Creating a Data Visualization Toolkit¶
To start developing a data visualization toolkit beyond data analysis/wrangling (i.e. the LibreOffice Calc, Excel, Python, or R) you might also want to consider tools for static prototyping and/or drawing as well as those various tools and libraries for visualization. Taking on more than one tool may seem like a lot for those starting out, but it can help avoid becoming too dependent on any one technology or resource.
This section will connect you with a few tools you might consider using for your next visualization project.
Note: “Traditional” in the below refers to tools for making the standard charts many think of, i.e. bar charts, line charts, pie charts & etc. “Creative” in the below refers to tools that particularly lend themselves to forms outside of those “traditional” chart styles. “Coding” versus “Non-Coding” in the below refers to whether or not you will need to know how to code to use that tool.
LibreOffice, Excel (not free), or Google Sheets can allow for the generation of simple charts.
Gephi is a tool for creating network graphs. Note: the Gephi site includes free tutorials.
Tableau (free for students) is a popular drag-and-drop style data visualization tool. Note: if you are not a student or employed somewhere that pays for a corporate Tableau package this can become costly.
RAWGraphs is a great alternative to Tableau, and is built on top of D3.JS and the visualizations can be later edited with a tool like Inkscape. Note: the RAWGraphs site includes free tutorials.
Google Data Studio allows you to create simple data visualization dashboards.
Pen and Paper! See the “Making a Case for Data Sketching, Analog Visualization, and Prototyping” if you need to be convinced about hand-drawn work.
Inkscape or Adobe Illustrator (not free) Note: those using Illustrator might also be interested in ai2html for converting Illustrator files into HTML & CSS.
GIMP or Adobe Photoshop (not free)
Traditional, Coding Required¶
Python Altair, Bokeh, matplotlib, Plotly, and Seaborn
Vega and/or Vega-Lite These tools are built on top of D3.JS, which makes them look like the graphics that many are familiar with. Vega-Lite is useful for producing traditional visualizations that require little personalization. Vega has a bit more breadth in terms of options. Note: the Vega site includes free tutorials.
Andy Kirk’s collection of visualization tools might be the most extensive list there is; however it might be an overwhelming list to peruse for those just starting out.
Jon Olav H. Eikenes’ article “Designing a Custom Data Visualisation” speaks usefully to using a ranging data visualization toolkit (R, Illustrator, and P5.JS) to create a visualization.
Note: I have provided additional materials on many of the above tools in the Additional Resources section in order to avoid cluttering this page.