Question

将df作为：

  ID  Status     Created_Date  Booking_Date   Price_Booking
  1   Confirmed  "2013-03-01"  "2013-08-21"   400
  1   Confirmed  "2013-03-01"  "2013-10-01"   350
  2   Confirmed  "2013-04-11"  "2013-10-01"   299
  2   Confirmed  "2013-04-11"  "2013-10-01"   178
  3   Cancelled  "2013-02-21"  "2014-04-01"   99
  4   Confirmed  "2013-08-30"  "2013-10-01"   525
  5   Confirmed  "2014-01-01"  "2014-12-01"   439
  6   Confirmed  "2015-02-22"  "2015-11-18"   200
  6   Confirmed  "2015-07-13"  "2017-04-09"   100

想根据Created_Date变量计算第一年每位客户的收入。

我尝试过：

 with(df$ID[df$Status=="Confirmed" & format(as.Date(df$Created_Date), "%Y") == 2013 & format(as.Date(df$Booking_Date), "%Y") == 2013]))

但是，这仅计算每个日历年的收入，我希望相对于Created_Date

预期输出为：

   ID    Sum_Price_Booking
   1     750
   2     477
   3     NA
   4     525
   5     439
   6     200

Answer 1

对于group_by和ID之间的差异小于1年的那些值，我们可以sum Price_Booking和Booking_Date Created_Date个值

library(dplyr)
df %>%
  mutate_at(vars(ends_with("Date")), as.Date) %>%
  group_by(ID) %>%
  summarise(sum = sum(Price_Booking[Booking_Date - Created_Date < 365]))

#     ID   sum
#  <int> <int>
#1     1   750
#2     2   477
#3     3     0
#4     4   525
#5     5   439
#6     6   200

数据

df <- structure(list(ID = c(1L, 1L, 2L, 2L, 3L, 4L, 5L, 6L, 6L), 
Status = structure(c(2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L), 
.Label = c("Cancelled", "Confirmed"), class = "factor"), 
Created_Date = structure(c(2L, 2L, 3L, 3L, 1L, 4L, 5L, 6L, 7L), 
.Label = c("2013-02-21", "2013-03-01", "2013-04-11", "2013-08-30", "2014-01-01", 
"2015-02-22", "2015-07-13"), class = "factor"), Booking_Date = 
structure(c(1L, 2L, 2L, 2L, 3L, 2L, 4L, 5L, 6L), 
.Label = c("2013-08-21", "2013-10-01", "2014-04-01", "2014-12-01", "2015-11-18", 
"2017-04-09"), class = "factor"), Price_Booking = c(400L, 350L, 299L, 178L, 99L, 
525L, 439L,200L, 100L)), class = "data.frame", row.names = c(NA, -9L))

Answer 2

您可以使用data.table方式通过=选择聚合

UIView

有没有一种方法可以根据as.Date变量

2 个答案: