R group by with each grouped element associated with most common factor

I want to group by column a and choose the most common factor b for each unique a. For example:

tibble(a = c(1,1,1,2,2,2), b = factor(c('cat', 'dog', 'cat', 'cat', 'dog', 'dog'))) %>%
    reframe(b = most_common(b), .by = a)

I want this to produce:

However, the most_common function doesn't exist. Is there an efficient R function for this purpose? This must be a pretty common need for data cleaning (what I need it for). I searched and found people implementing mode functions. I could use one of those, but they seemed inefficient. Is there a better approach to this overall problem?

We can use table + max.col

d <- table(df)
data.frame(
  a = as.numeric(row.names(d)),
  b = colnames(d)[max.col(d)]
)

which gives

  a   b
1 1 cat
2 2 dog

or using dplyr like below

  group_by(a) %>%
  summarise(b = names(which.max(table(b))))

which gives

# A tibble: 2 × 2
      a b
  <dbl> <chr>
1     1 cat
2     2 dog