Paperback. 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. Organised 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, manipulation, and visualisation; 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. After all this youll make your code reproducible with LaTeX, RMarkdown, and Shiny. 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 Explore R, RStudio, and R packages Use R for math: variable types, vectors, calling functions, and more Exploit data structures, including data.frames, matrices, and lists Read many different types of data Create attractive, intuitive statistical graphics Write user-defined functions Control program flow with if, ifelse, and complex checks Improve program efficiency with group manipulations Combine and reshape multiple datasets Manipulate strings using Rs facilities and regular expressions Create normal, binomial, and Poisson probability distributions Build linear, generalised linear, and nonlinear models Program basic statistics: mean, standard deviation, and t-tests Train machine learning models Assess the quality of models and variable selection Prevent overfitting and perform variable selection, using the Elastic Net and Bayesian methods Analyse univariate and multivariate time series data Group data via K-means and hierarchical clustering Prepare reports, slideshows, and web pages with knitr Display interactive data with RMarkdown and htmlwidgets Implement dashboards with Shiny Build reusable R packages with devtools and Rcpp Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Detalles bibliogr�ficos

T�tulo: R for Everyone (Paperback)
Editor: Pearson Education (US), Boston
Fecha de publicaci�n: 2017
Idioma: Ingl�s
ISBN 10: 013454692X
ISBN 13: 9780134546926
Encuadernaci�n: Paperback
Edici�n: 2� Edici�n
Estado: new

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Sinopsis

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

Acerca del autor

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.

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