Merge pull request #25 from ryanlovett/workflow-ext

Move external workflow doc to workflow.

_toc.yml

@@ -31,7 +31,6 @@ parts:
   - file: technology/vscode.md
   - file: technology/real-time-collaboration
   - file: technology/syncthing
-  - file: technology/pedagogy-and-technology/external
 - caption: Workflow Basics
   chapters:
   - file: workflow/creating-notebooks
@@ -61,6 +60,7 @@ parts:
     title: Work with Large Datasets
   - file: workflow/bestpractices
     title: Instructional Design Best Practices for Creating Jupyter Notebooks
+  - file: workflow/external-technology
   # FIXME: r-datahub has very little to do with using R on DataHub
   - file: workflow/r-datahub
   - file: faq/share

technology/rstudio.md

@@ -17,7 +17,7 @@ RStudio is available on many of our R-based hubs, and is the default application
 
 ## RStudio Applications
 
-RStudio has integration with applications such as [Quarto](quarto) and [Shiny](shiny). These applications should work as they do on desktop machines. Windows and tabs will be launched as expected.
+RStudio has integration with applications such as [Quarto](quarto.md) and [Shiny](shiny.md). These applications should work as they do on desktop machines. Windows and tabs will be launched as expected.
 
 ## Using nbgitpuller
 

technology/shiny.md

@@ -9,7 +9,7 @@ dashboards.
 ```{figure} ../images/shiny_combined.gif
 :width: 500px
 :align: center
-:name: Shiny Demo
+:name: Shiny demo on DataHub
 
 Short demo of Shiny application on DataHub
 ```

workflow/external-technology.md

@@ -0,0 +1,46 @@
+---
+title: External Computing
+---
+
+People who cannot access DataHub may still be able to do computing in other locations.
+
+## Personal Computer with Miniforge
+
+[Miniforge](https://conda-forge.org/miniforge) is a distribution of [Anaconda](https://docs.anaconda.com/anaconda/install/index.html) that has easy access to a wide range of open source scientific computing software. Download and run the appropriate `Miniforge3` installer from the link above.
+
+Launch a terminal on your computer and install software.
+
+```bash
+# Install Jupyter with Python kernel
+mamba install jupyterlab ipython
+
+# Install R and its Jupyter kernel
+mamba install r-essentials r-tidyverse r-irkernel
+```
+
+Launch Jupyter from a terminal:
+```bash
+jupyter lab
+```
+
+The following is a screenshot of Jupyter running the R kernel.
+
+```{figure} ../images/jupyter_notebook_r_kernel.jpg
+:width: 500px
+:align: center
+:name: Jupyter Notebook with R Kernel
+```
+
+## Binder
+
+[Binder](https://mybinder.org) enables users to launch Python and R notebooks in a free and open executable environment. Launch [Jupyter R notebook](https://notebooks.gesis.org/binder/jupyter/user/binder-examples-r-conda-ofu958y9/lab) or [R Studio](https://mybinder.org/v2/gh/binder-examples/r-conda/master?urlpath=rstudio) using the shared binder links.
+
+## RStudio Cloud and Desktop
+
+You can purchases a hosted [RStudio Cloud](https://rstudio.cloud/plans/instructor) environment for your coursework. Students would need to create RStudio accounts. They would also need to upload and download assignments to/from their RStudio cloud accounts. The is also a [free tier account](https://rstudio.cloud/plans/free).
+
+If you don't want to use RStudio Cloud, you can ask students to download and install [RStudio Desktop](https://www.rstudio.com/products/rstudio/) onto their own device.
+
+## Google Colab
+
+You can launch Python or R based notebook environments on [Google Colab](https://colab.research.google.com).