OxShef: Tools

Visualisation Tools

There are hundreds of visualisation tools and packages used by researchers for communicating research datasets. OxShef: Tools cannot hope to be a reference or a recommendation engine for all of these solutions. We maintain a collection of the most popular/versatile tools below. For a birds eye view of the entire range of dataviz tools avaialble, you might find this resource useful: http://www.visualisingdata.com/resources/.

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Data can be pulled from external sources

Tools that can pull data from external sources fullfil OxShef: Tools reproducible dataviz workflow requirements and are highly recommended by us. In general, we will provide a dedicated website for such tools in the table below.

Data cannot pulled from external sources

As data cannot be pulled directly from a data repository, these tools do not meet OxShef: Tools reproducible dataviz workflow requirements. You will be required to duplicate your data on the visualisation service’s website, which makes keeping your visualisation consistent with your canonical datset difficult.

Visualisation tools fit neatly into these two categories, either of these may be suitable for a reproducible dataviz workflow.

Point and Click Tools

These allow users to build visualisations interactively, for instance selecting columns from a spreadsheet-like view of your data and clicking “Create BarChart”. Examples of this type of tool include: Excel, SPSS and Tableau.

Scripting Tools

These require users to write code (or scripts) to generate visualisations, such tools in general have a steeper initial learning curve than “point and click tools” but allow greater overall flexibility and extensibility. Examples of this type of tool include: Python, R.

Visualisation Tool	Brief Description	Allows external data to be accessed?	Type of tool
	Dash allows Python users to build rich interactive web applications and visualisations through a combination of different technologies, including React and Flask. OxShef are currently investigating the reproducability of a Dash-driven dataviz visualisation workflow, which may lead to a dedicated site about this tool in the future.
	Jupyter (the spiritual successor to iPython notebooks) is a powerful tool for creating rich documents incorporating code, data and visualisation outputs. Jupyter notebooks allow code written in Python, R and more to be combined together easily. Oxshef are currently developing a site dedicated to using this tool in a reproducible dataviz workflow.
	Plotly provides a free tool for creating interactive visualisations from dataset uploaded to the Plotly service. It's a great tool for creating "one off" visualisations but does not fit into a fully reproducible workflow as data must be siloed in the plot.ly website.
	R is a very popular scripting language that includes a wide range of popular visualisation tools, including ggplot2 and htmlwidgets for interactive visualisation. It is possible to build fully functioning web applications with R by using Shiny. We thoroughly recommend that R users browse OxShef: Shiny to see what's possible.
	Shiny is a technology that allows users of R to create interactive web applications without strictly having to learn HTML, CSS, JavaScript or anything about web-hosting. There is a dedicated OxShef: Shiny website with tutorials and templates for creating your own data visualisations with Shiny. [Read more...]
	Vega-Lite provides a high-level grammar of interactive graphics, allowing users to specify "charts as data" in well designed JSON format. OxShef are currently investigating the reproducability of a Vega-Lite driven dataviz workflow, which may lead to a dedicated site about this tool in the future.