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

by

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

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