Question

我目前正在处理以下数据结构：

属性df：

  ID Begin_A      End_A        Interval                          Value
1  5 1990-03-01   2017-03-10   1990-03-01 UTC--2017-03-10 UTC    Cat1
2 10 1993-12-01   2017-12-02   1993-12-01 UTC--2017-12-02 UTC    Cat2
3  5 1991-03-01   2017-03-03   1991-03-01 UTC--2017-03-03 UTC    Cat3
4 10 1995-12-05   2017-12-10   1995-12-05 UTC--2017-12-10 UTC    Cat4

预订df：

  ID    Begin_A      End_A                       Interval
1  5 2017-03-03 2017-03-05 2017-03-03 UTC--2017-03-05 UTC
2  6 2017-05-03 2017-05-05 2017-05-03 UTC--2017-05-05 UTC
3  8 2017-03-03 2017-03-05 2017-03-03 UTC--2017-03-05 UTC
4 10 2017-12-05 2017-12-06 2017-12-05 UTC--2017-12-06 UTC

正如以下帖子中提到的：Matching values conditioned on overlapping Intervals and ID，我打算进行以下数据重组：从预订中获取ID，过滤属性ID与预订ID匹配的属性数据框的所有行。检查具有匹配属性ID的哪些行也具有重叠的时间间隔（来自lubridate的int_overlaps）。然后从“值”列中获取相应的值，并在Attribute_value列中打印每个值。

预期结果如下：

ID  Begin_A      End_A        Interval                        Attribute_value
5   2017-03-03   2017-03-05   2017-03-03 UTC--2017-03-05 UTC  Cat1,Cat3
6   2017-05-03   2017-05-05   2017-05-03 UTC--2017-05-05 UTC  NA
8   2017-03-03   2017-03-05   2017-03-03 UTC--2017-03-05 UTC  NA
10  2017-12-05   2017-12-06   2017-12-05 UTC--2017-12-06 UTC  Cat4

ycw已经在这里提供了对这个问题的部分答案：（https://stackoverflow.com/a/46819541/8259308）。此解决方案不允许在属性数据框中的Begin_A和End_A之间存在长时间段，因为使用此命令创建具有单独日期的向量：

complete(Date = full_seq(Date, period = 1), ID) %>%

由于我的原始数据集在属性数据框中具有非常大量的具有长时间帧的观察，因此R不能处理这些大量观察。我的想法是修改上面提到的行以减少日期跳转到几个月（这也会降低精度）或尝试新的方法。以下代码生成上面显示的数据框：

library(lubridate)
library(tidyverse)
# Attributes data frame:
date1 <- as.Date(c('1990-3-1','1993-12-1','1991-3-1','1995-12-5'))
date2 <- as.Date(c('2017-3-10','2017-12-2','2017-3-3','2017-12-10'))
attributes <- data.frame(matrix(NA,nrow=4, ncol = 5)) 
names(attributes) <- c("ID","Begin_A", "End_A", "Interval", "Value")
attributes$ID <- as.numeric(c(5,10,5,10))
attributes$Begin_A <-date1
attributes$End_A <-date2
attributes$Interval <-attributes$Begin_A %--% attributes$End_A
attributes$Value<- as.character(c("Cat1","Cat2","Cat3","Cat4"))

### Bookings data frame:

date1 <- as.Date(c('2017-3-3','2017-5-3','2017-3-3','2017-12-5'))
date2 <- as.Date(c('2017-3-5','2017-5-5','2017-3-5','2017-12-6'))
bookings <- data.frame(matrix(NA,nrow=4, ncol = 4)) 
names(bookings) <- c("ID","Begin_A", "End_A", "Interval")
bookings$ID <- as.numeric(c(5,6,8,10))
bookings$Begin_A <-date1
bookings$End_A <-date2
bookings$Interval <-bookings$Begin_A %--% bookings$End_A

这是ycw提供的上一篇文章的解决方案：

library(tidyverse)

attributes2 <- attributes %>%
  select(-Interval) %>%
  gather(Type, Date, ends_with("_A")) %>%
  select(-Type) %>%
  group_by(Value) %>%
  complete(Date = full_seq(Date, period = 1), ID) %>%
  ungroup()

bookings2 <- bookings %>%
  select(-Interval) %>%
  gather(Type, Date, ends_with("_A")) %>%
  select(-Type) %>%
  group_by(ID) %>%
  complete(Date = full_seq(Date, period = 1)) %>%
  ungroup()

bookings3 <- bookings2 %>%
  left_join(attributes2, by = c("ID", "Date")) %>%
  group_by(ID) %>%
  summarise(Attribute_value = toString(sort(unique(Value)))) %>%
  mutate(Attribute_value = ifelse(Attribute_value %in% "", NA, Attribute_value))

bookings4 <- bookings %>% left_join(bookings3, by = "ID")
bookings4
  ID    Begin_A      End_A                       Interval Attribute_value
1  5 2017-03-03 2017-03-05 2017-03-03 UTC--2017-03-05 UTC      Cat1, Cat3
2  6 2017-05-03 2017-05-05 2017-05-03 UTC--2017-05-05 UTC            <NA>
3  8 2017-03-03 2017-03-05 2017-03-03 UTC--2017-03-05 UTC            <NA>
4 10 2017-12-05 2017-12-06 2017-12-05 UTC--2017-12-06 UTC            Cat4

Answer 1

您可以考虑允许“非等联接”的data.table，即基于>=，>，<=和<的联接。在同一调用中，可以对LHS数据集中的组执行聚合操作，RHS数据集中的每一行（i）匹配（by = .EACHI）。

d1[d2, on = .(id = id, end >= begin),
         .(i.begin, i.end, val_str = toString(val)), by = .EACHI]

#    id        end    i.begin      i.end    val_str
# 1:  5 2017-03-03 2017-03-03 2017-03-05 Cat3, Cat1
# 2:  6 2017-05-03 2017-05-03 2017-05-05         NA
# 3:  8 2017-03-03 2017-03-03 2017-03-05         NA
# 4: 10 2017-12-05 2017-12-05 2017-12-06       Cat4

数据准备：

d1 <- data.frame(id = c(5, 10, 5, 10),
                 begin = as.Date(c('1990-3-1','1993-12-1','1991-3-1','1995-12-5')),
                 end = as.Date(c('2017-3-10','2017-12-2','2017-3-3','2017-12-10')),
                 val = c("Cat1", "Cat2", "Cat3", "Cat4"))

d2 <- data.frame(id = c(5, 6, 8, 10),
                 begin = as.Date(c('2017-3-3','2017-5-3','2017-3-3','2017-12-5')),
                 end = as.Date(c('2017-3-5','2017-5-5','2017-3-5','2017-12-6')))

library(data.table)
setDT(d1)
setDT(d2)

基于重叠日期匹配数据框之间的值

1 个答案: