Predictions from brms

I’m new to Bayes and brms. Apologize if this question has been asked before (a brief search did not help). I’m following the radon examples in Gelman and Hill (2007).

Suppose I fit a multilevel model in brms:

f1 <- brm(log_radon ~ floor + (1 | county), data = radon, family = "gaussian")

I want to plot the predictions by county for each level of floor (fig 12.4 p.257 of Gelman and Hill). I suppose one way is:

radon_yhats  <- radon %>% 
  mutate(log_radon_multilev = predict(f1, radon))

ggplot(data = radon_yhats, aes(y = log_radon, x = floor)) + 
  geom_jitter() +
  geom_line(aes(x = floor, y = log_radon_multilev)) +

However, elsewhere I’ve seen references to tidybayes::add_epred_draws()or sometimes people use modlr.
I guess I’m a bit confused about the difference between these approaches. For instance, is tidybayes::add_epred_draws() the same as predict()? What would be tidy-esque approach to plotting this figure?

Many thanks.