Forest plot solution

Here is a solution to the forest plot question.

compute_posterior_summary = function(
    nA, nB,
    prior = c( nA = 0, nB = 0 )
) {
    return(
        data.frame(
            posterior_mean = (nB+prior['nB'])/(nA+prior['nA']+nB+prior['nB']),
            q2.5 = qbeta( p = 0.025, shape1 = nB+prior['nB']+1, shape2 = nA+prior['nB']+1 ),
            q97.5 = qbeta( p = 0.975, shape1 = nB+prior['nB']+1, shape2 = nA+prior['nB']+1 )
        )
    )
}


posterior_summary = rbind(
    data
    %>% mutate(
        nA = ( `C/C` + `-/C` ),
        nB = ( `-/-` )
    )
    %>% group_by( population )
    %>% summarise( compute_posterior_summary( nA, nB, prior = c( nA = 0, nB = 0 )))
    %>% mutate( prior = "none" ),
        data
    %>% mutate(
        nA = ( `C/C` + `-/C` ),
        nB = ( `-/-` )
    )
    %>% group_by( population )
    %>% summarise( compute_posterior_summary( nA, nB, prior = c( nA = 5, nB = 5 )))
    %>% mutate( prior = "5,5" )
)

p = (
    ggplot( data = posterior_summary )
    + geom_point( aes( x = posterior_mean, y = population ))
    + geom_segment( aes( x = q2.5, xend = q97.5, y = population, yend = population ))
    # Always make your plot have good axis labels!
    + xlab( "Posterior mean and 95% CI" )
    + ylab( "Population" )
    # A clean, large-font theme:
    + theme_minimal(16)
    # Rotate y axis label so it's not at 90 degrees:
    + theme( axis.title.y = element_text( angle = 0, vjust = 0.5 ))
    + facet_grid( . ~ prior)
)
print(p)