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Category: Visualization

Shrinking Dot Sizes in Power BI

Published 2019-04-02 by Kevin Feasel

David Eldersveld shows how we can reduce the point size of dots in POwer BI as of March 2019:

One of the Power BI improvements in the March 2019 Desktop release was reduced bubble size for the Map visual. I previously wrote about the benefit of the reduction in point/bubble size. I was unaware until recently that this change made it into more than the Map visual.

The ability to reduce the point size also appears in the Format options for the Power BI Scatter chart. Previously, you could change the size option from 0 to 100 under the Shapes area. As with the Map, the Scatter now allows you to reduce the size as low as -30. I did not see this mentioned in the March Desktop blog post. I must have missed it if it was part of a previous month’s release. In any case, if you were not aware that you could set the point size from -30 to 100with the Scatter chart, now you do.

For most scenarios, I think the dot size is probably a little too big. -30 is generally too small, but I’m happy that they offer us options to get it right.

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Databricks Dashboards

Published 2019-03-27 by Kevin Feasel

Megan Quinn takes us through building dashboards with Apache Zeppelin on Databricks:

The first step in any type of analysis is to understand the dataset itself. A Databricks dashboard can provide a concise format in which to present relevant information about the data to clients, as well as a quick reference for analysts when returning to a project.

To create this dashboard, a user can simply switch to Dashboard view instead of Code view under the View tab. The user can either click on an existing dashboard or create a new one. Creating a new dashboard will automatically display any of the visualizations present in the notebook. Customization of the dashboard is easily achieved by clicking on the chart icon in the top right corner of the desired command cells to add new elements.

This isn’t quite a step-by-step guide but does spur on ideas.

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Conditional Formatting on Text Fields in Power BI

Published 2019-03-26 by Kevin Feasel

Matt Allington shows how you can apply conditional formatting to non-numeric fields in Power BI:

The high level process is to:
1. Create a measure that returns a colour as the result

1. It can be a word, such as blue, red, green
2. It can be a hex code for a colour, like #40E0D0″, “#FFA07A”
2. Use conditional formatting and use the measure to apply the formatting on the text as a rule.

Read on for a demo.

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Running Totals in Tableau and Power BI

Published 2019-03-25 by Kevin Feasel

David Eldersveld shows how to create running totals in both Tableau and Power BI:

What about a separate Power BI Date table?
This setup is built for consistency of comparison. As people go deeper into Power BI, they typically add a separate Date table as part of a more robust data model and add relationships between tables. At the same time, they disable the default Auto Date/Time built-in hierarchies. This more advanced setup with a separate Date table allows several conveniences as well as performance and storage benefits. It’s especially true with larger models that include many facttables that each join to Date and other possible dimension tables. Tableau doesn’t currently have a comparable data model. We’ll stay conveniently away from that setup in Power BI because we only have one simple sample table.

I think both of them make this an easy operation, though Tableau is probably easier here.

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Interactive ggplot Plots with plotly

Published 2019-03-20 by Kevin Feasel

Laura Ellis takes us through ggplotly:

As someone very interested in storytelling, ggplot2 is easily my data visualization tool of choice. It is like the Swiss army knife for data visualization. One of my favorite features is the ability to pack a graph chock-full of dimensions. This ability is incredibly handy during the data exploration phases. However, sometimes I find myself wanting to look at trends without all the noise. Specifically, I often want to look at very dense scatterplots for outliers. Ggplot2 is great at this, but when we’ve isolated the points we want to understand, we can’t easily examine all possible dimensions right in the static charts.

Enter plotly. The plotly package and ggploty function do an excellent job at taking our high quality ggplot2 graphs and making them interactive.

Read on for several quality, interactive visuals.

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Custom ggplot2 Fonts

Published 2019-03-18 by Kevin Feasel

Daniel Oehm shares two techniques for using custom fonts in your ggplot2 visuals:

ggplot – You can spot one from a mile away, which is great! And when you do it’s a silent fist bump. But sometimes you want more than the standard theme.

Fonts can breathe new life into your plots, helping to match the theme of your presentation, poster or report. This is always a second thought for me and need to work out how to do it again, hence the post.

There are two main packages for managing fonts – extrafont, and showtext.

Read on to see how to use each of these packages. H/T R-bloggers

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Showing Totals on Power BI Stacked Column Charts

Published 2019-03-12 by Kevin Feasel

Reza Rad shows us how to add a totals figure to Power BI stacked column charts:

As you can see, there are data labels for each subcategory (means gender and education), but no data label showing the total of each education category. for example, we want to know how much was the total sales in the High School category. Now that you know the problem, let’s see a way to fix it.

Read on for Reza’s solution to the problem. In general, if people might care about the total, do them a favor and show the total.

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New Version of ggforce Available

Published 2019-03-08 by Kevin Feasel

Thomas Lin Pedersen announces a new version of ggforce for R:

If there is one thing of general utility lacking in ggplot2 it is probably the ability to annotate data cleanly. Sure, there’s geom_text()/geom_label()but using them requires a fair bit of fiddling to get the best placement and further, they are mainly relevant for labeling and not longer text. ggrepelhas improved immensely on the fiddling part, but the lack of support for longer text annotation as well as annotating whole areas is still an issue.

In order to at least partly address this, ggforce includes a family of geoms under the geom_mark_*() moniker. They all behaves equivalently except for how they encircle the given area(s). 

There are some really interesting features in the ggforce package, so check them out.

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Simplify Visuals: No Unnecessary Lines

Published 2019-03-07 by Kevin Feasel

Stephanie Evergreen shows how you can improve your visuals by removing most of the lines:

The Lines section of the Data Visualization Checklist helps us enhance reader interpretability by handling a lot of the junk, or what Edward Tufte called the “noise” in the graph. I’m referring to all of the parts of the graph that don’t actually display data or assist reader cognition. Create more readability by deleting unnecessary lines. 

The default chart, on the left, has black gridlines. These stand out quite a bit because of how well black contrasts against the white chart background. But the gridlines shouldn’t be standing out so much because they are not the most important part of the graph 

I like that Stephanie keeps the gridlines. I’ve seen Tufte advocate removing them altogether but there’s a lot of value in keeping them in; just don’t make them the sharpest focus color.

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