R - 基于一系列开始和结束日期复制行

Question

我有一个像这样的数据框"DF":

Flight.Start   Flight.End   Device      Partner   Creative   Days.in.Flight 
2015-08-31     2015-08-31   Standard    MSN       Video      35

我需要做的是"吹嘘",如下:

Flight.Start   Flight.End   Date         Device      Partner   Creative   Days.in.Flight 
2015-08-31     2015-10-04   2015-08-31   Standard    MSN       Video      35
2015-08-31     2015-10-04   2015-09-01   Standard    MSN       Video      35
2015-08-31     2015-10-04   2015-09-02   Standard    MSN       Video      35
2015-08-31     2015-10-04   2015-09-03   Standard    MSN       Video      35
2015-08-31     2015-10-04   2015-09-04   Standard    MSN       Video      35
2015-08-31     2015-10-04   2015-09-05   Standard    MSN       Video      35
2015-08-31     2015-10-04   2015-09-06   Standard    MSN       Video      35
2015-08-31     2015-10-04   2015-09-07   Standard    MSN       Video      35

ETC ......直到Date变量达到2015-10-04,然后继续下一个重复

基本上每一行都会被飞行中的天数重复- 1(因为已存在的行可以在该时间间隔内占一天,然后为该航班中的相关日期填写新列"日期"因此,如果一行的开始和结束日期分别为9/1和9/5,则将有4个重复行附加到已存在的行,将创建新列(日期),以及任何日期序列飞行开始和结束日期是原始行将填写列值.

所有日期值都格式化为日期,飞行天数为num,其余为因子.

编辑

响应重复的问题标记:

为了澄清,这不像被标记为重复的情况,因为我的问题并没有真正关注如何根据飞行天数复制(我已经知道如何做到了!),而是我怎么能那么将列添加到该输出数据框并在相应的航班期间内依次插入日期.谢谢你的抬头......

Answer 1

这是与splitstackshape和的一种方式dplyr.expandRows()从splitstackshape包中使用,您可以按照描述扩展数据框.然后,您要使用添加日期序列mutate().我所做的是通过组合Flight.Start和分组数据Flight.End,并seq()用于为每个组创建一个日期序列.first()走的第一个元素Flight.Start和Flight.End.通过这种方式,您可以创建所需的序列.我希望这能帮到您.

数据和代码

mydf <- data.frame(Flight.Start = as.Date(c("2015-09-01", "2015-09-10")),
                   Flight.End = as.Date(c("2015-09-03", "2015-09-15")),
                   Device = "Standard",
                   Creative = "Video",
                   Days.in.Flight = c(3, 6),
                   stringsAsFactors = FALSE)

#  Flight.Start Flight.End   Device Creative Days.in.Flight
#1   2015-09-01 2015-09-03 Standard    Video              3
#2   2015-09-10 2015-09-15 Standard    Video              6

library(splitstackshape)
library(dplyr)

expandRows(mydf, "Days.in.Flight", drop = FALSE) %>%
group_by(Flight.Start, Flight.End) %>%
mutate(Date = seq(first(Flight.Start),
                  first(Flight.End),
                  by = 1))

#  Flight.Start Flight.End   Device Creative Days.in.Flight       Date
#        (date)     (date)    (chr)    (chr)          (dbl)     (date)
#1   2015-09-01 2015-09-03 Standard    Video              3 2015-09-01
#2   2015-09-01 2015-09-03 Standard    Video              3 2015-09-02
#3   2015-09-01 2015-09-03 Standard    Video              3 2015-09-03
#4   2015-09-10 2015-09-15 Standard    Video              6 2015-09-10
#5   2015-09-10 2015-09-15 Standard    Video              6 2015-09-11
#6   2015-09-10 2015-09-15 Standard    Video              6 2015-09-12
#7   2015-09-10 2015-09-15 Standard    Video              6 2015-09-13
#8   2015-09-10 2015-09-15 Standard    Video              6 2015-09-14
#9   2015-09-10 2015-09-15 Standard    Video              6 2015-09-15

Answer 2

或者使用data.table,我们将'data.frame'转换为'data.table'(setDT(mydf)),通过'Days.in.Flight'复制行序列,基于该索引,我们对数据集(.SD[rep(...)进行子集化,按'Flight分组.Start'和'Flight.End',我们创建'Date'列.

library(data.table)
setDT(mydf)[, .SD[rep(1:.N, Days.in.Flight)]][, 
     Date:= seq(Flight.Start , Flight.End, by = '1 day'),
     by = .(Flight.Start, Flight.End)][]

Answer 3

这是使用基本 R 实现此目的的一种方法：

mydf <- data.frame(Flight.Start = as.Date(c("2015-09-01", "2015-09-10")),
                   Flight.End = as.Date(c("2015-09-03", "2015-09-15")),
                   Device = "Standard",
                   Creative = "Video",
                   Days.in.Flight = c(3, 6),
                   stringsAsFactors = FALSE)

expanded <-mydf[rep(row.names(mydf), mydf$ Days.in.Flight), ]
data.frame(expanded,Date=expanded$Flight.Start+(sequence(mydf$Days.in.Flight)-1))

> data.frame(expanded,Date=expanded$Flight.Start+(sequence(mydf$Days.in.Flight)-1))
    Flight.Start Flight.End   Device Creative Days.in.Flight       Date
1     2015-09-01 2015-09-03 Standard    Video              3 2015-09-01
1.1   2015-09-01 2015-09-03 Standard    Video              3 2015-09-02
1.2   2015-09-01 2015-09-03 Standard    Video              3 2015-09-03
2     2015-09-10 2015-09-15 Standard    Video              6 2015-09-10
2.1   2015-09-10 2015-09-15 Standard    Video              6 2015-09-11
2.2   2015-09-10 2015-09-15 Standard    Video              6 2015-09-12
2.3   2015-09-10 2015-09-15 Standard    Video              6 2015-09-13
2.4   2015-09-10 2015-09-15 Standard    Video              6 2015-09-14
2.5   2015-09-10 2015-09-15 Standard    Video              6 2015-09-15