Resources
This carefully curated collection of resources will help you find packages and learning resources to help you on your R journey.
Rapid RAG Prototyping
Rapid RAG Prototyping leverages the power of R through the ellmer package and DuckDB to build a Retrieval-Augmented Generation (RAG) prototype, enhancing a large language model with domain-specific knowledge. This solution addresses the limitations of large language models, which often lack current or specific information. The ellmer package provides an interface for working with various LLM providers, adding functions like tool calling and data extraction. DuckDB contributes with high-performance data processing, enabling efficient query handling. Together, they offer a formidable toolkit for fast prototyping of LLM-powered applications.
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raster
The raster package is an R package for spatial data manipulation and analysis. It provides classes and functions for creating, manipulating, and analyzing raster data. The package includes high-level methods for raster algebra, overlay operations, distance calculations, and more. It also supports writing and reading raster files in various formats. The raster package is commonly used for remote sensing image analysis, species distribution modeling, and other spatial data science tasks.
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Re-constructing Google Forms responses with Quarto and {glue}
This blog post by Eric R. Scott explains how to use Quarto and the {glue} R package to transform Google Forms responses into a more readable application format. Initially dealing with a Google Sheet that collated 50 applicants' data for a short course, Scott outlines the process of converting cumbersome, lengthy answers into clean, application-like documents. The use of the {googlesheets4} package to import data and the manipulation of column names with {janitor} and {stringr} are detailed. Key to the transformation is utilizing Quarto's 'asis' output chunk option alongside {glue} to programmatically create markdown from the dataset.
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reactable
Interactive data tables for R, based on the React Table library and made with reactR.
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Reactable Tutorial
This tutorial provides a guide on using the reactable package in R to create interactive data tables. It covers topics such as setting up the package, modifying column properties, adjusting width and alignment, rendering cell values as HTML, and grouping and aggregation.
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Read files on the web into R
June Choe's tutorial provides valuable insights for R users desiring to read files directly from the web into their R environment. It caters to individuals seeking to streamline their workflow by skipping the download process. The focus is on various data sources like GitHub public repos, gists, private repos, and OSF. Techniques include utilizing the 'raw.githubusercontent.com' URLs for reading CSV files and handling binary files which can't be displayed as plain text. The content covers sessionInfo(), streaming with {duckdb}, and miscellaneous tips for efficient data import in R.
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Read hundreds of Excel files into one dataset with one line of code #shorts #excel #rstats - YouTube
Learn how to read multiple Excel files into one dataset using R with just one line of code.
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readr
The readr package provides a fast and friendly way to read rectangular data from delimited files, such as CSV and TSV. It supports various file formats and allows you to specify column types or guess them. This overview provides information on installation, usage, and column type guessing in readr.
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readxl
The readxl package makes it easy to get data out of Excel and into R. It supports both the legacy .xls format and the modern xml-based .xlsx format, and can read data from specific worksheets or cell ranges.
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Rebecca Barter - Learn to purrr
Learn about the purrr package in R, which provides map functions for iteration and manipulating lists.
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Recreate a real-world, complex dataviz with R & ggplot - YouTube
Recreate a real-world, complex dataviz with R & ggplot - YouTube
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