Search Results for "programming languages learning"

The wch/r-source repository is a read-only mirror of the official R language source code, maintained to reflect the upstream Subversion (SVN) R core development tree. This mirror provides public visibility into R’s internals—everything from the interpreter, base and recommended packages, documentation, and C/Fortran code under the hood. It is updated hourly to stay in sync with the upstream SVN. Although it mirrors the R source for browsing and reference, it is not the “canonical development...

awesome-network-analysis is a curated list of resources focused on network and graph analysis, including libraries, frameworks, visualization tools, datasets, and academic papers. It covers multiple programming languages and domains like sociology, biology, and computer science. This repository serves as a central reference for researchers, analysts, and developers working with network data.

swirl is an R package that allows interactive, in-R learning of statistics, data science, R programming etc. The idea is that you load swirl in R, and it presents you with lessons (within R’s console or RStudio) that ask you to type commands, check results, and progress through tutorial material—without leaving the R environment. It is used for teaching R, especially for beginners, as well as for self-paced learning of packages, data manipulation, visualization, etc. ...

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, and reproducible workflows. ...

...The repository is designed as a shared space for code examples, datasets, and instructional materials, helping learners follow along with lectures and assignments. It spans essential topics such as R programming, data cleaning, exploratory data analysis, statistical inference, regression models, machine learning, and practical data science projects. By providing centralized resources, the repo makes it easier for students to practice concepts and replicate examples from the curriculum. It also offers a structured view of how multiple disciplines—programming, statistics, and applied data analysis—come together in a professional workflow.