Data Visualization

Why Create Visualizations of Your Data?

Alison Link

  • Promotes efficient communication about quantitative and qualitative data
  • Facilitates exploratory analysis; finding and interpreting patterns in data
  • Makes use of art, design, and communication theory as well as statistics and data science
  • Mike Bostock uses a variety of visualization methods to present data on topics from the NFL Playoffs to Senate races, health care laws, trends in manufacturing, and Oscar nominees

Data visualization is an ever-evolving technique, with new modes of visualization emerging constantly for both quantitative and qualitative data. It can be a powerful approach to help notice and emphasize patterns, add emotional impact to an argument, or simply help present information more efficiently. Part of the reason data visualization is so compelling to scholars, learners, and the public at large is that it leverages humans’ strongest and most nuanced sensory power: vision. As Ware (2004) has suggested: “Why should we be interested in visualization? Because the human visual system is a pattern seeker of enormous power and subtlety.” (cited in Few, p. 29). Reading text, numeric, or tabular information in a traditional print format can be a slow and cognitively intensive process. Visualization can take this same type of information and make patterns more salient and readily perceptible.

Data visualization draws inspiration from fields as diverse as statistics, art, design, computer science, communication theory, among others. Modern data visualization also rests upon a long a tradition of using graphical elements to aid and enhance human thought. As Card, et al. suggest: “graphic aids for thinking have an ancient and venerable history. What is new is the evolution of computers in making possible a medium for graphics with dramatically improved rendering, real-time interactivity, and dramatically lower cost” (p. 1).

There are a number of ways you may consider engaging with data visualization in your own scholarly processes, including:

Sense-making: exploratory analysis; theory building

Efficient communication: enhance research presentations & reports with data displays

Enhance rhetoric: enhance an argument or story with visualizations; critically analyze the persuasiveness of visualizations in media

One big challenge, particularly in humanities disciplines, can be obtaining data in a format that can be readily visualized using digital tools. Sometimes data can be downloaded from archives or open data portals, or you may need to contact the data provider to get a data export. In some cases, you may also be able to “scrape” data from the internet and rearrange it into a format that is ready to be visualized.

For any questions or assistance, please contact us (

Exemplary Projects

Journalism in the Age of Data
(Stanford University)

Mike Bostock
(Mike Bostock)

Humanities + Design Lab
(Stanford University)

Recommended Tools for Data Visualization

Learn more about the following tools that can facilitate data visualization.



Widely available on most computers; free for UMN students and faculty.

Can be unwieldy for large datasets. Limited to 1,048,576 rows and 16,384 columns.

Prerequisite Knowledge
Accessible to most users who have some familiarity with spreadsheets. Advanced statistical features, plugins, and scripting/automating may require some additional background in statistics or programming.

Support Guides & Tutorials
Taylor, D. (2016). “Excel Charts in Depth”.

Microsoft. “Analyzing and Visualizing Data with Excel”. EdX


An easy-to-use drag-and-drop interface for data visualization that is accessible to beginners, but also grows to meet a number of advanced data processing and visualization needs.

Tableau is very different from basic spreadsheet programs like Excel and more sophisticated statistical programs like R, SPSS, SAS, etc. Beginners and advanced users alike will likely need to set aside a little time to learn and adjust to Tableau’s relatively unique interface.

Prerequisite Knowledge
A lot of Tableau’s visualizations require the user to make decisions about how to group or ungroup data rows to make effective visualizations, so some conceptual understanding of data aggregation and/or pivot tables is helpful.

Support Guides & Tutorials
Tableau. “Free Training Videos”

Frye, C. (2016). “Tableau 10 Essential Training”.

R (ggplot, plotly)

R is free and open source. It has a large range of packages that offer highly specialized visualization functionality. ggplot and plotly, for example, are two libraries that have become popular for creating data visualizations in R. Users can also create reports in RMarkdown that integrate R visualizations and that can be exported and shared as HTML or PDF files.

May be difficult for users unfamiliar with statistical programming. The setup and syntax can vary a lot between packages.

Prerequisite Knowledge
Requires some familiarity with programming or scripting to get started, or a willingness to spend some time learning statistical programming.

Support Guides & Tutorials
Carlberg, C. (2016). “R for Excel Users”.

Arnold, T. & Tilton, L. (2015). Humanities Data in R. Berlin: Springer


A very fast, highly flexible tool for manipulating both small and large datasets and creating visualizations that can be viewed in a web browser.

D3 requires very specialized coding syntax that will likely require some self-study time to learn. Spin-off libraries based on D3, such as C3.js, may help simplify some of this complexity for users who want basic charting functionality based on D3, but with a less laborious learning curve.

Prerequisite Knowledge
Requires some familiarity with JavaScript to get started. Even users with prior JavaScript experience should expect to spend some time familiarizing themselves with the specific “grammar” of D3.

Support Guides & Tutorials
D3 documentation [GitHub wiki].


Guides on Data Visualization


Card, et al. (1999). Readings in Information Visualization: Using Vision to Think. San Diego, CA: Academic Press.

Few, S. Now You See It: Simple Visualization Techniques for Quantitative Analysis. Oakland, CA: Analytics Press.

Wong, D. M. (2010). The Wall Street Journal Guide to Information Graphics: The Dos and Don’ts of Presenting Data, Facts, and Figures. New York: W.W. Norton & Company.

Zepel, T. (2013). “Visualization as a Digital Humanities ______?”. HASTAC blog post.


Shander, B. (2016) “Data Visualization Storytelling Essentials”.

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