reklama - zainteresowany?

Efficient R Programming. A Practical Guide to Smarter Programming - Helion

Efficient R Programming. A Practical Guide to Smarter Programming
ebook
Autor: Colin Gillespie, Robin Lovelace
ISBN: 978-14-919-5073-9
stron: 222, Format: ebook
Data wydania: 2016-12-08
Księgarnia: Helion

Cena książki: 29,90 zł (poprzednio: 135,91 zł)
Oszczędzasz: 78% (-106,01 zł)

Dodaj do koszyka Efficient R Programming. A Practical Guide to Smarter Programming

Osoby, które kupiły tę książkę, wybierały także »
Tagi: Analiza danych | R - Programowanie

There are many excellent R resources for visualization, data science, and package development. Hundreds of scattered vignettes, web pages, and forums explain how to use R in particular domains. But little has been written on how to simply make R work effectively—until now. This hands-on book teaches novices and experienced R users how to write efficient R code.

Drawing on years of experience teaching R courses, authors Colin Gillespie and Robin Lovelace provide practical advice on a range of topics—from optimizing the set-up of RStudio to leveraging C++—that make this book a useful addition to any R user’s bookshelf. Academics, business users, and programmers from a wide range of backgrounds stand to benefit from the guidance in Efficient R Programming.

  • Get advice for setting up an R programming environment
  • Explore general programming concepts and R coding techniques
  • Understand the ingredients of an efficient R workflow
  • Learn how to efficiently read and write data in R
  • Dive into data carpentry—the vital skill for cleaning raw data
  • Optimize your code with profiling, standard tricks, and other methods
  • Determine your hardware capabilities for handling R computation
  • Maximize the benefits of collaborative R programming
  • Accelerate your transition from R hacker to R programmer

Dodaj do koszyka Efficient R Programming. A Practical Guide to Smarter Programming

 

Osoby które kupowały "Efficient R Programming. A Practical Guide to Smarter Programming", wybierały także:

  • Fundamentals of Metadata Management. Uncover the Meta Grid and Unlock IT, Data, Information, and Knowledge Management
  • Semantic Modeling for Data
  • Power BI Desktop. Kurs video. Wykorzystanie narzędzia w analizie i wizualizacji danych
  • The Practitioner's Guide to Graph Data. Applying Graph Thinking and Graph Technologies to Solve Complex Problems
  • R Cookbook. Proven Recipes for Data Analysis, Statistics, and Graphics. 2nd Edition

Dodaj do koszyka Efficient R Programming. A Practical Guide to Smarter Programming

Spis treści

Efficient R Programming. A Practical Guide to Smarter Programming eBook -- spis treści

