a sliceable Gantt chart that automatically updates and populates itself when you add more rows to your dataset (i.e., no tedious manual updates).

How to Make Sliceable Gantt Charts in Excel

You can watch the high-level tutorial here:

What’s Inside

• 0:00 Intro
• 1:08 The end product: Sliceable in Excel. or printed/PDFd
• 1:52 Gantt chart options in Excel: 1) Stacked bar chart or 2) Inside cells, like this
• 3:50 Dataset
• 5:51 Pivot table
• 6:29 Slicer
• 6:40 List of projects and their amounts
• 8:30 Helper cells to the left and above
• 9:55 AND formula to fill in the body of the table
• 11:18 Conditional formatting
• 12:50 Theme Colors
• 13:40 Your Homework List
• 14:17 Want more details? Watch the 2.5-hr livestream
• 14:37 Download this Gantt chart

Download the Excel File

It’s here.

Related Resources

• The full livestream where I created this from scratch
• Helper cells

The post Sliceable Gantt Charts in Excel appeared first on Depict Data Studio.

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For the past few months, I’ve been developing dashboards to support students’ understanding of local ecology and equip them to use that local understanding as a baseline to explore the rest of the world.

Imagine, for example, being a 10-year-old in St. Louis.

Your neighborhood has plenty of trees since you’re at the western edge of the forests typical of the eastern US. This can only happen because the temperature is warm enough and there is enough precipitation to support tree growth.

Heading west from here the ecology shifts pretty quickly to grasslands, with the grass getting shorter as you approach the Rocky Mountains. A quick look at the data shows decreasing levels of precipitation as you head west.

For a more extreme contrast, Yuma, Arizona is much hotter, but the area gets about 10% of the precipitation St. Louis does. How is this heat and lack of precipitation reflected in the plants and animals in southern Arizona?

All of this learning is wrapped under the heading “What’s It Like Where You Live?” – a program I used as a 4th grade teacher 25 years ago, developed by the Missouri Botanical Garden and now undergoing a major reworking.

As we flesh out the curriculum, we’ll be supporting kids’ local field work with dashboards synthesizing climate data and images of plants and animals typically found in different ecoregions.

First Forays

At a basic level, students can compare temperature and precipitation data for their local community with data for other cities around the world. Is it warmer or cooler, wetter or drier?

A simple table or a scatter plot serves the purpose quite well. The limit in this approach is the image students often develop when data from one city represents “the desert” or “the rainforest.”

Resolving this conundrum has opened the door to some exploratory work crafting dashboards which encompass both similarities and variation within an ecoregion. (Mostly I’ve just been geeking out with the data, but “exploratory research and development” sounds so much better!)

Refinements

Taking the data visualizations further has pushed me to walk a fine line between interesting visualizations and the developmental capacities pre-teen students bring to the task.

Most kids have limited experience with data tables and graphs, and what work they have done is pretty specific (such as graphing pizza preferences among class members).

Graphs showing means (or even means of means) risks becoming too abstract without the right supports.

After exploring a few options, I settled on a representation which captured both the spread of data typical of cities in a given ecoregion and the mean value of these cities.

Major thanks are due here to Ann Emery for streamlining the look and feel of this version. Her focused, uncluttered design aesthetic is a perfect match for this work.

I’ve tested this out with a few kids with good results, but COVID restrictions have kept me from seeing how a broader pool of students make sense of this display. I’m hopeful that restrictions will be lifted in the new school year so we can move forward with some pilot testing.

Going Further

To be sure students remain connected to their local base, I needed an anchor which is ideally movable so that students in other areas can use the materials.  For this, I’m indebted to Jon Schwabish of the Urban Institute and PolicyViz.

While participating in a workshop he led, a couple of techniques we were using came together. By combining a single point scatter plot and error bars, a reference line can be inserted to mark local conditions.

If this strategy proves useful in our pilot testing, I expect that we will be able to support localization so that students anywhere could enter their own data and have VLOOKUP or a similar procedure to change my St. Louis reference line to one appropriate for any student’s home city.

The work so far has been an enjoyable way to explore data and apply the many things I’ve learned in Ann’s workshops and elsewhere. I’m looking forward to seeing how students use the data when we begin pilot testing. 

