Replace missing values (NA) with most recent non-NA by group

Question

I would like to solve the following problem with dplyr. Preferable with one of the window-functions. I have a data frame with houses and buying prices. The following is an e

Answer 1

Since data.table v1.12.4, the package has a nafill() funciton, similar to tidyr::fill() or zoo::na.locf() and you can do:

require(data.table)
setDT(df)

df[ , price := nafill(price, type = 'locf'), houseID ]

There is also setnafill(), though not allowing for a group by, but multpile columns.

setnafill(df, type = 'locf', cols = 'price')

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Data taken from @G. Grothendieck's answer:

df = data.frame(houseID = c(1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 
                            2L, 3L, 3L, 3L, 3L, 3L),
                year = c(1995L, 1996L, 1997L, 1998L, 1999L, 1995L, 1996L,
                         1997L, 1998L, 1999L, 1995L, 1996L, 1997L, 1998L, 1999L),
                price = c(NA, 100L, NA, 120L, NA, NA, NA, NA, 30L, NA, NA, 44L,
                          NA, NA, NA))