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

A dplyr and imputeTS combination.

library(dplyr)
library(imputeTS)
df %>% group_by(houseID) %>% 
mutate(price = na.locf(price, na.remaining="keep"))  

You could also replace na.locf with more advanced missing data replacement (imputation) functions from imputeTS. For example na.interpolation or na.kalman. For this just replace na.locf with the name of the function you like.