The JupyterLab half of a 1-hour session for Love Data Week 2024, which will overview Quarto and JupyterLab, two popular tools for integrating code, documentation, and text into reproducible research projects. This workshop will teach you the basics of using Quarto and Jupyter Notebooks to improve the legibility of your code in R, Python, or other programming languages, focusing on features that help you create reproducible research and code for technical and non-technical audiences.
Website: dataservices.library.jhu.edu/
Contact us: dataservices@jhu.edu
JHU Data Services, part of the Johns Hopkins University Sheridan Libraries, helps the JHU community find, use, visualize, manage, and share data. We offer live webinars and self-paced online trainings on computational research and coding, GIS, data management, data visualization, and more. See all of our training topics on our website.
This repository contains materials for one of our live webinars open to JHU students, faculty, and staff. Please contact us with any questions.
As of March 2020, Data Services workshops are being held virtually on Zoom. See our calendar to register for upcoming workshops.
The easiest way to install JupyterLab, and the preferred if you don't have Python installed already, is using Anaconda Python: https://www.anaconda.com/download. Anaconda Python is a "batteries included" version of Python with JupyterLab and common packages already installed. This is the fastest and easiest way to get started with JupyterLab and Python.
If you already have the Conda package manager installed (which is installed with the Anaconda or Miniconda Python installer), you can use conda install -c conda-forge jupyterlab to install JupyterLab.
If you would prefer to use Python's default package manager pip, use pip install jupyterlab to install JupyterLab.
All user actions in JupyterLab can be accessed with the Command Palette.
To access Command Palette:
- Windows/Linux:
Ctrl Shift C - Mac:
Command Shift C
A list of all the JupyterLab commands and corresponding keyboard shortcuts are available: https://jupyterlab.readthedocs.io/en/latest/user/commands.html
To access a list of common keyboard shortcuts, use the command:
- Windows/Linux:
Ctrl Shift H - Mac:
Command Shift H
- In-ClassScripts: This folder contains code files you will need for the workshop:
project_final_version2.py: An example Python file that is not reproduciblepenguins_Iter.csv: A data file for the non-reproducible Python code- penguins-species-predictor: An example Python reproducible research project we will be using to demonstrate the reproducible research features of JupyterLab
.
└── penguin-species-predictor
├── README.md
├── code
│ ├── 01_data_cleaning.ipynb
│ ├── 02_data_visualization.ipynb
│ └── 03_machine_learning.ipynb
├── data
│ ├── cleaned
│ │ ├── penguins_cleaned.csv
│ │ └── penguins_size.csv
│ └── raw
│ └── penguins_lter.csv
└── output
├── figures
│ ├── body_mass_boxplot.png
│ └── flipper_to_mass_scatter.png
├── manuscript
└── reports
├── 01_data_cleaning.html
├── 02_data_visualization.html
└── 03_machine_learning.html
- PresentationMaterials: This folder contains slides and other presentation materials used in the workshop
- Resources: This folder contains cheatsheets to assist you during the workshop and links to external sources for you to continue your learning
The presentation materials are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0), attributable to Data Services, Johns Hopkins University.
See LICENSE file for additional code licensing and re-use information.
The images, external resources, and cheatsheets linked in this repository may have other licenses and terms of use.
Please cite this material as:
Johns Hopkins University Data Services. February 13, 2024. JupyterLab for Reproducible Research. https://github.com/jhu-data-services/reproducible-research-jupyterlab.