根据日期按组比较值并创建值下降的新数据框

分类：编程问答

如何解决根据日期按组比较值并创建值下降的新数据框

我有一个 df

+-----+------+--------+--------------------+------+---------+
| ID1 | ID2  | DOC_NO |        DATE        | COST | CLIENT  |
+-----+------+--------+--------------------+------+---------+
| ABC | A123 |      1 | 2021-01-01 0:10:00 |   11 | ABC123  |
| DEF | B456 |      2 | 2021-01-01 0:10:00 |   12 | DEF256  |
| GHI | C789 |      3 | 2021-01-01 0:10:00 |   13 | GHI389  |
| JKL | D890 |      4 | 2021-01-01 0:10:00 |   14 | JKL490  |
| MNO | E012 |      5 | 2021-01-01 0:10:00 |   15 | MNO512  |
| ABC | A123 |      6 | 2021-01-01 0:15:00 |   11 | ABC623  |
| DEF | B456 |      7 | 2021-01-01 0:15:00 |   12 | DEF756  |
| GHI | C789 |      8 | 2021-01-01 0:15:00 |   13 | GHI889  |
| JKL | D890 |      9 | 2021-01-02 0:15:00 |   14 | JKL990  |
| MNO | E012 |     10 | 2021-01-03 0:15:00 |   15 | MNO1012 |
| ABC | A123 |     11 | 2021-01-03 0:20:00 |   10 | GHI890  |
| DEF | B456 |     12 | 2021-01-03 0:20:00 |   11 | JKL991  |
| GHI | C789 |     13 | 2021-01-03 0:20:00 |   12 | MNO1013 |
| JKL | D890 |     14 | 2021-01-03 0:20:00 |   13 | GHI891  |
| MNO | E012 |     15 | 2021-01-03 0:20:00 |   14 | JKL992  |
| ABC | A123 |     16 | 2021-01-03 0:20:00 |   12 | MNO1014 |
| DEF | B456 |     17 | 2021-01-03 0:20:00 |   13 | GHI892  |
| GHI | C789 |     18 | 2021-01-03 0:20:00 |   14 | JKL993  |
| JKL | D890 |     19 | 2021-01-03 0:20:00 |   15 | MNO1015 |
| MNO | E012 |     20 | 2021-01-03 0:20:00 |   16 | GHI893  |
| ABC | A123 |     21 | 2021-01-03 0:25:00 |   11 | ABC124  |
| DEF | B456 |     22 | 2021-01-03 0:25:00 |   12 | DEF257  |
| GHI | C789 |     23 | 2021-01-03 0:25:00 |   13 | GHI390  |
| JKL | D890 |     24 | 2021-01-03 0:25:00 |   14 | JKL491  |
| MNO | E012 |     25 | 2021-01-03 0:25:00 |   15 | MNO513  |
+-----+------+--------+--------------------+------+---------+

我想将 ID1 和 ID2 分组并按 DOC_NO 和 DATE 排列 df 发布我想创建一个新列 REFERENCE_COST，其中 REFERENCE_COST 是相对于时间和 DOC_NO 安排的最高成本，这意味着如果 COST 随 TIME 和 DOC_NO 增加，则较高的 COST 现在将被设置为 REFERENCE_COST 所以新的 df 如下所示：

