我有两个非常相似的csv文件。以相同格式从同一来源下载的2种不同股票的股票价格。但是,R中的read.csv以不同的方式读取它们。
> tab1=read.csv(path1)
> tab2=read.csv(path2)
> head(tab1)
Date Open High Low Close Volume Adj.Close
1 2014-12-01 158.35 162.92 157.12 157.12 2719100 156.1488
2 2014-11-03 153.14 160.86 152.98 160.09 2243400 159.1004
3 2014-10-01 141.16 154.44 130.60 153.77 3825900 152.0036
4 2014-09-02 143.30 147.87 140.66 141.68 2592900 140.0525
5 2014-08-01 140.15 145.39 138.43 144.00 2027100 142.3459
6 2014-07-01 143.41 146.43 140.60 140.89 2131100 138.4461
> head(tab2)
Date Open High Low Close Volume Adj.Close
1 12/1/2014 73.39 75.20 71.75 72.29 1561400 71.92211
2 11/3/2014 69.28 74.92 67.88 73.74 1421600 72.97650
3 10/1/2014 66.18 74.95 63.42 69.21 1775400 68.49341
4 9/2/2014 68.34 68.57 65.49 66.32 1249200 65.63333
5 8/1/2014 67.45 68.99 65.88 68.26 1655400 67.20743
6 7/1/2014 64.07 69.50 63.09 67.46 1733600 66.41976
如果我尝试在read.csv中使用colClasses,那么第二个表的日期将被错误地读取。
> tab1=read.csv(path1,colClasses=c("Date",rep("numeric",6)))
> tab2=read.csv(path2,colClasses=c("Date",rep("numeric",6)))
> head(tab1)
Date Open High Low Close Volume Adj.Close
1 2014-12-01 158.35 162.92 157.12 157.12 2719100 156.1488
2 2014-11-03 153.14 160.86 152.98 160.09 2243400 159.1004
3 2014-10-01 141.16 154.44 130.60 153.77 3825900 152.0036
4 2014-09-02 143.30 147.87 140.66 141.68 2592900 140.0525
5 2014-08-01 140.15 145.39 138.43 144.00 2027100 142.3459
6 2014-07-01 143.41 146.43 140.60 140.89 2131100 138.4461
> head(tab2)
Date Open High Low Close Volume Adj.Close
1 0012-01-20 73.39 75.20 71.75 72.29 1561400 71.92211
2 0011-03-20 69.28 74.92 67.88 73.74 1421600 72.97650
3 0010-01-20 66.18 74.95 63.42 69.21 1775400 68.49341
4 0009-02-20 68.34 68.57 65.49 66.32 1249200 65.63333
5 0008-01-20 67.45 68.99 65.88 68.26 1655400 67.20743
6 0007-01-20 64.07 69.50 63.09 67.46 1733600 66.41976
如果不附加.csv文件,我不知道如何才能使此问题重现。我附上了这两个文件的快照。任何帮助将不胜感激。
由于
答案 0 :(得分:1)
这可以通过将日期作为字符向量读取,然后在strptime()
内调用transform()
来解决:
transform(read.csv(path2,colClasses=c('character',rep('numeric',6))),Date=as.Date(strptime(Date,'%m/%d/%Y')));
## Date Open High Low Close Volume Adj.Close
## 1 2014-12-01 73.39 75.20 71.75 72.29 1561400 71.92211
## 2 2014-11-03 69.28 74.92 67.88 73.74 1421600 72.97650
## 3 2014-10-01 66.18 74.95 63.42 69.21 1775400 68.49341
## 4 2014-09-02 68.34 68.57 65.49 66.32 1249200 65.63333
## 5 2014-08-01 67.45 68.99 65.88 68.26 1655400 67.20743
## 6 2014-07-01 64.07 69.50 63.09 67.46 1733600 66.41976
编辑:您可以尝试使用自己的假设动态“检测”日期格式,但这只会像您的假设一样可靠:
readStockData <- function(path) {
tab <- read.csv(path,colClasses=c('character',rep('numeric',6)));
tab$Date <- as.Date(tab$Date,if (grepl('^\\d+/\\d+/\\d+$',tab$Date[1])) '%m/%d/%Y' else '%Y-%m-%d');
tab;
};
readStockData(path1);
## Date Open High Low Close Volume Adj.Close
## 1 2014-12-01 158.35 162.92 157.12 157.12 2719100 156.1488
## 2 2014-11-03 153.14 160.86 152.98 160.09 2243400 159.1004
## 3 2014-10-01 141.16 154.44 130.60 153.77 3825900 152.0036
## 4 2014-09-02 143.30 147.87 140.66 141.68 2592900 140.0525
## 5 2014-08-01 140.15 145.39 138.43 144.00 2027100 142.3459
## 6 2014-07-01 143.41 146.43 140.60 140.89 2131100 138.4461
readStockData(path2);
## Date Open High Low Close Volume Adj.Close
## 1 2014-12-01 73.39 75.20 71.75 72.29 1561400 71.92211
## 2 2014-11-03 69.28 74.92 67.88 73.74 1421600 72.97650
## 3 2014-10-01 66.18 74.95 63.42 69.21 1775400 68.49341
## 4 2014-09-02 68.34 68.57 65.49 66.32 1249200 65.63333
## 5 2014-08-01 67.45 68.99 65.88 68.26 1655400 67.20743
## 6 2014-07-01 64.07 69.50 63.09 67.46 1733600 66.41976
在上面我假设文件中至少有一条记录,并且所有记录都使用相同的Date格式,因此第一个Date值(tab$Date[1]
)可用于检测