Welcome Back!

Principal Skinner from Simpsons saying

Hello students, faculty, and everyone else who makes up the amazing community of the University of Illinois at Urbana-Champaign! We hope the beginning of this new academic year has been an exciting and only-mildly-hectic time. The Scholarly Commons, your central hub for qualitative and quantitative research assistance, has officially resumed our extended hours.

That’s right, for the entirety of this beautiful fall semester we will be open Monday-Friday, 8:30am-6:00pm!

In addition to our expansive software and numerous scanners, the Scholarly Commons is here to provide you with access to both brand new and continued services

Ted Mosby from How I Met Your Mother asking

New additions to the Scholarly Commons this semester include two, new, high-powered computers featuring: 6-core processors, NVidia 1080 video cards, 32GB RAM, and solid-state drives. 

For the first time, we’ll also be offering REDCap (Research Electronic Data Capture) consultations to help you with data collection and database needs. Drop-in hours are available during this fall on Tuesdays, 9:00-11:00am in the Scholarly Commons.

CITL Statistical Consulting is back to help you with all your research involving R, Stata, SPSS, SAS, and more. Consultations can be requested through this form.
Drop-in hours are available with CITL Consultants:
Monday: 10:00am-4:00pm
Tuesday: 10:00am-4:00pm
Wednesday: 10:00am-1:00pm, 2:00-5:00pm
Thursday: 10:00am-4:00pm
Friday: 10:00am-4:00pm

Billy Mays saying

Once again our wonderful Data Analytics and Visualization Librarian, Megan Ozeran, is offering office hours every other Monday, 2:00-4:00pm (next Office Hours will be held 9/9). Feel free to stop by with your questions about data visualization!

And speaking of data visualization, the Scholarly Commons will be hosting the Data Viz Competition this fall. Undergraduate and graduate student submissions will be judged separately, and there will be first and second place awards for each. All awards will be announced at the finale event on Tuesday, October 22nd. Check out last year’s entries.  

As always, please reach out to the Scholarly Commons with any questions at sc@library.illinois.edu and best of luck in all your research this upcoming year!

Exploring Data Visualization #14

In this monthly series, I share a combination of cool data visualizations, useful tools and resources, and other visualization miscellany. The field of data visualization is full of experts who publish insights in books and on blogs, and I’ll be using this series to introduce you to a few of them. You can find previous posts by looking at the Exploring Data Visualization tag.

A day in the life of Americans: a data comic

A comic demonstrating the amount of time Americans spend sleeping, at work, free, or doing other activities from 4 a.m. to 3 p.m.

By illustrating the activity most Americans are doing at a given hour, Hong highlights what the average day looks like for an American worker.

Happy May, researchers! With the semester winding down and summer plans on the horizon, a lot of us are reflecting on what we’ve done in the past year. Sometimes it can be hard to determine what your daily routine looks like when you are the one doing it every day. Matt Hong created a cute and informative data comic about how we spend our time during the day, based on data from the Census Bureau. Check out Hong’s Medium page for more data comics.

What Qualifies as Middle-Income in Each State

A bar chart that shows the range of incomes that qualify as "middle-income" for households made up of four people, organized by state.

The distribution of middle-income for households made up of four people.

Nathan Yau at Flowing Data created an interesting chart that shows the range of income that is considered “middle-income” in each state and the District of Columbia in the United States. The design of the chart itself is smooth and watching the transitions between income ranges based on number of people in the household is very enjoyable. It is also enlightening to see where states fall on the spectrum of what “middle-income” means, and this visualization could be a useful tool for researchers working on wage disparity.

When People Find a New Job

A frequency trail chart that shows peaks based on the age when people change jobs.

The bottom of the chart shows jobs that people transition into later in life.

The end of the semester also means a wave of new graduates entering the workforce. While we extend our congratulations to those people, we often inquire about what their upcoming plans are and where they will be working in the future. For some, that question is straightforward; for others, a change of pace may be on the horizon. Nathan Yau of Flowing Data also created a frequency trail chart that shows at what age many people change career paths. As Yau demonstrates in a bar chart that accompanies the frequency trail chart, the majority of job switches happen early and late in life, a phenomenon which he offers some suggestions for.

A bar chart showing the distribution of the age at which people switch jobs. 15-19 is the highest percent (above 30%) and 55-64 is the lowest (around 10%)

The peak at the “older” end of the chart indicates some changes post-retirement, but also makes you wonder why people are still finding new jobs at age 85 to 89.

I hope you enjoyed this data visualization news! If you have any data visualization questions, please feel free to email the Scholarly Commons.

Exploring Data Visualization #13

In this monthly series, I share a combination of cool data visualizations, useful tools and resources, and other visualization miscellany. The field of data visualization is full of experts who publish insights in books and on blogs, and I’ll be using this series to introduce you to a few of them. You can find previous posts by looking at the Exploring Data Visualization tag.

One Way to Spot a Partisan Gerrymander

Even though it feels like it was 2016 yesterday, we are more than a quarter of the way through 2019 and the 2020 political cycle is starting to heat up. A common issue in the minds of voters and politicians is fraudulent and rigged elections—voters increasingly wonder if their votes really matter in the current political landscape. Last week, the Supreme Court heard two cases on partisan gerrymandering in North Carolina and Maryland. FiveThirtyEight made an elegant visualization about gerrymandering in North Carolina. The visualization demonstrates how actual election outcomes can be used to extrapolate what percentage of seats will go to each party.

A graph that shows the Average Democratic vote share in the U.S. House plotted against the actual outcome. A pink line represents the average outcome and since it does not pass through (0, 0), that indicates partisan bias in the House election being studied.

If there is no partisan bias in voting districts, the outcome should be 50/50.

As you scroll, the chart continues to develop and become more complicated. It adds results from past elections to contextualize the severity of the current problems with gerrymandering. It also provides an example of the outcomes of a redrawn district map in Pennsylvania.

Mistakes, we’ve drawn a few

Two different charts that both represent attitudes in the UK toward Britain voting to leave the EU. The chart on the left is a sine chart which looks erratic while the chart on the right shows the averages of plotted lines and demonstrates clear trends.

The change from line chart to plotted points better demonstrates the trend of the attitudes toward Brexit.

Sarah Leo from The Economist re-creates past visualizations from the publication that were misleading or poorly designed. The blog post calls out the mistakes made very effectively and offers redesigns, when possible. They also make their data available after each visualization.

Seeing two visualizations of the same data next to one another really helps drive home how data can be represented differently–and how that causes different impacts upon a reader.

FastCharts

The Financial Times has made an online version of their quick chart-making tool available for the public. Appropriately titled FastCharts, the site lets you upload your own data or play around with sample data they have provided. Because this tool is so simple, it seems like it would be useful for exploratory data, but maybe not for creating more complex explanations of your data.

The interface of FastCharts, showing a line chart of global temperature anomalies from 1850 to 2017.

FastCharts automatically selects which type of chart it thinks will work best for your data.

Play with the provided example data or use your own data to produce an interesting result! For a challenge, see if any of the data in our Numeric Data Library Guide can work for this tool.

I hope you enjoyed this data visualization news! If you have any data visualization questions, please feel free to email the Scholarly Commons.

Exploring Data Visualization #12

In this monthly series, I share a combination of cool data visualizations, useful tools and resources, and other visualization miscellany. The field of data visualization is full of experts who publish insights in books and on blogs, and I’ll be using this series to introduce you to a few of them. You can find previous posts by looking at the Exploring Data Visualization tag.

American segregation, mapped day and night

Is segregation in the United States improving? And if it is, what race sees the most people of different races? And do the answers to these questions change based on the time of day? Vox sets out to answer some of these questions through a video essay and an interactive map about segregation in the United States cities at work and at home.

A map of Champaign County showing data peaks where the highest population of Black people live.

This map shows the population density of Black people living in Champaign-Urbana, IL. The brighter the pink, the higher the percentage of Black people living only near Black people.

A map showing the areas in Champaign County populated by white people.

This map shows the population density of white people living in Champaign-Urbana, IL. The brighter the pink, the higher the percentage of white people living only near white people.

The map is interesting and effectively demonstrates the continued presence of segregation in communities across the United States. However, there is little detail on the map about the geographical features of the region being examined. This isn’t too much of a problem if you are familiar with the region you are looking at, but for more unfamiliar communities it leads to more questions than it answers.

NASA’s Opportunity Rover Dies on Mars

 

After 15 years on Mars, the Opportunity Rover Mission was officially declared finished on February 13th, 2019. The New York Times created a visualization that lets you follow Opportunity’s 28 mile path across the surface of Mars, which includes a bird’s eye view of Oppy’s path as well as images sent by the rover back to NASA. Opportunity was responsible for discovering evidence of drinkable water on Mars.

