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• R Cookbook

• By: Paul Teetor

• Publisher: O'Reilly Media, Inc.

• Pub. Date: March 15, 2011

• Print ISBN-13: 978-0-596-80915-7

• Pages in Print Edition: 438

• Subscriber Rating: [6 Ratings] Subscriber Reviews

• 
  

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• R Cookbook.pdf

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关键词：Cookbook Book Cook COO Subscriber Media

1. Printing Something
   
2. 
   
3. Problem
   
4. You want to display the value of a variable or expression.
   
5. 
   
6. Solution
   
7. If you simply enter the variable name or expression at the command prompt, R will print its value. Use the print function for generic printing of any object. Use the cat function for producing custom formatted output

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1. Setting Variables
   
2. 
   
3. Problem
   
4. You want to save a value in a variable.
   
5. 
   
6. Solution
   
7. Use the assignment operator (<-). There is no need to declare your variable first:
   
8. 
   
9. > x <- 3

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1. Listing Variables
   
2. 
   
3. Problem
   
4. You want to know what variables and functions are defined in your workspace.
   
5. 
   
6. Solution
   
7. Use the ls function. Use ls.str for more details about each variable.
   
8. 
   
9. Discussion
   
10. The ls function displays the names of objects in your workspace:
    
11. 
    
12. > x <- 10
    
13. > y <- 50
    
14. > z <- c("three", "blind", "mice")
    
15. > f <- function(n,p) sqrt(p*(1-p)/n)
    
16. > ls()

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1. Deleting Variables
   
2. 
   
3. Problem
   
4. You want to remove unneeded variables or functions from your workspace or to erase its contents completely.
   
5. 
   
6. Solution
   
7. Use the rm function.
   
8. 
   
9. Discussion
   
10. Your workspace can get cluttered quickly. The rm function removes, permanently, one or more objects from the workspace:
    
11. 
    
12. > x <- 2*pi
    
13. > x
    
14. [1] 6.283185
    
15. > rm(x)
    
16. > x
    
17. Error: object "x" not found

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1. Creating a Vector
   
2. 
   
3. Problem
   
4. You want to create a vector.
   
5. 
   
6. Solution
   
7. Use the c(...) operator to construct a vector from given values.
   
8. 
   
9. Discussion
   
10. Vectors are a central component of R, not just another data structure. A vector can contain either numbers, strings, or logical values but not a mixture.
    
11. 
    
12. The c(...) operator can construct a vector from simple elements:
    
13. 
    
14. > c(1,1,2,3,5,8,13,21)
    
15. [1]  1  1  2  3  5  8 13 21
    
16. > c(1*pi, 2*pi, 3*pi, 4*pi)
    
17. [1]  3.141593  6.283185  9.424778 12.566371
    
18. > c("Everyone", "loves", "stats.")
    
19. [1] "Everyone" "loves"    "stats."
    
20. > c(TRUE,TRUE,FALSE,TRUE)
    
21. [1]  TRUE  TRUE FALSE  TRUE
    
22. If the arguments to c(...) are themselves vectors, it flattens them and combines them into one single vector:
    
23. 
    
24. > v1 <- c(1,2,3)
    
25. > v2 <- c(4,5,6)
    
26. > c(v1,v2)
    
27. [1] 1 2 3 4 5 6
    
28. Vectors cannot contain a mix of data types, such as numbers and strings. If you create a vector from mixed elements, R will try to accommodate you by converting one of them:
    
29. 
    
30. > v1 <- c(1,2,3)
    
31. > v3 <- c("A","B","C")
    
32. > c(v1,v3)

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1. Computing Basic Statistics
   
2. 
   
3. Problem
   
4. You want to calculate basic statistics: mean, median, standard deviation, variance, correlation, or covariance.
   
5. 
   
6. Solution
   
7. Use one of these functions as applies, assuming that x and y are vectors:
   
8. 
   
9. mean(x)
   
10. 
    
11. median(x)
    
12. 
    
13. sd(x)
    
14. 
    
15. var(x)
    
16. 
    
17. cor(x, y)
    
18. 
    
19. cov(x, y)
    
20. 
    
21. Discussion
    
22. When I first opened the documentation for R, I begin searching for material entitled “Procedures for Calculating Standard Deviation.” I figured that such an important topic would likely require a whole chapter.
    
23. 
    
24. It’s not that complicated.
    
25. 
    
26. Standard deviation and other basic statistics are calculated by simple functions. Ordinarily, the function argument is a vector of numbers and the function returns the calculated statistic:
    
27. 
    
28. > x <- c(0,1,1,2,3,5,8,13,21,34)
    
29. > mean(x)
    
30. [1] 8.8
    
31. > median(x)
    
32. [1] 4
    
33. > sd(x)
    
34. [1] 11.03328
    
35. > var(x)
    
36. [1] 121.7333
    
37. The sd function calculates the sample standard deviation, and var calculates the sample variance.
    
38. 
    
39. The cor and cov functions can calculate the correlation and covariance, respectively, between two vectors:
    
40. 
    
41. > x <- c(0,1,1,2,3,5,8,13,21,34)
    
42. > y <- log(x+1)
    
43. > cor(x,y)
    
44. [1] 0.9068053
    
45. > cov(x,y)
    
46. [1] 11.49988

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1. Creating Sequences
   
2. 
   
3. Problem
   
4. You want to create a sequence of numbers.
   
5. 
   
6. Solution
   
7. Use an n:m expression to create the simple sequence n, n+1, n+2, ..., m:
   
8. 
   
9. > 1:5
   
10. [1] 1 2 3 4 5
    
11. Use the seq function for sequences with an increment other than 1:
    
12. 
    
13. > seq(from=1, to=5, by=2)
    
14. [1] 1 3 5
    
15. Use the rep function to create a series of repeated values:
    
16. 
    
17. > rep(1, times=5)
    
18. [1] 1 1 1 1 1

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1. Comparing Vectors
   
2. 
   
3. Problem
   
4. You want to compare two vectors or you want to compare an entire vector against a scalar.
   
5. 
   
6. Solution
   
7. The comparison operators (==, !=, <, >, <=, >=) can perform an element-by-element comparison of two vectors. They can also compare a vector’s element against a scalar. The result is a vector of logical values in which each value is the result of one element-wise comparison.
   
8. 
   
9. Discussion
   
10. R has two logical values, TRUE and FALSE. These are often called Boolean values in other programming languages.
    
11. 
    
12. The comparison operators compare two values and return TRUE or FALSE, depending upon the result of the comparison:
    
13. 
    
14. > a <- 3
    
15. > a == pi     # Test for equality
    
16. [1] FALSE
    
17. > a != pi     # Test for inequality
    
18. [1] TRUE
    
19. > a < pi
    
20. [1] TRUE
    
21. > a > pi
    
22. [1] FALSE
    
23. > a <= pi
    
24. [1] TRUE
    
25. > a >= pi
    
26. [1] FALSE

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1. Selecting Vector Elements
   
2. 
   
3. Problem
   
4. You want to extract one or more elements from a vector.
   
5. 
   
6. Solution
   
7. Select the indexing technique appropriate for your problem:
   
8. 
   
9. Use square brackets to select vector elements by their position, such as v[3] for the third element of v.
   
10. 
    
11. Use negative indexes to exclude elements.
    
12. 
    
13. Use a vector of indexes to select multiple values.
    
14. 
    
15. Use a logical vector to select elements based on a condition.
    
16. 
    
17. Use names to access named elements.

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