交叉连接 ID 从同一 R 数据帧中的其他列中提取数据

Question

I have an R data frame like this one (but data wouldn't be sorted by any column):

ppl <- structure(list(id = c("I0000", "I0001", "I0002", "I0003", "I0004","I0005", "I0006", "I0007", "I0008", "I0009"), Birth_Date = structure(c(NA, 517, -10246, -8723, 2349, -25125, NA, -12141, 2349, NA), class = "Date"), Father_id = c(NA, "I0002", "I0005", "I0037", "I0002", "I0018", "I0056", "I0005", "I0002", "I0005"), Mother_id = c(NA, "I0003", "I0006", "I0038", "I0003", "I0019", "I0057", "I0006", "I0003", "I0006"), marriage = structure(c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_, -12119, -12119, NA_real_, NA_real_, NA_real_), class = "Date")), row.names = c(NA, -10L), class = c("tbl_df", "tbl", "data.frame"))

> ppl
# A tibble: 10 x 5
   id    Birth_Date Father_id Mother_id marriage  
   <chr> <date>     <chr>     <chr>     <date>    
 1 I0000 NA         NA        NA        NA        
 2 I0001 1971-06-02 I0002     I0003     NA        
 3 I0002 1941-12-13 I0005     I0006     NA        
 4 I0003 1946-02-13 I0037     I0038     NA        
 5 I0004 1976-06-07 I0002     I0003     NA        
 6 I0005 1901-03-19 I0018     I0019     1936-10-27        
 7 I0006 NA         I0056     I0057     1936-10-27        
 8 I0007 1936-10-05 I0005     I0006     NA        
 9 I0008 1976-06-07 I0002     I0003     NA        
10 I0009 NA         I0005     I0006     NA    

Children and parents relationships are stablished through their different IDs.

For each individual ( id ) without a marriage date value, I want to estimate a date value for that column, based on the Birth_date of his/her first child (of course this is just an assumption, since for some people Birth_Date is not available).

So, in this example, some individues which would get a marriage date would be I0002 and I0003 (calculated marriage would be "1971-06-02" in rows 3 and 4, because it is the minimum Birth_Date of the 3 people which have Father_id =='I0002' and Mother_id =='I0003' -rows 2, 5 and 9-).

The same way, individues I0005 and I0006 would get marriage date "1936-10-05", which is the minimum known Birth_Date of their children (I0002, I0007 and I0009 -which has NA as Birth_Date -). But in this case, all children Birth_Date values should not be taken in account because the data frame has already a real marriage_date value for these individues ("1936-10-27").

As you can see, dataframe structure has not to be changed (same number of rows and same columns; but the last one gets some NA updated with a Date value).

Expected result:

> ppl
# A tibble: 10 x 5
   id    Birth_Date Father_id Mother_id marriage  
   <chr> <date>     <chr>     <chr>     <date>    
 1 I0000 NA         NA        NA        NA        
 2 I0001 1971-06-02 I0002     I0003     NA        
 3 I0002 1941-12-13 I0005     I0006     1971-06-02
 4 I0003 1946-02-13 I0037     I0038     1971-06-02
 5 I0004 1976-06-07 I0002     I0003     NA        
 6 I0005 1901-03-19 I0018     I0019     1936-10-27
 7 I0006 NA         I0056     I0057     1936-10-27
 8 I0007 1936-10-05 I0005     I0006     NA        
 9 I0008 1976-06-07 I0002     I0003     NA        
10 I0009 NA         I0005     I0006     NA        

Is it possible to accomplish this task avoiding a function to iterate the data frame?

I know there are libraries dealing with joins, like those mentioned here . But I still can't figure out how to use them to do this task.

I was thinking to calulate it row by row (one marriage date per iteration), but I guess there must be some fasters ways to do it. Please, elaborate a little bit your answer because I am a complete R-newbie. It's not just a matter of making it work, but of understanding how it works.

Answer 1

We can select a row with minimum value of Birth_Date for each father and mother and join with the dataframe itself.

library(dplyr)

ppl %>%
   #Keep only NA values
   filter(is.na(marriage)) %>%
   #For each father and mother
   group_by(Father_id, Mother_id) %>%
   #Select the minimum date
   slice(which.min(Birth_Date)) %>%
   #Get father and mother in same column
   tidyr::pivot_longer(cols = c(Father_id, Mother_id)) %>%
   #rename Birth_Date to marriage and select it with value
   select(marriage = Birth_Date, value) %>%
   #Join with the dataframe itself
   right_join(ppl, by = c('value' = 'id')) %>%
   #If marriage data is already present select that
   mutate(marriage_date = coalesce(marriage.y, marriage.x)) %>%
   #select only columns needed. 
   select(id = value, Birth_Date, Father_id, Mother_id, marriage_date)

   id    Birth_Date Father_id Mother_id marriage_date
   <chr> <date>     <chr>     <chr>     <date>       
 1 I0000 NA         NA        NA        NA           
 2 I0001 1971-06-02 I0002     I0003     NA           
 3 I0002 1941-12-13 I0005     I0006     1971-06-02   
 4 I0003 1946-02-13 I0037     I0038     1971-06-02   
 5 I0004 1976-06-07 I0002     I0003     NA           
 6 I0005 1901-03-19 I0018     I0019     1936-10-27   
 7 I0006 NA         I0056     I0057     1936-10-27   
 8 I0007 1936-10-05 I0005     I0006     NA           
 9 I0008 1976-06-07 I0002     I0003     NA           
10 I0009 NA         I0005     I0006     NA