R Data Analysis Cookbook and R Data Visualization Cookbook

1. How to do it...
   
2. Internally, R represents dates as the number of days from 1 January, 1970:
   
3. 
   
4. Get today's date:
   
5. > Sys.Date()
   
6. Create a date object from a string:
   
7. > # Supply year as two digits
   
8. > # Note correspondence between separators in the date string and the format string
   
9. > as.Date("1/1/80", format = "%m/%d/%y")
   
10. 
    
11. [1] "1980-01-01"
    
12. 
    
13. > # Supply year as 4 digits
    
14. > # Note uppercase Y below instead of lowercase y as above
    
15. > as.Date("1/1/1980", format = "%m/%d/%Y")
    
16. 
    
17. [1] "1980-01-01"
    
18. 
    
19. > # If you omit format string, you must give date as "yyyy/mm/dd" or as "yyyy-mm-dd"
    
20. > as.Date("1970/1/1")
    
21. 
    
22. [1] "1970-01-01"
    
23. 
    
24. > as.Date("70/1/1")
    
25. 
    
26. [1] "0070-01-01"
    
27. Use other options for separators (this example uses hyphens) in the format string, and also see the underlying numeric value:
    
28. > dt <- as.Date("1-1-70", format = "%m-%d-%y")
    
29. > as.numeric(dt)  
    
30. 
    
31. [1] 0
    
32. Explore other format string options:
    
33. > as.Date("Jan 15, 2015", format = "%b %d, %Y")
    
34. 
    
35. [1] "2015-01-15"
    
36. 
    
37. > as.Date("January 15, 15", format = "%B %d, %y")
    
38. 
    
39. [1] "2015-01-15"
    
40. Create dates from numbers by typecasting:
    
41. > dt <- 1000
    
42. > class(dt) <- "Date"
    
43. > dt                 # 1000 days from 1/1/70
    
44. 
    
45. [1] "1972-09-27"
    
46. 
    
47. > dt <- -1000
    
48. > class(dt) <- "Date"
    
49. > dt                 # 1000 days before 1/1/70
    
50. 
    
51. [1] "1967-04-07"
    
52. Create dates directly from numbers by setting the origin date:
    
53. > as.Date(1000, origin = as.Date("1980-03-31"))
    
54. 
    
55. [1] "1982-12-26"
    
56. 
    
57. > as.Date(-1000, origin = as.Date("1980-03-31"))
    
58. 
    
59. [1] "1977-07-05"
    
60. Examine date components:
    
61. > dt <- as.Date(1000, origin = as.Date("1980-03-31"))
    
62. > dt
    
63. 
    
64. [1] "1982-12-26"
    
65. 
    
66. > # Get year as four digits
    
67. > format(dt, "%Y")
    
68. 
    
69. [1] "1982"
    
70. 
    
71. > # Get the year as a number rather than as character string
    
72. > as.numeric(format(dt, "%Y"))
    
73. 
    
74. [1] 1982
    
75. 
    
76. > # Get year as two digits
    
77. > format(dt, "%y")
    
78. 
    
79. [1] "82"
    
80. 
    
81. > # Get month
    
82. > format(dt, "%m")
    
83. 
    
84. [1] "12"
    
85. 
    
86. > as.numeric(format(dt, "%m"))
    
87. 
    
88. [1] 12
    
89. 
    
90. > # Get month as string
    
91. > format(dt, "%b")
    
92. 
    
93. [1] "Dec"
    
94. 
    
95. > format(dt, "%B")
    
96. 
    
97. [1] "December"
    
98. 
    
99. > months(dt)
    
100. 
     
101. [1] "December"
     
102. 
     
103. > weekdays(dt)
     
104. 
     
105. [1] "Sunday"
     
106. 
     
107. > quarters(dt)
     
108. [1] "Q4"
     
109. 
     
110. > julian(dt)
     
111. 
     
112. [1] 4742
     
113. attr(,"origin")
     
114. [1] "1970-01-01"
     
115. 
     
116. > julian(dt, origin = as.Date("1980-03-31"))
     
117. 
     
118. [1] 1000
     
119. attr(,"origin")
     
120. [1] "1980-03-31"

复制代码

1. Operating on date objects
   
2. 
   
3. R supports many useful manipulations with date objects such as date addition and subtraction, and the creation of date sequences. This recipe shows many of these operations in action. For details on creating and examining date objects, see the previous recipe Creating and examining date objects, in this chapter.
   
4. 
   
5. Getting ready
   
6. The base R package provides date functionality, and you do not need any preparatory steps.
   
7. 
   
8. How to do it...
   
9. Perform the addition and subtraction of days from date objects:
   
10. > dt <- as.Date("1/1/2001", format = "%m/%d/%Y")
    
11. > dt
    
12. 
    
13. [1] "2001-01-01"
    
14. 
    
15. > dt + 100                 # Date 100 days from dt
    
16. 
    
17. [1] "2001-04-11"
    
18. 
    
19. > dt + 31
    
20. 
    
21. [1] "2001-02-01"
    
22. Subtract date objects to find the number of days between two dates:
    
23. > dt1 <- as.Date("1/1/2001", format = "%m/%d/%Y")
    
24. > dt2 <- as.Date("2/1/2001", format = "%m/%d/%Y")
    
25. > dt1-dt1
    
26. 
    
27. Time difference of 0 days
    
28. 
    
29. > dt2-dt1
    
30. 
    
31. Time difference of 31 days
    
32. 
    
33. > dt1-dt2
    
34. 
    
35. Time difference of -31 days
    
36. 
    
37. > as.numeric(dt2-dt1)
    
38. 
    
39. [1] 31
    
40. Compare date objects:
    
41. > dt2 > dt1
    
42. 
    
43. [1] TRUE
    
44. 
    
45. > dt2 == dt1
    
46. 
    
47. [1] FALSE
    
48. Create date sequences:
    
49. > d1 <- as.Date("1980/1/1")
    
50. > d2 <- as.Date("1982/1/1")
    
51. > # Specify start date, end date and interval
    
52. > seq(d1, d2, "month")
    
53. 
    
54. [1] "1980-01-01" "1980-02-01" "1980-03-01" "1980-04-01"
    
55. [5] "1980-05-01" "1980-06-01" "1980-07-01" "1980-08-01"
    
56. [9] "1980-09-01" "1980-10-01" "1980-11-01" "1980-12-01"
    
57. [13] "1981-01-01" "1981-02-01" "1981-03-01" "1981-04-01"
    
58. [17] "1981-05-01" "1981-06-01" "1981-07-01" "1981-08-01"
    
59. [21] "1981-09-01" "1981-10-01" "1981-11-01" "1981-12-01"
    
60. [25] "1982-01-01"
    
61. 
    
62. > d3 <- as.Date("1980/1/5")
    
63. > seq(d1, d3, "day")
    
64. 
    
65. [1] "1980-01-01" "1980-01-02" "1980-01-03" "1980-01-04"
    
66. [5] "1980-01-05"
    
67. 
    
68. > # more interval options
    
69. > seq(d1, d2, "2 months")
    
70. 
    
71. [1] "1980-01-01" "1980-03-01" "1980-05-01" "1980-07-01"
    
72. [5] "1980-09-01" "1980-11-01" "1981-01-01" "1981-03-01"
    
73. [9] "1981-05-01" "1981-07-01" "1981-09-01" "1981-11-01"
    
74. [13] "1982-01-01"
    
75. 
    
76. > # Specify start date, interval and sequence length
    
77. > seq(from = d1, by = "4 months", length.out = 4 )
    
78. 
    
79. [1] "1980-01-01" "1980-05-01" "1980-09-01" "1981-01-01"
    
80. Find a future or past date from a given date, based on an interval:
    
81. > seq(from = d1, by = "3 weeks", length.out = 2)[2]
    
82. 
    
83. [1] "1980-01-22"

复制代码

1. Performing preliminary analyses on time series data
   
2. 
   
3. Before creating proper time series objects, we may want to do some preliminary analyses. This recipe shows you how.
   
4. 
   
5. Getting ready
   
6. The base R package provides all the necessary functionality. If you have not already downloaded the data files for this chapter, please do it now and ensure that they are located in your R working directory.
   
7. 
   
8. How to do it...
   
9. Read the file. We will use a data file that has the share prices of Walmart (downloaded from Yahoo Finance) between March 11, 1999 and January 15, 2015:
   
10. > wm <- read.csv("walmart.csv")
    
11. View the data as a line chart:
    
12. > plot(wm$Adj.Close, type = "l")
    
13. 
    
14. How to do it...
    
15. Compute and plot daily price movements:
    
16. > d <- diff(wm$Adj.Close)
    
17. > plot(d, type = "l")
    
18. The plotted daily price movements appear as follows:
    
19. 
    
20. How to do it...
    
21. Generate a histogram of the daily price changes, along with a density plot:
    
22. > hist(d, prob = TRUE, ylim = c(0,0.8), main = "Walmart stock", col = "blue")
    
23. > lines(density(d), lwd = 3)
    
24. 
    
25. How to do it...
    
26. Compute one-period returns:
    
27. > wmm <- read.csv("walmart-monthly.csv")
    
28. > wmm.ts <- ts(wmm$Adj.Close)
    
29. > d <- diff(wmm.ts)
    
30. > wmm.return <- d/lag(wmm.ts, k=-1)
    
31. > hist(wmm.return, prob = TRUE, col = "blue")

复制代码