R Programming for Data Science

Description

This book brings the fundamentals of R programming to you, using the same material developed as part of the industry-leading Johns Hopkins Data Science Specialization. The skills taught in this book will lay the foundation for you to begin your journey learning data science. Preface History and Overview of R What is R? What is S? The S Philosophy Back to R Basic Features of R Free Software Design of the R System Limitations of R R Resources Getting Started with R Installation Getting started with the R interface R Nuts and Bolts Entering Input Evaluation R Objects Numbers Attributes Creating Vectors Mixing Objects Explicit Coercion Matrices Lists Factors Missing Values Data Frames Names Summary Getting Data In and Out of R Reading and Writing Data Reading Data Files with read.table() Reading in Larger Datasets with read.table Calculating Memory Requirements for R Objects Using Textual and Binary Formats for Storing Data Using dput() and dump() Binary Formats Interfaces to the Outside World File Connections Reading Lines of a Text File Reading From a URL Connection Subsetting R Objects Subsetting a Vector Subsetting a Matrix Subsetting Lists Subsetting Nested Elements of a List Extracting Multiple Elements of a List Partial Matching Removing NA Values Vectorized Operations Vectorized Matrix Operations Dates and Times Dates in R Times in R Operations on Dates and Times Summary Control Structures if-else for Loops Nested for loops while Loops repeat Loops next, break Summary Functions Functions in R Your First Function Argument Matching Lazy Evaluation The ... Argument Arguments Coming After the ... Argument Summary Scoping Rules of R A Diversion on Binding Values to Symbol Scoping Rules Lexical Scoping: Why Does It Matter? Lexical vs. Dynamic Scoping Application: Optimization Plotting the Likelihood Summary Coding Standards for R Loop Functions Looping on the Command Line lapply() sapply() split() Splitting a Data Frame tapply apply() Col/Row Sums and Means Other Ways to Apply mapply() Vectorizing a Function Summary Debugging Something’s Wrong! Figuring Out What’s Wrong Debugging Tools in R Using traceback() Using debug() Using recover() Summary Profiling R Code Using system.time() Timing Longer Expressions The R Profiler Using summaryRprof() Summary Simulation Generating Random Numbers Setting the random number seed Simulating a Linear Model Random Sampling Summary Data Analysis Case Study: Changes in Fine Particle Air Pollution in the U.S. Synopsis Loading and Processing the Raw Data Results