Dockerizing R: A DOCKERFILE Guide For Your Lessons

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Dockerizing R: A DOCKERFILE Guide for Your Lessons

Hey guys! Today, we're diving into the awesome world of Docker and how you can use it to set up your R environment and serve lessons seamlessly. If you're involved in the ImperialCollegeLondon's rse_further_git_course or just looking to streamline your R development workflow, this guide is for you. We'll walk through creating a DOCKERFILE step by step, ensuring that your R setup is reproducible and consistent across different environments. So, buckle up and let's get started!

Why Docker for R?

Before we jump into the DOCKERFILE, let's quickly chat about why Docker is a game-changer for R developers. Imagine you've spent hours, maybe even days, configuring your R environment with all the necessary packages and dependencies. Now, you want to share your work with a colleague or deploy it to a server. Without Docker, you're likely to run into the dreaded "it works on my machine" problem. Docker solves this by packaging your R environment into a container, which is a lightweight, standalone executable that includes everything needed to run your code: code, runtime, system tools, system libraries, and settings.

Using Docker ensures that your R code runs the same way everywhere, regardless of the underlying infrastructure. This is particularly useful for collaborative projects, continuous integration, and deployment. Plus, Docker makes it easy to manage different versions of R and R packages, avoiding conflicts and ensuring reproducibility. For courses like the rse_further_git_course at ImperialCollegeLondon, Docker can be a lifesaver by providing a consistent environment for all students, eliminating setup headaches and allowing everyone to focus on learning.

Another fantastic benefit of Docker is its ability to isolate your R environment from the host system. This means you can experiment with different packages and configurations without messing up your main system. It's like having a sandbox where you can play around without any consequences. And when you're done, you can simply delete the container, leaving your host system clean and tidy. So, if you're not already using Docker for your R projects, now is the perfect time to start!

Creating Your DOCKERFILE

Alright, let's get our hands dirty and create a DOCKERFILE for setting up our R environment and serving lessons. A DOCKERFILE is a simple text file that contains a set of instructions for building a Docker image. Each instruction adds a layer to the image, creating a series of snapshots of your environment. Here’s a step-by-step guide to creating an effective DOCKERFILE for R:

1. Base Image

First, we need to choose a base image. A base image is the foundation upon which we'll build our R environment. Docker Hub provides a variety of pre-built R images that you can use. For this example, we'll use the rocker/verse image, which comes with R, RStudio, and a collection of commonly used R packages from the tidyverse. This is a great starting point for most R projects. Add this line to your DOCKERFILE:

FROM rocker/verse

This line tells Docker to pull the rocker/verse image from Docker Hub and use it as the base for our image. You can also use other R-related images like rocker/r-base (for a minimal R installation) or rocker/rstudio (if you need RStudio Server). Choose the one that best fits your needs.

2. Install System Dependencies

Next, we need to install any system dependencies that our R packages or lessons might require. System dependencies are libraries and tools that are not available through CRAN or Bioconductor. For example, if you're working with geospatial data, you might need to install the libgeos library. To install system dependencies, we'll use the apt-get command. Here’s an example:

RUN apt-get update && apt-get install -y \
    libgeos-dev \
    libproj-dev \
    --no-install-recommends

This command first updates the package lists and then installs the libgeos-dev and libproj-dev packages. The --no-install-recommends flag tells apt-get not to install recommended packages, which can help reduce the size of the image. Make sure to list all the system dependencies that your project needs.

3. Install R Packages

Now, let's install the R packages that our lessons require. We'll use the install.packages() function for packages available on CRAN and BiocManager::install() for Bioconductor packages. To do this, we'll create an R script called install_packages.R and then copy it to the Docker image and run it. Here’s an example install_packages.R script:

# install_packages.R
install.packages(c("shiny", "ggplot2", "dplyr"))
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install(c("DESeq2", "AnnotationDbi"))

This script installs the shiny, ggplot2, and dplyr packages from CRAN and the DESeq2 and AnnotationDbi packages from Bioconductor. Now, let's add the following lines to our DOCKERFILE:

COPY install_packages.R /
RUN Rscript /install_packages.R

These lines copy the install_packages.R script to the root directory of the Docker image and then run it using Rscript. This will install all the R packages listed in the script.

