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!

person icon Aniket Jain

ebook icon Kindle

language icon Language - English

updated on icon Updated on Feb, 2025

category icon Development ,Data Science,R Programming

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This eBook includes

Formats : PDF (Downlodable)

Pages : 141

ISBN : 9798306899091

Edition : 1st

Language : English

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.