Master your Minecraft
 
 

Special Offers see all

Enter to WIN a $100 Credit

Subscribe to PowellsBooks.news
for a chance to win.
Privacy Policy

Tour our stores


    Recently Viewed clear list


    Contributors | November 26, 2014

    Chris Faatz: IMG The Collected Poems of James Laughlin



    Fall has brought us a true gift in the publication of the massive The Collected Poems of James Laughlin, published by New Directions in an... Continue »

    spacer
Qualifying orders ship free.
$31.00
List price: $44.99
Used Trade Paper
Ships in 1 to 3 days
Add to Wishlist
Qty Store Section
1 Burnside Mathematics- Software

R for Everyone: Advanced Analytics and Graphics (Addison-Wesley Data and Analytics)

by

R for Everyone: Advanced Analytics and Graphics (Addison-Wesley Data and Analytics) Cover

 

Synopses & Reviews

Publisher Comments:

Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals

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

 

Synopsis:

Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals

 

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 nonstatisticians 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 all newcomers 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, hierarchical clustering, and other techniques

• Preparing reports, slideshows, and web pages

• Building reusable R packages with devtools and Rcpp

• Getting involved with the R global community

 

Synopsis:

Statistical computation for non-statisticians like computer programmers, social scientists, biologists, physicists, and quants.

Using the free, open source R language, scientists, financial analysts, public policy professionals, and programmers can build powerful statistical models capable of answering many of their most challenging questions. But, for non-statisticians, R can be difficult to learn—and most books on the subject assume far too much knowledge to help the non-statistician.

 

R for Everyone is the solution. Drawing on his extensive experience teaching new users through the New York City R User Group, professional statistician Jared Lander has written the perfect R tutorial for everyone who’s new to statistical programming and modeling. Offering extensive hands-on practice and sample code, Lander covers all this and more:

  • Downloading, installing, and getting started with R
  • Navigating and mastering the R environment
  • Learning basic techniques, from control statements to data manipulation
  • Importing data from SAS, SPSS, Stata, web sites, or elsewhere
  • Performing essential statistical tests
  • Building, comparing, and diagnosing models
  • Developing your own R packages
  • Connecting with and learning from the global R user community

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

About the Author

Jared P. Lander is the owner of Lander Analytics, a statistical consultanting 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

