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Software Review: Tableau as a Teaching Tool

Tableau is unique and a valuable teaching tool because it provides an easy interface for the creation of charts, graphs and even maps.  Students can explore data in sophisticated ways with only a short training session.  Even better, as students they can get free licenses for the software, allowing faculty to use it for classes without ensuing large financial commitments.

A map showing fatality and even types of different violent events in Africa

What sets Tableau apart from other data visualization or business intelligence software is its intuitive, user-friendly drag-and-drop interface. For more sophisticated applications this is supplemented by a variety of easy to understand menus. By using contextual menus and panels instead of typing in code, Tableau lowers the learning curve needed to create visualizations. For example, creating a line graph or a map is as easy as selecting the variables in question and selecting the appropriate type of visualization.

Classic tables like the one below are easy to construct and can also be augmented with color-coded hotspot analyses.

A highlight table showing the number of violent events happening in Egypt, Libya, South Sudan, and Sudan broken down by Country and Event Type

Tableau provides the opportunity to construct data visualizations that are more complex than those generated by most traditional statistical packages.  For example, the graphic below compares the number of conflicts over time for four North African countries in a fairly normal plot, but add an additional variable, the number of fatalities by varying line thickness.

A line graph showing the trend of the number of violent events in 4 African countries (Egypt, Libya, South Sudan, and Sudan) between 1997 and 2015. The thickness of the lines represent number of fatalities.

For classes working with data, Tableau presents a significant opportunity for instructors to integrate more data into the classroom, especially with students who might not have experience with more advanced statistical software. Making it easier for students to explore and understand data, as well as to ask their own questions through investigative learning, encourages them to gain a deeper appreciation for data as it relates to their discipline. In fact, as of the time of writing, Tableau is currently being successfully used in several of our classes at Grinnell College.

However, Tableau does have its drawbacks. In particular, visualizations created with Tableau are not as customizable as more powerful languages such as R or Javascript. In addition, Tableau is not created for data analysis.  It is a data visualization tool, not a statistical package. Another small downside is that data entered into Tableau must be formatted in a specific way.  While Tableau is able to do some data manipulation, spreadsheet programs like Excel are much easier for this. So, Tableau’s role in classrooms or in research might only be restricted to surface-level explorations of the data in question. Despite this limitation, Tableau remains a tool with great potential, especially in the possibilities it presents to the user in creating quick and easy visualizations.

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5 Must-See TED Talks on Data Visualization!

Data visualization is crucial in understanding data and identifying hidden connections that matter. Below are 5 TED talks on data visualization you don’t want to miss!

1. Hans Rosling: The best stats you’ve ever seen

Han Rosling, cofounder of the Gapminder Foundation, developed the Trendalyzer software that converts international statistics – such as life expectancy and child mortality rate – into innovative, interactive graphics. The statistics guru is a strong advocate for public access to data and the development of tools that make it accessible and usable for all.  In this classic talk, Rosling highlights the importance of data in debunking myths about the gap between developed countries and the so-called “developing world.” Even though the talk was filmed 10 years ago, it still carries very important and relevant messages.

Watch more of Rosling’s TED talks here.

2. David McCandless: The beauty of data visualization

In this visually captivating talk, data journalist David McCandless suggests that data visualization is a quick solution to our current problem of information overload. Visualizations allow us to see the hidden patterns, identify connections that matter, and tell stories with data. To McCandless, “even when the information is terrible, the visual can be quite beautiful”; this is a controversial claim, however, since the main goal of data visualization should be to communicate information effectively through graphical means.

3. Dave Troy: Social maps that reveal a city’s intersections – and separations

A serial entrepreneur and data-viz fan, Dave Troy takes a people-focused approach to data visualization. Troy has been mapping tweets among city dwellers, revealing what connects communities and what separates them – above and beyond demographic factors such as race or ethnicity. He compares a city to a “giant high school cafeteria” and suggests that we see “how everybody arranged themselves in a seating chart”, arguing that “maybe it’s time to shake up the seating chart a little bit” to reshape our cities.

4. Eric Berlow & Sean Gourley: Mapping ideas worth spreading

An ecologist and a physicist, Eric Berlow and Sean Gourley, collaborate in this presentation to create stunning 3D visualizations demonstrating the interconnectedness of ideas. Taking 4,000 TEDx talks from 147 countries representing 50 languages, they explore their “meme-omes” – the mathematical structures that underlie the ideas behind these talks – and discover similarities between seemingly unconnected topics. Berlow and Gourley also broke down complex themes into multiple more specific ones, seeing what topics resonated with viewers and what kind of audience looked at what topic. To Gourley, mapping ideas in this way will help us “to see what’s being said, to see what’s not being said, and to be a little bit more human and, hopefully, a little smarter.”

5. Manuel Lima: A visual history of human knowledge

Founder of VisualComplexity.com Manuel Lima, described by Wired Magazine as “the man who turns data into art,” explains the visual metaphor shift from the tree to the network as “a new lens to understand the world around us.” Lima argues that the tree – an important tool to map everything from genealogy to systems of law to Darwin’s “Tree of Life” – is being replaced by a new metaphor – the network. Rigid structures are evolving into interdependent systems, and networks emerge to embody the nonlinearity, decentralization, interconnectedness, and multiplicity of ideas and knowledge. The shift in visual metaphor also represents a new way of thinking – one that is critical for us to solve many complex problems we are facing.

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Visualizing the Production Function and Cost Curves

Single, static images of data trends aren’t the most effective way to communicate the ways the different elements of an equation or formula contribute to a trend.  This is especially true for introductory economics concepts such as cost curves or the production function. Dynamic, interactive visualizations that allow users to manipulate the variables contributing to a relationship which enables the audience to better understand how equations express trends.

