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Viser: R Cookbook - Proven Recipes for Data Analysis, Statistics, and Graphics
R Cookbook Vital Source e-bog
Jd Long og Paul Teetor
(2019)
R Cookbook
Proven Recipes for Data Analysis, Statistics, and Graphics
J. D. Long og Paul Teetor
(2019)
Sprog: Engelsk
om ca. 10 hverdage
Detaljer om varen
- 2. Udgave
- Vital Source searchable e-book (Reflowable pages)
- Udgiver: O'Reilly Media, Inc (Juni 2019)
- Forfattere: Jd Long og Paul Teetor
- ISBN: 9781492040637
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: ubegrænset dage fra købsdato.
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Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)
Detaljer om varen
- Paperback: 598 sider
- Udgiver: O'Reilly Media, Incorporated (Juli 2019)
- Forfattere: J. D. Long og Paul Teetor
- ISBN: 9781492040682
Perform data analysis with R quickly and efficiently with more than 275 practical recipes in this expanded second edition. 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 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