Someone asked a question about finding which records (rows) in their data frame are duplicated by other records. If you just want to know which records are duplicates, base R has a duplicated() function that will do just that. It occurred to me, though, that the questioner might have wanted to know not just which records were duplicates but also which records were the corresponding "originals". Here's a bit of R code that creates a small data frame with duplicated rows and then identifies original/duplicate pairs by row number.
# Create source data.
df <- data.frame(a = c(3, 1, 1, 2, 3, 1, 3), b = c("c", "a", "a", "b", "c", "a", "c"))
# Find the indices of duplicated rows.
dup <- df |> duplicated() |> which()
# Split the source data into two data frames.
df1 <- df[-dup, ] # originals (rows 1, 2 and 4)
df2 <- df[dup, ] # duplicates (rows 3, 5, 6 and 7)
# The row names are the row indices in the original data frame df. Assign them to columns.
df1$Original <- row.names(df1)
df2$Duplicate <- row.names(df2)
# Perform an inner join to find the original/duplicate pairings. The "NULL" value for "by"
# (which is actually the default and can be omitted) means rows of df1 and df2 are paired
# based on identical values in all columns they have in common (i.e., all the original
# columns of df).
inner_join(df1, df2, by = NULL) |> select(Original, Duplicate)
# Result:
# Original Duplicate
# 1 1 5
# 2 1 7
# 3 2 3
# 4 2 6
The key here is that the inner_join function pairs rows from each data frame (originals and duplicates) based on matching values in the "by" columns. The default value of "by" (NULL) tells it to match by all the columns the two data frames have in common -- which in the is case is all the columns in the source data frame. The resulting data frame will have the columns from the source data frame (here "a" and "b") plus the columns unique to each data frame ("Original" and "Duplicate"). We use the select() command to drop the source columns and just keep the indices of the original and duplicate rows.