4. Copy Lesson Files

Next, we need to copy our lesson files to the Docker image. This includes the R scripts, data files, and any other resources that are needed to run the lessons. Let's assume that our lesson files are located in a directory called lessons. We can copy them to the Docker image using the COPY command:

COPY lessons /lessons

This line copies the entire lessons directory to the /lessons directory in the Docker image. You can also copy individual files if you prefer.

5. Set Working Directory

To make it easier to run our lessons, we can set the working directory in the Docker image to the /lessons directory. This means that when we run commands in the container, they will be executed from the /lessons directory. We can set the working directory using the WORKDIR command:

WORKDIR /lessons

This line sets the working directory to /lessons. Now, any commands we run in the container will be executed from this directory.

6. Expose Ports (if necessary)

If your lessons involve running a web server (e.g., a Shiny app), you'll need to expose the port that the server is listening on. This allows you to access the server from outside the container. You can expose a port using the EXPOSE command. For example, if your Shiny app is listening on port 3838, you can add the following line to your DOCKERFILE:

EXPOSE 3838

This line exposes port 3838. You can expose multiple ports if needed.

7. Command to Run

Finally, we need to specify the command that should be executed when the container starts. This is typically the command that starts the R server or runs the main lesson script. We can specify the command using the CMD command. For example, if we want to run a Shiny app called app.R, we can add the following line to our DOCKERFILE:

CMD ["R", "-e", "shiny::runApp('app.R', host = '0.0.0.0', port = 3838)"]

This command starts the Shiny app using the shiny::runApp() function. The host = '0.0.0.0' argument tells the app to listen on all interfaces, and the port = 3838 argument specifies the port to listen on. If you just want to be able to access an R terminal use:

CMD ["R"]

Example DOCKERFILE

Here’s an example DOCKERFILE that combines all the steps we've discussed:

FROM rocker/verse

RUN apt-get update && apt-get install -y \
    libgeos-dev \
    libproj-dev \
    --no-install-recommends

COPY install_packages.R /
RUN Rscript /install_packages.R

COPY lessons /lessons
WORKDIR /lessons

EXPOSE 3838

CMD ["R", "-e", "shiny::runApp('app.R', host = '0.0.0.0', port = 3838)"]

Building and Running the Docker Image

Now that we have our DOCKERFILE, we can build the Docker image using the docker build command. Open a terminal, navigate to the directory containing the DOCKERFILE, and run the following command:

docker build -t r-lesson .

This command builds the Docker image and tags it with the name r-lesson. The . at the end of the command specifies that the DOCKERFILE is located in the current directory. Once the image is built, you can run it using the docker run command:

docker run -p 3838:3838 r-lesson

This command runs the r-lesson image and maps port 3838 on the host to port 3838 on the container. You can then access the Shiny app (or whatever service is running on port 3838) by opening a web browser and navigating to http://localhost:3838.

Optimizing Your DOCKERFILE

To get the most out of Docker, it's important to optimize your DOCKERFILE. Here are a few tips:

  • Use multi-stage builds: Multi-stage builds allow you to use multiple FROM instructions in a single DOCKERFILE. This can be useful for separating the build environment from the runtime environment, reducing the size of the final image.
  • Cache dependencies: Docker caches the layers of the image, so it's important to order the instructions in your DOCKERFILE in a way that takes advantage of this caching. For example, you should install system dependencies and R packages before copying your lesson files, as these are less likely to change.
  • Use .dockerignore: Create a .dockerignore file to exclude unnecessary files and directories from being copied to the Docker image. This can significantly reduce the size of the image and speed up the build process.
  • Keep it lean: Only install the packages and dependencies that are absolutely necessary for your lessons. The smaller the image, the faster it will build and run.

Conclusion

So, there you have it! A comprehensive guide to creating a DOCKERFILE for setting up your R environment and serving lessons. Dockerizing your R projects can greatly improve reproducibility, collaboration, and deployment. Whether you're involved in the rse_further_git_course at ImperialCollegeLondon or just looking to streamline your R workflow, Docker is a powerful tool that you should definitely consider. Happy Dockering, and see you in the next one!