0

我有这个数据框：

            DATE    pc       SERVER
1  2013-02-16 01:00:00  3.83 server1
2  2013-02-16 02:00:00  3.45 server1
3  2013-02-16 03:00:00  3.34 server1
4  2013-02-16 04:00:00  3.73 server1
5  2013-02-16 05:00:00  3.16 server1
6  2013-02-16 06:00:00  3.16 server1
7  2013-02-16 01:00:00 4.74 server2
8  2013-02-16 02:00:00 5.70 server2
9  2013-02-16 03:00:00  8.54 server2
10 2013-02-16 04:00:00  9.25 server2
11 2013-02-16 05:00:00 10.12 server2
12 2013-02-16 06:00:00 10.15 server2

在 SERVER 列上有 8 个服务器。我需要按日期对每个服务器进行分组。例如，

这就是我需要这个 df 来查看的内容；

 DATE               server1   server2
 2013-02-16 01:00:00  3.83     4.74
 2013-02-16 02:00:00  3.45     5.50
 2013-02-16 03:00:00  3.34     8.54
 2013-02-16 04:00:00  3.73     9.25

ETC

我将如何做到这一点，重新组织我的数据框

4

2 回答 2

3

这是一个非常基本的reshape问题。假设你data.frame被称为“mydf”：

> reshape(mydf, direction = "wide", idvar="DATE", timevar="SERVER")
                 DATE pc.server1 pc.server2
1 2013-02-16 01:00:00       3.83       4.74
2 2013-02-16 02:00:00       3.45       5.70
3 2013-02-16 03:00:00       3.34       8.54
4 2013-02-16 04:00:00       3.73       9.25
5 2013-02-16 05:00:00       3.16      10.12
6 2013-02-16 06:00:00       3.16      10.15

或者，使用“reshape2”包：

> library(reshape2)
> dcast(mydf, DATE ~ SERVER, value.var="pc")
                 DATE server1 server2
1 2013-02-16 01:00:00    3.83    4.74
2 2013-02-16 02:00:00    3.45    5.70
3 2013-02-16 03:00:00    3.34    8.54
4 2013-02-16 04:00:00    3.73    9.25
5 2013-02-16 05:00:00    3.16   10.12
6 2013-02-16 06:00:00    3.16   10.15

如果您有重复的“DATE”和“SERVER”组合，那么您需要在数据中添加一个辅助“ID”变量。

以下是一些示例数据（请在以后以这种形式分享您的数据）：

mydf <- structure(list(DATE = c("2013-02-16 01:00:00", "2013-02-16 02:00:00", 
    "2013-02-16 03:00:00", "2013-02-16 04:00:00", "2013-02-16 05:00:00", 
    "2013-02-16 06:00:00", "2013-02-16 01:00:00", "2013-02-16 02:00:00", 
    "2013-02-16 03:00:00", "2013-02-16 04:00:00", "2013-02-16 05:00:00", 
    "2013-02-16 06:00:00", "2013-02-16 01:00:00"), pc = c(3.83, 3.45, 
    3.34, 3.73, 3.16, 3.16, 4.74, 5.7, 8.54, 9.25, 10.12, 10.15, 
    5.83), SERVER = c("server1", "server1", "server1", "server1", 
    "server1", "server1", "server2", "server2", "server2", "server2", 
    "server2", "server2", "server1")), .Names = c("DATE", "pc", "SERVER"
    ), row.names = c("1", "2", "3", "4", "5", "6", "7", "8", "9", 
    "10", "11", "12", "13"), class = "data.frame")
mydf
#                   DATE    pc  SERVER
# 1  2013-02-16 01:00:00  3.83 server1
# 2  2013-02-16 02:00:00  3.45 server1
# 3  2013-02-16 03:00:00  3.34 server1
# 4  2013-02-16 04:00:00  3.73 server1
# 5  2013-02-16 05:00:00  3.16 server1
# 6  2013-02-16 06:00:00  3.16 server1
# 7  2013-02-16 01:00:00  4.74 server2
# 8  2013-02-16 02:00:00  5.70 server2
# 9  2013-02-16 03:00:00  8.54 server2
# 10 2013-02-16 04:00:00  9.25 server2
# 11 2013-02-16 05:00:00 10.12 server2
# 12 2013-02-16 06:00:00 10.15 server2
# 13 2013-02-16 01:00:00  5.83 server1

请注意，由于第 1 行和第 13 行中重复的“DATE”+“SERVER”组合，我们将无法在reshape没有收到您提到的警告的情况下使用。解决方案：添加辅助 ID：

mydf$ID <- ave(as.character(mydf$DATE), mydf$DATE, mydf$SERVER, FUN = seq_along)
reshape(mydf, direction = "wide", idvar=c("DATE", "ID"), timevar="SERVER")
#                   DATE ID pc.server1 pc.server2
# 1  2013-02-16 01:00:00  1       3.83       4.74
# 2  2013-02-16 02:00:00  1       3.45       5.70
# 3  2013-02-16 03:00:00  1       3.34       8.54
# 4  2013-02-16 04:00:00  1       3.73       9.25
# 5  2013-02-16 05:00:00  1       3.16      10.12
# 6  2013-02-16 06:00:00  1       3.16      10.15
# 13 2013-02-16 01:00:00  2       5.83         NA
于 2013-08-20T16:42:43.680 回答
0

使用该reshape软件包，您可以简单地做到这一点。考虑数据框df：

 df = data.frame(DATE = c("2013-02-16", "2013-02-17", "2013-02-18", "2013-02-16", "2013-02-17", "2013-02-18"), SERVER = c("server1","server1","server1","server2","server2","server2"), pc = c(1,2,3,4,5,6))

 cast(df, DATE ~ SERVER, value = 'pc', mean)

你得到：

        DATE server1 server2
1 2013-02-16       1       4
2 2013-02-17       2       5
3 2013-02-18       3       6
于 2013-08-20T16:45:28.577 回答