问题描述
我有一个 excel 电子表格，其中包含第一行日期和随后的列，这些列指的是这些日期不同证券的价格.
I have a spreadsheet in excel which consists of first row of dates and then subsequent columns that refer to prices of different securities on those dates.
我将excel文件保存为csv，然后使用
I saved the excel file as a csv and then imported to excel using
prices=read.csv(file="C:/Documents and Settings/Hugh/My Documents/PhD/Option prices.csv",header = TRUE, sep = ",")
这将创建正确的时间序列数据
This creates the correct time series data
x<-ts(prices[,2])
但没有附上日期.
然而，日期指的是工作日.因此，尽管它们通常代表周一至周五，但由于假期等原因，情况并非总是如此.
However the dates refer to working days. So although in general they represent Monday-Friday this is not always the case because of holidays etc.
然后如何创建一个时间序列，其中从 csv 文件的第一列读取日期?我在 R 中找不到完成此操作的示例
How then can I create a time series where the dates are read in from the first column of the csv file? I can not find an example in R where this is done
推荐答案
由于你没有提供任何数据，这里是一个虚构的 data.frame:
As you didn't give any data, here is a made-up data.frame:
R> DF <- data.frame(date="2011-05-15", time=c("08:25:00", "08:45:00",
+ "09:05:11"), val=rnorm(3, 100, 5))
R> DF
date time val
1 2011-05-15 08:25:00 99.5926
2 2011-05-15 08:45:00 95.8724
3 2011-05-15 09:05:11 96.6436
R> DF <- within(DF, posix <- as.POSIXct(paste(date, time)))
R> DF
date time val posix
1 2011-05-15 08:25:00 99.5926 2011-05-15 08:25:00
2 2011-05-15 08:45:00 95.8724 2011-05-15 08:45:00
3 2011-05-15 09:05:11 96.6436 2011-05-15 09:05:11
R>
我使用了within()，您可以使用其他方式来分配新列.关键是 paste() 允许您组合列，您可以根据需要使用其他 R 函数来修改数据.
I used within(), you can use other means to in order to assign new columns. The key is that paste() allows you to combine columns, and you could use other R functions to modify the data as needed.
以合适的类型(如POSIXct)解析日期和时间的主要优点是其他函数可以使用它.这是动物园:
The key advantage of having dates and times parsed in a suitable type (like POSIXct) is that other functions can then use it. Here is zoo:
R> z <- with(DF, zoo(val, order.by=posix))
R> summary(z)
Index z
Min. :2011-05-15 08:25:00.00 Min. :95.9
1st Qu.:2011-05-15 08:35:00.00 1st Qu.:96.3
Median :2011-05-15 08:45:00.00 Median :96.6
Mean :2011-05-15 08:45:03.67 Mean :97.4
3rd Qu.:2011-05-15 08:55:05.50 3rd Qu.:98.1
Max. :2011-05-15 09:05:11.00 Max. :99.6
R>
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