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Details of R for Everyone: Advanced Analytics and Graphics

R for Everyone: Advanced Analytics and Graphics
Exact title of book
R for Everyone: Advanced Analytics and Graphics
Book author
Jared Lander
ISBN
9780321888037
Published
Jan 02, 2014
Language
English
Format
PDF, FB2, EPUB, MOBI
Pages
464
File size (in PDF)
4176 kB

Some brief overview of book

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 youll need to accomplish 80 percent of modern data tasks.

Landers self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. Youll 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, youll construct several complete models, both linear and nonlinear, and use some data mining techniques.

By the time youre done, you wont just know how to write R programs, youll 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 Rs 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

About book author

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 Scotts 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 inder 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 youll need to accomplish 80 percent of modern data tasks. Landers self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. Youll 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, youll construct several complete models, both linear and nonlinear, and use some data mining techniques. By the time youre done, you wont just know how to write R programs, youll 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 Rs 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

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