdown-based Content

Helpers for Automatic Translation of Markdown-based Content • babeldown

babeldown is an R package designed for automatically translating Markdown-based R content with the help of the DeepL API. It facilitates the translation of Markdown strings, files, Quarto book chapters, and Hugo blog posts. The package offers a straightforward installation process through rOpenSci R-universe or GitHub. It supports the free and Pro plans of the DeepL API, requiring configuration of the API URL and key. Features like recommended line-wrapping practices and troubleshooting tips for common issues, such as punctuation mix-ups and API credit exhaustion, are provided. RStudio users can also benefit from integrated features.

Hillshade effects

Dr. Dominic Royé's blog post on hillshade effects explains creating relief maps in R with shadow effects to generate visual depth. He uses several R packages, including 'sf' for vector data, 'elevatr' for elevation API access, 'terra' for raster manipulation, 'whitebox' for geospatial analysis, and 'ggplot2' extensions for scales and color blending. The tutorial covers installing packages, importing and filtering lake boundaries, and manipulating Digital Elevation Models (DEMs) for Switzerland, with reproducible R code snippets.

Homelessness and Rents in Canada

This content is a comprehensive R code walkthrough for analyzing homelessness and rent data in Canada. It uses multiple R libraries, including the tidyverse for data wrangling, can census for accessing census data, and patchwork for visualizing data. Important steps include data import, cleaning, and transforming with functions like mutate, filter, and summarize. Quantile calculations for rents and adjustments for CPI are shown to assess real rents over time. It highlights metros like Vancouver and Toronto, using colors to represent different years. The code indicates a rich, data-driven analysis and visualization process focusing on socio-economic issues of homelessness and rents.

How (and Why) I came to Use R for Data Analysis and Evaluation

Alberto Espinoza recounts his journey with R for data analysis and evaluation, marking his 10-year experience since first encountering R during his graduate assistantship. Initially clueless about R, he was tasked with assisting and leading statistics labs using R. Despite early challenges and a steep learning curve, he recognized R's power over software like SPSS or Excel. His continued use of R spanned graduate projects, market research, data preparation for Tableau, and Survey Monkey analysis. Espinoza outlines R's advantages: reproducibility, efficiency, clarity, and an extensive package ecosystem, underlining R's significance in his professional growth.

How Do You Organise Your R Project? This Is What We Do.

This blog post discusses how the Biometrics group at Telethon Kids Institute organizes their R projects using a standardized template project directory. The post covers the project directory structure, reproducible research practices, and the use of version control.

How Major League Teams use R to Analyze Baseball Data

Keith Woolner, on September 27, 2023, delivers a presentation showcasing how Major League Baseball teams utilize the R programming language to perform data analysis on baseball statistics. The video, available on YouTube, dives into methodologies and tools used within the industry to crunch numbers and derive insights that can potentially give teams a competitive edge. It touches upon predictive modeling, player performance evaluation, and related statistical techniques, evidencing R's pivotal role in sports analytics and data-driven decision-making in professional baseball.

How to automatically crop images using R ("autocrop" / "smart crop" tutorial) - YouTube

This tutorial on YouTube demonstrates how to automatically crop images using R. The tutorial focuses on the autocrop or smart crop technique.