The post Dashboards for 10-Year-Olds: Connecting Data to Students’ Lived Experience appeared first on Depict Data Studio.

This year, one of my many New Year’s resolutions was to participate in The 100 Day Project, a creativity project that requires making one thing, every day for 100 days.

I’m a data visualization designer with a master’s degree in Information Visualization. And I love working with clay. I was a ceramics instructor at a summer camp for six years, and recently finished taking an advanced wheel throwing class. Molding things with my hands comes naturally to me.

I wanted to merge these two seemingly disparate passions of data visualization and ceramics in a playful way. Day-Doh-Viz was born: Data visualizations created out of Play-Doh produced “daily”. #DayDohViz

Play-Doh is a great medium because it’s brightly colored and photogenic. It’s easily moldable; you can pretty much do anything with it. It’s reusable and relatively inexpensive. And taking something from childhood and elevating it for a different use has a playful, nostalgic feel.

Working with Play-Doh also limits precision. It isn’t going to be perfect, but that’s part of the fun. It stops me from being a perfectionist and lets me focus on the bigger theme. It also allows me to visualize things I might not want to show with absolute precision, like my financial data.

My (45 so far!) play-doh visualizations have mostly fallen into 3 categories. Financial, personal data, and remakes of other data visualizations. Here are 6 of my favorite DayDohViz:

Income and Expenditures in April

Talking about money is such a taboo subject. Especially sharing your own financial information. Most of us aren’t great at personal finance, and it doesn’t help that we’re afraid to share this information with others. I like to help destigmatize this by sharing my own information. Play-doh feels like a low risk way to do this. It’s a little imprecise, and I don’t have to label the exact numbers, but the viewer gets the feel of what’s going on, and can maybe relate.

Perceptions of Probability in Play-Doh

What does it mean when something is “probable”? Is it more certain than “highly likely”? I love the original perceptions of probability joyplots, which plotted survey respondents numerical probability estimates for a variety of phrases. It quantifies how people perceive these usually squishy terms. It’s also colorful and fun. Doing a remake of it in Play-Doh was an enjoyable experience.

In doing so, I realized that joyplots are like the side view of violin plots. This Day-Doh-Vis won the most informative #CraftyDataViz Award.

Beverage Enjoyment

I love liquids. When you’re trying to create a whole new visualization every day, sometime you just need to graph something easy with “data” that you know. To me, the chart details make this one especially fun. All the carbonated beverages have a slight bubble texture to them. I added little suggestions of fruit to the drinks that typically come with a lemon, lime or cherry. The size of the sphere is the amount I want to drink and the proximity to the top is my enjoyment level. This was a quick viz to build probably about an hour.

Costs of New Dog Ownership

This one was another way of showing and talking about personal finance using my own experience and data. I knew there were going to be lots of start up costs to adopting a dog. I was surprised at how varied and spread out they were. The gif format helps the labeling not seem overwhelming, and allowed me to try my hand at something different. For just the bar graph, the extruding and assemble portion, took me about 40 minutes.

Birthdays Parties Change with Age

I’m at an age where on a Saturday I can attend a friend’s child’s party and an adult’s birthday. The contrast is quite interesting. When the little ones crash, they crash HARD. It was fun to build and make the numbers out of Play-Doh.

Birth Rate Viz in 3D

Inspired by the popular Scientific America visualization “Why Are so Many Babies Born around 8:00 A.M.” The original visualization has aspects of being 3D but is visualized in two dimensions. I played with that and built it on top of a glass jar. The original chart was so great, I didn’t want to mess with that, just bring out a different aspect that building it in real life could highlight. There was a lot of measuring over three days to create this one and I plastic wrapped it overnight.

