5

试图弄清楚这一点。假设你有一个 data.table：

dt <- data.table (person=c('bob', 'bob', 'bob'), 
                  door=c('front door', 'front door', 'front door'),
                  type=c('timeIn', 'timeIn', 'timeOut'),
                  time=c(
as.POSIXct('2016 12 02 06 05 01', format = '%Y %m %d %H %M %S'),
as.POSIXct('2016 12 02 06 05 02', format = '%Y %m %d %H %M %S'),
as.POSIXct('2016 12 02 06 05 03', format = '%Y %m %d %H %M %S')                     )
)

我想把它旋转成这样

person        door        timeIn             timeOut

bob           front door  min(<date/time>) max(<date/time>)

我似乎无法为 dcast.data.table 找到正确的语法。我试过

dcast.data.table(
  dt, person + door ~ type, 
  value.var = 'time', 
  fun.aggregate = function(x) ifelse(type == 'timeIn', min(x), max(x))
)

引发错误：

聚合函数应采用向量输入并返回单个值（长度=1）。

我也试过：

 dcast.data.table(dt, person + door ~ type, value.var = 'time')

但结果抛弃了我的约会

   person       door timeIn timeOut
1:    bob front door      2       1

任何建议，将不胜感激。TIA

4

2 回答 2

7

有几种方法可以使用dcast. jazzurro的解决方案在重塑结果之前进行聚合。这里的方法dcast直接使用，但可能需要一些后处理。我们正在使用jazzurro的数据，这些数据经过调整以符合UTC时区和 CRAN 版本 1.10.0 的data.table.

1.ifelse开始工作

正如 Q 中所报道的，

dcast(
  dt, person + door ~ type, 
  value.var = 'time', 
  fun.aggregate = function(x) ifelse(type == 'timeIn', min(x), max(x))
)

返回错误消息。错误消息的全文包括使用fill参数的提示。不幸ifelse()的是，它不尊重POSIXct类（有关详细信息，请参阅?ifelse），因此需要强制执行。

和

dcast(
  dt, person + door ~ type, 
  value.var = 'time', 
  fun.aggregate = function(x) 
    lubridate::as_datetime(ifelse(type == 'timeIn', min(x), max(x))),
  fill = 0
)

我们确实得到

#   person       door              timeIn             timeOut
#1:    ana front door 2016-12-02 07:06:01 2016-12-02 07:06:05
#2:    bob front door 2016-12-02 06:05:01 2016-12-02 06:05:05

2. 替代ifelse

ifelse的帮助页面建议

(tmp <- yes; tmp[!test] <- no[!test]; tmp)

作为替代。按照这个建议，

dcast(
  dt, person + door ~ type, 
  value.var = 'time', 
  fun.aggregate = function(x) {
    test <- type == "timeIn"; tmp <- min(x); tmp[!test] = max(x)[!test]; tmp
    }
)

返回

#   person       door              timeIn             timeOut
#1:    ana front door 2016-12-02 07:06:01 2016-12-02 07:06:05
#2:    bob front door 2016-12-02 06:05:01 2016-12-02 06:05:05

请注意，既不需要fill参数也不需要强制转换POSIXct。

3.使用增强dcast

使用最新版本，dcast.data.table我们可以提供以下功能列表fun.aggregate：

dcast(dt, person + door ~ type, value.var = 'time', fun = list(min, max))

返回

#   person       door     time_min_timeIn    time_min_timeOut     time_max_timeIn    time_max_timeOut
#1:    ana front door 2016-12-02 07:06:01 2016-12-02 07:06:03 2016-12-02 07:06:02 2016-12-02 07:06:05
#2:    bob front door 2016-12-02 06:05:01 2016-12-02 06:05:03 2016-12-02 06:05:02 2016-12-02 06:05:05

我们可以删除不需要的列并重命名其他列

dcast(dt, person + door ~ type, value.var = 'time', fun = list(min, max))[
  , .(person, door, timeIn = time_min_timeIn, timeOut = time_max_timeOut)]

这让我们

#   person       door              timeIn             timeOut
#1:    ana front door 2016-12-02 07:06:01 2016-12-02 07:06:05
#2:    bob front door 2016-12-02 06:05:01 2016-12-02 06:05:05

数据

如上所述，我们使用的是jazzurro的数据

dt <- structure(list(person = c("bob", "bob", "bob", "bob", "ana", 
"ana", "ana", "ana"), door = c("front door", "front door", "front door", 
"front door", "front door", "front door", "front door", "front door"
), type = c("timeIn", "timeIn", "timeOut", "timeOut", "timeIn", 
"timeIn", "timeOut", "timeOut"), time = structure(c(1480658701, 
1480658702, 1480658703, 1480658705, 1480662361, 1480662362, 1480662363, 
1480662365), class = c("POSIXct", "POSIXt"))), .Names = c("person", 
"door", "type", "time"), row.names = c(NA, -8L), class = c("data.table", 
"data.frame"))

但将时区强制为UTC.

和

dt[, time := lubridate::with_tz(time, "UTC")]

我们有

dt
#   person       door    type                time
#1:    bob front door  timeIn 2016-12-02 06:05:01
#2:    bob front door  timeIn 2016-12-02 06:05:02
#3:    bob front door timeOut 2016-12-02 06:05:03
#4:    bob front door timeOut 2016-12-02 06:05:05
#5:    ana front door  timeIn 2016-12-02 07:06:01
#6:    ana front door  timeIn 2016-12-02 07:06:02
#7:    ana front door timeOut 2016-12-02 07:06:03
#8:    ana front door timeOut 2016-12-02 07:06:05

独立于当地时区。

于 2016-12-24T08:31:35.243 回答
6

这将是实现目标的一种方式。我修改了您dt并创建了以下数据集。对于每个人，我寻找 的最小时间timeIn和最大时间timeOut。然后，我申请dcast()了结果。

#   person       door    type                time
#1:    bob front door  timeIn 2016-12-02 06:05:01
#2:    bob front door  timeIn 2016-12-02 06:05:02
#3:    bob front door timeOut 2016-12-02 06:05:03
#4:    bob front door timeOut 2016-12-02 06:05:05
#5:    ana front door  timeIn 2016-12-02 07:06:01
#6:    ana front door  timeIn 2016-12-02 07:06:02
#7:    ana front door timeOut 2016-12-02 07:06:03
#8:    ana front door timeOut 2016-12-02 07:06:05

library(data.table)

dcast(
   dt[, .SD[(type == "timeIn" & time == min(time))|(type == "timeOut" & time == max(time))], by = person],
   person + door ~ type)

#   person       door              timeIn             timeOut
#1:    ana front door 2016-12-02 07:06:01 2016-12-02 07:06:05
#2:    bob front door 2016-12-02 06:05:01 2016-12-02 06:05:05

数据

dt <- structure(list(person = c("bob", "bob", "bob", "bob", "ana", 
"ana", "ana", "ana"), door = c("front door", "front door", "front door", 
"front door", "front door", "front door", "front door", "front door"
), type = c("timeIn", "timeIn", "timeOut", "timeOut", "timeIn", 
"timeIn", "timeOut", "timeOut"), time = structure(c(1480658701, 
1480658702, 1480658703, 1480658705, 1480662361, 1480662362, 1480662363, 
1480662365), class = c("POSIXct", "POSIXt"))), .Names = c("person", 
"door", "type", "time"), row.names = c(NA, -8L), class = c("data.table", 
"data.frame"))
于 2016-12-23T23:47:37.610 回答