replace NA value with the group value

Question

I have a df as follows which has 20 people across 5 households. Some people within the household have missing data for whether they have a med_card or not. I want to give these people the same value as the other people in their household (not an NA value, a real binary value which is either 0 or 1). I have tried the following code, which is a step in the right direction I think - but isn't 100% correct because a) it doesn't work if the first value for med_card per household is NA and b) it doesn't replace NA for all people in household 1. ``` DF<- ddply(df, .(hhold_no), function(df) {df$med_card[is.na(df$med_card)] <- head(df$med_card, na.rm=TRUE); return(df)}) ``` Any pointers would be greatly appreciated, Thank you Sample df ``` df person_id hhold_no med_card 1 1 1 1 2 2 1 1 3 3 1 NA 4 4 1 NA 5 5 1 NA 6 6 2 0 7 7 2 0 8 8 2 0 9 9 2 0 10 10 3 NA 11 11 3 NA 12 12 3 NA 13 13 3 1 14 14 3 1 15 15 4 1 16 16 4 1 17 17 5 1 18 18 5 1 19 19 5 NA 20 20 5 NA ``` and code to make ``` person_id<-a…

Accepted Answer

Try `ave`. It applies a function to groups. Have a look at `?ave` for details, e.g.:

```
df$med_card_new <- ave(df$med_card, df$hhold_no, FUN=function(x)unique(x[!is.na(x)]))

#   person_id hhold_no med_card med_card_new
#1          1        1        1            1
#2          2        1        1            1
#3          3        1       NA            1
#4          4        1       NA            1
#5          5        1       NA            1
#6          6        2        0            0
#7          7        2        0            0
#8          8        2        0            0
#9          9        2        0            0
```

Please note that this will only work if not all values in a household are `NA` and the should not differ (e.g. person 1 == 1, person 2 == 0).

replace NA value with the group value

Solution

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