Beginning Programming With Python R For Dummies R
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Learn R Programming in 24 Hours
R is a programming language developed is widely used for statistical and graphical analysis. It can execute advance machine learning algorithms including earning algorithm, linear regression, time series, statistical inference. R programming language is used by Fortune 500 companies and tech bellwethers like Uber, Google, Airbnb, Facebook, Apple. R provides a data scientist tools and libraries (Dplyr) to perform the 3 steps of analysis 1) Extract 2) Transform, Cleanse 3) Analyze. Table of Contents Chapter 1: What is R Programming Language? Introduction & Basics Chapter 2: How to Download & Install R, RStudio, Anaconda on Mac or Windows Chapter 3: R Data Types, Arithmetic & Logical Operators with Example Chapter 4: R Matrix Tutorial: Create, Print, add Column, Slice Chapter 5: Factor in R: Categorical & Continuous Variables Chapter 6: R Data Frame: Create, Append, Select, Subset Chapter 7: List in R: Create, Select Elements with Example Chapter 8: R Sort a Data Frame using Order() Chapter 9: R Dplyr Tutorial: Data Manipulation(Join) & Cleaning(Spread) Chapter 10: Merge Data Frames in R: Full and Partial Match Chapter 11: Functions in R Programming (with Example) Chapter 12: IF, ELSE, ELSE IF Statement in R Chapter 13: For Loop in R with Examples for List and Matrix Chapter 14: While Loop in R with Example Chapter 15: apply(), lapply(), sapply(), tapply() Function in R with Examples Chapter 16: Import Data into R: Read CSV, Excel, SPSS, Stata, SAS Files Chapter 17: How to Replace Missing Values(NA) in R: na.omit & na.rm Chapter 18: R Exporting Data to Excel, CSV, SAS, STATA, Text File Chapter 19: Correlation in R: Pearson & Spearman with Matrix Example Chapter 20: R Aggregate Function: Summarise & Group_by() Example Chapter 21: R Select(), Filter(), Arrange(), Pipeline with Example Chapter 22: Scatter Plot in R using ggplot2 (with Example) Chapter 23: How to make Boxplot in R (with EXAMPLE) Chapter 24: Bar Chart & Histogram in R (with Example) Chapter 25: T Test in R: One Sample and Paired (with Example) Chapter 26: R ANOVA Tutorial: One way & Two way (with Examples) Chapter 27: R Simple, Multiple Linear and Stepwise Regression [with Example] Chapter 28: Decision Tree in R with Example Chapter 29: R Random Forest Tutorial with Example Chapter 30: Generalized Linear Model (GLM) in R with Example Chapter 31: K-means Clustering in R with Example Chapter 32: R Vs Python: What's the Difference? Chapter 33: SAS vs R: What's the Difference?
Beginning Programming For Dummies
Discover the latest programming tips and techniques Start writing programs for Windows, Linux, Macintosh, Palms, and PocketPCs Ready to take control of your computer by writing your own programs? Mixing concepts with humor, author Wallace Wang shows you how to begin programming as simply and quickly as possible. In no time, you'll find out how to create and debug programs in Liberty BASIC, use algorithms, build interactive Web pages, and more! All this on the bonus CD-ROM * Liberty BASIC and other compilers * All the examples and code from the book * IBM Robocode V1.0.6 * JBuilder 9 Personal, C# Builder Personal, and Kylix Enterprise Trial See the "About the CD-ROM" appendix for details and complete system requirements. Discover how to: * Choose the best programming language for your project * Work with data structures * Install and use Liberty BASIC * Create interactive Web sites * Program multiple platform devices
R Programming for Beginners
Author: Anshuman Mishra
language: en
Publisher: Independently Published
Release Date: 2025-01-09
"R Programming for Beginners: Unlock the Power of Data" is designed as the ultimate guide for those who are new to programming and data analysis. Whether you're a student, professional, or someone just starting their journey with R, this book will provide you with the foundation needed to work confidently with data. Through easy-to-follow explanations, practical examples, and hands-on exercises, this book introduces the basic concepts of R programming, including its syntax, data structures, and built-in functions. You'll learn how to import, manipulate, and visualize data using R and develop a strong understanding of statistical analysis. By the end of this book, you will have the skills necessary to solve real-world problems using R and be well-prepared to dive into more advanced topics or data science projects. How to Study This Book 1. Follow the Structure Step-by-Step: Each chapter is designed to build upon the previous one. It's essential to go through the chapters sequentially to understand the logical flow of learning. Start from the basics and gradually move toward more complex topics. 2. Hands-On Practice: R programming is all about practice. Make sure to work through the exercises and examples provided in each chapter. Don't just read; actively try to write code, run it in RStudio, and experiment with different commands. The more you practice, the more comfortable you'll become with R. 3. Use the R Help System: If you encounter any difficulties or are unsure about a particular function, use R's built-in help system. Each chapter includes tips on how to search for documentation within RStudio or the online R community. 4. Learn by Solving Problems: After completing each chapter, challenge yourself by solving small problems. You can find simple datasets online and apply what you've learned to analyze them, or create your own datasets for practice. 5. Review and Reflect: After finishing each section, take a moment to review the key concepts. Try explaining them to someone else or write them down in your own words to solidify your understanding. Benefits of Studying This Book Solid Foundation in R Programming: This book is perfect for beginners, and it helps you develop a strong foundational knowledge of R. By learning the basics correctly, you'll set yourself up for success in more advanced topics in programming and data analysis. Practical, Hands-On Learning: Learning programming requires practice, and this book emphasizes that. With hands-on examples and exercises, you'll gain experience working with real data, making it easier to apply R in real-world scenarios. Data Science Skills: R is one of the most popular programming languages for data analysis, statistics, and visualization. By mastering R through this book, you will acquire essential data science skills, opening doors to numerous opportunities in fields like business, research, and technology. Understand Data Visualization: The book provides detailed coverage of data visualization techniques using R, particularly through the ggplot2 package. Mastering data visualization is an invaluable skill for anyone working with data and helps communicate insights effectively. Boost Confidence in Data-Driven Decision Making: By learning how to manipulate and analyze data, you will develop the ability to make informed, data-driven decisions. Whether you're conducting research, making business decisions, or pursuing academic goals, knowing R will give you the tools to work with data effectively. Prepare for Advanced Learning: This book serves as a stepping stone to more advanced R programming or data science topics.