Note from Ann: Today’s guest post is from Johanna Morariu, Director of Innovation Network, AEA DVRTIG Chair, and dataviz aficionado.

snaBasic social network analysis is something EVERYONE can do. So let’s try out one social network analysis tool, NodeXL, and take a peek at the Twitter hashtag #eval13.

Using NodeXL (a free Excel plug-in) I will demonstrate step-by-step how to do a basic social network analysis (SNA). SNA is a dataviz approach for data collection, analysis, and reporting. Networks are made up of nodes (often people or organizations) and edges (the relationships or exchanges between nodes). The set of nodes and edges that make up a network form the dataset for SNA. Like other types of data, there are quantitative metrics about networks, for example, the overall size and density of the network.

There are four basic steps to creating a social network map in NodeXL: get NodeXL, open NodeXL, import data, and visualize.

Do you want to explore the #eval13 social network data? Download it here.

Here’s where SNA gets fun—there is a lot of value in visually analyzing the network. Yes, your brain can provide incredible insight to the analysis process. In my evaluation consulting experience, the partners I have worked with have consistently benefited more from the exploratory, visual analysis they have benefited from reviewing the quantitative metrics. Sure, it is important to know things like how many people are in the network, how dense the relationships are, and other key stats. But for real-world applications, it is often more important to examine how pivotal players relate to each other relative to the overall goals they are trying to achieve.

So here’s your challenge—what do you learn from analyzing the #eval13 social network data? Share your visualizations and your findings!

Last week, more than 3000 evaluators descended on my hometown of Washington, DC for the American Evaluation Association’s annual conference. I learned this much + slept this much = rockstar conference.


I had the pleasure of spending Monday and Tuesday in Michael Quinn Patton’s Developmental Evaluation workshop. Due 10% to my bad vision and 90% to being starstruck, I sought out front-row seats:


Best seats in the house for the best speaker in the field. #omgMQP #eval13

— Ann K. Emery (@AnnKEmery) October 14, 2013

Along with many other nuggets of gold, MQP shared the Mountain of Accountability, a simple visualization demonstrating a Maslow’s hierarchy for organizations. (Start with the basics like auditing, personnel review, and outputs; then progress to typical program evaluation; then progress to developmental evaluation and strategic learning.) This visual was a fan favorite; the ipads and iphones were flying around as everyone tried to snap a picture. Anyone else think that MQP would be a great addition to the dataviz TIG?


A must see! “World debut” of @MQuinnP‘s Mountain of Accountability at #eval13 – like a Maslow’s hierarchy for orgs — Ann K. Emery (@AnnKEmery) October 15, 2013

My biggest takeaway? Developmental evaluation is probably the future of evaluation, or at least the future of my evaluation career. Also, many evaluators wouldn’t call this approach “evaluation,” which means I’m not an evaluator, but an “evaluation facilitator.” Time to order new business cards!


On Tuesday night I had Dataviz Drinks with Stephanie Evergreen, Tania Jarosewich, Andy Kirk, Johanna Morariu, Jon Schwabish, and Robert Simmon, along with a few more poor souls who had to listen to our endless enthuasiam about charts, fonts, and other things “worth staying up late for.” We’ve each been trying to reshape the dataviz community from one of frequent criticism to one of encouragement and peer learning (e.g., the Dataviz Hall of Fame.) A few beers later, the #thumbsupviz hashtag was born.

Stay tuned for our growing gallery of superb visualizations at


@AnnKEmery plugs #thumbsupviz

— Stephanie Evergreen (@evalu8r) October 17, 2013

omg Factor Analysis…

On Wednesday I attended a pre-conference workshop about factor analysis. I learned the approach in grad school a few years ago, have only used it twice, and wanted to brush up my skills. The instructor provided a wealth of resources:


#Eval13 workshop instructors have really gone above and beyond! Entire beautiful binder on factor analysis:

— Ann K. Emery (@AnnKEmery) October 16, 2013

My biggest takeaway? Ouch. My brain was hurting. Leave the factor analysis to the experts because 99% of us are doing it wrong anyway. You don’t have to tell me twice!

Performance Management & Evaluation: Two Sides of the Same Coin

On Wednesday afternoon, I gave an Ignite presentation with my former supervisor and performance management expert, Isaac Castillo. Paired Ignites are rarely attempted, and I’m glad we took a risk. I had a lot of fun giving this talk. Stay tuned for future collaborations from Isaac and I!

