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Category: Visualization

Horizontal Dumbbell Dot Plots in Excel

Stephanie Evergreen walks us through an interesting technique for creating dumbbell-style dot plots in Excel:

Ok, babes, prepare to be amazed. It used to be that making this viz was pretty tedious but I’ve recently refined a totally new hack (thanks to a lollipop chart example provided by Sevinc Rende, one of my mentees) that makes this soooooooo easier. It used to be ninja level 9. Now it is ninja level 5, if that.

We will create a dumbbell dot plot out of a stacked bar, where the first stack is composed of our first set of dot values and the second stack is composed of *the difference* between our first and second values (so that it would end at our second values on the x-axis scale). So let’s calculate the difference between the 2020 and 2010 scores.

Read on to see how.

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Finding Below-Average Sales Per Hierarchy Level in Power BI

Soheil Bakhshi has an interesting problem to solve:

Now, the requirement is to show the above behaviour in a “Column Chart” (yes! visualising time series with column chart, that’s what the customer wants) and highlight the columns with values below average amount in Orange and leave the rest in default theme colour.

So, I need to create Measures to conditionally format the column chart. I also need to add a bit of intelligent in the measures to:

– Detect which hierarchy level I am in
– Calculate the average of sales for that particular hierarchy level
– Change the colour of the columns that are below the average amount

Let’s get it done!

Read on to see how you can do exactly this.

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All About Line Graphs

Mike Cisneros takes us through one of the most useful visuals out there:

A typical line graph will have continuous data along both the vertical (y-axis) and horizontal (x-axis) dimensions. The y-axis usually shows the value of whatever variable we are measuring; the x-axis is most often used to show when we measured it, either chronologically or based on some independent variable (e.g., as we rev our old car’s engine, we measure the decibel level at different RPM). 

While some line graphs do not use continuous data on the x-axis (particularly slopegraphs and parallel coordinates diagrams, which are specialized variants of line graphs), what we absolutely can’t use on our x-axis is data that doesn’t have any meaningful relationship among the categories shown. 

Read on for a lot of good information on a workhorse visual.

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Color Palettes in R

Paul van der Laken talks to us about paleteer:

I often cover tools to pick color palettes on my website (e.g. herehere, or here) and also host a comprehensive list of color packages in my R programming resources overview.

However, paletteer is by far my favorite package for customizing your colors in R!

The paletteer package offers direct access to 1759 color palettes, from 50 different packages!

Just make sure to run your graphics through something like Coblis afterward to ensure that they’re CVD-friendly. H/T R-Bloggers.

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Visualizing “Check All that Apply” Options

Stephanie Evergreen shows a couple of ways to visualize multi-select results:

Which means a bar chart, ordered greatest to least, is your alternative. But that can have many variations.

In this example, created by Dr. Sheila B. Robinson, she used 100% stacked bars for each survey item, to indicate that each item could have totaled 100% if all respondents checked that box. This is a nice way to show that, while the response options as a whole can’t add to 100%, each option on its own CAN. Plus, look at the cute icons.

Click through for several alternatives depending upon the story you’re trying to tell.

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Developing Shiny Apps in Databricks

Yifan Cao, Hossein Falaki, and Cyirelle Simeone announce something cool:

We are excited to announce that you can now develop and test Shiny applications in Databricks! Inside the RStudio Server hosted on Databricks clusters, you can now import the Shiny package and interactively develop Shiny applications. Once completed, you can publish the Shiny application to an external hosting service, while continuing to leverage Databricks to access data securely and at scale.

That’s really cool. Databricks dashboards are nice for simple stuff, but when you really need visualization power, having Shiny available is great.

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Dynamic Binning with Power BI

Reza Rad has fun with dynamic binning in Power BI:

In the previous article/video, I explained how to create dynamic bins by choosing the count of bins in a slicer in the Power BI report. In this article, I’ll explain, how you can do it the other way around, which is by selecting the size of the bin, you will have bins and buckets dynamically generated.

I like this for its ability to let you select the proper number and size of bins when Power BI is being particularly obstinate about something. In an ideal world, I don’t like this so much as a user-facing feature because we as designers should know the proper number and size of bins.

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Visualizing Rating Data

Stephanie Evergreen shows various ways to visualize ratings data:

Stacked Bars *seem* like a good idea – we show 100%, we can fit more questions and data into a similar amount of space – advantages, right? Except that stacked bars are difficult for people to read. How well can you compare the values of the orange segments? Not so much.

If you are going to use stacked bars, make some helpful formatting tweaks, like smarter color coding and an order from greatest to least.

A lot of this comes down to simplification and reduction of possibilities. Read the whole thing.

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Making a Better Pie Chart

Elizabeth Ricks tries the impossible:

A friend called me recently and started our conversation with: “I know you dislike pie charts, but…can you help me create one?” 

Spoiler alert: I don’t hate pie charts. They’ve received a bad rap over the years and with good reason—they are very commonly used when another chart type would be better suited. The appropriate use case for a pie chart is expressing a part-to-whole relationship. Their limitation is that it can be difficult to accurately judge the relative size of and compare the segments. Here are some related articles on our blog: the great pie debate and an updated post on pies

Elizabeth does put together the best possible case, but I’m still in favor of burning pie charts to the ground.

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