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

require(tidyverse) #fill is part of tidyrps1 %>%   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      F2     21   blue   2yrs      F3     21    red   2yrs      M4     22   blue   3yrs      F5     22   blue   3yrs      F6     22   blue   3yrs      F7     23    red   4yrs      F8     23    red   4yrs      F9     23   gold   4yrs      F

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.

Using @agenis method with na.locf() combined with purrr, you could do:

library(purrr)library(zoo)ps1 %>%   slice_rows("userID") %>%   by_slice(function(x) {     na.locf(na.locf(x), fromLast=T) },     .collate = "rows")