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lintr is a static code analysis tool for R that identifies syntax errors, style inconsistencies, and other potential issues in R scripts and packages. It supports customizable lint rules and integrates with many editors to provide realtime feedback and enforce coding standards (e.g., tidyverse style).

data.table is an R package that extends base R’s data.frame for high-performance data manipulation. It offers concise syntax, blazing speed, and memory-efficient operations. It supports fast file reading/writing, joins, grouping, reshaping, and updates by reference. It is heavily used in large data workflows, big data in R, production pipelines, etc. Extremely efficient grouping/aggregation/summarization; can handle very large datasets (hundreds of millions to billions of rows) in memory (if...

box is an R package providing a modular system / module loader for organizing reusable R code outside of full packages. It allows users to treat R scripts (files/folders) as modules — possibly nested — with explicit exports, imports, and scoping. The idea is to let users structure code in a more modular, composable way, without needing every reusable component to be a full CRAN-style package. It also provides a cleaner syntax for importing functions or modules (via box::use) that allows...

Scala and polyglot Spark library (Scala, PySpark, SparkSQL, ... ) focused on reading OpenStreetMap Pbf files.

The DataScienceR repository is a curated collection of tutorials, sample code, and project templates for learning data science using the R programming language. It includes an assortment of exercises, sample datasets, and instructional code that cover the core steps of a data science project: data ingestion, cleaning, exploratory analysis, modeling, evaluation, and visualization. Many of the modules demonstrate best practices in R, such as using the tidyverse, R Markdown, modular scripting,...