Learning R: A Step-by-Step Function Guide to Data Analysis
ISBN-13:
9781449357108
ISBN-10:
1449357105
Edition:
1
Author:
Richard Cotton
Publication date:
2013
Publisher:
O'Reilly Media
Format:
Paperback
396 pages
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Book details
ISBN-13:
9781449357108
ISBN-10:
1449357105
Edition:
1
Author:
Richard Cotton
Publication date:
2013
Publisher:
O'Reilly Media
Format:
Paperback
396 pages
Summary
Learning R: A Step-by-Step Function Guide to Data Analysis (ISBN-13: 9781449357108 and ISBN-10: 1449357105), written by authors
Richard Cotton, was published by O'Reilly Media in 2013.
With an overall rating of 4.5 stars, it's a notable title among other
Data Modeling & Design
(Databases & Big Data, Data Mining, Data Processing, Microsoft Programming, Programming, Mathematical & Statistical, Software, Programming Languages, Bioinformatics, Biological Sciences) books. You can easily purchase or rent Learning R: A Step-by-Step Function Guide to Data Analysis (Paperback, Used) from BooksRun,
along with many other new and used
Data Modeling & Design
books
and textbooks.
And, if you're looking to sell your copy, our current buyback offer is $2.
Description
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
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