A map of the surface of mars with a yellow line showing the path of NASA's Opportunity rover. There is a small image in the corner of Santa Maria Crater taken by the rover.

The map of Opportunity’s path is accompanied by images from the rover and artists’ renderings of the surface of Mars.

The periodic table is a scatterplot. (Among others.)

 

The periodic table: a data visualization familiar to anyone who has ever set foot in a grade school science classroom. As Lisa Rost points out, the periodic table is actually just a simple scatter plot, with group as the x-axis and period as the y-axis. Or at least, that’s true of the Mendeleev periodic table, the one we are most familiar with. See some other examples of how to break down the periodic table on Rost’s post, which links to the Wikipedia article on alternative periodic tables. If you find a favorite, be sure to tweet it to us @ScholCommons! We are always curious to see what visualizations get people excited.

A visualization of the periodic table of the elements with the elements represented by different colored dots. The dot colors correspond to when in time the elements were discovered, which is coded in a key at the top of the chart. Yellow is before Mendeleev, blue is after Mendeleev, orange is BC, and black is since 2000.

A periodic table color coded by Lisa Rost to show when in time different elements where discovered.

I hope you enjoyed this data visualization news! If you have any data visualization questions, please feel free to email the Scholarly Commons.

Exploring Data Visualization #11

 In this monthly series, I share a combination of cool data visualizations, useful tools and resources, and other visualization miscellany. The field of data visualization is full of experts who publish insights in books and on blogs, and I’ll be using this series to introduce you to a few of them. You can find previous posts by looking at the Exploring Data Visualization tag.

Data Visualization Office Hours and Workshops

A headshot of Megan Ozeran with a border above her reading Data Viz Help and a banner below that reads The Librarian is In

Our amazing Data Visualization Librarian Megan Ozeran is holding open office hours every other Monday for the Spring 2019 semester! Drop by the Scholarly Commons from 2-4 on any of the dates listed below to ask any data viz questions you might have.

Office hours on: February 25, March 11, March 25, April 8, April 22, and May 6.

Additionally, Megan will teach a joint workshop as part of our Savvy Researcher series titled “Network Analysis in Digital Humanities” on Thursday, March 7th. Megan and SC GA Kayla Abner will cover the basics of how to use NodeXL, Palladio, and Cytoscape to show relationships between concepts in your research. Register online on our Savvy Researcher Calendar!

Lifespan of News Stories

A chart showing the search interest for different news stories in October 2018, represented as colored peaks with the apex labeled with a world event.

October was one of the busier times of the year, with eight overlapping news stories. Hurricane Michael tied with Hurricane Florence for the largest number of searches in 2018.

According to trends compiled by the news site Axios, “news cycles for some of the biggest moments of 2018 only lasted for a median of 7 days.” Axios put together a timeline of the year which shows the peaks and valleys of 49 of the top news stories from 2018. A simplified view of the year in the article “What captured America’s attention in 2018” shows the distribution of those 49 stories, while a full site, “The Lifespan of News Stories,” shows search interest by region and links to an article from Axios about the event (clever advertising on their part).

#SWDchallenge: visualize variance

A graph showing the average minimum temperature in Milwaukee, Wisconsin, for January 2000 through January 2019. The points on the chart are connected with light blue lines and filled in with blue to resemble icicles.

Knaflic’s icicle-style design for minimum temperature.

If there were to be a search interest visualization for the past few weeks in the Midwest, I have no doubt that the highest peak would be for the term “polar vortex.” The weather so far this year has been unusual, thanks to the extreme cold due to the polar vortex we had in the last week of January. Cole Nussbaumer Knaflic from Storytelling with Data used the cold snap as inspiration for the #SWDchallenge this month: visualize variance. Knaflic went through a series of visualizations in a blog post to show variation in average temperature in Milwaukee.

I hope you enjoyed this data visualization news! If you have any data visualization questions, please feel free to email the Scholarly Commons.

Exploring Data Visualization #10

In this monthly series, I share a combination of cool data visualizations, useful tools and resources, and other visualization miscellany. The field of data visualization is full of experts who publish insights in books and on blogs, and I’ll be using this series to introduce you to a few of them. You can find previous posts by looking at the Exploring Data Visualization tag.

A collage of images of sticky notes in different configurations from the article "stickies!"