Check out our slides and the recording of our presentation:


Excel Elbow Grease: How to Fool Excel into Making (Pretty Much) Any Chart You Want

On Thursday morning, I shared four strategies for making better evaluation charts: 1) adjusting default settings until your chart passes the Squint Test; 2) building two charts in one; 3) creating invisible bars; and 4) really really exploiting the default chart types, like using stacked bars to create a timeline or using a scatter plot to create a dot plot.

The section about dot plots was pretty popular, so I recorded it later:

I thought the presentation went okay, but afterwards, an audience member came up to me and asked, “So if I wanted to make a different type of chart in Excel, like anything besides a typical bar chart, how would I do it? What could I make?” “That’s what I just spent the last 45 minutes showing you.” “No I mean, if I wanted to make one of these in Excel, could I do it?” “Weren’t you in the audience for the presentation I just did?” “Yes, that would be a cool presentation, you should show us how to make those charts in Excel.” Thanks for the great idea buddy, I’ll submit that idea to next year’s conference. :)

East-coast happy hour

For the second year in a row, the east-coast AEA affiliates got together for a joint happy hour on Thursday night. Good vibes and familiar faces.


The Washington Evaluators, Baltimore Area Evaluators, New York City Consortium of Evaluators, and the Eastern Evaluation Research Society

The Conference is Over, Now What? Professional Development for Novice Evaluators

On Friday afternoon I led a roundtable with tips for novice evaluators. The discussion was awesome, especially the great chats I had with people afterwards. I’m going to write a full post recapping that session. Stay tuned!

How to Climb the R Learning Curve Without Falling Off the Cliff: Advice from Novice, Intermediate, and Advanced R Users

On Saturday morning I had the pleasure of presenting with a former teammate, Tony Fujs, and my new teammate, Will Fenn. Tony dazzled the audience with strategies for automating reports and charts with just a few lines of R code, and Will shared tips to help novices avoid falling off the learning curve cliff. Check out their resources and tips in this handout.


Tony Fujs (left) and Will Fenn (right)

I thought the presentation went okay, but afterwards, an audience member commented, “It would be really cool if you got some evaluators together to show us what kinds of things are possible in R.” “Umm yep, that’s what we just did, Will and Tony showed how to automate reports and create data visualizations in R.” “Yep exactly, that would be a great panel, you could get several evaluators together and show how to automate reports and make data visualizations in R.” “Did you see the panel we just did?” “Yeah you should put a panel together like that.” Okay thanks, I’ll consider it. :)

Evaluation Blogging: Improve Your Practice, Share Your Expertise, and Strengthen Your Network

Dozens of evaluators have influenced and guided my blogging journey, and I was fortunate to co-present with three of them on Saturday: Susan Kistler, Chris Lysy, and Sheila Robinson. I first started blogging after watching Chris’ Ignite presentation at Eval11, Susan’s initial encouragement kept me going, and Sheila provides a sounding board for my new ideas.


Left to right: Susan Kistler, Chris Lysy, and Sheila B. Robinson

Can you tell we presented on Saturday morning?! Chris and I arrived early. I almost panicked, but instead Chris and I started laughing hysterically, and then a second person arrived. Close call!


By the time we started, we drew a good crowd of 30-40 bloggers and soon-to-be bloggers. Same time next year??

Evaluation Practice in the Early 21st Century

Where have we come from, and where are we headed? Evaluators have accomplished some amazing things, and the future is bright. Patrick Germain and Michelle Portlock, evaluation directors at nonprofit organizations, shared strategies for making evaluation happen when you are not in the room:


For me, the mark of a good presentation is when the evaluator shows vs. tells us something new. Kim Sabo Flores, Chad Green, Robert Shumer, David White, Javier Valdes, and Manolya Tanyu talked about incorporating youth voices into policymaking decisions. The best part: the panelists invited a youth participant to speak alongside them on the panel so that she could share her experiences firsthand.


They taught us about youth presence vs. participation, and then they showed us about youth presence vs. participation. Well done!

A dataviz panel shared a brief history of dataviz; strategies for displaying qualitative data; and ideas for using graphic recording:

One of many, many graphic recording examples shared by Jara Dean-Coffey

One of many, many graphic recording examples shared by Jara Dean-Coffey

The Innovation Network team is pretty fond of graphic recording too, and Kat Athanasiades even recorded an entire advocacy evaluation panel. Thanks to Cindy Banyai for capturing this awesome video!

