R Programming for Data Science

Description

Data science has taken the world by storm. Every field of study and area of business has been affected as people increasingly realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be trained in the proper data science skills. The R programming language has become the de facto programming language for data science. Its flexibility, power, sophistication, and expressiveness have made it an invaluable tool for data scientists around the world. This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code. With the fundamentals provided in this book, you will have a solid foundation on which to build your data science toolbox. Table of Contents Stay in Touch! 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 the readr Package 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 Managing Data Frames with the dplyr package Data Frames The dplyr Package dplyr Grammar Installing the dplyr package select() filter() arrange() rename() mutate() group_by() %>% 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 Regular Expressions Before You Begin Primary R Functions grep() grepl() regexpr() sub() and gsub() regexec() The stringr Package 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 Parallel Computation Hidden Parallelism Embarrassing Parallelism The Parallel Package Example: Bootstrapping a Statistic Building a Socket Cluster Summary Why I Indent My Code 8 Spaces About the Author