r - Replace values in a data.frame column based on values in a different column -

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i have data.frame:

df <- data.frame(id = rep(c("one", "two", "three"), each = 10), week.born = na) df$week.born[c(5,15,28)] <- c(23,19,24)  df     id week.born 1    1        na 2    1        na 3    1        na 4    1        na 5    1        23 6    1        na 7    1        na 8    1        na 9    1        na 10   1        na 11   2        na 12   2        na 13   2        na 14   2        na 15   2        19 16   2        na 17   2        na 18   2        na 19   2        na 20   2        na 21 3        na 22 3        na 23 3        na 24 3        na 25 3        na 26 3        na 27 3        na 28 3        24 29 3        na 30 3        na

for one week.born values should 23. two week.born values should 19. one week.born values should 24.

whats best way this?

i create data.frame containing mapping , simple join:

require(dplyr) map <- data.frame(id=c("one","two","three"), new.week.born=c(23,19,24)) left_join(df, map, by="id")  # id week.born new.week.born # 1    1        na            23 # 2    1        na            23 # ... # 16   2        na            19 # 17   2        na            19 # 18   2        na            19 # 19   2        na            19 # 20   2        na            19 # 21 3        na            24 # 22 3        na            24 # 23 3        na            24 # ...

see benchmark below.

library(microbenchmark) library(dplyr) # v 0.4.1 library(data.table) # v 1.9.5  df <- data.frame(id = rep(c("one", "two", "three"), each = 1e6)) df2 <- copy(df) map <- data.frame(id=c("one","two","three"), new.week.born=c(23,19,24))  dplyr_join <- function() {    left_join(df, map, by="id") }  r_merge <- function() {   merge(df, map, by="id") }  data.table_join <- function() {   setkey(setdt(df2))[map] }  unit: milliseconds               expr         min         lq       mean     median         uq       max neval       dplyr_join()   409.10635   476.6690   910.6446   489.4573   705.4021  2866.151    10          r_merge() 41589.32357 47376.0741 55719.1752 50133.0918 54636.3356 83562.931    10  data.table_join()    94.14621   132.3788   483.4220   225.3309  1051.7916  1416.946    10

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