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From Zero to Binder in R!

Sarah Gibson, The Alan Turing Institute & Anna Krystalli, University of Sheffield

The Turing Way - making reproducible Data Science "too easy not to do"

Based on Tim Head's Zero-to-Binder workshops which can be found here: and

To follow these instructions on your own machine, follow this link:

Binder can take a long time to load, but this doesn't necessarily mean that your Binder will fail to launch. You can always refresh the window if you see the "... is taking longer to load, hang tight!" message.

Running Code is more complicated than Displaying Code

GitHub is a great service for sharing code, but the contents are static.

How could you run a GitHub repository without installing complicated requirements? Or even in your browser?

To run code, you need:

  • Hardware on which to run the code
  • Software, including:
    • The code itself
    • The programming language (e.g. Python, R, Julia, and so on)
    • Relevant packages (e.g. pandas, matplotlib, tidyverse, ggplot)

What Binder Provides

Binder is a service that provides your code and the hardware and software to execute it.

You can create a link to a live, interactive version of your code!

In R, we can use package holepunch to binderise our projects! The package provides functionality to create the necessary files and automatically configure them with the required information. All files created with holepunch are store in a hidden .binder/ directory and Binder knows to use those files to build your environment.

1. Creating a repo to Binderize

TO DO: 🚦

  1. Create a new repo on GitHub called "my-first-binder".
    • Make sure the repository is public, not private!
    • Don't forget to initialise with a README!
  2. Create a file called hello.R via the web interface with print("Hello from Binder!") on the first line and commit to the main branch.
  3. Create a file called runtime.txt with r-YYYY-MM-DD on the first line, where YYYY-MM-DD is today's date (eg r-2020-05-26). Commit to the main branch.
    • In R you can use holepunch::write_runtime() to create a runtime.txt in the .binder/ directory, configured with today's date.

Why did the repo have to be public? cannot access private repositories as this would require a secret token. The Binder team choose not to take on the responsibility of handling secret tokens as is a public service and proof of technological concept. If accessing private repositories is a feature you/your team need, we advise that you look into building your own BinderHub.

2. Launch your first repo!

TO DO: 🚦

  1. Go to

  2. Type the URL of your repo into the "GitHub repo or URL" box. It should look like this:

  3. As you type, the webpage generates a link in the "Copy the URL below..." box It should look like this:

  4. Copy it, open a new browser tab and visit that URL.

    • You will see a "spinner" as Binder launches the repo.

If everything ran smoothly, you'll see a Jupyter Notebook interface.

What's happening in the background? - Part 1

While you wait, BinderHub (the backend of Binder) is:

  • Fetching your repo from GitHub
  • Analysing the contents
  • Creating a Docker file based on your repo
  • Launching that Docker image in the Cloud
  • Connecting you to it via your browser

3. Run hello.R

TO DO: 🚦

  1. In the top right corner, click "New" ➡️ "Rstudio"
  2. In the console (the left-side panel) in Rstudio, type source("hello.R") and press return

Hello from Binder! should be printed to the terminal.

4. Pinning Dependencies

It was easy to get started, but our environment is barebones - let's add a dependency!

TO DO: 🚦

  1. In your repo, create a file called install.R
  2. Add a line that says: install.packages("readr")
    • In R you can create an install.R file and automatically add the code to install all dependencies in your project using holepunch::write_install().
  3. Check for typos! Then commit to the main branch.
  4. Visit again in a new tab

This time, click on "Build Logs" in the big, horizontal, grey bar. This will let you watch the progress of your build. It's useful when your build fails or something you think should be installed is missing.

N.B.: Sometimes Binder's build logs prints things in red font, such as warnings that pip is not up-to-date (pip is often out of date because it's regularly updated!) or installation messages, especially if you're using R. These red messages don't necessarily mean there's a problem with your build and it will fail - it's just an unfortunate font colour choice!

What's happening in the background? - Part 2

This time, BinderHub will run install.R and install package readr into our project.

More on pinning dependencies

In the above example, we specified that we want to use R in our project included a date in runtime.txt. The date tells Binder which MRAN snapshot to source R and packages from. MRAN hosts a "CRAN Time Machine" of daily snapshots of the CRAN R packages and R releases as far back as Sept. 17, 2014. In the above example, the MRAN snapshot dated r-2020-05-26 is used and the version of R and readr available at that date and installed. For the workflow to work correctly, please ensure you do not supply a date earlier than this example date.

