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    1. Data och IT
    2. Databaser

    R for Everyone

    Advanced Analytics and Graphics

    AvJared Lander

    Häftad, Engelska, 2017

    Del i serien Addison-Wesley Data & Analytics Series

    373 kr

    Beställningsvara. Skickas inom 7-10 vardagar. Fri frakt över 249 kr.

    Fler format och utgåvor

    Häftad

    Kommande

    Beskrivning

    Using the open source R language, you can build powerful statistical models to answer many of your most challenging questions. R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone is the solution.

     

    Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you’ll need to accomplish 80 percent of modern data tasks. Lander’s self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You’ll download and install R; navigate and use the R environment; master basic program control, data import, and manipulation; and walk through several essential tests. Then, building on this foundation, you’ll construct several complete models, both linear and nonlinear, and use some data mining techniques.

     

    By the time you’re done, you won’t just know how to write R programs, you’ll be ready to tackle the statistical problems you care about most.

     

    Coverage Includes:

    • Exploring R, RStudio, and R packages
    • Using R for math: variable types, vectors, calling functions, and more
    • Exploiting data structures, including data.frames, matrices, and lists
    • Creating attractive, intuitive statistical graphics
    • Writing user-defined functions
    • Controlling program flow with if, ifelse, and complex checks
    • Improving program efficiency with group manipulations
    • Combining and reshaping multiple datasets
    • Manipulating strings using R’s facilities and regular expressions
    • Creating normal, binomial, and Poisson probability distributions
    • Programming basic statistics: mean, standard deviation, and t-tests
    • Building linear, generalized linear, and nonlinear models
    • Assessing the quality of models and variable selection
    • Preventing overfitting, using the Elastic Net and Bayesian methods
    • Analyzing univariate and multivariate time series data
    • Grouping data via K-means and hierarchical clustering
    • Preparing reports, slideshows, and web pages with knitr
    • Building reusable R packages with devtools and Rcpp
    • Getting involved with the R global community

    Produktinformation

    • Utgivningsdatum:2017-06-28
    • Mått:178 x 231 x 18 mm
    • Vikt:700 g
    • Format:Häftad
    • Språk:Engelska
    • Serie:Addison-Wesley Data & Analytics Series
    • Antal sidor:560
    • Upplaga:2
    • Förlag:Pearson Education
    • ISBN:9780134546926

    Utforska kategorier

    • Databaser inom Data och IT
    • Programspråk inom Data och IT

    Mer om författaren

    Jared P. Lander is the owner of Lander Analytics, a statistical consulting firm based in New York City, the organizer of the New York Open Statistical Programming Meetup and an adjunct professor of statistics at Columbia University. He is also a tour guide for Scott’s Pizza Tours and an advisor to Brewla Bars, a gourmet ice pop startup. With an M.A. from Columbia University in statistics and a B.A. from Muhlenberg College in mathematics, he has experience in both academic research and industry. His work for both large and small organizations spans politics, tech startups, fund raising, music, finance, healthcare, and humanitarian relief efforts. He specializes in data management, multilevel models, machine learning, generalized linear models, visualization, data management, and statistical computing.

