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Category: Visualization

Changing the Style of a Legend in R

Published 2023-10-11 by Kevin Feasel

Steven Sanderson is a legend:

Before diving into code examples, let’s understand the basics. In R, legends are essential for explaining the meaning of different elements in your plot, such as colors, lines, or shapes. Legends help your audience interpret the data effectively.

In most cases, R’s base plotting system provides you with control over the legend’s size. The key functions we’ll explore are legend() and guides(). We’ll also delve into how to modify legend size in popular plotting packages like ggplot2.

Click through for those demonstrations.

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Conditional Formatting in Power BI with Field Parameters and Calculation Groups

Published 2023-10-10 by Kevin Feasel

Marco Russo and Alberto Ferrari perform some formatting:

If you want to build a report where the user can choose what measure to show, you have two features available in Power BI: field parameters and calculation groups. There are pros and cons to either technique – however, we are not about to talk about those. We narrow our scenario down to a specific requirement: we want to change the color of the value depending on the measure selected.

For example, suppose we let users choose between Sales Amount, Margin, or Total Cost. In that case, we might provide visual feedback about the measure selected through different colors: black for Sales Amount, green for Margin, and red for Total Cost.

Click through for that example, though I will say that the color choices are hard to differentiate if you have protanopia and even more difficult if you have deuteranopia, so about 2% of the male population would struggle with interpreting this measure. People with protanomaly and deuteranomaly (about 6% of men) wouldn’t have too much difficulty with this particular color pairing.

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Power BI Themes and Gallery

Published 2023-10-09 by Kevin Feasel

Seth Bauer has an announcement:

The Power BI Tips Theme Generator tool already allows you to easily interact with, and adjust, all the visual properties, wireframes, etc… How could we possibly make Power BI Theme building an effortless experience? We start with building it all for you, then letting you adjust it!
The all new Gallery feature represents a significant leap forward in simplifying the theming process for all. This feature is especially for the business users! But, it also opens up exciting opportunities for the Power BI community to contribute in the future as well.

Read on to see how this works.

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Radar Charts in R

Published 2023-10-06 by Kevin Feasel

Steven Sanderson has radar love:

Radar charts, also known as spider, web, polar, or star plots, are a useful way to visualize multivariate data. In R, we can create radar charts using the fmsb library. Here are several examples of how to create radar charts in R using the fmsb library:

Radar charts are a guilty pleasure of mine. They are rarely the right choice, but when they are, I love it so much.

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Tightening up Dashboards

Published 2023-10-06 by Kevin Feasel

Rita Fainshtein improves that dashboard:

One of our challenges as dashboard developers is effectively presenting all the necessary information to decision-makers while working within the constraints of limited ‘real estate’ on the dashboard. To tackle this challenge, I’ve compiled a list of 5 tips that will help you complete the task without the need for excessive buttons or constant screen switching.

I heartily agree with 4 out of the five and agree with caveats concerning the tooltip example. The only reason I might disagree with moving information into tooltips is that dashboards are intended to be glanceable, meaning you can get all relevant information by looking at the dashboard but without needing to click, drag, scroll, drill, or otherwise manipulate the dashboard. I like tooltips for ancillary information—which, in fairness, is also the point Rita drives at.

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Visualizing Kusto Graphs with Plotly and Python

Published 2023-10-05 by Kevin Feasel

Henning Rauch creates some plots:

Graphs are a powerful way to model and analyse complex relationships between entities, such as cybersecurity incidents, network traffic, social networks, and more. Kusto, the query and analytics engine of Azure Data Explorer, Microsoft Fabric Real-Time Analytics and many more recently introduced a new feature that enables users to contextualize their data using graphs. In this blog post, we will show you how to use graph semantics to create and explore graph data in Kusto, and how to visualize it using Plotly, a popular library for interactive data visualization in Python.

Graph semantics are a set of operators that allow users to work with graph data in Kusto, without the need to use a separate graph database or framework.

Click through for the KQL you’ll need, as well as how to display that in Plotly.

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Drawing Horizontal Box Plots in R

Published 2023-10-04 by Kevin Feasel

Steven Sanderson is not limited to one axis:

Boxplots are a great way to visualize the distribution of a numerical variable. They show the median, quartiles, and outliers of the data, and can be used to compare the distributions of multiple groups.

Horizontal boxplots are a variant of the traditional boxplot, where the x-axis is horizontal and the y-axis is vertical. This can be useful for visualizing data where the x-axis variable is categorical, such as species or treatment group.

Click through for an example using base R and ggplot2.

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Overlaying Lines with Points in Base R

Published 2023-09-29 by Kevin Feasel

Steven Sanderson adds points to those lines:

In this blog post, we’ll explore how to overlay points or lines on a plot using Base R. We’ll use the plot() function to create the initial plot and then show how to overlay points with points() and lines with lines(). We’ll provide several examples, explaining each code block in simple terms, and encourage you to try them out on your own datasets.

Read on to see how. It’s also pretty easy to do in ggplot2 or other visualization libraries.

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Faceted Images in ggplot2

Published 2023-09-28 by Kevin Feasel

Steven Sanderson shows multiple plots on one image:

Data visualization is a crucial tool in the data scientist’s toolkit. It allows us to explore and communicate complex patterns and insights effectively. In the world of R programming, one of the most powerful and versatile packages for data visualization is ggplot2. Among its many features, ggplot2 offers the facet_grid() function, which enables you to create multiple plots arranged in a grid, making it easier to visualize different groups of data simultaneously.

In this blog post, we’ll dive into the fascinating world of facet_grid() using a practical example. We’ll generate some synthetic data, split it into multiple groups, and then use facet_grid() to create a visually appealing grid of plots.

Read on for the demo script. The text talks about facet_grid() and the demo is facet_wrap(). The two behave very similarly, though they have slightly different use cases.

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