Match in R while disregarding order [duplicate] - r

Question

This question already has an answer here:
Match Dataframes Excluding Last Non-NA Value and disregarding order
(1 answer)
Closed 5 years ago.
I am trying to do a match in R regardless of the order of the columns.
Basically the problem I am trying to solve is that if all of the values in the columns of df2, from column 2-to the end, are found in df1 (after Partner), then match df1.
Here's the catch: disregard the last non-NA value in each row when doing this match but include it in the final output. So don't take the last non-NA value into account when matching but include it.
After the match, determine if that last non-na value exists in any of the columns with it's respective row.
df1
Partner Col1 Col2 Col3 Col4
A A1 A2 NA NA
B A2 B9 NA NA
C B7 V9 C1 N9
D Q1 Q3 Q4 NA
df2
lift rule1 rule2 rule3
11 A2 A1 A9
10 A1 A3 NA
11 B9 A2 D7
10 Q4 Q1 NA
11 A2 B9 B1
How do I match df1 with df2 so that the following happens:
1) Disregards the order of the columns found in both dataframes.
2) Then determine if the last non-na value exists in the row currently.
Final output:
df3
Partner Col1 Col2 Col3 Col4 lift rule1 rule2 rule3 EXIST?
A A1 A2 NA NA 11 A2 A1 A9 YES
A A1 A2 NA NA 10 A1 A3 NA NOPE
B A2 B9 NA NA 11 B9 A2 D7 YES
B A2 B9 NA NA 11 A2 B9 B1 YES
D Q1 Q3 Q4 NA 10 Q4 Q1 NA YES

Answer 1

I get one more B match than you, but this solution is very close to what you want. You first have to add an id column as we use it to reconstruct the data. Then to perform the match, you first need to melt it with gather from tidyr and use inner_join from dplyr. We then cbind using the ids and the original data.frames.
library(tidyr);library(dplyr)
df1 <- read.table(text="Partner Col1 Col2 Col3 Col4
A A1 A2 NA NA
B A2 B9 NA NA
C B7 V9 C1 N9
D Q1 Q3 Q4 NA",header=TRUE, stringsAsFactors=FALSE)
df2 <- read.table(text="lift rule1 rule2 rule3
11 A2 A1 A9
10 A1 A3 NA
11 B9 A2 D7
10 Q4 Q1 NA
11 A2 B9 B1",header=TRUE, stringsAsFactors=FALSE)
df1 <- cbind(df1_id=1:nrow(df1),df1)
df2 <- cbind(df2_id=1:nrow(df2),df2)
#melt with gather
d11 <- df1 %>% gather(Col, Value,starts_with("C")) #Long
d11 <- d11 %>% na.omit() %>%group_by(df1_id) %>% slice(-n()) #remove last non NA
d22 <- df2 %>% gather(Rule, Value,starts_with("r")) #Long
res <- inner_join(d11,d22)
cbind(df1[res$df1_id,],df2[res$df2_id,])
df1_id Partner Col1 Col2 Col3 Col4 df2_id lift rule1 rule2 rule3
1 1 A A1 A2 <NA> <NA> 2 10 A1 A3 <NA>
1.1 1 A A1 A2 <NA> <NA> 1 11 A2 A1 A9
2 2 B A2 B9 <NA> <NA> 1 11 A2 A1 A9
2.1 2 B A2 B9 <NA> <NA> 5 11 A2 B9 B1
2.2 2 B A2 B9 <NA> <NA> 3 11 B9 A2 D7
4 4 D Q1 Q3 Q4 <NA> 4 10 Q4 Q1 <NA>