+-----+------+--------+--------------------+------+---------+----------+
| ID1 | ID2  | DOC_NO |        DATE        | COST | CLIENT  | REF_COST |
+-----+------+--------+--------------------+------+---------+----------+
| ABC | A123 |      1 | 2021-01-01 0:10:00 |   11 | ABC123  |       11 |
| DEF | B456 |      2 | 2021-01-01 0:10:00 |   12 | DEF256  |       12 |
| GHI | C789 |      3 | 2021-01-01 0:10:00 |   13 | GHI389  |       13 |
| JKL | D890 |      4 | 2021-01-01 0:10:00 |   14 | JKL490  |       14 |
| MNO | E012 |      5 | 2021-01-01 0:10:00 |   15 | MNO512  |       15 |
| ABC | A123 |      6 | 2021-01-01 0:15:00 |   11 | ABC623  |       11 |
| DEF | B456 |      7 | 2021-01-01 0:15:00 |   12 | DEF756  |       12 |
| GHI | C789 |      8 | 2021-01-01 0:15:00 |   13 | GHI889  |       13 |
| JKL | D890 |      9 | 2021-01-02 0:15:00 |   14 | JKL990  |       14 |
| MNO | E012 |     10 | 2021-01-03 0:15:00 |   15 | MNO1012 |       15 |
| ABC | A123 |     11 | 2021-01-03 0:20:00 |   10 | GHI890  |       11 |
| DEF | B456 |     12 | 2021-01-03 0:20:00 |   11 | JKL991  |       12 |
| GHI | C789 |     13 | 2021-01-03 0:20:00 |   12 | MNO1013 |       13 |
| JKL | D890 |     14 | 2021-01-03 0:20:00 |   13 | GHI891  |       14 |
| MNO | E012 |     15 | 2021-01-03 0:20:00 |   14 | JKL992  |       15 |
| ABC | A123 |     16 | 2021-01-03 0:20:00 |   12 | MNO1014 |       12 |
| DEF | B456 |     17 | 2021-01-03 0:20:00 |   13 | GHI892  |       13 |
| GHI | C789 |     18 | 2021-01-03 0:20:00 |   14 | JKL993  |       14 |
| JKL | D890 |     19 | 2021-01-03 0:20:00 |   15 | MNO1015 |       15 |
| MNO | E012 |     20 | 2021-01-03 0:20:00 |   16 | GHI893  |       16 |
| ABC | A123 |     21 | 2021-01-03 0:25:00 |   11 | ABC124  |       12 |
| DEF | B456 |     22 | 2021-01-03 0:25:00 |   12 | DEF257  |       13 |
| GHI | C789 |     23 | 2021-01-03 0:25:00 |   13 | GHI390  |       14 |
| JKL | D890 |     24 | 2021-01-03 0:25:00 |   14 | JKL491  |       15 |
| MNO | E012 |     25 | 2021-01-03 0:25:00 |   15 | MNO513  |       16 |
+-----+------+--------+--------------------+------+---------+----------+

不，我希望能够将 REFERENCE_COST 与 COST 进行比较，并过滤 COST 小于 REFERENCE_COST 的所有行，并添加两个新列 DATE_LAST_REF_COST_MET & CLIENT_LAST_REF_COST_MET 显示 REFERENCE_COST 的日期和来自REFERENCE_COST 所以产生的 df 将如下所示：

+-----+------+--------+--------------------+------+---------+----------+------------------------+--------------------------+
| ID1 | ID2  | DOC_NO |        DATE        | COST | CLIENT  | REF_COST | DATE_LAST_REF_COST_MET | CLIENT_LAST_REF_COST_MET |
+-----+------+--------+--------------------+------+---------+----------+------------------------+--------------------------+
| ABC | A123 |     11 | 2021-01-03 0:20:00 |   10 | GHI890  |       11 | 2021-01-01 0:15:00     | ABC623                   |
| DEF | B456 |     12 | 2021-01-03 0:20:00 |   11 | JKL991  |       12 | 2021-01-01 0:15:00     | DEF756                   |
| GHI | C789 |     13 | 2021-01-03 0:20:00 |   12 | MNO1013 |       13 | 2021-01-01 0:15:00     | GHI889                   |
| JKL | D890 |     14 | 2021-01-03 0:20:00 |   13 | GHI891  |       14 | 2021-01-02 0:15:00     | JKL990                   |
| MNO | E012 |     15 | 2021-01-03 0:20:00 |   14 | JKL992  |       15 | 2021-01-03 0:15:00     | MNO1012                  |
| ABC | A123 |     21 | 2021-01-03 0:25:00 |   11 | ABC124  |       12 | 2021-01-03 0:20:00     | MNO1014                  |
| DEF | B456 |     22 | 2021-01-03 0:25:00 |   12 | DEF257  |       13 | 2021-01-03 0:20:00     | GHI892                   |
| GHI | C789 |     23 | 2021-01-03 0:25:00 |   13 | GHI390  |       14 | 2021-01-03 0:20:00     | JKL993                   |
| JKL | D890 |     24 | 2021-01-03 0:25:00 |   14 | JKL491  |       15 | 2021-01-03 0:20:00     | MNO1015                  |
| MNO | E012 |     25 | 2021-01-03 0:25:00 |   15 | MNO513  |       16 | 2021-01-03 0:20:00     | GHI893                   |
+-----+------+--------+--------------------+------+---------+----------+------------------------+--------------------------+