And just in case you’re not familiar with my plans for our field…

Wave goodbye to the Dusty Shelf Report!

Wave goodbye to the Dusty Shelf Report!

Lookin’ good, Eval! See you next year in Denver!

Lately I’ve been feeling let down by summary statistics: the min and max, mean and median, quartiles and standard deviation… They do their job well enough. Summary statistics tell a summary. An aggregate story, bringing all the messy scores together into some sort of cohesion. We grab the averages and stick them in bar charts.

But sometimes we don’t want to summarize, we want to highlight the variety in scores and remind readers that the chart is actually made up of individual people, not just the mean or median. Long live the messy data, the dispersion, the distribution, the spread!


I could tell you a few descriptive statistics: min = 26%, max = 100%, Q1 = 64%, Q3 = 83%, median = 74%, mean = 73%, standard deviation = 15%. Or, I could show you the spread in this unit-chart-turned-histogram.

Unit charts are not your new go-to chart. They do not replace bar charts. They are not appropriate for all datasets. They’re best for those few moments when you choose to emphasize individual units of data. A unit could be 1 person, or 10 people, or 1 school, and so on. Units can be represented in circles or squares or triangles. Units can be stacked on top of each other to form a histogram, or they can be plotted along a line.

The dataviz challenge: Re-create the chart in in Excel, R, or some other free software program. Then, tweet a screenshot to @annkemery. Bonus: Make a unit chart for your own data. Or, do you emphasize individual differences with other chart types? Share your ideas with the community!

The prize for playing: A professional development opportunity and bragging rights. I’ll post the how-to guide in a couple weeks.

Want to learn more? I’m presenting about charting techniques at the American Evaluation Association’s annual conference on Thursday, October 17, 2013 at 11am in Washington, DC. Hope to see you there!

Hi, I’m Corey Newhouse, the Founder and Principal of Public Profit. We help public service organizations measure and manage what matters. As the leader of an 8-person evaluation firm, I think often about staff training and common organizational practices.

Anyone who has been in the field knows that there are hundreds of tips and tricks that we pick up along the way, ranging from the global (“Don’t falsify your data”) to the very local (“Meg the attendance clerk always has the file you want”).

And, to complex-ify things, one person’s “must-do-every-time-without-fail” tip is another person’s “what-the-heck-are-you-talking-about?” non-tip. So what’s an evaluation team to do?

Our team recently developed a “Top Ten Tips for Evaluation at Public Profit” in order to codify the most important of these practices for our work. To develop the list, each member of the team drafted as many tips as they wanted, and we discussed the tips as a group. We were able to whittle our list down to a set of tips that we agreed were essential to our work.


The exercise was hugely helpful for three reasons:

  • We were relieved to find that many of our tips were similar, suggesting that our team was already pretty good at sharing good ideas with one another.
  • The tip nominations process stimulated important conversations about the ways in which we work together, such as whether it was OK to ask for uninterrupted time to complete a task. (And leading to the tip, “Ask for what you need, even if it is time to focus.”)
  • We used our tips list to create a professional development calendar, in which some of the more complex tips were covered in a 30-60 minute training.

Our tips are now part of our data operations manual, and a key part of our staff on-boarding process. We’ll update the list every year or so to make sure that our best thinking is reflected.

I’ve been in love with diverging stacked bar charts since I saw Joe Mako’s submission to Cole Nussbaumer’s dataviz challenge last December. Joe made this contest-winning chart. But in Tableau! The amazing but expensive software!

Could I ever create one in Excel?!

Yes! Luckily I’d learned about the Values in Reverse Order feature from Stephanie Evergreen. With Joe’s inspiration and Stephanie’s strategy, I started making these beauties for myself in Excel.

I wanted to share the chart secrets with all of you, so last month, I challenged readers to re-create a diverging stacked bar chart like this one:


It looks like I’m not the only one who loves diverging stacked bar charts. Congratulations to the 12 contestants! In order of submission, they are:

    • David Napoli
    • Anjie Raber
    • David Bonachea
    • Sheila Robinson
    • Amanda Drescher
    • Kristin Minichello
    • @luno1972
    • Hornyik Jozsef (in d3! with code!)
    • Stephanie Evergreen
    • Angelina Lopez
    • Kevin Gilds
    • Praveen Gowda

Most contestants seized the opportunity to use their own datasets and made adjustments as needed. For example, Sheila’s dataset fit a traditional stacked bar chart better than a diverging stacked bar chart, and Anjie needed to display cut-off scores.