    Innehållsförteckning

    • Foreword xvPreface xviiAcknowledgments xxiAbout the Author xxv Chapter 1: Getting R 11.1 Downloading R 11.2 R Version 21.3 32-bit vs. 64-bit 21.4 Installing 21.5 Microsoft R Open 141.6 Conclusion 14 Chapter 2: The R Environment 152.1 Command Line Interface 162.2 RStudio 172.3 Microsoft Visual Studio 312.4 Conclusion 31 Chapter 3: R Packages 333.1 Installing Packages 333.2 Loading Packages 363.3 Building a Package 373.4 Conclusion 37 Chapter 4: Basics of R 394.1 Basic Math 394.2 Variables 404.3 Data Types 424.4 Vectors 474.5 Calling Functions 524.6 Function Documentation 524.7 Missing Data 534.8 Pipes 544.9 Conclusion 55 Chapter 5: Advanced Data Structures 575.1 data.frames 575.2 Lists 645.3 Matrices 705.4 Arrays 735.5 Conclusion 74 Chapter 6: Reading Data into R 756.1 Reading CSVs 756.2 Excel Data 796.3 Reading from Databases 816.4 Data from Other Statistical Tools 846.5 R Binary Files 856.6 Data Included with R 876.7 Extract Data from Web Sites 886.8 Reading JSON Data 906.9 Conclusion 92 Chapter 7: Statistical Graphics 937.1 Base Graphics 937.2 ggplot2 967.3 Conclusion 110 Chapter 8: Writing R functions 1118.1 Hello, World! 1118.2 Function Arguments 1128.3 Return Values 1148.4 do.call 1158.5 Conclusion 116 Chapter 9: Control Statements 1179.1 if and else 1179.2 switch 1209.3 ifelse 1219.4 Compound Tests 1239.5 Conclusion 123 Chapter 10: Loops, the Un-R Way to Iterate 12510.1 for Loops 12510.2 while Loops 12710.3 Controlling Loops 12710.4 Conclusion 128 Chapter 11: Group Manipulation 12911.1 Apply Family 12911.2 aggregate 13211.3 plyr 13611.4 data.table 14011.5 Conclusion 150 Chapter 12: Faster Group Manipulation with dplyr 15112.1 Pipes 15112.2 tbl 15212.3 select 15312.4 filter 16112.5 slice 16712.6 mutate 16812.7 summarize 17112.8 group_by 17212.9 arrange 17312.10 do 17412.11 dplyr with Databases 17612.12 Conclusion 178 Chapter 13: Iterating with purrr 17913.1 map 17913.2 map with Specified Types 18113.3 Iterating over a data.frame 18613.4 map with Multiple Inputs 18713.5 Conclusion 188 Chapter 14: Data Reshaping 18914.1 cbind and rbind 18914.2 Joins 19014.3 reshape2 19714.4 Conclusion 200 Chapter 15: Reshaping Data in the Tidyverse 20115.1 Binding Rows and Columns 20115.2 Joins with dplyr 20215.3 Converting Data Formats 20715.4 Conclusion 210 Chapter 16: Manipulating Strings 21116.1 paste 21116.2 sprintf 21216.3 Extracting Text 21316.4 Regular Expressions 21716.5 Conclusion 224 Chapter 17: Probability Distributions 22517.1 Normal Distribution 22517.2 Binomial Distribution 23017.3 Poisson Distribution 23517.4 Other Distributions 23817.5 Conclusion 240 Chapter 18: Basic Statistics 24118.1 Summary Statistics 24118.2 Correlation and Covariance 24418.3 T-Tests 25218.4 ANOVA 26018.5 Conclusion 263 Chapter 19: Linear Models 26519.1 Simple Linear Regression 26519.2 Multiple Regression 27019.3 Conclusion 287 Chapter 20: Generalized Linear Models 28920.1 Logistic Regression 28920.2 Poisson Regression 29320.3 Other Generalized Linear Models 29720.4 Survival Analysis 29720.5 Conclusion 302 Chapter 21: Model Diagnostics 30321.1 Residuals 30321.2 Comparing Models 30921.3 Cross-Validation 31321.4 Bootstrap 31821.5 Stepwise Variable Selection 32121.6 Conclusion 324 Chapter 22: Regularization and Shrinkage 32522.1 Elastic Net 32522.2 Bayesian Shrinkage 34222.3 Conclusion 346 Chapter 23: Nonlinear Models 34723.1 Nonlinear Least Squares 34723.2 Splines 35023.3 Generalized Additive Models 35323.4 Decision Trees 35923.5 Boosted Trees 36123.6 Random Forests 36423.7 Conclusion 366 Chapter 24: Time Series and Autocorrelation 36724.1 Autoregressive Moving Average 36724.2 VAR 37424.3 GARCH 37924.4 Conclusion 388 Chapter 25: Clustering 38925.1 K-means 38925.2 PAM 39725.3 Hierarchical Clustering 40325.4 Conclusion 407 Chapter 26: Model Fitting with Caret 40926.1 Caret Basics 40926.2 Caret Options 40926.3 Tuning a Boosted Tree 41126.4 Conclusion 415 Chapter 27: Reproducibility and Reports with knitr 41727.1 Installing a LaTeX Program 41727.2 LaTeX Primer 41827.3 Using knitr with LaTeX 42027.4 Conclusion 426 Chapter 28: Rich Documents with RMarkdown 42728.1 Document Compilation 42728.2 Document Header 42728.3 Markdown Primer 42928.4 Markdown Code Chunks 43028.5 htmlwidgets 43228.6 RMarkdown Slideshows 44428.7 Conclusion 446 Chapter 29: Interactive Dashboards with Shiny 44729.1 Shiny in RMarkdown 44729.2 Reactive Expressions in Shiny 45229.3 Server and UI 45429.4 Conclusion 463 Chapter 30: Building R Packages 46530.1 Folder Structure 46530.2 Package Files 46530.3 Package Documentation 47230.4 Tests 47530.5 Checking, Building and Installing 47730.6 Submitting to CRAN 47930.7 C++ Code 47930.8 Conclusion 484 Appendix A: Real-Life Resources 485A.1 Meetups 485A.2 Stack Overflow 486A.3 Twitter 487A.4 Conferences 487A.5 Web Sites 488A.6 Documents 488A.7 Books 488A.8 Conclusion 489 Appendix B: Glossary 491 List of Figures 507List of Tables 513General Index 515Index of Functions 521Index of Packages 527Index of People 529Data Index 531

    Betyg & recensioner

    3/5