Open Source R Education Software for ChromeOS

This repository holds the R Markdown (.Rmd) source files for the PH525x / HarvardX course series (Data Analysis for the Life Sciences / Genomics) managed by GenomicsClass. It functions as the canonical source for course lab exercises, lecture modules, and reading materials in reproducible format. Students and learners use these R Markdown files to follow along, knit notebooks, run code samples, and complete the lab-based assignments. The repo is licensed under MIT, allowing reuse and modification. It is part of a larger ecosystem: the compiled HTML / book version of the labs is published via a companion “book” repository, which presents a polished, browsable version of the materials. The content covers topics such as data wrangling in R, statistical inference, genomics workflows, Bioconductor packages, and project-based analyses. Because it’s open and modular, contributors can suggest improvements, update modules, or add new exercises.

This repository contains the second programming assignment for an R course, focused on caching expensive computations by leveraging R’s scoping rules. The assignment walks you through creating a special matrix object that stores both a matrix and its cached inverse, avoiding repeated calls to costly operations. It builds on a worked example that caches the mean of a numeric vector, demonstrating how the operator preserves state across function calls. You then implement analogous logic for matrices via two functions, one to construct the cache-aware object and another to compute or retrieve the cached inverse. The instructions emphasize using solve for inversion and assuming that the supplied matrix is always invertible. The repository outlines the workflow for forking, editing the provided R stub, committing your solution, and submitting your repository URL as the final deliverable.

The Data Science Specialization Courses repository is a collection of materials that support the Johns Hopkins University Data Science Specialization on Coursera. It contains the source code and resources used throughout the specialization’s courses, covering a broad range of data science concepts and techniques. 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.

“R for Data Science” (r4ds) is the source material (book + examples) by Hadley Wickham et al., intended to teach data science using R and the tidyverse. It covers the workflow from importing data, tidying, transforming, visualizing, modelling, communicating results, and programming in R. The repository contains the source files (Quarto / RMarkdown), example datasets, visualizations, exercises, and all content needed to build the book. Includes many example datasets, diagrams, code samples, and “hands-on” exercises. Comprehensive coverage of data-science workflow: data import, cleaning, transformation, exploration, modelling etc. Includes topics beyond basics: relational data (joins), date/time, strings, working with missing values, visualizing data, etc.