| Author | Andy Vickler |
| Publisher | Ladoo Publishing LLC |
| Year | 2023 |
| Language | English |
| Pages | 172 |
| Size | 648 KB |
| Extension | EPUB |
Summary
"R Programming: R Data Analysis and Statistics" by Andy Vickler, published in 2023 by Ladoo Publishing LLC, is a comprehensive guide designed to introduce readers to the powerful world of R programming specifically tailored for data analysis and statistical computing. As the second book in the R Programming series, this 172-page volume builds upon foundational concepts while remaining accessible to beginners with no prior statistical knowledge. The book emphasizes practical applications, making it an ideal resource for aspiring data scientists, analysts, and researchers who want to harness R's capabilities for real-world data challenges. Vickler starts with an overview of R's ecosystem, explaining why R has become a staple in statistical computing due to its open-source nature, extensive package library, and robust community support. He covers the installation and setup of R and RStudio, ensuring readers can quickly get started with hands-on coding. The core of the book delves into data manipulation using packages like dplyr and tidyr, teaching techniques for cleaning, transforming, and reshaping datasets efficiently. Readers learn to handle missing values, merge datasets, and perform complex operations with ease through step-by-step examples. Data visualization is another key focus, utilizing ggplot2 to create informative and aesthetically pleasing graphs, charts, and plots. Vickler explains the grammar of graphics, enabling users to customize visualizations for exploratory data analysis and presentation purposes. The book progresses to statistical analysis, covering descriptive statistics, hypothesis testing, regression models, and ANOVA. Practical examples illustrate how to apply these methods to datasets, interpret results, and draw meaningful conclusions. A dedicated section on handling large datasets addresses performance issues, introducing efficient data structures like data.tables and parallel computing techniques in R. Supervised learning is introduced with machine learning concepts using packages such as caret and randomForest, guiding readers through model building, training, evaluation, and prediction. Vickler includes discussions on cross-validation, feature selection, and model tuning to avoid overfitting. Throughout the book, real-world case studies from fields like finance, healthcare, and social sciences demonstrate R's versatility. Common pitfalls and best practices are highlighted to help readers write clean, reproducible code. Advanced topics touch on integrating R with other tools like SQL databases, web scraping with rvest, and creating interactive dashboards with Shiny. The EPUB format, at just 648 KB, makes it portable and easy to reference on various devices. This book not only equips readers with technical skills but also fosters a problem-solving mindset essential for data-driven decision-making. Whether you're a student, professional transitioning to data roles, or a hobbyist interested in statistics, this guide provides a solid foundation to advance your R proficiency and tackle complex data problems confidently.
Key Features
- Beginner-friendly approach with no prior statistical knowledge required.
- Comprehensive coverage of data manipulation using dplyr and tidyr.
- In-depth tutorials on data visualization with ggplot2.
- Practical statistical analysis including hypothesis testing and regression.
- Techniques for handling large datasets efficiently.
- Introduction to supervised learning with machine learning packages.
- Real-world examples and case studies from various industries.
- Best practices for writing reproducible and efficient R code.
- Integration with other tools like SQL and Shiny for advanced applications.
- Compact EPUB format for easy digital access and portability.
About Author
Andy Vickler is a prolific author and educator specializing in programming, data science, and technical skills development. With a portfolio exceeding 130 books, Vickler has made significant contributions to fields such as Python, SQL, R programming, Linux, and more. His R Programming series, including "R Data Analysis and Statistics," reflects his dedication to making complex topics accessible to beginners while providing value to experienced practitioners. Vickler's writing style is characterized by clear explanations, practical examples, and a structured progression from basics to advanced concepts. He often self-publishes through platforms like Ladoo Publishing LLC or independently, allowing for timely updates in fast-evolving tech areas. Although detailed biographical information is sparse, his works suggest a background in software engineering and education, with a focus on empowering self-learners. Vickler's books have garnered positive reviews on platforms like Goodreads and Amazon, praised for their hands-on approach and relevance to real-world applications. His commitment to demystifying programming has helped countless individuals build careers in data science and analytics.
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Frequently Asked Questions
Q: What can I learn by reading this book ?
A: By reading this book, you can learn the fundamentals and advanced techniques of R programming for data analysis and statistics, including data manipulation, visualization, statistical modeling, handling large datasets, and supervised learning, all through practical examples and hands-on exercises.
Q: This book is suitable for beginners?
A: Yes, this book is suitable for beginners as it is written in an accessible way, requires no prior statistical knowledge, and starts with the basics before progressing to more complex topics.
Q: This book is recommended for professionals?
A: Absolutely, this book is recommended for professionals looking to expand their skills in R programming, data analysis, and statistics, offering practical insights and advanced techniques applicable to real-world scenarios.
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