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r - 基于分组一列或多列计算日期之间的差异

问题描述

我的数据集示例如下：

| id |       Date | Buyer    |
|:--:|-----------:|----------|
|  9 | 11/29/2018 | Jenny    |
|  9 | 11/29/2018 | Jenny    |
|  9 | 11/29/2018 | Jenny    |
| 4  | 5/30/2018  | Chang    |
| 4  | 7/4/2018   | Chang    |
| 4  | 8/17/2018  | Chang    |
| 5  | 5/25/2018  | Chunfei  |
| 5  | 2/13/2019  | Chunfei  |
| 5  | 2/16/2019  | Chunfei  |
| 5  | 2/16/2019  | Chunfei  |
| 5  | 2/23/2019  | Chunfei  |
| 5  | 2/25/2019  | Chunfei  |
| 8  | 2/28/2019  | Chunfei  |
| 8  | 2/28/2019  | Chunfei  |

我对这个数据集有两组问题：

  1. 我需要计算日期之间的差异，但这个差异将基于分组“买家”和“id”计算，这意味着买家“珍妮”和标识“9”的日期差异将是一组，买家“张” ' ID 为“4”的将是另一个组，ID 为“5”的买家“Chunfei”将是另一个组，ID 为“8”的“Chunfei”将是另一个组。因此，输出将是：
| id |       Date | Buyer_id | Diff |
|:--:|-----------:|----------|------|
|  9 | 11/29/2018 | Jenny    | NA   |
|  9 | 11/29/2018 | Jenny    | 0    |
|  9 | 11/29/2018 | Jenny    | 0    |
| 4  | 5/30/2018  | Chang    | NA   |
| 4  | 7/4/2018   | Chang    | 35   |
| 4  | 8/17/2018  | Chang    | 44   |
| 5  | 5/25/2018  | Chunfei  | NA   |
| 5  | 2/13/2019  | Chunfei  | 264  |
| 5  | 2/16/2019  | Chunfei  | 3    |
| 5  | 2/16/2019  | Chunfei  | 0    |
| 5  | 2/23/2019  | Chunfei  | 7    |
| 5  | 2/25/2019  | Chunfei  | 2    |
| 8  | 2/28/2019  | Chunfei  | NA   |
| 8  | 2/28/2019  | Chunfei  | 0    |

问题是我不明白为什么 group_by 不起作用。以下代码减去连续的行，而不是将它们分组为相同的买家和 id，然后减去。

df=data.frame(id=c("9","9","9","4","4","4","5","5","5","5","5","5","8","8"), 
              Date=c("11/29/2018","11/29/2018","11/29/2018","5/30/2018","7/4/2018", 
                      "8/17/2018","5/25/2018","2/13/2019","2/16/2019","2/16/2019","2/23/2019",
                      "2/25/2019","2/28/2019","2/28/2019"),Buyer=c("Jenny","Jenny","Jenny",
                      "Chang","Chang","Chang","Chunfei","Chunfei","Chunfei","Chunfei","Chunfei",
                      "Chunfei","Chunfei","Chunfei"))
df$id=as.numeric(as.character(df$id))
df$Date=as.Date(df$Date, "%m/%d/%Y")
df$Buyer=as.character(df$Buyer)

df1=df %>% group_by(Buyer,id) %>%
  mutate(diff=as.numeric(difftime(Date,lag(Date),units='days')))
  1. 计算日期差后，我需要过滤那些日期差为 5 天的记录。在上面的示例中，“5/25/2018”、“2/13/2019”、“2/16/2019”、“2/16/2019”、“2/23/2019”、 2/25/2019" 将是 NA,264,3,0,7,2。但是，如果我为 n<6 提供过滤器，我会错过日期“2/13/2019”和“2/23/2019”。这些日期对于保留在最终输出中很重要，因为即使日期“2/13/2019”和“5/25/2018”之间的差异是 264，“2/16/2019”和“2 /13/2019" 是 3。同样，即使 "2/16/2019" 和 "2/23/2019" 之间的差异是 7，但 "2/23/2019" 和 "2/25/2019" 之间的差异" 是 2。所以，我需要保留这些日期。如何做到这一点？

我们可以在最终输出中屏蔽“diff”列，它应该如下所示：

| id |    Date    | Buyer_id |
|----|:----------:|---------:|
| 9  | 11/29/2018 |    Jenny |
| 9  | 11/29/2018 |    Jenny |
| 9  | 11/29/2018 |    Jenny |
| 5  | 2/13/2019  | Chunfei  |
| 5  | 2/16/2019  | Chunfei  |
| 5  | 2/16/2019  | Chunfei  |
| 5  | 2/23/2019  | Chunfei  |
| 5  | 2/25/2019  | Chunfei  |
| 8  | 2/28/2019  | Chunfei  |
| 8  | 2/28/2019  | Chunfei  |

标签： rgroup-bydate-difference

解决方案

我们可以diff用来减去Date和选择至少有一个小于等于 5 天的值的组。

library(dplyr)

df %>%
  group_by(id, Buyer) %>%
  filter(any(diff(Date) <= 5))

#      id Date       Buyer  
#   <dbl> <date>     <chr>  
# 1     9 2018-11-29 Jenny  
# 2     9 2018-11-29 Jenny  
# 3     9 2018-11-29 Jenny  
# 4     5 2018-05-25 Chunfei
# 5     5 2019-02-13 Chunfei
# 6     5 2019-02-16 Chunfei
# 7     5 2019-02-16 Chunfei
# 8     5 2019-02-23 Chunfei
# 9     5 2019-02-25 Chunfei
#10     8 2019-02-28 Chunfei
#11     8 2019-02-28 Chunfei

重新阅读问题后，我认为您可能不会查看filter整个组，而只会查看那些相差 5 天的行。我们可以得到diff值小于 5 的索引，并选择它的前一个索引。

df %>%
  group_by(id, Buyer) %>%
  mutate(diff = c(NA, diff(Date))) %>%
  slice({i1 <- which(diff <= 5); unique(c(i1, i1-1))}) %>%
  select(-diff)

#      id Date       Buyer  
#   <dbl> <date>     <chr>  
# 1     5 2019-02-16 Chunfei
# 2     5 2019-02-16 Chunfei
# 3     5 2019-02-25 Chunfei
# 4     5 2019-02-13 Chunfei
# 5     5 2019-02-23 Chunfei
# 6     8 2019-02-28 Chunfei
# 7     8 2019-02-28 Chunfei
# 8     9 2018-11-29 Jenny  
# 9     9 2018-11-29 Jenny  
#10     9 2018-11-29 Jenny

数据

df <- structure(list(id = c(9, 9, 9, 4, 4, 4, 5, 5, 5, 5, 5, 5, 8, 
8), Date = structure(c(17864, 17864, 17864, 17681, 17716, 17760, 
17676, 17940, 17943, 17943, 17950, 17952, 17955, 17955), class = "Date"), 
Buyer = c("Jenny", "Jenny", "Jenny", "Chang", "Chang", "Chang", 
"Chunfei", "Chunfei", "Chunfei", "Chunfei", "Chunfei", "Chunfei", 
"Chunfei", "Chunfei")), row.names = c(NA, -14L), class = "data.frame")

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