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R for Everyone

Advanced Analytics and Graphics

Paperback Engels 2017 9780134546926
Verkooppositie 5415
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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, Second Edition, 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, manipulation, and visualization; 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.


Aantal pagina's:532
Hoofdrubriek:IT-management / ICT


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About the Author

1. Getting R
2. The R Environment
3. R Packages
4. Basics of R
5. Advanced Data Structures
6. Reading Data into R
7. Statistical Graphics
8. Writing R Functions
9. Control Statements
10. Loops, the Un-R Way to Iterate
11. Group Manipulation
12. Faster Group Manipulation with dplyr
13. Iterating with purrr
14. Data Reshaping
15. Reshaping Data in the Tidyverse
16. Manipulating Strings
17. Probability Distributions
18. Basic Statistics
19. Linear Models
20. Generalized Linear Models
21. Model Diagnostics
22. Regularization and Shrinkage
23. Nonlinear Models
24. Time Series and Autocorrelation
25. Clustering
26. Model Fitting with Caret
27. Reproduciblility and Reports with knitr
28. Rich Documents with RMarkdown
29. Interactive Dashboards with Shiny
30. Building R Packages

Appendix A: Real-Life Resources
Appendix B: Glossary

List of Figures
List of Tables
General Index
Index of Functions
Index of Packages
Index of People
Data Index

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