Sticky notes in all different shapes, sizes, and colors provide a perfect medium for project planning.

1. Sometimes when you want to visualize your thinking, digital tools just don’t cut it and you have to go back to cold, hard paper. At the beginning of November, Cole Nussbaumer Knaflic at Storytelling with Data made a #SWDchallenge for readers to use sticky notes to represent their thinking and plan out a data visualization the old fashioned way! The images that resulted from that challenge, seen in the post stickies!, are an office-supply lover’s dream. I’ve taken inspiration from these posts in my own project planning for the past month—here’s a sneak peek of my thoughts for a sign that will be displayed in a library study space:

A piece of paper that reads "Welcome to Room 220" at the top with sticky notes stuck to the page underneath.

2. In a feature from February of this year, the digital branch of German newspaper Die Zeit, ZEIT ONLINE, showed some interesting finds from their database of approximately 450,000 street names used across Germany. They call the project Streetscapes and use them to explore important parts of German history. These street names show the legacy of political division in Germany, as well as noting what the most common names for streets are and what the age of different streets in Berlin are.

A map of Berlin with streets highlighted in different colors based on the age of the street name.

Older street names are clearly concentrated toward the center of Berlin.

3. Google Maps updated their display this year to zoom out to a globe instead of a flat Mercator projection, noting in a tweet on August 2nd that “With 3D Globe Mode…, Greenland’s projection is no longer the size of Africa.” Adapting the shape of countries from a globe to a flat map has always been a challenge and has resulted in some confusion as to how the Earth’s geography actually looks. In the third part of a series of Story Maps about “The World’s Troubled Lands & Geopolitical Curiosities,” John Nelson outlines some of those misconceptions. In a National Geographic write-up titled “Why your mental map of the world is (probably) wrong,” Betsy Mason goes deeper into why we hold these misconceptions and why they are so hard to let go of.

The title slide of a story map with text that reads "Misconceptions Some Common Geographic Mental Misplacements..."

The story map shows which three different regions people often misplace in their minds.

I hope you enjoyed this data visualization news! If you have any data visualization questions, please feel free to email the Scholarly Commons.

Exploring Data Visualization #9

In this monthly series, I share a combination of cool data visualizations, useful tools and resources, and other visualization miscellany. The field of data visualization is full of experts who publish insights in books and on blogs, and I’ll be using this series to introduce you to a few of them. You can find previous posts by looking at the Exploring Data Visualization tag.

Map of election districts colored red or blue based on predicted 2018 midterm election outcome

This map breaks down likely outcomes of the 2018 Midterm elections by district.

 

Seniors at Montgomery Blair High School in Silver Spring, Maryland created the ORACLE of Blair 2018 House Election Forecast, a website that hosts visualizations that predict outcomes for the 2018 Midterm Elections. In addition to breakdowns of voting outcome by state and district, the students compiled descriptions of how the district has voted historically and what are important stances for current candidates. How well do these predictions match up with the results from Tuesday?

A chart showing price changes for 15 items from 1998 to 2018

This chart shows price changes over the last 20 years. It gives the impression that these price changes are always steady, but that isn’t the case for all products.

Lisa Rost at Datawrapper created a chart—building on the work of Olivier Ballou—that shows the change in the price of goods using the Consumer Price Index. She provides detailed coverage of how her chart is put together, as well as making clear what is missing from both hers and Ballou’s chart based on what products are chosen to show on the graph. This behind-the-scenes information provides useful advise for how to read and design charts that are clear and informative.

An image showing a scale of scientific visualizations from figurative on the left to abstract on the right.

There are a lot of ways to make scientific research accessible through data visualization.

Visualization isn’t just charts and graphs—it’s all manner of visual objects that contribute information to a piece. Jen Christiansen, the Senior Graphics Editor at Scientific American, knows this well, and her blog post “Visualizing Science: Illustration and Beyond” on Scientific American covers some key elements of what it takes to make engaging and clear scientific graphics and visualizations. She shares lessons learned at all levels of creating visualizations, as well as covering a few ways to visualize uncertainty and the unknown.

I hope you enjoyed this data visualization news! If you have any data visualization questions, please feel free to email the Scholarly Commons.

