replace NA value with the group value

by

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).

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