Transfer values from one dataframe to another

Using data.table:

require(data.table)
dt1 <- data.table(df1, key="id")
dt2 <- data.table(df2)

dt1[dt2$id, value]

#    id     value
# 1:  1  1.000000
# 2:  2  6.210526
# 3:  3 11.421053
# 4:  4 16.631579
# 5:  5 21.842105
# 6: 21        NA
# 7: 22        NA
# 8: 23        NA

or using base merge as @TheodoreLytras mentioned under comment:

# you don't need to have `v2` column in df2
merge(df2, df1, by="id", all.x=T, sort=F)

#   id v2     value
# 1  1 NA  1.000000
# 2  2 NA  6.210526
# 3  3 NA 11.421053
# 4  4 NA 16.631579
# 5  5 NA 21.842105
# 6 21 NA        NA
# 7 22 NA        NA
# 8 23 NA        NA

One way to do this using merge():

df2Needed <- merge(df2,df1,by="id",all.x=TRUE, sort=FALSE)
df2Needed <- df2Needed[,c("id","value")]
colNames(df2Needed) <- c("id","v2")

and another (more elegant, I think) using match():

df2Needed <- df2
df2Needed$v2 <- df1$value[match(df2$id, df1$id)]

Using LEFT join sql with sqldf

library(sqldf)
sqldf('SELECT df2.id , df1.value
      FROM df2
      LEFT JOIN df1
      ON df2.id = df1.id')

 id     value
1  1  1.000000
2  2  6.210526
3  3 11.421053
4  4 16.631579
5  5 21.842105
6 21        NA
7 22        NA
8 23        NA

EDIT add some benchamrking:

The match as expected is very fast here. sqldf is really slow!

Test on the OP data

library(microbenchmark)
microbenchmark(ag(),ar.dt(),ar.me(),tl())

Unit: microseconds
     expr       min         lq     median        uq       max
1    ag() 23071.953 23536.1680 24053.8590 26889.023 34256.354
2 ar.dt()  3123.972  3284.5890  3348.1155  3523.333  7740.335
3 ar.me()   950.807  1015.2815  1095.1160  1128.112  6330.243
4    tl()    41.340    45.8915    68.0785    71.112   187.735

Test with big data 1E6 rows of data.

here how I generate my data:

N <- 1e6
df1 <- data.frame(id=as.character(1:N), 
                  value=seq(1, 100), 
                  stringsAsFactors=F)
n2 <- 1000
df2 <- data.frame(id=sample(df1$id,n2),
                  v2=NA, 
                  stringsAsFactors=F)

Surprise !! merge is 16 times faster than sqldf and data.table solution is the slowest one!

Unit: milliseconds
     expr       min        lq    median        uq        max
1    ag() 5678.0580 5865.3063 6034.9151 6214.3664  8084.6294
2 ar.dt() 8373.6083 8612.9496 8867.6164 9104.7913 10423.5247
3 ar.me()  387.4665  451.0071  506.8269  648.3958  1014.3099
4    tl()  174.0375  186.8335  214.0468  252.9383   667.6246

Where the function ag, ar.dt,ar.me, tl are defined by :

ag <- function(){
require(sqldf)
sqldf('SELECT df2.id , df1.value
      FROM df2
      LEFT JOIN df1
      ON df2.id = df1.id')
}


ar.dt <- function(){
  require(data.table)
  dt1 <- data.table(df1, key="id")
  dt2 <- data.table(df2)
  dt1[dt2$id, value]
}

ar.me  <- function(){
 merge(df2, df1, by="id", all.x=T, sort=F)
}

tl <- function(){
  df2Needed <- df2
 df2Needed$v2 <- df1$value[match(df2$id, df1$id)]
}

EDIT 2

It seems that including the data.table creation in the benchmarking it a little bit unfair. To avoid any confusion , I add a new function where I suppose that I have already data.table structures.

ar.dtLight <- function(){
  dt1[dt2$id, value]
}

library(microbenchmark)
microbenchmark(ag(),ar.dt(),ar.me(),tl(),ar.dtLight,times=1)

Unit: microseconds
        expr         min          lq      median          uq         max
1       ag() 7247593.591 7247593.591 7247593.591 7247593.591 7247593.591
2    ar.dt() 8543556.967 8543556.967 8543556.967 8543556.967 8543556.967
3 ar.dtLight       1.139       1.139       1.139       1.139       1.139
4    ar.me()  462235.106  462235.106  462235.106  462235.106  462235.106
5       tl()  201988.996  201988.996  201988.996  201988.996  201988.996

It seems that the creation of the keys (indexes) is the time consuming. But once the indexes are created data.table method is unbeatable.