So how do you make these diverging stacked bar charts, anyways?! There are at least two strategies: Either a) create two separate charts, a strategy demonstrated in previous posts like this one, or b) use floating bars, a strategy demonstrated in previous posts like this one. Stephanie Evergreen blogged about strategy B a few weeks ago and her explanation is pretty awesome, so I’m going to focus on strategy A today.
Want to learn more? I’ll be sharing my top 5 must-have chart strategies at the American Evaluation Association’s annual conference on Thursday, October 17.

For discussion: Nearly all of the contestants requested friendly feedback on their graphs. In most cases, contestants were trying these charts for the first time and thinking about whether or not these charts could be adapted for their datasets. What do you think?

Last week I shared strategies for improving any chart’s colors. One of the examples was a diverging stacked bar chart:

diverging_before-afterI love stacked bar charts because they’re pretty versatile, and because they’re a great chart for lots of evaluation and survey data. In my example, I looked at the percentage of survey respondents who selected strongly agree, agree, disagree, and strongly disagree on a satisfaction survey. But stacked bar charts can be used in dozens of different ways.

So when can you use a stacked bar chart?

  • Stacked bar charts are for part-to-whole relationships. Use them when you want readers to see both a) one portion of the bar and b) compare that piece to the entire bar.
  • Stacked bar charts can be used for tallies or percentages. A tally is the number of actual people, dollars, etc. For example, a nonprofit could display their funding sources in a stacked bar chart – $100K from a foundation, $200K from a government grant, and so on. The reader can see the size of each grant as well as how the grants stack up as a whole.
  • Stacked bar charts can be used for nominal, ordinal, or diverging data. An example of nominal data is the racial/ethnic categories of your survey respondents. Ordinal data has a natural order – from best to worst, most to least, something to nothing – like my example. Diverging data is a subtype of ordinal data – when the categories are polar opposites and there’s a clear middle ground or neutral zone in between two ends.

And when can you use a diverging stacked bar chart? Diverging stacked bar charts are just for comparing several sets of ordinal data at once. They work best when you’ve got an even number of categories (like the 4 survey choices). Then, you can easily line up the midpoints along an invisible y-axis.

The dataviz challenge: Re-create the “after” version in Excel, R, or some other free software program. When you’re finished, email me or tweet a screenshot to @annkemery.

Bonus! 1) Adapt this chart for own data. Think outside the box! 2) There are at least two different ways to create diverging stacked bar charts in Excel. Can you find more than one solution? (And these charts are so awesome that you’ll even see one solution on Stephanie Evergreen’s blog next week!) 3) Don’t forget to use custom colors!

The prize for playing: Beer or coffee, my treat, the next time you’re in DC; a professional development opportunity; and bragging rights.

I’ll post the how-to guide in 3 weeks, on September 6. Happy charting!

Are you facilitating a focus group? If so, you’ve probably got a lot on your mind – greeting participants, making sure everyone feels comfortable, covering all the key questions… It’s a lot to remember!

As I was learning to facilitate focus groups, I started taking notes of all the good advice I received from experienced facilitators. I turned that advice into a checklist for myself. I  review my checklist before every focus group (to keep the advice fresh on my mind) and after every focus group (as a self-evaluation). You could also use this checklist to assess colleagues who are learning to facilitate focus groups (i.e., use the checklist as a conversation starter when you’re debriefing together after the focus group).

Here’s my checklist for focus group facilitators. Did the facilitator:

  • Introduce him or herself?
  • Explain the purpose of the focus group?
  • Explain the rules for the discussion?
  • Address issues of confidentiality?
  • Use verbal and non-verbal expressions to make participants feel comfortable during the focus group?
  • Facilitate real interaction among group members (not just a series of individual interviews)?
  • Draw everyone into the discussion?
  • Demonstrate genuine interest in the discussion?
  • If asked for their personal opinion on a topic during the group, deflect requests by participants to give an opinion?
  • Maintain a good pace during the discussion (kept things moving but don’t rush questions)?
  • Ask questions in a neutral way (not in a leading way)?
  • Link comments to previous comments or themes?
  • Address comments or behaviors that could take the discussion off course?
  • Address disruptive and disrespectful behavior?
  • Ask participants for validation if rewording or summarizing comments?
  • Courteously but firmly stop a chatty participant?
  • Demonstrate sensitivity to emotional reactions of participants?
  • Display awareness and respect for cultural issues that emerge in the discussion?
  • Re-explain or re-phrase questions as needed?
  • Sincerely thank participants for contributing?

