gap <- gapminder %>%
  group_by(country) %>%
  mutate(latestLifeExp = tail(lifeExp, 1))

gap

# A tibble: 1,704 x 7
# Groups:   country [142]
   country     continent  year lifeExp      pop gdpPercap latestLifeExp
   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>         <dbl>
 1 Afghanistan Asia       1952    28.8  8425333       779          43.8
 2 Afghanistan Asia       1957    30.3  9240934       821          43.8
 3 Afghanistan Asia       1962    32.0 10267083       853          43.8
 4 Afghanistan Asia       1967    34.0 11537966       836          43.8
 5 Afghanistan Asia       1972    36.1 13079460       740          43.8
 6 Afghanistan Asia       1977    38.4 14880372       786          43.8
# ... with 1,698 more rows