Q: what book on Bayesian statistics, preferably with R? I am frequentist by training and practice, but I'd like to learn more about Bayesian statistics. I know the basics, but I would be at a loss if I had to, for example, replace my normal ANOVA hypothesis testing approach with a Bayesian alternative.
What book would you recommend to learn practical Bayesian approaches? Preferably using R.
 A: Fortuitous timing, as Bayesian Data Analysis, 3rd ed was just released. It's a good general-purpose text, with an emphasis on hierarchical methods, a section on  advanced computation (that is, Markov chain Monte Carlo), and an appendix on Gelman's Bayesian inference tool, rstan. 
The text focuses on statistics rather than programming, though, so perhaps this answer does not fit your R needs. That said, I've been able to recreate the text examples in R simply based on his clear prose descriptions.
A: 
Peter D. Huff. A First Course in Bayesian Statistical Methods. Springer (2010)

Also

Andrew Gelman et. al. Bayesian Data Analysis (3rd ed.). CRC (2013)

The Gelman book isn't constrained to R but also uses Stan, a probabilistic programming language similar to BUGS or JAGS. I believe earlier editions of the book used BUGS instead of Stan, which is probably very similar.
And finally:

John Kruschke. Doing Bayesian Data Analysis: A tutorial with R and BUGS. Academic Press (2011)

More BUGS than R, but probably the most pragmatic of the three books I've suggested. Don't let the cover deter you, this is a perfectly respectable text.
A: Both are introductory, but useful imho:
Bayesian Computation With R, by Jim Albert
Applied Bayesian Statistics, With R and OpenBUGS Examples, by
