Implement geom_beeswarm using a quasi-random algorithm

[const-ae]

Fixes #318.

This PR implements a beeswarm algorithm that produces a (in my opinion) visually more appealing output than the random sampling implemented in geom_sina.

n = 200
df = pd.concat([
    pd.DataFrame({'y': np.random.RandomState(125).normal(0, 10, n), 'x': "beeswarm"}),
    pd.DataFrame({'y': np.random.RandomState(125).normal(0, 10, n), 'x': "sina"}),
])
(ggplot(df, aes(x = 'x', y = 'y')) +
    geom_beeswarm(data = lambda df: df[df['x'] == 'beeswarm'], maxwidth = 0.4) +
    geom_sina(data = lambda df: df[df['x'] == 'sina'], maxwidth = 0.4)
)

The code and unit testes are mostly copied from [geom|stat]_sina. I removed the random_state parameter as it isn't needed anymore (except when jitter is called). I wasn't sure what the best way to do here is, so any feedback would be appreciated.

I could also refactor the PR, so that stat_sina and stat_beeswarm use a common code base.