Do you have additional tips to add to the checklist? Which tips have been most valuable to you when facilitating a focus group?

Note from Ann: Last week I shared a dataviz how-to guide. Paul Denninger has a great alternative solution to share! He uses hidden bars, also known in the Excel community as floating bars or white bars, to build “two” charts in one. Enjoy!

Getting excel to do complex work is easy; making the output look nice is a different matter. I took the challenge to recreate Ann’s graph because I’m always looking for better ways to display information.

The solution required a stacked bar chart, so the only problem was lining up the starting point for the second dataset. A buffer needed to sit between the end of the first set of bars and the start of the second so that it would force the second set to line up correctly. Hidden data had to fill the gap. Here’s how I did it.

First, start by inputting the original data. Sorting the rows from large to small will help make the chart easier to build.

Original Data

First, input the original data

The next step is creating the dummy data. Insert a column between the two existing datasets and populate it by subtracting the first column from “1.” This gives you a series of percentages that start at the end of the first bar and end at “1.” The problem of lining up the second dataset is solved at this point.

Dummy Column

Create a column of dummy data

With all of the data in place, creating the chart is easy. Use a stacked bar and include all 3 columns.

Setting Chart

Insert a stacked bar chart

Once the chart has been built, it’s time to hide the dummy column. Highlight the red data series and choose the format menu. Fill the series with the same color as your background. This makes the bars blend into the background and leaves them invisible. Do the same for the data labels. The dummy information is now hidden and your second dataset lines up properly.

Removing Dummy Bars

Make the dummy data invisible

The y-axis still needs to be fixed since the values are organized from low to high. Invert it by highlighting it and opening the format axis menu. Tick the “categories in reverse order” radio button to have the bars arrange themselves from high to low.

Fix the vertical axis

Flip the y-axis

Here is the primary display difference between this solution and Ann’s. Due to the hidden data, the x-axis begins at 0 and goes up to 200%. Since it’s a big challenge to make repeating axes in one chart, and one that I haven’t solved, it’s probably best to remove the x-axis altogether. Since the data values are shown in the bars, removing the axis should not make data interpretation more difficult. In fact, it will be easier for people looking at the chart since they don’t have to rescale the second data series in their heads.

Finally, decrease the gap between the bars and invert the fill and text colors for the overall bar. This makes it stand out from the other series. It’s personal preference whether you want to make the data labels bold or not.

Expand, invert the colors and remove the x-axis

Expand, invert the colors and remove the x-axis

Your chart is finished and you have improved on excel’s built in data presentation immensely. Other than the problem with the x-axis, this solution gives the same result as Ann’s. It’s a visually appealing way to compare two or more groups across the same categories.

When he isn’t playing with Excel and learning to use R better, Paul Denninger is trying to start a company. He currently manages investments for people and is on a constant search for data to analyze. He spends enough time working with nonprofits that he might make a career change and do it full time. You can see more of his work on his blog, Risk Assets.

Two weeks ago, I challenged readers to re-create the “after” version of a side by side bar chart. You can read the full post here.

Congratulations to the 9 contestants! Click on the contestant’s name to see their chart.

  • Regan Grandy
  • Sara Vaca
  • Alex Kadis
  • Katrina Brewsaugh
  • @CaptainCane64
  • @PD_ssp
  • Will Fenn
  • Angie Ficek (Check out how she used her own data!)
  • James Coyle (Check out how he used his own pre-post data!)

Now it’s time to post the how-to guide.

Step 1: Study the chart that you’re trying to reproduce in Excel.

We’re trying to re-create a side by side bar chart like the one shown below. We’re comparing how Coalition A and Coalition B scored on Innovation Network’s Coalition Assessment Tool.


