Replace NA with previous or next value, by group, using dplyr

Question

I have a data frame which is arranged by descending order of date.

ps1 = data.frame(userID = c(21,21,21,22,22,22,23,23,23),               color = c(NA,'blue','red','blue',NA,NA,'red',NA,'gold'),               age = c('3yrs','2yrs',NA,NA,'3yrs',NA,NA,'4yrs',NA),               gender = c('F',NA,'M',NA,NA,'F','F',NA,'F')  ) 

I wish to impute(replace) NA values with previous values and grouped by userID In case the first row of a userID has NA then replace with the next set of values for that userid group.

I am trying to use dplyr and zoo packages something like this...but its not working

cleanedFUG <- filteredUserGroup %>%  group_by(UserID) %>%  mutate(Age1 = na.locf(Age),       Color1 = na.locf(Color),       Gender1 = na.locf(Gender) )  

I need result df like this:

                      userID color  age gender                 1     21  blue 3yrs      F                 2     21  blue 2yrs      F                 3     21   red 2yrs      M                 4     22  blue 3yrs      F                 5     22  blue 3yrs      F                 6     22  blue 3yrs      F                 7     23   red 4yrs      F                 8     23   red 4yrs      F                 9     23  gold 4yrs      F 

Answer 1

require(tidyverse) #fill is part of tidyr  ps1 %>%    group_by(userID) %>%    fill(color, age, gender) %>% #default direction down   fill(color, age, gender, .direction = "up") 

Which gives you:

Source: local data frame [9 x 4] Groups: userID [3]    userID  color    age gender    <dbl> <fctr> <fctr> <fctr> 1     21   blue   3yrs      F 2     21   blue   2yrs      F 3     21    red   2yrs      M 4     22   blue   3yrs      F 5     22   blue   3yrs      F 6     22   blue   3yrs      F 7     23    red   4yrs      F 8     23    red   4yrs      F 9     23   gold   4yrs      F 

Answer 2

Using zoo::na.locf directly on the whole data.frame would fill the NA regardless of the userID groups. Package dplyr's grouping has unfortunately no effect on na.locf function, that's why I went with a split:

library(dplyr); library(zoo) ps1 %>% split(ps1$userID) %>%    lapply(function(x) {na.locf(na.locf(x), fromLast=T)}) %>%    do.call(rbind, .) ####      userID color  age gender #### 21.1     21  blue 3yrs      F #### 21.2     21  blue 2yrs      F #### 21.3     21   red 2yrs      M #### 22.4     22  blue 3yrs      F #### 22.5     22  blue 3yrs      F #### 22.6     22  blue 3yrs      F #### 23.7     23   red 4yrs      F #### 23.8     23   red 4yrs      F #### 23.9     23  gold 4yrs      F 

What it does is that it first splits the data into 3 data.frames, then I apply a first pass of imputation (downwards), then upwards with the anonymous function in lapply, and eventually use rbind to bring the data.frames back together. You have the expected output.