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Viser: Learning R
Learning R Vital Source e-bog
Richard Cotton
(2013)
Learning R Vital Source e-bog
Richard Cotton
(2013)
Learning R
Richard Cotton
(2013)
Sprog: Engelsk
om ca. 10 hverdage
Detaljer om varen
- 1. Udgave
- Vital Source searchable e-book (Reflowable pages): 400 sider
- Udgiver: O'Reilly Media, Inc (September 2013)
- ISBN: 9781449357184
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: ubegrænset dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: -1 sider kan printes ad gangen
Copy: højest -1 sider i alt kan kopieres (copy/paste)
Detaljer om varen
- 1. Udgave
- Vital Source searchable e-book (Fixed pages): 400 sider
- Udgiver: O'Reilly Media, Inc (September 2013)
- ISBN: 9781449357191
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: ubegrænset dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 10 sider kan printes ad gangen
Copy: højest 10 sider i alt kan kopieres (copy/paste)
Detaljer om varen
- Paperback: 400 sider
- Udgiver: O'Reilly Media, Incorporated (September 2013)
- ISBN: 9781449357108
Learn how to perform data analysis with the R language and software environment, even if you have little or no programming experience. With the tutorials in this hands-on guide, you'll learn how to use the essential R tools you need to know to analyze data, including data types and programming concepts.
The second half of Learning R shows you real data analysis in action by covering everything from importing data to publishing your results. Each chapter in the book includes a quiz on what you've learned, and concludes with exercises, most of which involve writing R code.
- Write a simple R program, and discover what the language can do
- Use data types such as vectors, arrays, lists, data frames, and strings
- Execute code conditionally or repeatedly with branches and loops
- Apply R add-on packages, and package your own work for others
- Learn how to clean data you import from a variety of sources
- Understand data through visualization and summary statistics
- Use statistical models to pass quantitative judgments about data and make predictions
- Learn what to do when things go wrong while writing data analysis code
Chapter 1: Introduction
Chapter 2: A Scientific Calculator
Chapter 3: Inspecting Variables and Your Workspace
Chapter 4: Vectors, Matrices, and Arrays
Chapter 5: Lists and Data Frames
Chapter 6: Environments and Functions
Chapter 7: Strings and Factors
Chapter 8: Flow Control and Loops
Chapter 9: Advanced Looping
Chapter 10: Packages
Chapter 11: Dates and Times The Data Analysis Workflow
Chapter 12: Getting Data
Chapter 13: Cleaning and Transforming
Chapter 14: Exploring and Visualizing
Chapter 15: Distributions and Modeling
Chapter 16: Programming
Chapter 17: Making Packages Appendixes Bibliography Index Colophon