Learn R in 24 Hours: The Ultimate Crash Course [ebook]
Master the Basics of R Programming in Just 24 Hours and Unlock the Power of Data Analysis and Visualization!
About the Book
Book description
Your Complete Guide to Mastering R Programming in Just One Day!
Whether you're a beginner or an aspiring data scientist, Learn R in 24 Hours: The Ultimate Crash Course is designed to make your journey into the world of R programming seamless and effective. This hands-on guide simplifies key concepts, empowering you to explore R’s capabilities for data science, statistical analysis, and visualization.
Why Learn R?
R is a powerful, open-source language widely used for data science, statistical computing, and machine learning. Its robust ecosystem, user-friendly syntax, and versatility make it a go-to tool for professionals and beginners alike. From building visualizations to performing complex data analysis, this book equips you with the essential skills to master the art of R programming.
What’s Inside?
This R programming for beginners guide is structured into 24 dynamic hours of learning:
- Hour 1-2: Introduction to R Programming and Setting Up Your Environment (Understanding R Programming, Real-World Applications, Installing R and RStudio, Navigating the Interface, Creating Your First Project).
- Hour 3-5: R Programming Basics and Working with Data Structures (Variables and Constants, Data Types, Vectors, Lists, Matrices, Arrays, Data Frames, Input/Output Operations).
- Hour 6-10: Importing and Manipulating Data (Reading and Writing Data, dplyr for Data Manipulation, Filtering, Grouping, Summarizing, Sorting Records).
- Hour 11-15: Data Visualization and Exploratory Data Analysis (Visualizations with ggplot2, Summary Statistics, Correlation and Trend Analysis).
- Hour 16-24: Statistical Analysis, Machine Learning, and Projects (Regression, Classification, Clustering, Time Series, Big Data Handling, Debugging, Shiny Apps).
Who Should Read This Book?
This crash course is perfect for:
- Beginners seeking a practical introduction to R programming for data science.
- Aspiring data scientists building strong foundations in statistical computing.
- Professionals in need of an R for dummies guide to upskill their analytics capabilities.
- Students and researchers seeking hands-on experience with R for data science.
What You’ll Learn:
- How to set up R and RStudio effortlessly.
- The fundamentals of R syntax, variables, data structures, and functions.
- Best practices for working with data frames, matrices, and lists.
- How to clean and manipulate data with R’s libraries.
- The art of R programming for creating stunning data visualizations.
- Statistical modeling and performing data analysis confidently.
- Real-world applications like building reports with R Markdown.
- Efficient coding practices, debugging, and version control.
Why This Book Stands Out:
With hands-on programming with an R approach, step-by-step exercises, and real-world coding examples, Learn R in 24 Hours: The Ultimate Crash Course ensures you gain practical knowledge and confidence in your R programming abilities.
Whether you're pursuing a career in data science, enhancing your portfolio, or exploring analytics, this crash course will help you learn R programming and unlock new opportunities.
Start your R programming journey today and experience the power of data science mastery!
Goals
- Learn R Basics: Understand the fundamentals of R programming, including variables, constants, and syntax.
- Set Up the Environment: Install and configure R and RStudio for an efficient programming workflow.
- Master Data Structures: Work with R’s data structures, including vectors, lists, matrices, arrays, and data frames.
- Perform Data Import and Manipulation: Learn to read and write data, manipulate datasets using libraries like dplyr, and apply operations like filtering, grouping, and summarizing.
- Create Stunning Visualizations: Build interactive and static data visualizations using ggplot2 and other visualization libraries.
- Explore Data Analysis Techniques: Conduct exploratory data analysis (EDA), compute summary statistics, and identify trends and correlations.
- Perform Statistical Analysis: Gain confidence in statistical methods, including regression, classification, clustering, and time series analysis.
- Handle Big Data: Learn techniques to manage and analyze large datasets efficiently.
- Develop Real-World Projects: Build practical applications, including reports using R Markdown and interactive apps with Shiny.
- Enhance Debugging and Optimization Skills: Adopt best practices for debugging and writing optimized R code.
- Prepare for Advanced Data Science: Lay a solid foundation for machine learning, advanced analytics, and R-based research projects.
- Complete Projects in 24 Hours: Gain hands-on experience by completing practical projects within a structured learning framework.
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Author Details

<a href="https://market.tutorialspoint.com/author/aniket_jain">Aniket Jain</a>
Full Stack DeveloperOur students work
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