Getting started

Step 0. Install R and RStudio

RStudio is the preferred development environment for R. Download the latest R binary for Mac OSX

Since CRAN does not check the binaries for viruses it’s a good idea to check the hash. e.g.,

shasum R-3.6.3.nn.pkg 

Run the package installer and it should overwrite the previous R installation by default. You can check the installation in from the R command line with .libPaths(). It should return something like:

/Library/Frameworks/R.framework/Versions/3.6/Resources/library

Finally download RStudio and follow the install instructions.

You can check the version of R from the RStudio terminal (or the bash terminal) with R --version e.g.,

R version 3.6.3 (2020-02-29) -- "Holding the Windsock"
Copyright (C) 2020 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin15.6.0 (64-bit)

Step 1. Install Jupyter notebooks

Jupyter notebooks are also a popular development tool. Anaconda (miniconda) provides an install for Jupyter notebooks. Anaconda has a lot of cool stuff but I normally just install miniconda to reduce the overhead. See here: https://conda.io/en/latest/miniconda.html for instructions and download the shell script. Then:

bash ~/Downloads/Miniconda3-latest-MacOSX-x86_64.sh

After installing miniconda then you will need to install the jupyter notebook package:

conda install jupyter

Step 2. Install IRkernel for R

The instructions for getting Jupyter to run with an R kernel are here: https://irkernel.github.io/installation/

First open an R session in terminal R. Then run the following commands in that R session:

install.packages('IRkernel')  # this will download the package from CRAN

The kernel spec can be installed for the current user with the following line:

IRkernel::installspec()

Step 3. Run a jupyter notebook from the bash terminal

jupyter notebook

Step 4. Install RStan

Instructions for installing RStan are here https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started

it is sometimes necessary to remove any existing RStan via

remove.packages("rstan")
if (file.exists(".RData")) file.remove(".RData")

Then, restart R.

In most cases, you can simply type (exactly like this)

install.packages("rstan", repos = "https://cloud.r-project.org/", dependencies = TRUE)

You can check the C++ Toolchain in RStudio

pkgbuild::has_build_tools(debug = TRUE)