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我有两个非常相似的 csv 文件。从同一来源以相同格式下载的 2 只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，则第二个表的日期读取不正确。

> 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

不知道如何在不附加 .csv 文件的情况下使这个问题重现。我附上了这两个文件的快照。任何帮助将不胜感激。选项卡1 选项卡2

谢谢

4

1 回答 1

2

这可以通过将日期作为字符向量读取然后调用strptime()inside来解决transform()：

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：您可以尝试使用您自己的假设动态“检测”日期格式，但这只会与您的假设一样可靠：

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日期格式，因此第一个日期值 ( tab$Date[1]) 可用于检测。

于 2015-04-28T19:59:08.653 回答