OxShef: Tools

OxShef: Tools cannot be a complete overview of all visualisation tools available, nor can we maintain a complete overview of training and templates available for the dataviz tools that meet our reproducible dataviz workflow criteria. However, you might find these resources generally useful:

  • Awesome Lists (Data Viz): This is a crowd sourced collection of resources about data visualisation tools, tutorials and other resources. It’s maintained via GitHub, so anyone can contribute.

  • Lynda.com: Subscription learning service with courses on a wide variety of dataviz tools, many Universities provide access to Lynda.com for free. Contact your local research support teams for support.

  • Datacamp.com: Subscription learning service with courses on a variety of dataviz tools.

Materials specific to dataviz tools that meet our reproducible dataviz workflow criteria.

Dash

Dash is a technology developed by plot.ly that allows Python users to create rich, interactive data visualisations and interfaces - commonly abbreviated to “data dashboards”. This is a fairly new technology (first released June 2017) that does not have much coverage outside of the first-party resources on the plot.ly website.

OxShef: dataviz are currently drafting a dedicated site with training, tutorials and templates for Dash dashboards. In the meantime, we recommend you check out the blog annoucement and Chris’ conference presentation at SciPy 2017.

Jupyter

Jupyter notebooks are the successor to iPython notebooks, a literate programming technology that allows text, code, charts and interactive content to be combined together into a single document. Jupyter allows code from multiple languages to be combined in the same document, the easiest to use languaes are Julia, Python and R. Using a combination of these technologies it is possible to build rich, interactive data visualisations. With some additional effort it is possible to host Jupyter notebooks in such a fashion that enables a reproducible dataviz workflow.

OxShef: dataviz are currently drafting a dedicated site with training, tutorials and templates for using Jupyter in a reproducible dataviz workfow. In the meantime, you might find this Datacamp.com free tutorial useful and there is also a Lynda.com course that introduces the basics of Jupyter notebooks

Shiny

Shiny is an R library developed by RStudio that allows R users to create interactive web applications without having to learn HTML, CSS or JavaScript. OxShef: Shiny provides a host of training, tutorials and templates for creating Shiny apps within the context of a fully reproducible dataviz workflow.

There are many excellent resources for learning more (and keeping up to date with) Shiny:

  • Awesome Shiny Resources: This is a crowd sourced collection of resources for Shiny users.
  • Datacamp.com Shiny Course: This is a free entirely-in-browser introduction to Shiny that is highly recommended to complete beginners to Shiny.
  • RStudio’s Shiny Gallery: This is an excellent showcase of examples and template Shiny apps, along with in-depth technical documentation on how the internals of Shiny works.

There is also a Lynda.com course dedicated to creating Shiny apps and interactive presentations with RMarkdown.

OxShef