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This carefully curated collection of resources will help you find packages and learning resources to help you on your R journey.

Screenshot of yonder


yonder is a reactive web framework built on shiny. It features new reactive inputs and Bootstrap components on the UI side, and tools for alerts, modals, and more on the server side.

Screenshot of You ‘tidyr::complete()’ me

You ‘tidyr::complete()’ me

Luis D. Verde Arregoitia's article demonstrates using the 'complete()' function from the tidyr package to expand a data frame's sequences based on start and end values within columns. The example showcases how to pivot data and use 'complete()' and 'full_seq()' functions for filling in sequences of days for different categories, while repeating longitude values accordingly. This technique is useful for managing tabular data in wide format, facilitating transformations into a long format ready for analysis. The article is instructional for those working with R in ecology, conservation, and biogeography, focusing on data wrangling challenges.

Screenshot of You're Already Ready: Zen and the Art of R Package Development

You're Already Ready: Zen and the Art of R Package Development

R packages make it easier to write robust, reproducible code, and modern tools in R development like usethis make it easy to work with packages. In this video, Malcolm Barrett discusses why your project is already an R package, why you’re already an R package developer, and why you already have the skills to walk the path of development.

Screenshot of Your first R package in 1 hour

Your first R package in 1 hour

This blog post provides a step-by-step guide on how to create an R package in just one hour. It covers the use of devtools and usethis packages to automate folder structure and file creation in package development.

Screenshot of zipcodeR


zipcodeR is an R package that makes working with ZIP codes in R easier. It provides data on all U.S. ZIP codes using multiple open data sources, making it easier for social science researchers and data scientists to work with ZIP code-level data in data science projects using R.