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Contents of the Book

Part I: Working in Base R

• Working with objects
  • Vectors
  • Matrices
  • Lists
• Class and mode of an R object
• Ordered factors
  • Months example
  • SES example
• Exploring data sets
  • Infertility data set
  • Preliminary statistics
  • Frequency tables
  • Installing and using a package
  • Plots
  • US Arrest data set
• Calculations
  • Temperature conversion
  • Body mass index
  • AIDS transmission
  • Cumulative risk
  • Attributable fractions
• Rates, risks, odds, and logits
  • HIV transmission example
  • Scottish Health Study
• Cross tabulations and stratified analysis
  • UGDP
  • Cars data set
  • Stratified analysis
• Understanding the *apply() family
  • tapply()
  • lapply()
  • sapply()
  • mapply()
  • Other apply functions
• Indexing to manipulate data
  • Indexing vectors
  • Indexing matrices and arrays
  • Indexing lists
  • Indexing data frames
• Logistic regression: Titanic example
• Survival analysis: papal longevity

Part II: Bayesian Analysis in R

• Loops
  • For loops
  • While loops
• Writing functions
  • Odds ratio function
• Sampling and simulations
  • Simulating risk ratios
  • Bootstrap function
• Bayesian conjugate and MCMC analysis of airline fatalities
• Bayesian approach to drug trials
• Bayesian linear regression
• Bayesian analysis of repeated fetal growth
• Hierarchical Bayesian model of pelvic inflammatory disease
• Meta-analysis
  • R meta-analysis packages
  • Bayesian meta-analysis
• Gibbs sampler code
• Acknowledgments

Part III: Spatial Analysis in R

• Introduction to the sp package
• Understanding areal data
• New York City ZIP code neighbors
  • ZCTA tabulation area data set
  • Contiguity neighbors
  • Graph-based neighbors
• Spatial correlation of NYC pediatric traumatic brain injury
  • Merging data and map files
  • Plotting choropleths
  • Global tests of autocorrelation
• Modeling pediatric TBI in NYC
  • Entering and cleaning the data
  • Exploring the data
  • Modeling the data
  • Spatial models

Book Description

Ever felt lost in a sea of data? ‘R Practicals (PDF)’ is here to toss you a lifeline! This book walks you through R programming with hands-on exercises and clear explanations. Each chapter is packed with practical examples that make even complex topics feel manageable. Instead of dense theory, you get lessons you can actually use. It’s the kind of guide I wish I’d had when I started outstraightforward, friendly, and just technical enough.

Book Overview

This book covers the essentials of R, from basic syntax to advanced data visualization. You’ll start with simple tasks like importing data and plotting graphs. Gradually, you’ll tackle more challenging topics such as statistical modeling and automation. The format is super approachableno jargon overload or intimidating code dumps. It’s all about learning by doing, which honestly makes things stick so much better. Plus, if you’ve ever wondered how statisticians actually use R in real life, this book gives you a peek behind the curtain.

Why Read This Book

Why pick up ‘R Practicals’? Well, if you’ve ever stared blankly at an R error message, you’re not alone! This book doesn’t just show you what worksit tells you why things break and how to fix them. It’s packed with tips that save time and headaches. There’s even a bit of humor sprinkled in (because who says coding can’t be fun?). Seriously, whether you’re prepping for exams or trying to impress your boss with slick charts, this book has your back.

Who This Book Is For

This one’s for the curious minds: students getting their feet wet in statistics, professionals eager to upskill, or anyone who’s tired of copying code without knowing what it does. If you like learning by doing rather than memorizing theory, you’ll fit right in here. No need to be a math whizjust bring your curiosity and maybe a cup of coffee!

What You Will Learn

• The basics of R syntax and data types
• How to import and clean datasets
• Building visualizations that actually look good
• Applying statistical models in real-world scenarios
• Debugging code without losing your mind
• Automating repetitive tasks for efficiency

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