  • Preface
    • Conventions Used in This Book
    • Using Code Examples
    • OReilly Safari
    • How to Contact Us
    • Acknowledgments
      • Colin
      • Robin
  • 1. Introduction
    • Prerequisites
    • Who This Book Is for and How to Use It
    • What Is Efficiency?
    • What Is Efficient R Programming?
    • Why Efficiency?
    • Cross-Transferable Skills for Efficiency
      • Touch Typing
      • Consistent Style and Code Conventions
    • Benchmarking and Profiling
      • Benchmarking
      • Benchmarking Example
      • Profiling
        • Exercises
    • Book Resources
      • R Package
      • Online Version
    • References
  • 2. Efficient Setup
    • Prerequisites
    • Top Five Tips for an Efficient R Setup
    • Operating System
      • Operating System and Resource Monitoring
        • Exercises
    • R Version
      • Installing R
      • Updating R
      • Installing R Packages
      • Installing R Packages with Dependencies
      • Updating R Packages
        • Exercises
    • R Startup
      • R Startup Arguments
      • An Overview of Rs Startup Files
      • The Location of Startup Files
      • The .Rprofile File
      • Example .Rprofile File
        • Setting options
        • Setting the CRAN mirror
        • The fortunes package
        • Useful functions
        • Creating hidden environments with .Rprofile
      • The .Renviron File
        • Example .Renviron file
        • Exercises
    • RStudio
      • Installing and Updating RStudio
      • Window Pane Layout
        • Exercises
      • RStudio Options
      • Autocompletion
      • Keyboard Shortcuts
      • Object Display and Output Table
      • Project Management
        • Exercises
    • BLAS and Alternative R Interpreters
      • Testing Performance Gains from BLAS
      • Other Interpreters
      • Useful BLAS/Benchmarking Resources
        • Exercise
    • References
  • 3. Efficient Programming
    • Prerequisites
    • Top Five Tips for Efficient Programming
    • General Advice
      • Exercise
      • Memory Allocation
      • Vectorized Code
        • Exercises
        • Example: Monte Carlo integration
        • Exercise
    • Communicating with the User
      • Fatal Errors: stop()
      • Warnings: warning()
      • Informative Output: message() and cat()
        • Exercise
      • Invisible Returns
    • Factors
      • Inherent Order
      • Fixed Set of Categories
        • Exercise
    • The Apply Family
      • Example: Movies Dataset
      • Type Consistency
        • Other resources
        • Exercises
    • Caching Variables
      • Exercise
      • Function Closures
        • Exercises
    • The Byte Compiler
      • Example: The Mean Function
      • Compiling Code
    • References
  • 4. Efficient Workflow
    • Prerequisites
    • Top Five Tips for Efficient Workflow
    • A Project Planning Typology
    • Project Planning and Management
      • Chunking Your Work
      • Making Your Workflow SMART
      • Visualizing Plans with R
        • Exercises
    • Package Selection
      • Searching for R Packages
      • How to Select a Package
    • Publication
      • Dynamic Documents with R Markdown
      • R Packages
    • Reference
  • 5. Efficient Input/Output
    • Prerequisites
    • Top Five Tips for Efficient Data I/O
    • Versatile Data Import with rio
      • Exercises
    • Plain-Text Formats
      • Differences Between fread() and read_csv()
      • Preprocessing Text Outside R
    • Binary File Formats
      • Native Binary Formats: Rdata or Rds?
      • The Feather File Format
      • Benchmarking Binary File Formats
      • Protocol Buffers
    • Getting Data from the Internet
    • Accessing Data Stored in Packages
    • References
  • 6. Efficient Data Carpentry
    • Prerequisites
    • Top Five Tips for Efficient Data Carpentry
    • Efficient Data Frames with tibble
      • Exercise
    • Tidying Data with tidyr and Regular Expressions
      • Make Wide Tables Long with gather()
      • Split Joint Variables with separate()
      • Other tidyr Functions
      • Regular Expressions
        • Exercises
    • Efficient Data Processing with dplyr
      • Renaming Columns
      • Changing Column Classes
      • Filtering Rows
      • Chaining Operations
        • Exercises
      • Data Aggregation
        • Exercises
      • Nonstandard Evaluation
    • Combining Datasets
    • Working with Databases
      • Databases and dplyr
        • Exercises
    • Data Processing with data.table
    • References
  • 7. Efficient Optimization
    • Prerequisites
    • Top Five Tips for Efficient Optimization
    • Code Profiling
      • Getting Started with profvis
      • Example: Monopoly Simulation
    • Efficient Base R
      • The if() Versus ifelse() Functions
      • Sorting and Ordering
      • Reversing Elements
      • Which Indices are TRUE?
      • Converting Factors to Numerics
      • Logical AND and OR
      • Row and Column Operations
      • is.na() and anyNA()
      • Matrices
        • The integer data type
        • Sparse matrices
        • Exercises
    • Example: Optimizing the move_square() Function
      • Exercise
    • Parallel Computing
      • Parallel Versions of Apply Functions
      • Example: Snakes and Ladders
      • Exit Functions with Care
      • Parallel Code under Linux and OS X
    • Rcpp
      • A Simple C++ Function
      • The cppFunction() Command
      • C++ Data Types
      • The sourceCpp() Function
      • Vectors and Loops
        • Exercises
      • Matrices
      • C++ with Sugar on Top
        • Exercises
      • Rcpp Resources
    • References
  • 8. Efficient Hardware
    • Prerequisites
    • Top Five Tips for Efficient Hardware
    • Background: What Is a Byte?
    • Random Access Memory
      • Exercises
    • Hard Drives: HDD Versus SSD
    • Operating Systems: 32-Bit or 64-Bit
      • Exercises
    • Central Processing Unit
    • Cloud Computing
      • Amazon EC2
        • Exercise
  • 9. Efficient Collaboration
    • Prerequisites
    • Top Five Tips for Efficient Collaboration
    • Coding Style
      • Reformatting Code with RStudio
      • Filenames
      • Loading Packages
      • Commenting
      • Object Names
      • Example Package
      • Assignment
      • Spacing
      • Indentation
      • Curly Braces
        • Exercise
    • Version Control
      • Commits
      • Git Integration in RStudio
      • GitHub
      • Branches, Forks, Pulls, and Clones
    • Code Review
    • References
  • 10. Efficient Learning
    • Prerequisties
    • Top Five Tips for Efficient Learning
    • Using Rs Internal Help
      • Searching R for Topics
      • Finding and Using Vignettes
      • Getting Help on Functions
      • Reading R Source Code
      • swirl
    • Online Resources
      • Stack Overflow
      • Mailing Lists and Groups
    • Asking a Question
      • Minimal Dataset
      • Minimal Example
    • Learning In Depth
    • Spread the Knowledge
    • References
  • A. Package Dependencies