R 中的 read.csv 阅读日期不同

Question

我有两个非常相似的 csv 文件。从相同来源以相同格式下载的 2 种不同股票的股票价格。然而，R 中的 read.csv 读取它们的方式不同。

> 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

不确定如何在不附加 .csv 文件的情况下重现此问题。我附上了这两个文件的快照。任何帮助将不胜感激。

谢谢

Answer 1

这可以通过读入日期作为字符向量然后调用 strptime() 来解决。里面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。