Portfolio bar graph skill full#

I’ve experimented plenty with real-life clients to see whether they prefer regular bar charts or stacked bar charts. What do you think of remake #1? Makeover #2: A Clustered Stacked Bar Chart In real life, would I match the action color to my client’s RGB codes? Absolutely. Why the black and white? Just for fun, I want to show you that you can still emphasize patterns without using bright, showy colors.

For example, I added a 10-word title and a 1-sentence caption I deleted the border, grid lines, and tick marks I placed the percentages on the bars so I could delete the axis and I placed the years directly on the bars so I could delete the legend. Next, I transformed the raw scores (“2.6”) into percentages (“43%”).įinally, I addressed all the Low Hanging Fruit formatting issues. Not sure how to transform your vertical bar chart into a horizontal bar chart? I’ve got a tutorial.

It’s harder to read, so viewers get distracted, bored, or generally turned off and stop trying to decode your messy chart. Your goal is to avoid diagonal text at all costs. Horizontal bar charts are great when the data labels are pretty long, like in this example. Makeover #1: A Horizontal Clustered Bar Chartįirst and foremost, I changed the chart type from a vertical bar chart to a horizontal bar chart. My goal is to move more people into the third phase: actually improving your work. Some people stay in the first phase forever. The third phase is actually remaking your charts. What, exactly, is wrong with your work or someone else’s? How can you learn from those mistakes? The second phase is articulating specific ideas for how you’d make charts better (Data Visualization Checklist to the rescue!). The first phase in learning about data visualization is usually critiquing charts. Which means I’m certain that nobody would take the time to understand my poorly-titled, redundantly-labeled, cluttered, confusing chart. Guess how my old chart scored on the Data Visualization Checklist? 39%. I want you to learn from my mistakes and go forth and make charts that people actually understand. That’s why Stephanie Evergreen and I published the Data Visualization Checklist earlier this month. I’ve studied, practiced, gotten trained, and trained others. Introducing the Data Visualization Checklist the percentages? Can you imagine how much mental energy it must’ve taken my viewers to figure out what 2.9 vs.

Most importantly, why did I display the raw scores vs.

Our brains are drawn to darker colors. Why was I drawing attention to the “possible items correct” section with the dark blue?

Default color scheme (The client’s logo definitely didn’t contain Excel’s default blues.).Diagonal text (Should swap this vertical bar chart for a horizontal bar chart, leaving more room for the labels.).Speaking of direct labeling… why did I label the axes and the bars? Overkill.Legend (Should have direct labeling instead of the legend to avoid those back-and-forth eye movements.).

Portfolio bar graph skill full#

Full grid lines (Should be lightened or removed altogether.).Tick marks on vertical and horizontal axes (Unnecessary.).No subtitle or annotations (So the viewer can’t skim the information.).Generic and centered title (Should have a “so what?” and be left-justified.).Can’t they just tilt their head to read the words, you argue.Įach of these mistakes–from the before I knew better period of my career, never to be revisited again–kept viewers from understanding the information:

One of these charts was so horrific that I just had to share it with you: Data visualization is a new skill for most researchers and evaluators. Most of these charts from my prior work were ineffective. The great sobering thing about portfolios is that you can bask in the glory of your wonderful work bow your head in shame. Before that, I collected paper copies of reports I worked on: I’ve been building portfolios of my work for a while. Over the past year or so I’ve been building an online portfolio of speaking and writing engagements.