Adventures in Tableau

Tableau is one of the most popular data visualization tool used by scholars, researchers, and business-people.  But is that a good thing? Does data visualization give the viewer/reader an accurate picture of the information presented? Or does it allow the creator/researcher to hide flaws or omission in their data? The truth is any consolidation of information into easily-digestible chunks distorts the overall picture.  Whether it be an old-fashioned line graph or bar chart, or a dynamic information web, all data visualizations focus on some data while downplaying others.  Additionally, visualizations tend not to convey how data was gather or from where.

The pitfalls of data visualization aside, Tableau provides a fairly easy tool to create fun and vibrant visuals to convey information.  As a traveler, and a fan of cheap trips, I found Sara Loves Data’s visualization of the cheapest cities in Europe to travel to quite interesting and engaging. For the blog post on how she assembled her visualization, go here ( For the visualization alone, go here (!/vizhome/EuropeanCitiesonaBudget/EuropeanCitiesonaBudget)

I particularly liked her simplified use of icons for budget lines, inclusion of climate details for the best times to travel, and a clear disclosure at the top for how she evaluated the costs of a stay in each city.  Her visualization provides interactivity with the user when they scroll over the climate data, as it provides the number of rainy days/month or average temperature when the visitor moves the cursor over those elements.

Starting to Use Tableau

First, you have to download Tableau.  You can use the free version (but if you want to use your creations you must publish them publicly on Tableau Public). If that doesn’t float your boat, you can download and use Tableau Creator for $70/month/person.  Free = Tableau Public. Come on, share your creations with the world.

The first thing you need is a dataset, and you want that to be a cleaned up dataset.  What does that mean?  You want consistent entries, no blanks or nulls, and clear column and row headings.  As you clean up your data, you should think about how you go about doing that clean-up, how those tweaks, additions, or deletions change the data, and what those changes might do to your conclusions.

Tableau Public provides a number of sample datasets to use as you’re learning to use their software.  You can find them here.  You must download those files to your computer to use in the Tableau program.

When you open Tableau, and start a new project you will see this:

At this point you will want to connect your data source.  Select the correct file type under the Connect list on the left side of the screen.  Follow the prompts, which should lead you to this:

If you haven’t cleaned up your data, now is the time to do that.  All set? Now, the fun begins.  Click on the Sheet 1 tab at the bottom of the window.  This will open up the workspace where you can begin to play with the data, and decide which information from your dataset you want to compare and contrast.  This part is mainly trial and error.  The more you move things around the clearer it all becomes.  I chose a dataset of 633 Pokemon and their stats from the Tableau Public site.  After a lot of playing around, cursing, and talking with others who’ve used Tableau in their professional experiences, I began to get a better handle on where to put things, which visualization to use, and how filter the data.  First, you must move the column and row information to where you want it for your visualization.  Like this:

Here I am moving the “Type of Pokemon” to the column space, so my data will organize based upon Type.  Next, I move two of the other attributes that I want to show.  Like this:

These “measures” won’t be displayed on the x or y axis, but rather will be conveyed by size and color, which can be changed and added like this:

Still not a very interesting or helpful graphic.  At this point I also realized that the numbers the program was using were totals of these measures for each type not an average.  You can change this by right clicking (two finger clicking for those fellow Mac users) on the measure, selecting measure and changing the method of calculation.  Like this:

At this point, I went to the right side of the screen and began looking at the different visualizations available with these parameters.  Some work better than others to display the differences between each Pokemon type.  As seen here:

I went back to my initial setup, with the type in the columns and the attack and defense shown in the size and color (I also went back to SUM rather than AVG, why will soon be revealed).  I then added Speed to the row space.  This populated the columns with marks for all of the individual Pokemon in a particular category.  Now, I had something interesting.  I played with how the shapes looked by right clicking on the shape icon and playing with the options. I played with the color the same way as the shape.  When I hovered over a particular mark, it did not tell you the name of the Pokemon for that particular point.  I pulled over the Name “dimension” and bingo, it was all there.  How beautiful is that?   But wait, I learned one more trick.  You can filter the data within the visualization to allow for closer comparisons.  If you add a dimension to the filter box, you unlock a whole other level of comparison power.  Watch here:

Finished product can be found here: Pokemon Fun

I couldn’t get the embedding to work, so you’ll have to go to the source.


