将数据集中的同一列合并到 R 中另一列的所有列？

Question

I'm trying to do multiple merges/joins onto different columns in the same dataset, but when I do so the output is entirely wrong.

df1                 df2
P1  P2  P3  P4      P   Output  
A   B   C           C   1                 
A   B               B   2          
E   F   G   H       H   3
E                   E   4

I'm trying to merge df2 onto df1 and the output I would like to get would look like

df3
P1  P2 P3  P4  Output   
A   B  C   NA  1
A   B  NA  NA  2
E   F  G   H   3
E   NA NA  NA  4

I've tried

df3<- merge(df1,df2, by.x = "P1", by.y = "P", all.x = T, all.y = T)
df3<- merge(df1,df2, by.x = "P2", by.y = "P", all.x = T, all.y = T)
df3<- merge(df1,df2, by.x = "P3", by.y = "P", all.x = T, all.y = T)
df3<- merge(df1,df2, by.x = "P4", by.y = "P", all.x = T, all.y = T)

however it doesn't work the way I think it should. Is there an easier function that can cleanly merge like this that I am not aware of?

Answer 1

Based on the output showed, it seems that for each row, we need to get the last non-NA element and do a match with the second data.frame 'P' column to get the corresponding 'Output'. If that is the logic,

df3 <- df1
df3$Output <- apply(df1, 1, function(x) 
        setNames(df2$Output, df2$P)[tail(x[!is.na(x)], 1)])

---

Or with tidyverse

library(dplyr)
library(tidyr)
df1 %>%
   mutate(rn = row_number()) %>%
   pivot_longer(cols = -rn, values_drop_na = TRUE) %>% 
   group_by(rn) %>%
   slice(n()) %>%
   ungroup %>% 
   left_join(df2, by = c('value' = 'P')) %>% 
   select(Output) %>% 
   bind_cols(df1, .)

data

df1 <- structure(list(P1 = c("A", "A", "E", "E"), P2 = c("B", "B", "F", 
NA), P3 = c("C", NA, "G", NA), P4 = c(NA, NA, "H", NA)), class = "data.frame", 
row.names = c(NA, 
-4L))

df2 <- structure(list(P = c("C", "B", "H", "E"), Output = 1:4), 
class = "data.frame", row.names = c(NA, 
-4L))

Answer 2

You can use coalesce from the dplyr package to create a new field in df1 which will be the key between the two datasets.

library(dplyr)
#create column P, which takes first non null value
df1$P <- coalesce(df1$P4,df1$P3,df1$P2,df1$P1)
#Join data frames on P
df3 <- inner_join(df1, df2, by='P')
#Rmove P from df3
df3$P <- NULL

>> df3
  P1   P2   P3   P4 Output
1  A    B    C <NA>      1
2  A    B <NA> <NA>      2
3  E    F    G    H      3
4  E <NA> <NA> <NA>      4