Table of Contents

1. Introduction

2. Getting R

3. The R Environment

4. Basics

5. Reading Data

6. Plotting

7. Functions

8. Control Statements

9. Loops

10. String Manipulation

11. Manipulating Data

12. Basics

13. Non-Parametric

14. Modeling

15. Model Diagnostics a. Residuals

16. Regularization

17. Clustering

18. Sweave

19. Building Your Own Package

20. Resources

Product Details

ISBN:
9780321888037
Author:
Lander, Jared
Publisher:
Addison-Wesley Professional
Author:
Lander, Jared P.
Subject:
Database Management - General
Subject:
Computer Languages-SQL
Subject:
R; R programming; statistics; statistical computation; statistical modeling; predictive analytics; SPSS; SAS; Sweave; UseR; Strata; scientific computing; r for dummies; a beginners guide to r; the art of r programming; statistics for dummies; r cookbook
Subject:
R; R programming; statistics; statistical computation; statistical modeling; predictive analytics; SPSS; SAS; Sweave; UseR; Strata; scientific computing; r for dummies; a beginners guide to r; the art of r programming; statistics for dummies; r cookbook
Subject:
R; R programming; statistics; statistical computation; statistical modeling; predictive analytics; SPSS; SAS; Sweave; UseR; Strata; scientific computing; r for dummies; a beginners guide to r; the art of r programming; statistics for dummies; r cookbook
Subject:
R; R programming; statistics; statistical computation; statistical modeling; predictive analytics; SPSS; SAS; Sweave; UseR; Strata; scientific computing; r for dummies; a beginners guide to r; the art of r programming; statistics for dummies; r cookbook
Subject:
R; R programming; statistics; statistical computation; statistical modeling; predictive analytics; SPSS; SAS; Sweave; UseR; Strata; scientific computing; r for dummies; a beginners guide to r; the art of r programming; statistics for dummies; r cookbook
Subject:
R; R programming; statistics; statistical computation; statistical modeling; predictive analytics; SPSS; SAS; Sweave; UseR; Strata; scientific computing; r for dummies; a beginners guide to r; the art of r programming; statistics for dummies; r cookbook
Subject:
R; R programming; statistics; statistical computation; statistical modeling; predictive analytics; SPSS; SAS; Sweave; UseR; Strata; scientific computing; r for dummies; a beginners guide to r; the art of r programming; statistics for dummies; r cookbook
Subject:
R; R programming; statistics; statistical computation; statistical modeling; predictive analytics; SPSS; SAS; Sweave; UseR; Strata; scientific computing; r for dummies; a beginners guide to r; the art of r programming; statistics for dummies; r cookbook
Subject:
R; R programming; statistics; statistical computation; statistical modeling; predictive analytics; SPSS; SAS; Sweave; UseR; Strata; scientific computing; r for dummies; a beginners guide to r; the art of r programming; statistics for dummies; r cookbook
Subject:
R; R programming; statistics; statistical computation; statistical modeling; predictive analytics; SPSS; SAS; Sweave; UseR; Strata; scientific computing; r for dummies; a beginners guide to r; the art of r programming; statistics for dummies; r cookbook
Subject:
R; R programming; statistics; statistical computation; statistical modeling; predictive analytics; SPSS; SAS; Sweave; UseR; Strata; scientific computing; r for dummies; a beginners guide to r; the art of r programming; statistics for dummies; r cookbook
Subject:
R; R programming; statistics; statistical computation; statistical modeling; predictive analytics; SPSS; SAS; Sweave; UseR; Strata; scientific computing; r for dummies; a beginners guide to r; the art of r programming; statistics for dummies; r cookbook
Subject:
R; R programming; statistics; statistical computation; statistical modeling; predictive analytics; SPSS; SAS; Sweave; UseR; Strata; scientific computing; r for dummies; a beginners guide to r; the art of r programming; statistics for dummies; r cookbook
Subject:
R; R programming; statistics; statistical computation; statistical modeling; predictive analytics; SPSS; SAS; Sweave; UseR; Strata; scientific computing; r for dummies; a beginners guide to r; the art of r programming; statistics for dummies; r cookbook
Subject:
R; R programming; statistics; statistical computation; statistical modeling; predictive analytics; SPSS; SAS; Sweave; UseR; Strata; scientific computing; r for dummies; a beginners guide to r; the art of r programming; statistics for dummies; r cookbook
Copyright:
Edition Description:
Trade paper
Series:
Addison-Wesley Data & Analytics Series
Publication Date:
20130623
Binding:
TRADE PAPER
Language:
English
Pages:
464
Dimensions:
9 x 7 x 0.8 in 640 gr

Other books you might like

  1. Data Mining and Business Analytics... New Hardcover $125.00
  2. Genetic Algorithms in Search,... Used Hardcover $60.50

Related Subjects

Computers and Internet » Computer Languages » SQL
Computers and Internet » Computer Languages » The Attic
Computers and Internet » Computers Reference » General
Computers and Internet » Database » Design
Computers and Internet » Software Engineering » Programming and Languages
Computers and Internet » Software Engineering » Software Management
Science and Mathematics » Mathematics » General
Science and Mathematics » Mathematics » Software

R for Everyone: Advanced Analytics and Graphics (Addison-Wesley Data and Analytics) Used Trade Paper
0 stars - 0 reviews
$31.00 In Stock
Product details 464 pages Addison-Wesley Professional - English 9780321888037 Reviews:
"Synopsis" by ,

Statistical Computation for Programmers, Scientists, Quants, Excel Users, and Other Professionals

 

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 nonstatisticians 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 all newcomers 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, hierarchical clustering, and other techniques

• Preparing reports, slideshows, and web pages

• Building reusable R packages with devtools and Rcpp

• Getting involved with the R global community

 

"Synopsis" by ,

Statistical computation for non-statisticians like computer programmers, social scientists, biologists, physicists, and quants.

Using the free, open source R language, scientists, financial analysts, public policy professionals, and programmers can build powerful statistical models capable of answering many of their most challenging questions. But, for non-statisticians, R can be difficult to learn—and most books on the subject assume far too much knowledge to help the non-statistician.

 

R for Everyone is the solution. Drawing on his extensive experience teaching new users through the New York City R User Group, professional statistician Jared Lander has written the perfect R tutorial for everyone who’s new to statistical programming and modeling. Offering extensive hands-on practice and sample code, Lander covers all this and more:

  • Downloading, installing, and getting started with R
  • Navigating and mastering the R environment
  • Learning basic techniques, from control statements to data manipulation
  • Importing data from SAS, SPSS, Stata, web sites, or elsewhere
  • Performing essential statistical tests
  • Building, comparing, and diagnosing models
  • Developing your own R packages
  • Connecting with and learning from the global R user community

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

spacer
spacer
  • back to top

FOLLOW US ON...

     
Powell's City of Books is an independent bookstore in Portland, Oregon, that fills a whole city block with more than a million new, used, and out of print books. Shop those shelves — plus literally millions more books, DVDs, and gifts — here at Powells.com.