这就是我能做的：

df %>%
  group_by(ID1,ID2) %>%
  arrange(DATE,DOC_NO,.by_group = TRUE) %>%
  mutate(diff = COST - lag(COST,default = first(COST)))%>%
  mutate(REF_COST = case_when(diff < 0~lag(COST),TRUE~diff)) %>%
  mutate(DATE_LAST_REF_COST_MET= case_when(diff < 0~lag(DATE),TRUE~DATE)) %>%
  mutate(CLIENT_LAST_REF_COST_MET= case_when(diff < 0~lag(CLIENT),TRUE~CLIENT))

这样做的限制是它在进行计算时不会用 DATE 和 DOC_NO 改变 REFERENCE_COST

我不知道如何实现这一点

解决方法

您可以使用 cummax 设置 REF_COST 和 lag 以获取每个组中的前一个值。使用 filter 仅保留引用成本高于成本的那些行。

library(dplyr)

df %>%
  group_by(ID1,ID2) %>%
  mutate(REF_COST = cummax(COST),DATE_LAST_REF_COST_MET = lag(DATE),CLIENT_LAST_REF_COST_MET = lag(CLIENT)) %>%
  ungroup() %>%
  filter(REF_COST > COST)

#    ID1   ID2   DOC_NO DATE                COST CLIENT  REF_COST DATE_LAST_REF_COST_MET CLIENT_LAST_REF_COST_MET
#   <chr> <chr>  <int> <chr>              <int> <chr>      <int> <chr>                  <chr>                   
# 1 ABC   A123      11 2021-01-03 0:20:00    10 GHI890        11 2021-01-01 0:15:00     ABC623                  
# 2 DEF   B456      12 2021-01-03 0:20:00    11 JKL991        12 2021-01-01 0:15:00     DEF756                  
# 3 GHI   C789      13 2021-01-03 0:20:00    12 MNO1013       13 2021-01-01 0:15:00     GHI889                  
# 4 JKL   D890      14 2021-01-03 0:20:00    13 GHI891        14 2021-01-02 0:15:00     JKL990                  
# 5 MNO   E012      15 2021-01-03 0:20:00    14 JKL992        15 2021-01-03 0:15:00     MNO1012                 
# 6 ABC   A123      21 2021-01-03 0:25:00    11 ABC124        12 2021-01-03 0:20:00     MNO1014                 
# 7 DEF   B456      22 2021-01-03 0:25:00    12 DEF257        13 2021-01-03 0:20:00     GHI892                  
# 8 GHI   C789      23 2021-01-03 0:25:00    13 GHI390        14 2021-01-03 0:20:00     JKL993                  
# 9 JKL   D890      24 2021-01-03 0:25:00    14 JKL491        15 2021-01-03 0:20:00     MNO1015                 
#10 MNO   E012      25 2021-01-03 0:25:00    15 MNO513        16 2021-01-03 0:20:00     GHI893

数据

如果您以更容易复制的 dput 形式提供数据，则更容易获得帮助。

df <- structure(list(ID1 = c("ABC","DEF","GHI","JKL","MNO","ABC","MNO"),ID2 = c("A123","B456","C789","D890","E012","A123","E012"),DOC_NO = 1:25,DATE = c("2021-01-01 0:10:00","2021-01-01 0:10:00","2021-01-01 0:15:00","2021-01-02 0:15:00","2021-01-03 0:15:00","2021-01-03 0:20:00","2021-01-03 0:25:00","2021-01-03 0:25:00"
),COST = c(11L,12L,13L,14L,15L,11L,10L,16L,15L),CLIENT = c("ABC123","DEF256","GHI389","JKL490","MNO512","ABC623","DEF756","GHI889","JKL990","MNO1012","GHI890","JKL991","MNO1013","GHI891","JKL992","MNO1014","GHI892","JKL993","MNO1015","GHI893","ABC124","DEF257","GHI390","JKL491","MNO513")),row.names = c(NA,-25L),class = "data.frame")

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