R programming

Datacamp Project Solutions R

Your solutions to DataCamp Projects showcase a diverse range of data science skills applied to real-world datasets. Each project offers a unique opportunity to delve into different aspects of data analysis and modeling, ultimately contributing to the enhancement of your data science portfolio. From exploring textual data like Donald Trump's tweets to analyzing health survey data for BMI associations, your projects demonstrate your proficiency in using R for various analytical tasks. These projects not only help you refine your technical skills but also showcase your ability to derive valuable insights from data, making you a compelling candidate for prospective employers in the data science field.

Source https://github.com/veeralakrishna/Datacamp-Project-Solutions-R

Applied Predictive Modeling

The "Applied Predictive Modeling" project is a structured learning initiative designed to deepen comprehension of predictive modeling techniques. Through practical engagement with the provided questions and solutions, participants can sharpen their skills in data analysis. Accessible open-source datasets sourced from platforms like the UCI Machine Learning Repository and Kaggle enrich the learning experience, offering varied contexts for application. Each chapter's solutions folder contains both answers and accompanying R code, fostering a hands-on approach to mastering predictive modeling concepts. This project equips learners with practical experience and theoretical understanding, enabling them to tackle diverse real-world challenges in data analysis with confidence and proficiency.

Source https://github.com/anilsilwal98/AppliedPredictiveModeling

R Programming Tutorial

R serves as both a language and an environment tailored for statistical computing and graphics. Offering an extensive array of statistical techniques from linear and nonlinear modeling to time-series analysis, R excels in flexibility and extensibility. Notably, it enables the effortless creation of high-quality plots suitable for publication, complete with mathematical symbols and formulas. The R environment encompasses robust data manipulation and storage capabilities, a suite of operators for array calculations, and a rich collection of tools for data analysis. Moreover, its graphical facilities facilitate on-screen or hardcopy data visualization. Its programming language is straightforward yet powerful, featuring conditionals, loops, user-defined recursive functions, and robust input-output functionality.

Source https://github.com/TirendazAcademy/R-Programming-Tutorial

R_StatisticalProject

The "R_StatisticalProject" delves into probability and statistics through a focused assignment. This project aims to analyze the intricate relationships among different CPU specifications using advanced statistical methods such as Multi-factor Anova test and Multiple Linear Regression Models. By employing these techniques, the project seeks to uncover meaningful insights into how various CPU specifications interact and influence one another. Through rigorous analysis and interpretation of results, participants gain a deeper understanding of the complex dynamics within CPU specifications, contributing to enhanced knowledge and proficiency in statistical modeling using R.

Source https://github.com/cmq2002/R_StatisticalProject

Micronaut

Micronaut Framework: JVM-based, full-stack Java framework for modular, testable applications in Java, Kotlin, and Groovy. Inspired by Grails and lessons from Spring and Spring Boot. Provides DI/IoC, AOP, and sensible defaults. Supports message-driven apps, HTTP servers, and more, with features like distributed config and service discovery. Aims for fast startup, low memory, and minimal reflection/proxies, achieved by pre-computing framework infrastructure at compile time. Supported by Micronaut Foundation. Ideal for microservices and avoids downsides of other frameworks.

Source https://github.com/micronaut-projects/micronaut-core

mixOmics

This repository hosts the R package now available on Bioconductor, offering both stable and development versions on GitHub. For installation, ensure you have the latest R version and BiocManager package installed. For macOS users, XQuartz installation is necessary. Here are the installation steps:

Features:

• Latest Bioconductor Release
• GitHub Versions:
• Stable version
• Development version

For Docker containers, refer to option c). Contributions adhering to the code of conduct and Bioconductor’s coding guide are welcomed. Bug reports and pull requests are encouraged on GitHub. Set up a development environment and join discussions on the mixOmics forum. Led by a collaborative team across Australia, France, and Canada, mixOmics provides advanced multivariate methods for biological data integration and variable selection, enabling a holistic view of biological systems through comprehensive ’omics analyses.

Source https://github.com/mixOmicsTeam/mixOmics

QRcode Generator for R

The qrcode package in R facilitates the generation of QR codes, two-dimensional barcodes developed by Denso Wave. It supports numeric, alphanumeric, and byte modes. The qr_code() function returns a logical matrix object, with print and plot methods for visualization. For high-quality QR codes, plot() or generate_svg() is recommended. Installation options include CRAN, R universe, and GitHub. The legacy qrcode_gen() function has been replaced due to known bugs, with new functions introduced since version 0.1.4, and the old functions deprecated since version 0.2.0.

Source https://github.com/ThierryO/qrcode

Steve’s Academic R Projects

"Steve’s Academic R Projects" is an R package designed to facilitate the management of academic projects aimed at producing research papers. It leverages the {stevetemplates} ecosystem and Rstudio to create and manage projects efficiently. When creating a project, {steveproj} generates a directory structure containing subdirectories for data analysis, document rendering, and reports. Key functionalities include simplifying the research process with Make targets, rendering R Markdown documents to PDF or Word, anonymizing documents for peer review, and generating citations for bibliographies. Installation options include CRAN and GitHub. Notably, LaTeX, Make, and Rstudio are essential components for optimal package functionality.

Source https://github.com/svmiller/steveproj

Rockerverse

The "Rockerverse" is a comprehensive resource encompassing all aspects of containers and R. Originally based on a blog post, it evolved into a preprint on arXiv and eventually became a published article in The R Journal. Authored by a collaborative team, the article explores packages and applications for containerization with R, offering valuable insights into the integration of R within container environments. Covering diverse topics such as Docker, Binder, and RStudio, the Rockerverse provides a wealth of information for researchers and practitioners interested in leveraging containers for their R projects. The article DOI is 10.32614/RJ-2020-007.

Source https://github.com/nuest/rockerverse-paper

rocker-win

"rocker-win" is a proof of concept project aimed at running R in Windows Containers with Docker for Windows. It offers selected images based on different Windows Server versions and explores automated builds on various platforms. The project is currently in its conceptual stage, with minimal implementation done. Available images include those for Windows Server 2019, 2016, and 1803, each with different R versions. Users can build the images locally using Dockerfiles provided in the repository. The project also demonstrates potential use cases, such as deploying R-based web applications alongside Windows containers in organizations already using them. Automated builds are supported on platforms like Appveyor, facilitating validation and deployment of Docker images.

Source https://github.com/nuest/rocker-win

JumpDetectR

The QuantLet "JumpDetectR" provides a scalable implementation of Lee/Mykland (2012) and Ait-Sahalia/Jacod/Li (2012) jump tests for noisy high-frequency data. It enables users to detect jumps in equilibrium prices amidst market microstructure noise, particularly in digital asset markets like cryptocurrencies. The QuantLet includes functions for preprocessing data, conducting jump tests, and evaluating results. Users can split the dataset by ID, apply jump tests, and obtain results for further analysis. The code is available for further exploration and application in understanding jumps in high-frequency digital asset markets.

Source https://github.com/QuantLet/JumpDetectR