Step 2: And the secret to making side by side bar charts in Excel…

…is that we’re going to make two separate bar charts, one for Coalition A and one for Coalition B. When we copy and paste the charts from Excel into PowerPoint or Word, they’ll look like a single cohesive chart.


Step 3: Type the data into Excel.

Make sure you choose a purposeful order to your data, like the highest percentage on top and the lowest percentage on bottom.

Excel often flips the data table upside down when creating charts, i.e. if you want the overall scores to be at the bottom of the chart, then you need to put the overall scores in the top of your data table. (Or, you can reverse the order of the categories later on.)


Step 4: Create the first bar chart.

You know the drill: Add data labels inside the end of your bars. Choose a color palette that matches your client’s logo or your own logo. Use an action color to draw the reader’s eye where you want it (in my example, the overall score). Delete unnecessary ink like the tick marks, grid lines, and border. Use gray to de-emphasize things like (n=7) and the axis labels. Reduce the gap width.


Beginner Excel users: If you need extra instruction, check out how to make a basic bar chart and my Excel for Evaluation chart tutorials.

Step 5: Copy the first chart.

Rather than re-create the wheel when making the second bar chart, let’s save some time by simply copying the first chart.


Step 6: Populate the second chart with Coalition B’s data.

Use the “select data” feature to put Coalition B’s percentages into the chart.


Step 7: Adjust the second chart’s bar color and title.


Step 8: Delete the second chart’s axis labels.

Yep, you’re right, the second chart’s bars are going to get waaaaaay too long. We’ll fix this in Step 9.


Step 9: Re-size the second chart.

Here’s my super scientific secret for making sure each chart is the same size: I measure the plot area with a business card.

First, adjust the first chart's plot area so that it's the width of a business card or post-it note.

First, adjust the first chart’s plot area so that it’s the width of a business card or post-it note.

Next, I adjust the second chart's plot area so that it's the same width as my business card.

Next, I adjust the second chart’s plot area so that it’s the same width as my business card.

Step 10: Paste the charts into PowerPoint or Word.

Select both charts and paste them into PowerPoint or Word at the same time. Here’s what it looks like on a slide. Looks like a single chart!


Thanks to everyone who participated in the first and second dataviz challenges! I hope these challenges give you a chance to practice and build upon your Excel and visualization skills.

Last time, we made a streamlined version of the basic bar chart by adjusting Excel’s default settings. Basic bar charts are great when you’re just looking at simple patterns, like one series of data at a time.

However, sometimes we need to compare several series of data at one time. For example, the Innovation Network team and I recently compared the capacity of 12 different coalitions using our coalition assessment tool. We calculated coalition capacity in 7 different areas, such as Basic Functioning and Structure and the Ability to Cultivate and Develop Champions. To learn more about the tool, check out some materials from a recent presentation here.

The “before” chart: Here’s a clustered (aka cluttered) bar chart based on fictional data from the coalition assessment tool. The default Excel chart to compare Coalition A and Coalition B might look something like this:


A default clustered bar chart in Excel. Bleh.

Or, you could use the switch row/column feature to make a default chart like this:


Another default clustered bar chart in Excel.

These default clustered bar charts work… kind of. Well… not really. Actually, no, they really don’t work at all! My brain needs a couple minutes to read the default charts and really think about comparisons.

The “after” chart: Luckily, with a little Excel elbow grease, we can make the patterns pop out even faster. Here’s a side by side bar chart:


Now, my brain can see the patterns almost immediately: Both coalitions scored highest on Basic Functioning and Structure. Both coalitions scored lowest on Sustainability, with Reputation and Visibility scoring pretty low as well. Coalition A scored higher than Coalition B on every section of the assessment. The overall scores were 80% for Coalition A and 50% for Coalition B. Phew! This is much easier for my brain.

The dataviz challenge: Re-create the “after” version of the side by side bar chart in Excel, R, or some other free software program. When you’re finished, email me or tweet a screenshot to @annkemery.

Tips for beginner Excel users: First, learn how to make a basic bar chart. Then, check out this powerpoint for tips on making a side by side bar chart.

Bonus for advanced Excel users: Instead of copying this dataset exactly, think about how you might use a side by side bar chart in your own work. Can you re-create this chart using your own data or your own color scheme?

The prize for playing: Beer or coffee, my treat, the next time you’re in DC; a professional development opportunity; and bragging rights.

I’ll post the how-to guide in two weeks, on June 10, 2013. Happy charting!