While, I’m not sure that this tool will be immediately helpful in my research, I can certainly envision ways to use Tableau to help make sense of large chunks of complicated data.  However, in the advantages lies a weakness, over-simplification.  In the humanities, we live in the minutia, the grey area, the complexity, and exceptions.  Pretty, colorful charts are fun, but only as useful as the conclusions they afford.  It is those conclusions that we as curators of information must remain mindful of their limitations.

First foray into the DH digital forest; or where are my breadcrumbs?

I initially thought that there must be a robust engagement with the digital humanities in the Art History field, because we deal with images which make for visually alluring projects.  But as I began my general Google search I was surprised to find a somewhat limited result.  This is not to say that the field is vacant, but as one narrows the field, the results become much more sparsely populated.

At first look, many projects stem from museum collections, as museum curators seeks to engage an increasingly distracted visitor population.  Sit in any museum and watch as the majority of people spend minimal time engaging with the works, let alone the wall didactics.  More common still are the people taking multiple pictures with their phones to share on social media to prove they are cultured.  I’m not saying people should not take photos of artworks with their phones, they should.  It allows them to re-engage with the work periodically as they scroll through their photo feeds, or Facebook reminds them what they were doing on a specific day three years ago.  The issue is that the old format of museums, with lengthy wall inscription and somewhat vague image labels does not invigorate a modern audience.

Technology, rightly or wrongly pervades our daily existence, our job as scholars should be to evolve in how we present our research.  No, we do not need to abandon the scholarly papers that dominate academia, but utilizing digital platforms and methods of communication provide additional avenues that open up our scholarship and create new audiences.  In this new age of public distrust of the intellectual we should not double down on archaic practices that exclude participation.  Instead we should work to show the public why the humanities, and art history, matter.

In 2012, Diane M. Zorich, an information management consultant for cultural and education organization, adapted an excerpt from her longer report about the state of Digital Art History for an article in the Journal of Digital Humanities entitled “Transitioning to a Digital World: Art History, its Research Centers, and Digital Scholarship.”  In this article, she explores the ambivalent attitude of Art History toward digital humanities, why the digital humanities in the Art History field has failed to develop as robustly as other disciplines, and what can be done to ameliorate some of the barriers.

While she brings to the discussion many excellent points, ultimately the issue comes down to comfort, funding, and tenure.  Early career scholars are more likely to have an interest in digital methods and a comfort with technology to support that inquiry, but their careers fall in to jeopardy if they expend too much time and/or effort on digital projects as they do not hold sufficient weight in considerations for tenure.  Middle- to late-career scholars possess the freedom to explore digital projects as they’ve established themselves in their field and (likely) secured tenure, but either do not want to or do not know how to explore digital methodologies.

With much of this in mind, my own current scholarship is in a stage of transition.  Although trained as a medievalist through my post-bac experience and early graduate program, over the past year and a half I’ve turned my attention toward research interests in more contemporary issues such as decolonization, race, and environment.  Deeply rooted in a desire to make my research more relevant (pedagogical, personally, socially, culturally), I am moving away from questions about the past and toward questions about how the more recent past continues to affect us today.

Toward this end I begin my journey exploring the digital humanities of Art History and Environmental Studies.  Below I present some initial projects related to past, present, and future research interests.  As I explore my own interests and further develop my future research directions I anticipate adding more projects, textual references, and scholars to this list.  The first two I am particular drawn to for the use and organization of data, models for future projects.

One project that I have always admired is Mapping Gothic France, as it has grown over the years to include more data and technologies.  A project initiated by Stephen Murray, Professor of Art History at New York University and Andrew Tallon, Assistant Professor of Art at Vassar College, Mapping Gothic France uses digital photography including 360° images, floor plans, elevations, and mapping to present a robust sense of the gothic architecture of medieval France.  While some sites have more information than others, the project provides a tool for scholars, teachers, and interested public to visually explore these spaces.

While I personally am not that scholastically interested in Andy Warhol, the warhol: timeweb, a joint project between the Andy Warhol Museum and a local Pittsburgh design company Gradient Labs, provides a fascinating look at the intersections of Warhol’s life and art with wider historical events, social changes, and technological advances.  The visual elements of this particular project allow for the user to engage with the material and information on the site based upon their own interests, rather than being led through the site according to the museum’s narrative.

Art History DH Projects:

Smithsonian’s Freer-Seckler Galleries and Wayne State University:  The Peacock Room

University of California, Berkeley: Jan Brueghel’s Complete Catalogue


DH Scholars and Blogs of interest:

Alicia Peaker:

            Ant Spider Bee


More to come…