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"R for Data Science" by Garrett Grolemund and Hadley Wickham is a comprehensive guide that introduces readers to the powerful programming language R and its applications in data analysis and visualization. It covers fundamental concepts such as data manipulation, visualization, and modeling using tidyverse packages, making it an essential resource for both beginners and experienced data scientists looking to leverage R for their projects.
"The Book of R" by Tilman M. Davies is a distinctive beginner's companion to mastering the R programming language. Through its engaging narrative and practical approach, it navigates readers through essential concepts like data visualization, statistical analysis, and machine learning. With a focus on clarity and comprehension, this book ensures that readers not only grasp the fundamentals but also gain confidence in utilizing R for diverse data-related tasks, setting a distinct tone amidst the plethora of programming guides available.
"R For Dummies" by Andrew P. Negronio is a user-friendly guide designed to demystify the R programming language for beginners. With a humorous and approachable tone, it covers key concepts such as data analysis, visualization, and statistical modeling using R. The book provides step-by-step instructions and practical examples to help readers quickly grasp the fundamentals and start applying R to real-world data projects with confidence.
"Data Science in Action" by Alexis Géron is a hands-on guide that brings data science principles to life through practical examples and case studies. It explores various aspects of the data science process, including data wrangling, analysis, and interpretation, using tools like Python and R. With a focus on real-world applications, this book equips readers with the skills and techniques needed to tackle complex data problems and derive actionable insights in diverse domains.
"Advanced R" by Hadley Wickham delves deep into the intricacies of the R programming language, catering to experienced users and developers. Through detailed explanations and advanced techniques, Wickham explores topics like functional programming, object-oriented programming, and performance optimization in R. This comprehensive guide empowers readers to leverage R's full potential, enabling them to write efficient and elegant code for complex data analysis and visualization tasks.
"R Graphics Cookbook" by Winston Chang is a practical guide that provides solutions to common data visualization challenges in R. Through concise recipes and examples, Chang demonstrates how to create a wide range of static and interactive plots using R's powerful graphics capabilities. From basic charts to complex visualizations, this cookbook equips readers with the tools and techniques needed to effectively communicate insights from their data through compelling and informative graphics.
"Data Manipulation with R" by Winston Chang is a comprehensive guide that teaches readers how to effectively manipulate and transform data using R. Through clear explanations and practical examples, Chang covers essential techniques for data cleaning, reshaping, and summarization using R's powerful tools and packages. Whether you're a beginner or an experienced R user, this book equips you with the skills needed to efficiently prepare and preprocess your data for analysis and visualization tasks.
"R in a Nutshell" by Joseph Adler serves as a compact yet comprehensive compendium for users of all proficiency levels within the R programming ecosystem. Offering a diverse array of topics ranging from elementary data manipulation to intricate statistical analysis and visualization methods, Adler's work provides lucid explanations and practical illustrations. With its condensed yet expansive coverage, this book stands as an indispensable asset for anyone seeking to harness the full potential of R for data analysis and beyond.
"R for Everyone" by Jared P. Lander is like a friendly companion for anyone curious about R programming. It's written in a way that's easy to understand, whether you're just starting out or you've been using R for a while. Lander breaks down complicated topics into simple explanations and gives plenty of examples to help you learn. Whether you're interested in basic data tasks or diving into more advanced techniques, this book has something for everyone who wants to explore R.
This exploration delves into machine learning techniques using R, covering data preparation, model construction, and evaluation. Readers learn to preprocess data for analysis, select and train predictive models, and optimize their performance. Through practical examples, they gain proficiency in implementing diverse algorithms like decision trees and neural networks. Evaluation methods such as cross-validation and ROC curves help assess model accuracy, enabling informed decision-making based on the analysis results.
Topic |
Description |
---|---|
R Introduction |
An overview of the R programming language |
R Reserved Words |
Keywords in R that are reserved for specific purposes |
R Variables and Constants |
Explanation of variables and constants in R |
R Operators |
Different types of operators used in R |
R Operator Precedence and Associativity |
Precedence and associativity of operators in R |
Topic |
Description |
---|---|
R Flow Control |
Control structures in R for managing program flow |
R if…else Statement |
Conditional statement for decision making in R |
R ifelse() Function |
Conditional function for vectorized operations |
R for Loop |
Looping construct for iterating over a sequence |
R while Loop |
Looping construct for repeated execution as long as a condition is true |
R break and next Statement |
Statements for controlling loop execution |
R repeat loop |
Looping construct for repeating execution indefinitely |
Topic
Description
R Functions |
Introduction to functions in R |
R Return Value from Function |
Explanation of returning values from functions in R |
R Environment and Scope |
Discussion on environment and scope of variables in R |
R Recursive Function |
Functions that call themselves within their definition |
R Infix Operator |
Operators that are used in-between operands in R expressions |
R switch() Function |
Conditional function for multiple branch selection in R |
Topic |
Description |
---|---|
R Data Structures |
Overview of data structures in R |
R Vector |
Explanation of vectors in R |
R Matrix |
Description of matrices in R |
R Lists |
Overview of lists in R |
R Data Frame |
Explanation of data frames in R |
R Factor |
Description of factors in R |
Topic |
Description |
---|---|
R Object & Class |
Overview of classes and objects in R |
R Classes and Objects |
Explanation of classes and objects in R |
R S3 Class |
Description of S3 class in R |
R S4 Class |
Explanation of S4 class in R |
R Reference Class |
Overview of reference class in R |
Topic |
Description |
---|---|
R Graphs & Charts |
Overview of graphs and charts in R |
R Bar Plot |
Explanation of bar plots in R |
R Histograms |
Description of histograms in R |
R Pie Chart |
Explanation of pie charts in R |
R Box Plot |
Overview of box plots in R |
R Strip Chart |
Description of strip charts in R |
Topic |
Description |
---|---|
R Advanced Topics |
Overview of advanced topics in R |
R Plot Function |
Explanation of the plot function in R |
R Multiple Plots |
Description of creating multiple plots in R |
Saving a Plot in R |
Explanation of saving a plot in R |
R Plot Color |
Overview of specifying plot colors in R |
R 3D Plot |
Description of creating 3D plots in 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
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 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
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.
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.
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:
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.
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.
"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.
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.
"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.
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.