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Category: Visualization

Visualizing Data in Python

Published 2022-08-03 by Kevin Feasel

Mark Litwintschik provides some recommendations:

There are two major phases of data analysis. The first is building up a basic understanding of a new dataset. Once this is done there is a second phase of understanding what’s changing over time and if there are any new outliers.

For the first phase, I find Tableau to be more productive than writing code in a Jupyter Notebook. For the second phase, I like to build periotic Airflow jobs that send charts and Excel files to operational channels on Slack. These are formatted to be mobile-friendly and allow me to do more of my work on a phone rather than being chained to a laptop. This also means access is controlled via Slack rather than a custom web app.

Mark also covers some examples with Altair.

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Showing Filter Selections on Power BI Reports

Published 2022-08-03 by Kevin Feasel

Mara Pereira makes a Power BI breadcrumb:

When I’m developing reports, I’m asked multiple times to “hide” the slicers/filters from the report page. Usually this is to make space in the page for other visuals and because customers don’t want to use the filter pane for some reason.

This happened so many times, and only in the last couple of months I decided to try some things out and get a bit creative with Power BI.

I came up with two solutions, which I think work great in these scenarios.

Read on to see the solutions.

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A Primer on Contrast and CVD

Published 2022-08-01 by Kevin Feasel

Tim Brock covers color vision deficiency:

In this blog post and a follow up I’m going to describe why and how we used theming to make diffify.com more accessible to users who suffer from some common visual impairments. Here in Part 1 I’ll cover some of the science and the terminology. Part 2 will look at the actual changes we made.

Click through for a brief discussion of contrast sensitivity and a longer one on color vision deficiency, including why color choice matters.

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Target Areas on a Line Chart

Published 2022-07-28 by Kevin Feasel

Mara Pereira adds target bands to a line chart:

At the time I could not really find an easy way to achieve this… Until error bars came out!

Don’t be fooled though, it’s still a bit tricky to build a line chart like this, however I found it way easier now than before.

So, you must be thinking now “how did you do that?”.

Well, let’s find out!

The end result looks really nice, though it takes a lot of work to get there.

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The Benefit of Tick Marks on a Visual

Published 2022-07-21 by Kevin Feasel

Alex Velez lays out the case for tick marks:

Lately, I’ve noticed that more and more graphs don’t include gridlines. If it’s unclear, I believe this to be a positive trend. I, myself, rarely use gridlines, and often remove them when I find them in a graph I’m reviewing. But I don’t stop there. 

More often than not, if a chart has gridlines, it will be lacking tick marks along the axis, and possibly an axis line as well. 

Read on to see why.

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Rolling Average and Working Days in DAX

Published 2022-07-20 by Kevin Feasel

Marco Russo and Alberto Ferrari combine two common business requests:

In a previous article, Rolling 12 Months Average in DAX we showed you how to compute a rolling average over a time period. In this new article, we want to take you one step further and show how to compute a moving average over a certain timeframe, that takes into account only the working days. We present two variations of the same solution: one that is optimized, relying on a calculated column, and one that – despite being somewhat slower – works without requiring a calculated column. The latter can be useful in case you need to define the formula in a live-connected report, where calculated columns are not an option.

Because the formula needs to account for working versus non-working days, it cannot rely on standard time intelligence functions. Indeed, DAX time intelligence functions have no knowledge about what it means for a day to be either a working day or a rest day. The NETWORKDAYS DAX function would not be very useful in this case, because it would introduce a slow filter to compute the range of dates that includes the number of working days desired.

Read on to see how they solve this one.

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Power BI Field Parameters and Measures

Published 2022-07-20 by Kevin Feasel

Roland Szirmai has fun with field parameters in Power BI:

Meaning that report users can switch between “dimensions” of the data. This is great and already provides a much better UI and UX, but there was no information about the limitations of what “fields” can you add to the parameter table.

To be more specific, I couldn’t find any limitation about adding measures (Explicit Measures) to the Field Parameter.

I think you can see where my mind wandered after that…

Read on for the result of Roland’s wanderings.

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Extracting Numbers from a Stacked Density Plot

Published 2022-07-18 by Kevin Feasel

Derek Jones digs into an image:

A month or so ago, I found a graph showing a percentage of PCs having a given range of memory installed, between March 2000 and April 2020, on a TechTalk page of PC Matic; it had the form of a stacked density plot. This kind of installed memory data is rare, how could I get the underlying values (a previous post covers extracting data from a heatmap)?

Read on for an interesting attempt at reverse-engineering the original numbers used to create an image. H/T R-Bloggers.

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Mapping Income vs Rent in Counties

Published 2022-07-18 by Kevin Feasel

Rick Pack updates a package to support a project:

I am happy to announce a contribution to the biscale package that makes printing shorter labels using SI prefixes (e.g., 1,000,003 => 1M and 1,324 => 1.3k) far easier. This makes printing the legend in an attractive easier, although you can tell by the picture above that I still struggle with optimal uses of the cowplot package’s draw_plot(). I would love for the legend and map to be centered under the title.

The new si_levels argument for bi_class_breaks() takes a logical value of TRUE or FALSE for either a single or two-unit vector, with a single unit vector causing the specified value to be applied to both the X and Y variables. This matches Prener’s convenient functionality for the number of digits function dig_lab, as he requested in the Github Issue I created for this addition. Note that si_levels rounds the input number, if appropriate, based on the digits indicated by dig_lab, which defaults to 3.

Click through to get access to the update, as well as to see some of the visuals Rick put together with it.

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Quality Checks for Power BI Visuals

Published 2022-07-18 by Kevin Feasel

Meagan Longoria has a checklist:

For more formal enterprise Power BI development, many people have a checklist to ensure data acquisition and data modeling quality and performance. Fewer people have a checklist for their data visualization. I’d like to offer some ideas for quality checks on the visual design of your Power BI report. I’ll update this list as I get feedback or new ideas.

Read on for the list, as it’s a good one. For the most part, these also apply to visuals created in other tools.

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