Question

Given 2 data frames that are identical in terms of column names/datatypes, where some columns uniquely identify the rows, is there an efficient function/method for one data.frame to “update” the other?

For example, in the following, original and replacement are identified by 'Name' and 'Id'. goal is the result of finding all rows from replacement in original (by the unique id’s) and replacing with Value1 and Value2

original = data.frame( Name = c("joe","john") , Id = c( 1 , 2) , Value1 = c(1.2,NA), Value2 = c(NA,9.2) )
replacement = data.frame( Name = c("john") , Id = 2 , Value1 = 2.2 , value2 = 5.9)
goal = data.frame( Name = c("joe","john") , Id = c( 1 , 2) , Value1 = c(1.2,2.2), Value2 = c(NA,5.9) )

The solution should work for an original and replacement of arbitrary length (although replacement should never have more rows than original). In practice, I’m using 2 id columns.

Answer 1

Just set a unique ID as the row names. Then it is simple indexing:

rownames(original) = original$Id
rownames(replacement) = replacement$Id

original[rownames(replacement), ] = replacement