Election Forecasts and the Importance of Good Data Visualization

In the wake of the 2016 presidential election, many people, on the left and right alike, came together on the internet to express a united sentiment: that the media had called the election wrong. In particular, one man may have received the brunt of this negative attention. Nate Silver and his website FiveThirtyEight have taken nearly endless flak from disgruntled Twitter users over the past two years for their forecast which gave Hillary Clinton a 71.4% chance of winning.

However, as Nate Silver has argued in many articles and tweets, he did not call the race “wrong” at all, everyone else just misinterpreted his forecast. So what really happened? How could Nate Silver say that he wasn’t wrong when so many believe to this day that he was? As believers in good data visualization practice, we here in the Scholarly Commons can tell you that if everyone interprets your data to mean one thing when you really meant it to convey something else entirely, your visualization may be the problem.

Today is Election Day, and once again, FiveThirtyEight has new models out forecasting the various House, Senate, and Governors races on the ballot. However, these models look quite a bit different from 2016’s, and in those differences lie some important data viz lessons. Let’s dive in and see what we can see!

The image above is a screenshot taken from the very top of the page for FiveThirtyEight’s 2016 Presidential Election Forecast, which was last updated on the morning of Election Day 2016. The image shows a bar across the top, filled in blue 71.4% of the way, to represent Clinton’s chance of winning, and red the rest of the 28.6% to represent Trump’s chance of winning. Below this bar is a map of the fifty states, colored from dark red to light red to light blue to dark blue, representative of the percentage chance that each state goes for one of the two candidates.

The model also allows you to get a sense of where exactly each state stands, by hovering your cursor over a particular state. In the above example, we can see a bar similar the one at the top of the national forecast which shows Clinton’s 55.1% chance of winning Florida.

The top line of FiveThirtyEight’s 2018 predictions looks quite a bit different. When you open the House or Senate forecasts, the first thing you see is a bell curve, not a map, as exemplified by the image of the House forecast below.

At first glance, this image may be more difficult to take in than a simple map, but it actually contains a lot of information that is essential to anyone hoping to get a sense of where the election stands. First, the top-line likelihood of each party taking control is expressed as a fraction, rather than as a percent. The reasoning behind this is that some feel that the percent bar from the 2016 model improperly gave the sense that Clinton’s win was a sure thing. The editors at FiveThirtyEight hope that fractions will do a better job than percentages at conveying that the forecasted outcome is not a sure thing.

Beyond this, the bell curve shows forecasted percentage chances for every possible outcome (for example, at the time of writing, this, there is a 2.8% chance that Democrats gain 37 seats, a 1.6% chance that Democrats gain 20 seats, a <0.1% chance that Democrats gain 97 seats, and a <0.1% chance that Republicans gain 12 seats. This visualization shows the inner workings of how the model makes its prediction. Importantly, it strikes home the idea that any result could happen even if one end result is considered more likely. What’s more, the model features a gray rectangle centered around the average result, that highlights the middle 80% of the forecast: there is an 80% chance that the result will be between a Democratic gain of 20 seats (meaning Republicans would hold the House) and a Democratic gain of 54 (a so-called “blue wave”).

The 2018 models do feature maps as well, such as the above map for the Governors forecast. But some distinct changes have been made. First, you have to scroll down to get to the map, hopefully absorbing some important information from the graphs at the top in the meantime. Most prominently, FiveThirtyEight has re-thought the color palette they are using. Whereas the 2016 forecast only featured shades of red and blue, this year the models use gray (House) and white (Senate and Governors) to represent toss-ups and races that only slightly lean one way or the other. If this color scheme had been used in 2016, North Carolina and Florida, both states that ended up going for Trump but were colored blue on the map, would have been much more accurately depicted not as “blue states” but as toss-ups.

Once again, hovering over a state or district gives you a detail of the forecast for that place in particular, but FiveThirtyEight has improved that as well.

Here we can see much more information than was provided in the hover-over function for the 2016 map. Perhaps most importantly, this screen shows us the forecasted vote share for each candidate, including the average, high, and low ends of the prediction. So for example, from the above screenshot for Illinois’ 13th Congressional District (home to the University of Illinois!) we can see that Rodney Davis is projected to win, but there is a very real scenario in which Betsy Dirksen Londrigan ends up beating him.

FiveThirtyEight did not significantly change how their models make predictions between 2016 and this year. The data itself is treated in roughly the same way. But as we can see from these comparisons, the way that this data is presented can make a big difference in terms of how we interpret it. 

Will these efforts at better data visualization be enough to deter angry reactions to how the model correlates with actual election results? We’ll just have to tune in to the replies on Nate Silver’s twitter account tomorrow morning to find out… In the meantime, check out their House, Senate, and Governors  forecasts for yourself!

 

All screenshots taken from fivethirtyeight.com. Images of the 2016 models reflect the “Polls-only” forecast. Images of the 2018 models reflect the “Classic” forecasts as of the end of the day on November 5th 2018.

Lightning Review: Data Visualization for Success

Data visualization is where the humanities and sciences meet: viewers are dazzled by the presentation yet informed by research. Lovingly referred to as “the poster child of interdisciplinarity” by Steven Braun, data visualization brings these two fields closer together than ever to help provide insights that may have been impossible without the other. In his book Data Visualization for Success, Braun sits down with forty designers with experience in the field to discuss their approaches to data visualization, common techniques in their work, and tips for beginners.

Braun’s collection of interviews provides an accessible introduction into data visualization. Not only is the book filled with rich images, but each interview is short and meant to offer an individual’s perspective on their own work and the field at large. Each interview begins with a general question about data visualization to contribute to the perpetual debate of what data visualization is and can be moving forward.

Picture of Braun's "Data Visualization for Success"

Antonio Farach, one of the designers interviewed in the book, calls data visualization “the future of storytelling.” And when you see his work – or really any of the work in this book – you can see why. Each new image has an immediate draw, but it is impossible to move past without exploring a rich narrative. Visualizations in this book cover topics ranging from soccer matches to classic literature, economic disparities, selfie culture, and beyond.

Each interview ends by asking the designer for their advice to beginners, which not only invites new scholars and designers to participate in the field but also dispels any doubt of the hard work put in by these designers or the science at the root of it all. However, Barbara Hahn and Christine Zimmermann of Han+Zimmermann may have put it best, “Data visualization is not making boring data look fancy and interesting. Data visualization is about communicating specific content and giving equal weight to information and aesthetics.”

A leisurely, stunning, yet informative read, Data Visualization for Success offers anyone interested in this explosive field an insider’s look from voices around the world. Drop by the Scholarly Commons during our regular hours to flip through this wonderful read.

And finally, if you have any further interest in data visualization make sure you stay up to date on our Exploring Data Visualization series or take a look at what services the Scholarly Commons provides!

Exploring Data Visualization #8

Note from Megan Ozeran, Data Analytics & Visualization Librarian: It’s been a pleasure sharing data visualization news with you over the last seven months. Now, I am excited to announce that one of our awesome Graduate Assistants, Xena Becker, will oversee the Exploring Data Visualization series. Take it away, Xena!

In this monthly series, I share a combination of cool data visualizations, useful tools and resources, and other visualization miscellany. The field of data visualization is full of experts who publish insights in books and on blogs, and I’ll be using this series to introduce you to a few of them. You can find previous posts by looking at the Exploring Data Visualization tag.

Bar graph of energy consumption

From Chartable

1) The amount of energy required to make electronic devices and information centers run on a daily basis is significant—but just how much energy is used worldwide? Lisa Charlotte Rost used the principles from Alberto Cairo’s the truthful art to design and explain the choices behind a chart showing worldwide IT energy consumption.

Line graph showing income inequality over time

Junk Charts breaks down what works and what doesn’t in this graphic

2) Crazy Rich Asians was a box office hit this summer, gaining attention for its opulent set design and for being the first film to feature Asians and Asian Americans in most of the leading, directing, and other production roles since the 1990s. The New York Times used the opening of the film to write a report on Asian immigration and wealth disparity in the United States. Junkcharts wrote up a breakdown of the data visualizations used in the report, noting what the NYTimes did well and what areas could be improved in their representations.

A portion of a graphic that uses colored bars to indicate whether Brett Kavanaugh and Dr. Christine Blasey Ford answered the questions they were asked during the Senate confirmation hearing for Brett Kavanaugh.

The graphic incorporates the transcript to indicate what questions were answered or left unanswered.

3) The news cycle has been dominated by the confirmation hearing of Supreme Court nominee Brett Kavanaugh, who has been accused to sexual assault by Dr. Christine Blasey Ford. Vox created a simple but impactful chart that shows every time Ford or Kavanaugh answered (or did not answer) the question they had been asked.

I hope you enjoyed this data visualization news! If you have any data visualization questions, please feel free to email the Scholarly Commons.