JupyterLab Environment for Statistical Analysis using R

This project provides a pre-configured Docker image to effortlessly run JupyterLab with R as the default language. The image includes essential dependencies, R installation, and popular packages like tidyverse, ggplot2, dplyr, and BiocManager for seamless data manipulation and analysis. Your notebooks will be stored in the 'notebooks' directory, ensuring organized work. Enjoy a hassle-free, reproducible, and isolated environment for your data science tasks!

Table of Contents

1. Getting Started
   • Prerequisites
   • Installation
2. Pre-installed R Packages
3. Versioning
4. License

Getting Started

This guide will help you set up a running copy of JupyterLab Environment for Statistical Analysis using R on your machine. The Docker environment provides a seamless and isolated workspace for data science tasks, with R as the default language and popular packages pre-installed. Let's get started!

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Prerequisites

Ensure you have the following software installed on your machine. If not, download and install them from the provided official website:

• Docker Desktop
• Git or preferably Sourcetree

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Installation

Step 1: Clone the Repository

Clone this Git repository to your local machine using Git...

cd /Users/your_username/path_to_install

git clone [email protected]:SamThilmany/JupyterLab-with-R--Docker-Environment.git

... or using Sourcetree

New... > Clone from URL

• Source URL: [email protected]:SamThilmany/JupyterLab-with-R--Docker-Environment.git
• Destination Path: /Users/your_username/path_to_install/JupyterLab-with-R--Docker-Environment (path on your local machine where you want the environment to be installed)
• Name: JupyterLab-with-R--Docker-Environment

If you want to change the name, you can do so as long as the Name is equivalent to the project folder name in Destination Path, e.g.:

• Destination Path: /Users/your_username/path_to_install/Your-Awesome-Name (path on your local machine where you want the environment to be installed)
• Name: Your-Awesome-Name

Click "Clone".

Step 2: Build the Docker Image

Open the Docker Desktop app and go back to the terminal.

Build the Docker image:

cd /Users/your_username/path_to_install/JupyterLab-with-R--Docker-Environment/jupyter_r

docker-compose build

This step will take a few minutes, so feel free to grab a cup of coffee... ☕️

Step 3: Run the Docker Container

cd /Users/your_username/path_to_install/JupyterLab-with-R--Docker-Environment/jupyter_r

docker-compose up

Step 4: Access JupyterLab

Copy the URL from the terminal output into the adress bar of your favourite browser.

The URL has a format like http://127.0.0.1:8888/lab?token=14a67f16044064336d2c86c55a48c3c44a815c080cb5584e.

Step 5: Start Exploring

Congratulations! You now have a fully functional JupyterLab environment with R as the default language. Start creating new notebooks, analyzing data, and utilizing the power of R and its rich ecosystem of packages.

Additional Notes

• To stop the JupyterLab session, type Ctrl + C in the terminal where the container is running. Then type docker-compose down to stop the Docker container. You can now close the Docker Desktop app.
• To start the container again in the future, open the Docker Desktop app and repeat Step 3.
• Your notebooks will be saved in the /Users/your_username/path_to_install/JupyterLab-with-R--Docker-Environment/notebooks/ directory on your local machine, enabling easy access and collaboration.

Enjoy your productive data science journey with the JupyterLab Environment for Statistical Analysis using R! If you encounter any issues or have any questions, please feel free to reach out via GitLab Issues. Happy coding!

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Pre-installed R Packages

• tidyverse
• showtext
• ggplot2
• GGally
• ggVennDiagram
• cowplot
• ggrepel
• doParallel
• combinat
• BiocManager
• devtools
• ggtree
• disgenet2r

Pre-installed BiocManager Packages

• limma
• clusterProfiler
• AnnotationsDbi
• org.Hs.eg.db
• enrichplot
• rawrr

Versioning

Semantic Versioning is used for versioning. For the versions available, see the Releases.

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License

This project is distributed under the Apache License 2.0. See LICENSE for more information.

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