r - 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 example:

houseID      year    price 
1            1995    NA
1            1996    100
1            1997    NA
1            1998    120
1            1999    NA
2            1995    NA
2            1996    NA
2            1997    NA
2            1998    30
2            1999    NA
3            1995    NA
3            1996    44
3            1997    NA
3            1998    NA
3            1999    NA

I would like to make a data frame like this:

houseID      year    price 
1            1995    NA
1            1996    100
1            1997    100
1            1998    120
1            1999    120
2            1995    NA
2            1996    NA
2            1997    NA
2            1998    30
2            1999    30
3            1995    NA
3            1996    44
3            1997    44
3            1998    44
3            1999    44

Here are some data in the right format:

# Number of houses
N = 15

# Data frame
df = data.frame(houseID = rep(1:N,each=10), year=1995:2004, price =ifelse(runif(10*N)>0.15, NA,exp(rnorm(10*N))))

Is there a dplyr-way to do that?

Answer 1

tidyr::fill now makes this stupidly easy:

library(dplyr)
library(tidyr)
# or library(tidyverse)

df %>% group_by(houseID) %>% fill(price)
# Source: local data frame [15 x 3]
# Groups: houseID [3]
# 
#    houseID  year price
#      (int) (int) (int)
# 1        1  1995    NA
# 2        1  1996   100
# 3        1  1997   100
# 4        1  1998   120
# 5        1  1999   120
# 6        2  1995    NA
# 7        2  1996    NA
# 8        2  1997    NA
# 9        2  1998    30
# 10       2  1999    30
# 11       3  1995    NA
# 12       3  1996    44
# 13       3  1997    44
# 14       3  1998    44
# 15       3  1999    44