Steps of Day-Doh-Viz-ing

1. Conceptualize. Have a brilliant idea of what I want to visualize.
2. Research. Figure out what data is available for what I want to show and how do I get it.
3. Data collection. Find and organize the data. Sometimes I’m tracking my own data over time. Or going through and collecting specific data about a topic.
4. Design. Figure out how I want to show it and either sketch or do some test graphs in google sheets.
5. Gather materials. I have a bin of play doh, but sometimes you need to find the right jar, or custom tape, or print out a graph to build on top of.
6. Color mix. Squish colors together to create colors that will match the visualization or work well being photographed. Warm colors tend to blow out when you don’t add a little black or white to them.
7. Build. Create the visual out of play-doh, sometimes this involves rolling balls, extruding bars, or forming mounds.
8. Lighting set-up. Sometimes window lighting works nicely, sometime I need to use a ring light.
9. Photograph. I always take different angles so it’s usually at least 20+ photos of each visual. Sometimes a video, sometimes a tripod so I can create a gif.
10. Image edit. I try to only stick to apps to keep it simple, so I’ve been using the iphone photo app and Facetune for some detail editing.
11. Annotate. Again, I’m trying to be simple, so I’m using This app to annotate my viz. It limits the amount of choices I have, like font and style.
12. Post. Share on social media and add to my portfolio of creations.

When you’re creating every data point by hand, you get to know your data intimately. As I’ve created more of these visualizations with play-doh, I’ve tried to find and highlight the unique things about this medium. I’m working to push the 3D aspect. And the ability to manipulate data and show different stages, or effects, by just adding elements or building everything and removing parts.

What I’ve Learned

• Most successful = unique form + unique data + well executed
• To remember that I’m doing this for fun
• Play-doh is cheap at TJ Maxx

The project has been highlighted in Andy Kirk’s Best of the Data Visualization for April list, and on Alli Torban’s Data Viz Today podcast. I’ve gotten a few hundred new Twitter followers, and asked to write a couple blog posts. Which is pretty successful for making things out of play-doh.

The best thing, I think, is that I’ve inspired other people to use play-doh for data visualization. Anna Fergusson took play-doh into her class and had her intro stats and data science students make visualizations. I’m not saying that play-doh is the best medium for data viz, but I think there’s something to be said for stepping away from traditional tools, and creating visualizations by hand.

And I think it can be less intimidating and to work with an imperfect medium, rather than pen and paper, or a computer which can feel demandingly precise.

Learn More about #DayDohViz

Follow along on Twitter for more #DayDohViz

The post #DayDohViz: Play-Doh as a Tool for Data Visualization appeared first on Depict Data Studio.

Let’s zoom in and check out that data table:

Nevermind that the data (salary ranges) has nothing to do with the article (turnover).

Rather than displaying a mean or median, the article displayed a range. A range! What a challenge. I had to figure out how to best visualize that range.

During all my workshops, I encourage attendees to sit down and sketch on blank paper before turning to their computer and getting frustrated by Excel’s limited menu of chart options. Sketch first and free your mind, figure out the software second.

Draft #1

First I drew an exact replica of the data table from the article – CEO salaries, CDO salaries, Washington, and New York.

I actually hadn’t realized that Washington and New York salaries were nearly identical until I sat down to sketch.

Draft #2

Since New York and DC salaries are nearly identical, why display them a few inches away from each other? This draft places New York and DC’s salaries directly beside each other.

Draft #3

I decided that I wasn’t really interested in CDO salaries, only CEO salaries.

Draft #4

The data table gives us aggregate/summarized numbers.

If I had the raw data (I don’t), I could visualize each CEO’s unique salary in some sort of dot plot/pictograph/scatter plot-esque chart.

Draft #5

If I had the raw data, I could create an actual scatter plot that mapped out the connection between nonprofit size and CEO salary, maybe annotating one cluster of salaries.

On the Computer

There are probably a million more options, but I’d sketched a few ideas, and was ready to sit down at my computer and try to design the real thing.
I decided to create a twist on Draft #2.

Yes, this is good ol’ Excel.

Another Option: Displaying Ranges in Line Charts

You’ve seen how I might visualize a minimum and maximum value in a bar chart.

But what about line charts?

Here’s a great example from the New York Times.

Rather than displaying exact numbers, the New York Times displayed a range in this line chart. These are estimates, after all. Displaying an exact mean, median, or frequency count wouldn’t make sense.

As usual, the screenshot doesn’t do it justice. Head over to http://www.nytimes.com/interactive/2014/07/31/world/africa/ebola-virus-outbreak-qa.html#model to explore the charts.

How have you graphed your data when you only have a min and max? Share your ideas below.

The post Span Charts: When You’ve Only Got the Min and Max appeared first on Depict Data Studio.