This repo is to ensure reproducibility of analyses from Hu Chuan-Peng's team. This tutorial helped us to build and maintain this repo, many thanks.
The primary purpose of this docker image is to ensure my collaborators can run brms across different platforms, so that they don't need to worry about the installation problems.
This docker image can be used for Bayesian analyses, it includes the following packages: brms, cmdstanr, tidybayes. Also, it includes lme4, tidyverse, metafor. I will include more packages in the future. You can also easily install new packages and save the docker images locally, please see the tutorial I mentioned above.
Please see here for why using docker.
r4.3.1-arm64: for m1 chips; R 4.3.1; CmdStan 2.33.1; rstan 2.32.3; tidyverse 2.0.0; lme4 1.1-34
Linux (ubuntu): https://docs.docker.com/engine/install/ubuntu/
Windows 10: https://docs.docker.com/docker-for-windows/install/
Mac OS: https://docs.docker.com/docker-for-mac/install/
Make sure that docker was successfully installed on your machine
Choose the right tag!
docker pull hcp4715/rstudio_bayes # this doesn't work, because docker will try to find a image "hcp4715/rstudio_bayes:latest"
docker pull hcp4715/rstudio_bayes:cmdstanr # docker will try to find a image "hcp4715/rstudio_bayes:cmdstanr"
docker run -e PASSWORD=hcplab --cpus=4 -it --rm -p 8787:8787 -v /home/hcp4715/docker_R:/home/rstudio/tutorial hcp4715/rstudio_bayes:cmdstanr
docker run ---- Run a docker image in a container
-e PASSWORD=hcplab ---- set a password for rstudio, you can set your own password.
-it ---- Keep STDIN open even if not attached
--rm ---- Automatically remove the container when it exits
--cpus=4 ---- Number of cores will be used by docker. Make sure that your machine has more thread than the number here.
-v ---- Mount a folder to the container
/home/hcp4715/docker_R ---- The directory of a local folder where I stored my data. [For Linux]
/d/hcp4715/docker_R ---- The directory of a local folder under drive D. It appears as D:\hcp4715\docker_R in windows system.
/home/rstudio/tutorial ---- The directory inside the docker image (the mounting point of the local folder in the docker image). Note that the docker container itself likes a mini virtual linux system, so the file system inside it is linux style.
-p ---- Publish a container’s port(s) to the host
hcp4715/rstudio_bayes:cmdstanr ---- The docker image to run. Note that you shoud include the tag :cmdstanr part, otherwise docker will instead use "hcp4715/rstudio_bayes:latest".
After running the code above, you shall see output as below:
[s6-init] making user provided files available at /var/run/s6/etc...exited 0.
[s6-init] ensuring user provided files have correct perms...exited 0.
[fix-attrs.d] applying ownership & permissions fixes...
[fix-attrs.d] done.
[cont-init.d] executing container initialization scripts...
[cont-init.d] userconf: executing...
[cont-init.d] userconf: exited 0.
[cont-init.d] done.
[services.d] starting services
[services.d] done.
Then, open your broswer (e.g., firefox, chrome), and try one of the following url in the address:
localhost:8787
http://192.168.99.100:8787
You will be asked to input username and password
Username: rstudio Password: hcplab
Now, you will see the familiar interface of rstudio! In the broswer!
Open cmdstanrTest.r in the example folder and run, if you can run it without error, then this image works
This requires about 30 minutes.
docker build -t your_user_name/your_docker_image_name:your_tag -f arm64.Dockerfile .
Replace the your_user_name/your_docker_image_name:your_tag part with your own information. For example, I useddocker build -t hcp4715/rstudio_bayes:cmdstanr -f Dockerfile . Also replace the arm64.Dockerfile with other Dockerfile if your machine is not with apple chips.