Making Corporate Color Palettes Palatable

Meagan Longoria takes us through a corporate coloring problem:

Last week, I had a conversation on twitter about dealing with corporate color palettes that don’t work well for data visualization. Usually, this happens because corporate palettes are designed with websites and/or marketing collateral in mind rather than information graphic design. This often results in colors being too bright, dark, or dull to be used together in a report. Sometimes the colors aren’t easily distinguishable from each other. Other times, the colors needed for various situations (main color, ancillary colors, highlight color, error color, KPIs, text, borders) aren’t available in the corporate palette.

You can still stay on brand and create a consistent user experience with a color palette optimized for data visualization. But you may not be using the exact hex values as defined in the corporate palette. I like to say the data viz color palette is “inspired by” the marketing color palette.

Click through for lots of goodies, including a link to a really interesting color tester.

Automating Grafana Dashboard Creation

Rishi Khandelwal walks us through automating Grafana dashboard and alert creation:

We have already discussed the creation of Grafana dashboards and alerts in my previous blogs. We were doing that manually. But think of, if we need to do that in more than 10 environments then we need to repeat that manual process again and again and sometimes we get frustrated by doing all these repetitive stuff.

We should have some automated process for doing this. So let’s discuss that.

Read on for an example.

Decluttering Visuals

Elizabeth Ricks shows how you can remove extraneous noise from a visual and make it easier for a person to understand what you’re trying to show:

This caught my eye not because of the topic but because of how much time it took me to figure out what information it was trying to convey. What should I do with this? There’s a lot competing for my attention in this chart and distracting me from the data.

Spend a moment examining this graph and take note of which specific elements are challenging. Make a list: what might we eliminate or change to reduce cognitive burden?

I came up with eight specific design changes I would make. How does my list compare with yours?

There is a subjective element to this, but the end result does look better.

Visualizing Earthquake Data

Giorgio Garziano continues a series on analyzing earthquake data:

This is the third part of our post series about the exploratory analysis of a publicly available dataset reporting earthquakes and similar events within a specific 30 days time span. In this post, we are going to show static, interactive and animated earthquakes maps of different flavors by using the functionalities provided by a pool of R packages as specifically explained herein below.

Giorgio looks at 9 separate R mapping packages, so you get your money’s worth here.

Callout Lines

Lisa Charlotte Rost shows different sorts of callout lines and how they can affect the way a person views your chart:

After Defne’s awesome monster Weekly Charts in the last 14 days (here’s number 1, here’s number 2), this weeks’ Weekly Chart keeps it small. Today we launched callout lines for our locator maps (they are lines connecting marker and label), and I wrote a few words about how to create them and when – so I will simply refer you to this announcement blog post and explain the importance of our new feature with this map:

In moderation, they can be quite useful for indicating and explaining important but subtle points you’d like to make.

Horizontal Labels with ggplot

Michael Toth shows us how to ensure we use horizontal text labels in ggplot:

There are several things we could do to improve this graph, but in this guide let’s focus on rotating the y-axis label. This simple change will make your graph so much better. That way, people won’t have to tilt their heads like me to understand what’s going on in your graph:

It may not seem like much when you’re creating the visual, but it can make a difference for a viewer.

UpSet Plots for Set Analysis

Laura Ellis digs into the UpSetR package:

UpSet plots have a very cool parameter called queries. Queries can be used to define a subset of the data that you would like to highlight in your graph. The queries property takes in a list of query lists which means that you can pass multiple queries into the same graph. Each query list allows you to set a number of properties about how the query should function.

In this example we are viewing the Cycle and Walk set intersection (query and params). We want the query to be highlighted in a nice pink (color). We want to display the query as a highlighted overlap (active) and we will give it a name that we add to the chart legend (query.name)

I’ve not seen an UpSet plot before but it dumps a lot of information into a relatively small space. I’ll have to spend some time learning more about these plots.

Improving Diagrams

Stephanie Evergreen gives us some tips for improving diagrams:

As ubiquitous as diagrams are, they have some significant shortcomings.

Diagrams tend to be most useful as a mental organizing activity for the people who make the diagram. Outsiders coming in fresh have a much harder time seeing how the elements of the diagram fit together and make sense. I think that is generally because diagrams lack enough narrative to explain what is going on. We often use diagrams without explaining them. Or, more precisely, we use diagrams without connecting the diagram pieces to their associated parts of our narrative.

Read on for an example.

Controlling Power BI Visual Visibility

Matt Allington shows how we can take one Power BI visual and use it to control the visibility status of another visual:

I have written a few articles in the past that toy with the ideas of changing visibility and text colour based on selection.  I started to wonder if it was possible to make a visual appear (or not) based on a selection from the user.  There is no out of the box way to do that today. It is possible to use bookmarks to show an hide an object, but the user must click a specific button to do this. I want the user to be able to interact with a report and see (or not see) a chart based on some valid selection across the report.  Microsoft is already working on building expression based formatting across the breadth of Power BI however as of now the only item you can change is the header in a chart.

Hopefully this gets better over time.

Modifying HTML Rendering in Shiny

Senthil Thyagarajan gives us an example of uisng the htmltools package to change the way tables render in Shiny:

In order to build the html table I have used a function table_frame which can be used as a container in DT::renderdatatable. This function basically uses htmltools. For more references on the basics of html tables please refer here

In addition to changing the colors, Senthil also shows how to add a couple of buttons which call Javascript functions. H/T R-bloggers

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