通过一个精确匹配和最接近的日期匹配R中的两个日期框

icomxhvb 于 2023-02-06 发布在其他

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假设我们有两个 Dataframe ：
DF1

structure(list(ID = c("A", "A", "A", "A", "B", "B", "C", "C", 
"C", "C"), Date.X = structure(c(1452643200, 1452643200, 1452643200, 
1560556800, 1491177600, 1537142400, 1524700800, 1524787200, 1452297600, 
1417132800), class = c("POSIXct", "POSIXt"), tzone = "UTC"), 
    Score.X = c(40, 50, 10, 45, 60, 100, 90, 400, 376, 276)), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -10L))

DF2

structure(list(ID = c("A", "A", "A", "B", "B", "B", "B", "C", 
"C"), Date.Y = structure(c(1433462400, 1474588800, 1511740800, 
1431475200, 1442188800, 1537747200, 1577404800, 1451606400, 1493942400
), class = c("POSIXct", "POSIXt"), tzone = "UTC"), Score.Y = c(35, 
39, 130, 240, 126, 100, 95, 327, 257)), class = c("tbl_df", "tbl", 
"data.frame"), row.names = c(NA, -9L))

我想通过匹配相同的ID和近似日期，将分数从df1输入df2。
预期结果：

structure(list(ID = c("A", "A", "A", "A", "B", "B", "C", "C", 
"C", "C"), Date.X = structure(c(1452643200, 1452643200, 1452643200, 
1560556800, 1491177600, 1537142400, 1524700800, 1524787200, 1452297600, 
1417132800), class = c("POSIXct", "POSIXt"), tzone = "UTC"), 
    Score.X = c(40, 50, 10, 45, 60, 100, 90, 400, 376, 276), 
    Score.Y = c(35, 35, 35, 130, 100, 100, 257, 257, 327, 327
    ), Date.Y = structure(c(1433462400, 1433462400, 1433462400, 
    1511740800, 1537747200, 1537747200, 1493942400, 1493942400, 
    1451606400, 1451606400), class = c("POSIXct", "POSIXt"), tzone = "UTC")), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -10L))

我试着用dplyr自己用r编码，但没有得到结果。

library(dplyr)
result <- df1 %>% 
  left_join(df2, by="ID") %>% 
  group_by(ID) %>% 
  mutate(diff = abs(as.numeric(difftime(Date.X, Date.Y, units="days")))) %>% 
  slice_min(diff) %>% 
  select(-diff

r

来源：https://stackoverflow.com/questions/75347576/match-two-dateframes-in-r-by-one-exact-match-and-closest-date

1条答案

按热度按时间

vyswwuz21#

也许你可以用它来做，但我真的不知道你想在这里实现什么。

inner_join(df2,df1, by="ID") %>% 
  mutate(date_diff = abs(as.numeric(difftime(Date.X,Date.Y,units = "days")))) %>% 
  group_by(ID, Date.X, Score.X) %>% 
  slice_min(date_diff) %>% 
  select(-date_diff) %>% 
  relocate(ID, Date.X, Score.X, Score.Y, Date.Y)

输出：

ID    Date.X              Score.X Score.Y Date.Y             
   <chr> <dttm>                <dbl>   <dbl> <dttm>             
 1 A     2016-01-13 00:00:00      10      35 2015-06-05 00:00:00
 2 A     2016-01-13 00:00:00      40      35 2015-06-05 00:00:00
 3 A     2016-01-13 00:00:00      50      35 2015-06-05 00:00:00
 4 A     2019-06-15 00:00:00      45     130 2017-11-27 00:00:00
 5 B     2017-04-03 00:00:00      60     100 2018-09-24 00:00:00
 6 B     2018-09-17 00:00:00     100     100 2018-09-24 00:00:00
 7 C     2014-11-28 00:00:00     276     327 2016-01-01 00:00:00
 8 C     2016-01-09 00:00:00     376     327 2016-01-01 00:00:00
 9 C     2018-04-26 00:00:00      90     257 2017-05-05 00:00:00
10 C     2018-04-27 00:00:00     400     257 2017-05-05 00:00:00

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通过一个精确匹配和最接近的日期匹配R中的两个日期框

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