Krit Petrachaianan ‘17 of DASIL programmed a visualization using R that illustrates cost curves and the production function, two core concepts of introductory economics.  DASIL’s visualization allows users to manipulate the different parts of the equations that define cost curves and the production function. For instance, users can manipulate the costs per input (denoted r and w) and the amount of a particular input (denoted K for capital and L for labor). Users can also define the productivity of the firm’s inputs.

Cost curves visualize the costs of producing different levels of output. The total cost of production for a business can be subdivided into fixed and variable costs.  Some costs, such as raw materials and production supplies, change proportionally as more or less of the good or service is produced and are known as variable costs.  Other costs, such as the annual rent or salary of workers, are independent of the level of goods or services a business produces and are known as fixed costs.

The production function shows the relationship between the output produced by a firm from a given amount of inputs (i.e. labor and capital). The productivity of inputs in producing output can vary in three ways: 1) with constant productivity, the additional output produced by a given amount of input is constant as more of the input is used, 2) with diminishing productivity, the additional output produced by a given amount of input declines as more of the input is used, and 3) with increasing productivity, the additional output produced by a given amount of input increases as more of the input is used.

Explore DASIL’s latest R visualization below, as well as in the Graphs section of the Data Visualizations page and in the Economics tab of the DASIL website.


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Does Marriage Affect Earning Potential?

Using DASIL’s United States Income Data by Marital Status, Race, and Sex visualization, one can see how the effect of marriage on a person’s earnings is multifaceted in nature: it depends on who we focus on and other factors at play. However, there are general trends that do prevail.


Married people overall have higher earnings, although the difference between divorced people is smaller than that of single people. Married people with a spouse present earned over $33 annually, while single people earned on average well over $10,000 less than married people with a spouse present. While it may appear that being single correlates to lower earnings, inter-related variables may explain some of the earning discrepancies observed.


One important variable to consider is the effect of age. As we discuss in another blogpost, workers ages 15-24 earn less than those of other age brackets. Studies suggest that those belonging to the 15-24 age bracket are less likely to be married, so some of the earning trends shown may not be strictly due to marriage. In addition, as illustrated in the aforementioned blogpost, 25-34 year-olds and 65+ year-olds make about the same and the next least age demographic (about $25000 more in 2013 dollars), and 35-64 make about $20,000 more on average. The 35-64 year-olds are more likely to be established in their careers, earning their highest-paying years within this age bracket. So, some earnings trends may be attributed to the pace of a career’s trajectory.

Breaking down by gender, the general trend persists: married men make a lot more than divorced and single men of all races, $44k, $33K, and $20k respectively. Married women have been making more than single men in recent years, averaging about $2K more in 2006 and persisting into 2010. While single women made more than married women in the 80s, the trend has reversed in recent years.



Breaking down by race, both Asian single men and women make more than any other singles demographically, at both averaging about $21K in 2010. Hispanic single women make the least of all demographics of men and women, at $15.1K, although Black single men are a close second. Earnings of Black single men peaked in 1998, only separated from white men by about a $200 difference. Studies attribute this peak to the economic boom of the 1990s and the transition of Black men into higher-skilled service-industry jobs.



Married Hispanic women still make less in comparison to all other married women, at $19.1K, but still substantially more than if they are single. Black females top the earnings compared to women of other races, at $26.6K, with the trend moving more or less in the same way as Asian married women.

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The Mass Shooting Epidemic in the United States

An examination of Stanford University’s Mass Shootings of America (MSA) dataset shows why shootings have been making the headlines in the U.S. and gun violence has become a big issue addressed in the campaigns of presidential hopefuls. Stanford MSA defines a mass shooting as “3 or more shooting victims (not necessarily fatalities), not including the shooter. The shooting must not be identifiably gang or drug related” (Stanford Mass Shootings in America, courtesy of the Stanford Geospatial Center and Stanford Libraries).


The dramatic change in the number of mass shootings in the past two years is readily apparent. There were 121 mass shooting events from 1966 to 2009, but 116 just in the past 5 years. 2015 alone had 65 separate instances of mass shootings. In terms of total number of fatalities, the past 7 years are noticeably thicker than earlier years. Even in years with low numbers of mass shootings, such as 1991 which only had 5 incidences, there were a large number of fatalities (47).



The Southern states had the largest numbers of mass shootings in 2015. Florida led with 6. Even though Texas had fewer mass shootings (4), the state sustained the most fatalities, 20. North Dakota and New Hampshire are the only 2 states that have not experienced any mass shootings in the 49 year time period covered by the data (not shown). In 2015 39 mass shootings occurred in residential homes & neighborhoods, while 21 happened in public places. Back in the late 90s, schools were the primary target of mass shooters, with 3 incidents in 1997 and 1999, each.


Most of the mass shootings in the past 8 years have stemmed from a variant of an altercation, be it domestic, legal, financial, or school-related. Of course it can always be argued that all mass shooters have mental health issues, but contrary to popular belief, according to these classifications,  shooters’ mental health issues as a direct motive for shootings  has not increased in recent years, with only one incident in 2015 attributed to mental health issues. Perhaps what’s most troubling is the high number of cases where a motive can’t be identified, 23 in 2015, suggesting the need for further, more comprehensive study into the underlying causes of these mass shootings.

Many pundits largely attribute the US-specific phenomenon to things to lax gun policy. However, any progress to change gun laws, even to fund research into the causes of gun violence, has been (and continues to be) stymied by the gun lobby, led by the National Rifle Association (NRA). Re-examining the nation’s access to guns is imperative, and those in Congress who are funded by the gun lobby need to be open to that re-examination. While the data available is informative, unfettered research is integral to truly understanding the nature of gun violence and to finding effective policy solutions.

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