R Cookbook

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R Cookbook By Paul Teetor
Publisher: O'Reilly Media
Final Release Date: March 2011
Pages: 438

With more than 200 practical recipes, this book 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. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.

Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you’re a beginner, R Cookbook will help get you started. If you’re an experienced data programmer, it 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
• Input and output data
• 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
  

"Wonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language—one practical example at a time."—Jeffrey Ryan, software consultant and R package author

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关键词：Cookbook Book Cook COO distribution everything collection difficult practical structure

1. 3.2. Saving Your Workspace
   
2. 
   
3. Problem
   
4. You want to save your workspace without exiting from R.
   
5. 
   
6. Solution
   
7. Call the save.image function:
   
8. 
   
9. > save.image()

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1. 3.3. Viewing Your Command History
   
2. 
   
3. Problem
   
4. You want to see your recent sequence of commands.
   
5. 
   
6. Solution
   
7. Scroll backward by pressing the up arrow or Ctrl-P. Or use the history function to view your most recent input:
   
8. 
   
9. > history()
   
10. Discussion
    
11. The history function will display your most recent commands. By default it shows the most recent 25 lines, but you can request more:
    
12. 
    
13. > history(100)          # Show 100 most recent lines of history
    
14. > history(Inf)          # Show entire saved history

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1. 3.4. Saving the Result of the Previous Command
   
2. 
   
3. Problem
   
4. You typed an expression into R that calculated the value, but you forgot to save the result in a variable.
   
5. 
   
6. Solution
   
7. A special variable called .Last.value saves the value of the most recently evaluated expression. Save it to a variable before you type anything else.
   
8. 
   
9. Discussion
   
10. It is frustrating to type a long expression or call a long-running function but then forget to save the result. Fortunately, you needn’t retype the expression nor invoke the function again—the result was saved in the .Last.value variable:
    
11. 
    
12. > aVeryLongRunningFunction()     # Oops! Forgot to save the result!
    
13. [1] 147.6549
    
14. > x <- .Last.value               # Capture the result now
    
15. > x

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1. 3.5. Displaying the Search Path
   
2. 
   
3. Problem
   
4. You want to see the list of packages currently loaded into R.
   
5. 
   
6. Solution
   
7. Use the search function with no arguments:
   
8. 
   
9. > search()
   
10. Discussion
    
11. The search path is a list of packages that are currently loaded into memory and available for use. Although many packages may be installed on your computer, only a few of them are actually loaded into the R interpreter at any given moment. You might be wondering which packages are loaded right now.
    
12. 
    
13. With no arguments, the search function returns the list of loaded packages. It produces an output like this:
    
14. 
    
15. > search()

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1. 3.6. Accessing the Functions in a Package
   
2. 
   
3. Problem
   
4. A package installed on your computer is either a standard package or a package downloaded by you. When you try using functions in the package, however, R cannot find them.
   
5. 
   
6. Solution
   
7. Use either the library function or the require function to load the package into R:
   
8. 
   
9. > library(packagename)

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1. 3.7. Accessing Built-in Datasets
   
2. 
   
3. Problem
   
4. You want to use one of R’s built-in datasets.
   
5. 
   
6. Solution
   
7. The standard datasets distributed with R are already available to you, since the datasets package is in your search path.
   
8. 
   
9. To access datasets in other packages, use the data function while giving the dataset name and package name:
   
10. 
    
11. > data(dsname, package="pkgname")

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1. 3.8. Viewing the List of Installed Packages
   
2. 
   
3. Problem
   
4. You want to know what packages are installed on your machine.
   
5. 
   
6. Solution
   
7. Use the library function with no arguments for a basic list. Use installed.packages to see more detailed information about the packages.

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1. 3.9. Installing Packages from CRAN
   
2. 
   
3. Problem
   
4. You found a package on CRAN, and now you want to install it on your computer.
   
5. 
   
6. Solution
   
7. Command line
   
8. Use the install.packages function, putting the name of the package in quotes:
   
9. 
   
10. > install.packages("packagename")

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