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Category: Visualization

Building FAQs on a Power BI Dashboard

Published 2020-04-21 by Kevin Feasel

Evan Rhodes takes us through building out a FAQ for a dashboard:

I suppose you could add a bunch of text boxes with questions and answers. But, what if you have several questions and there isn’t enough space? I’m reminded of something a fantastic boss once told me, “Never pass up an opportunity to wow someone and grab their attention with your work.” So, let’s add some wow effect to this by leveraging the bookmarks and buttons functionality.

Bookmarks and buttons allow us to create a user experience that is intuitive to the user and that allows them to navigate around the page easily by just clicking. In this case, click on a FAQ and the answer appears. Click on the FAQ again or a different FAQ… I think you get the point.

If you need this on the dashboard itself, this is probably the right way to do it—there for the one time you need it and hidden away the rest of the time.

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The Importance of Gridlines

Published 2020-04-16 by Kevin Feasel

Stephanie Evergreen shows why (subtle) gridlines are so important in visuals:

Here’s the thing: This chart NEEDS gridlines. I’ve said this before but I find this anti-gridline trend so common that I need to address this topic explicitly.

The *medium gray not black* gridlines are necessary because I do not have data labels on every one of the dots in the chart. 

A quick reminder is that even Edward Tufte (a key proponent of the “gridlines are bad” school) doesn’t hate all gridlines. Subtlety is key with them: they should be there when you need them but easily ignored when you don’t.

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Changing the Graphics Device in RMarkdown Docs

Published 2020-04-14 by Kevin Feasel

Colin Gillespie shows us how to change PDF and PNG output settings within knitr:

In many workflows, function calls to graphic devices are not explicit. Instead, the call is made by another package, such as knitr.

When kniting an Rmarkdown document, the default graphics device when creating PDF documents is grDevices::pdf() and for HTML documents it’s grDevices::png(). As we demostrated, these are the worst possible choices!

Click through to see what you can do about it.

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Saving Graphics in R Across Multiple OSes

Published 2020-04-13 by Kevin Feasel

Colin Gillesipie takes us through exporting graphics in R and some of the cross-platform foibles you’ll find:

One of R’s outstanding features is that it is cross platform. You write R code and it magically works under Linux, Windows and Mac. Indeed, the above the code “runs” under all three operating systems. But does it produce the same graphic under each platform? Spoiler! None of the above functions produce identical output across OS’s. So for “same”, I going to take a lax view and I just want figures that look the same.

Read on to understand the differences and hopefully limit confusion around them.

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Understanding Area Graphs

Published 2020-04-10 by Kevin Feasel

Mike Cisneros takes us through the proper usage of area graphs:

Area graphs can be effective for:

– Showing the rise and fall of various data series over time
– Conveying total amounts over time as well as some sub-categorical breakdowns (but only to a point)
– Emphasizing a part-to-whole relationship over time when one part is very large, or changes from being very large to very small
– Showing change over time in individual panels of a small multiple chart

Area graphs are not the ideal choice for:

– Data sets on scales that do not have a meaningful relationship to zero
– Showing several volatile data sets over time
– Showing fine differences in values

In this post, we’ll talk about how an area graph works, and some of the challenges to keep in mind when you are considering creating one.

Click through for a detailed analysis. I will rarely use area graphs, but in the right use case, they can add a strong visual dynamic to a report.

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Horizontal Dumbbell Dot Plots in Excel

Published 2020-04-02 by Kevin Feasel

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

Published 2020-03-30 by Kevin Feasel

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

Published 2020-03-27 by Kevin Feasel

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

Published 2020-03-23 by Kevin Feasel

Paul van der Laken talks to us about paleteer:

I often cover tools to pick color palettes on my website (e.g. here, here, 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

Published 2020-03-20 by Kevin Feasel

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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