合并两个数据集，其中一个是 year_month，另一个是 year_month_week

Question

I practice data merging using R nowadays. Here are simple two data df1 and df2 .

df1<-data.frame(id=c(1,1,1,2,2,2,2),
                year_month=c(202205,202206,202207,202204,202205,202206,202207),
                points=c(65,58,47,21,25,27,43))

df2<-data.frame(id=c(1,1,1,2,2,2),
                year_month_week=c(2022052,2022053,2022061,2022043,2022051,2022052),
                temperature=c(36.1,36.3,36.6,34.3,34.9,35.3))

For df1 , 202205 in year_month column means May 2022. For df2 , 2022052 in year_month_week column means 2nd week of May, 2022. I want to merge df1 and df2 with respect to year_month_week . So, all the elements of df2 are left, but some values of df2 can be copied. For example, 202205 in year_month includes 2022052 and 2022053 . There is no column points in df2 . In this case, 65 is copied. My expected output looks like this:

df<-data.frame(id=c(1,1,1,2,2,2),
               year_month_week=c(2022052,2022053,2022061,2022043,2022051,2022052),
               temperature=c(36.1,36.3,36.6,34.3,34.9,35.3),
               points=c(65,65,58,21,25,25))

Answer 1

Create a temporary year_month column in df2 by taking the first six characters of year_month_week , then do a left join on df1 by year_month and id before removing the temporary column.

Using tidyverse, we could do this as follows:

library(tidyverse)

df2 %>%
  mutate(year_month = as.numeric(substr(year_month_week, 1, 6))) %>%
  left_join(df1, by = c('year_month', 'id')) %>%
  select(-year_month)
#>   id year_month_week temperature points
#> 1  1         2022052        36.1     65
#> 2  1         2022053        36.3     65
#> 3  1         2022061        36.6     58
#> 4  2         2022043        34.3     21
#> 5  2         2022051        34.9     25
#> 6  2         2022052        35.3     25

Or in base R using merge :

df2$year_month <- substr(df2$year_month_week, 1, 6)
merge(df2, df1, by = c('year_month', 'id'))[-1]
#>   id year_month_week temperature points
#> 1  2         2022043        34.3     21
#> 2  1         2022052        36.1     65
#> 3  1         2022053        36.3     65
#> 4  2         2022051        34.9     25
#> 5  2         2022052        35.3     25
#> 6  1         2022061        36.6     58