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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
• 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
• 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.
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
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
R for Everyone: Advanced Analytics and Graphics (Addison-Wesley Data and Analytics)
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Jared Lander
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464 pages
Addison-Wesley Professional -
English9780321888037
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"Synopsis"
by Pearson,
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
• 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 Pearson,
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.
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