This provides some rudimentary package versioning for R Users but is not as robust as pinning versions in a requirements.txt in python. For more robust and specific version pinning in R, have a look at package renv

5. Check the Environment

TO DO: 🚦

  1. In the top right corner, click "New" ➡️ "R" (under Notebook) to open a new R notebook

  2. Type the following into a new cell:

    read_csv(system.file("extdata/mtcars.csv", package = "readr"))
  3. Run the cell.

    • Press either SHIFT+RETURN or the "Run" button in the Menu bar. You should see the following output:
      • the version number of the installed version of readr.
      • a tibble of the contents of the mtcars.csv which is a csv file included in package readr.

N.B.: If you save this notebook, it will not be saved to the GitHub repo. Pushing changes back to the GitHub repo through the container is not possible with Binder. Any changes you have made to files inside the Binder will be lost once you close the browser window.

6. Sharing your Work

Binder is all about sharing your work easily and there are two ways to do it:

TO DO: 🚦

  1. Add the Markdown snippet from to the file in your repo
    • The grey bar displaying a binder badge will unfold to reveal the snippets. Click the clipboard icon next to the box marked with "m" to automatically copy the Markdown snippet.
  2. Click the badge to make sure it works!

7. Accessing data in your Binder

Another kind of dependency for projects is data. There are different ways to make data available in your Binder depending on the size of your data and your preferences for sharing it.

Small public files

The simplest approach for small, public data files is to add them directly into your GitHub repository. They are then directly encapsulated into the environment and versioned along with your code.

This is ideal for files up to 10MB.

Medium public files

To access medium files from a few 10s MB up to a few hundred MB, you can add a file called postBuild to your repo. A postBuild file is a shell script that is executed as part of the image construction and is only executed once when a new image is built, not every time the Binder is launched.

See Binder's postBuild example for more uses of the postBuild script.

N.B.: New images are only built when Binder sees a new commit, not every time you click the Binder link. Therefore, the data is only downloaded once when the Docker image is built, not every time the Binder is launched.

Large public files

It is not practical to place large files in your GitHub repo or include them directly in the image that Binder builds. The best option for large files is to use a library specific to the data format to stream the data as you're using it or to download it on demand as part of your code.

For security reasons, the outgoing traffic of your Binder is restricted to HTTP or GitHub connections only. You will not be able to use FTP sites to fetch data on

Private files

There is no way to access files which are not public from You should consider all information in your Binder as public, meaning that:

  • there should be no passwords, tokens, keys etc in your GitHub repo;
  • you should not type passwords into a Binder running on;
  • you should not upload your private SSH key or API token to a running Binder.

In order to support access to private files, you would need to create a local deployment of BinderHub where you can decide the security trade-offs yourselves.

N.B.: Building a BinderHub is not a simple task and is usually taken on by IT/RSE groups for reasons around managing maintenance, security and governance. However, that is not to say that they are the only groups of people who should/could build a BinderHub.

8. Get data with postBuild

TO DO: 🚦

  1. Go to your GitHub repo and create a file called postBuild

  2. In postBuild, add a single line reading: wget -q -O gapminder.csv

    • wget is a program which retrieves content from web servers. This line extracts the content from the bitly URL and saves it to the filename denoted by the -O flag (capital "O", not zero), i.e. gapminder.csv. The -q flag tells wget to do this quietly, i.e. don't print anything to the console.
  3. Update your install.R file to install two additional dependencies, "tidyr" and "ggplot2". To do so, supply a character vector of the required packages to install.packages() instead of a single character string. The installation command should now look like this:

    install.packages(c("readr", "tidyr", "ggplot2"))
    • These packages aren't necessary to download the data but we will use them to read the CSV file, process it and make a plot
  4. Click the binder badge in your README to launch your Binder

Once the Binder has launched, you should see a new file has appeared that was not part of your repo when you clicked the badge.

Now visualise the data by creating a new notebook ("New" ➡️ "R") and running the following code in a cell.


data <- read_csv("gapminder.csv") %>%
                 names_to = "year",
                 values_to = "gdpPercap",
                 names_prefix = "gdpPercap_",
                 names_transform = list(year = as.integer))

data[data$country == "Australia", ] %>%
    ggplot(aes(x = year, y = gdpPercap)) +

See this Software Carpentry lesson for more info.

Beyond Notebooks...

JupyterLab is installed into your containerized repo by default. You can access the environment by changing the URL you visit to:

N.B.: We've already seen how the ?filepath= argument can link to a specific file in the "What Binder Provides" section at the beginning of this workshop.

Here you can access:

  • Notebooks
  • IPython consoles
  • Terminals
  • A text editor

If you use R, you can also open RStudio using ?urlpath=rstudio.

Now over to you!

Now you've binderized (bound?) this demo repo, it's time to binderize the example script and data you brought along!

Some useful links: