Conditional merge/replacement in R

use match(), assuming values in df1 are unique.

df1 <- data.frame(x1=1:4,x2=letters[1:4],stringsAsFactors=FALSE)
df2 <- data.frame(x1=2:3,x2=c("zz","qq"),stringsAsFactors=FALSE)

df1$x2[match(df2$x1,df1$x1)] <- df2$x2
> df1
  x1 x2
1  1  a
2  2 zz
3  3 qq
4  4  d

If the values aren't unique, use :

for(id in 1:nrow(df2)){
  df1$x2[df1$x1 %in% df2$x1[id]] <- df2$x2[id]
}

The first part of Joris' answer is good, but in the case of non-unique values in df1, the row-wise for-loop will not scale well on large data.frames.

You could use a data.table "update join" to modify in place, which will be quite fast:

library(data.table)
setDT(df1); setDT(df2)
df1[df2, on = .(x1), x2 := i.x2]

Or, assuming you don't care about maintaining row order, you could use SQL-inspired dplyr:

library(dplyr)
union_all(
  inner_join( df1["x1"], df2 ), # x1 from df1 with matches in df2, x2 from df2
  anti_join(  df1, df2["x1"] )  # rows of df1 with no match in df2
) # %>% arrange(x1) # optional, won't maintain an arbitrary row order

Either of these will scale much better than the row-wise for-loop.