bayesian statistics

You want a basic understanding or intuition of bayesian statistics (as opposed to frequentist statistics)? The presentation Bayesian Statistics (a very brief introduction) by Ken Rice could be a good starting point.

learning bayesian statistics with R

There is Bayes Rules! An Introduction to Applied Bayesian Modeling by Alicia A. Johnson, Miles Q. Ott, and Mine Dogucu. They provide a thorough introduction to bayesian statistics, all with excercises in R (even including their own dedicated R package bayesrule to accompany the book).

A slightly shorter introduction to bayesian statistics in R can be found in the Bayesian statistics chapter of the book Learning Statistics with R by Danielle Navarro. It is aimed at a psychology audience, but the examples should all be generic enough to follow.

Or you could check out this Bayesian models in R r-bloggers post by Francisco Lima. It also has R code to follow along.

learning bayesian statistics with python

You would rather learn bayesian statistics using python? Look no further, here's Think Bayes 2 by Allen B. Downey. It comes with a Jupyter Notebook per chapter, so you can directly run (and play with) the code in the book.

Causal inference and Bayesian statistics

Richard McElreath has a nice introduction to causal inference and applying it to Bayesian methods called Statistical Rethinking. It is also available as a set of lectures on youtube, updated annually. There is also an R package rethinking to go with the book.