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I am trying to replicate the below sql query in R

select a.*, b.key from Table1 a 
LEFT OUTER JOIN Table2 b 
on a.key = b.key where b.key is null

I have read through this post however I am still struggling to code my specific case. https://stackoverflow.com/questions/1299871/how-to-join-merge-data-frames-inner-outer-left-right

I have tried the below but the result doesnot allow me to filter for b.key IS NULL

LoansToInsert_stg1 <- merge(x = Prior_stg2, y = BankLoans_stg2, 
                            by = "Account_ID", all.x = TRUE)

Any insights?

Example:

Key1 <- c("A1","A2","A3","A4","A5")  
Key2 <- c("A1","A2","A3","B4","B5")  
BV1 <- c(100, 200, 300, 400, 500)  
BV2 <- c(150, 250, 350, 450, 550)  
df1 <- as.data.frame(cbind(Key1, BV1))  
df2 <- as.data.frame(cbind(Key2, BV2))  

Expected Output as a new df:

Key1 BV1 Key2 BV2
A1 100 A1 150
A2 200 A2 250
A3 300 A3 350
A4 400 NA NA
A5 500 NA NA
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  • $\begingroup$ Could you provide some dummy data and the expected result? $\endgroup$ – Omri374 Feb 23 '16 at 19:28
  • $\begingroup$ @omri374 example added $\endgroup$ – Drj Feb 23 '16 at 21:39
  • $\begingroup$ see my updated answer $\endgroup$ – Omri374 Feb 23 '16 at 21:44
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If I understand correctly:

Table1 <- data.frame(key = seq(1,100),a.data = rnorm(100))
Table2 <- data.frame(key = c(seq(1,30),rep(NA,30)), b.data = seq(1,60))


##Assuming this is what you want
library(sqldf)
sql.ans <- sqldf("select a.*, b.key from Table1 a LEFT OUTER JOIN Table2 b on a.key = b.key where b.key is null")

## dplyr version
library(dplyr)

dplyr.ans <- Table1 %>% filter(!key %in% Table2$key)

## Regular R version
R.ans <- Table1[which(!Table1$key %in% Table2$key),]

EDIT after dummy data and expected output

dplyr.ans2 <- left_join(df1,df2, by = c("Key1" = "Key2"))
 Key1 BV1  BV2
 1   A1 100  150
 2   A2 200  250
 3   A3 300  350
 4   A4 400 <NA>
 5   A5 500 <NA>
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  • $\begingroup$ Yes, however I don't get b.key as part of my resultant. I understand my query has a where clause, however if I want to filter for other keys on Table2, I would not be able to. Basically the df should have key from table 1, key from table 2, other fields. If Key from table 2 (key2) does not exist in Key from table 1, that row could be NA for key2 $\endgroup$ – Drj Feb 23 '16 at 21:06
  • $\begingroup$ You don't need to add a WHERE clause, since using a LEFT JOIN instead of an INNER JOIN adds all the rows in df1 that don't have matching keys in df2. $\endgroup$ – Omri374 Feb 23 '16 at 21:43
  • $\begingroup$ Correct! That would give me the all records in Table1 and matching records in Table2. Your edit brings me closer to what I am expecting. Is there a way to get the key from table2 in the resultant df? $\endgroup$ – Drj Feb 23 '16 at 21:46
  • $\begingroup$ on second thought, I can still work through with this result. Even though this is not ideally what would help me, having BV2 as NA can be used as a proxy for Key2 being NA. $\endgroup$ – Drj Feb 23 '16 at 21:56

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