Download ebooks google kindle R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics (English Edition) CHM FB2 ePub by JD Long, Paul Teetor

R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics. JD Long, Paul Teetor

R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics


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ISBN: 9781492040682 | 600 pages | 15 Mb

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  • R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics
  • JD Long, Paul Teetor
  • Page: 600
  • Format: pdf, ePub, fb2, mobi
  • ISBN: 9781492040682
  • Publisher: O'Reilly Media, Incorporated
Download R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics


Download ebooks google kindle R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics (English Edition) CHM FB2 ePub by JD Long, Paul Teetor

With more than 275 practical recipes, this expanded edition helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. These task-oriented recipes make you productive with R immediately. Solutions range from basic tasks to input and output, general statistics, graphics, and linear regression. Each recipe addresses a specific problem and includes a discussion that explains the solution and provides insight into how it works. If you’re a beginner, R Cookbook will help get you started. If you’re an intermediate user, this book will jog your memory and expand your horizons. You’ll get the job done faster and learn more about R in the process. Create vectors, handle variables, and perform other basic functions Simplify data input and output Tackle data structures such as matrices, lists, factors, and data frames Work with probability, probability distributions, and random variables Calculate statistics and confidence intervals and perform statistical tests Create a variety of graphic displays Build statistical models with linear regressions and analysis of variance (ANOVA) Explore advanced statistical techniques, such as finding clusters in your data

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