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Category: Visualization

Visualizing Univariate Data Distributions in R

Published 2023-08-25 by Kevin Feasel

Steven Sanderson reviews the shape of the data:

Understanding the distribution of your data is a fundamental step in any data analysis process. It gives you insights into the spread, central tendency, and overall shape of your data. In this blog post, we’ll explore two popular functions in R for visualizing data distribution: density() and hist(). We’ll use the classic Iris dataset for our examples. Additionally, we will introduce the {TidyDensity} library and show how it can be used to create distribution plots.

Click through for three different functions for visualizing the density of a variable.

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Adding Mean to Box Plots in R

Published 2023-08-24 by Kevin Feasel

Steven Sanderson tracks the sixth number of a five-number summary:

Data visualization is a powerful tool for understanding and interpreting data. In this blog post, we will explore how to create box plots with mean values using both base R and ggplot2. We will use the famous iris dataset as an example. So, grab your coding tools and let’s dive into the world of box plots!

Note that this is mean in addition to median in these visuals, not replacing the median.

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Creating a Box Plot in R

Published 2023-08-21 by Kevin Feasel

Steven Sanderson builds up a box plot:

Are you ready to dive into the world of data visualization in R? One powerful tool at your disposal is the box plot, also known as a box-and-whisker plot. This versatile chart can help you understand the distribution of your data and identify potential outliers. In this blog post, we’ll walk you through the process of creating box plots using R’s ggplot2 package, using the airquality dataset as an example. Whether you’re a beginner or an experienced R programmer, you’ll find something valuable here.

Click through to learn what kind of information a box plot can provide, as well as how to create one using a variety of R libraries.

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Fallback Fonts in Power BI and Deneb Visuals

Published 2023-08-18 by Kevin Feasel

Meagan Longoria gets a request:

This week, I was working with a client who requested I use the Segoe UI font in their Power BI report. The report contained a mix of core visuals and Deneb visuals. I changed the fonts on the visuals to Segoe UI and published the report. But my client reported back that they were seeing serif fonts in some visuals. I couldn’t replicate this on my machine while viewing the report in a web browser or in Power BI Desktop.

Read on to see what the problem was, as well as the workaround.

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Creating Curves in R

Published 2023-08-17 by Kevin Feasel

Steven Sanderson draws a curve:

In the vast world of R programming, there are numerous functions that provide powerful capabilities for data visualization and analysis. One such function that often goes under appreciated is the curve() function. This neat little function allows us to plot mathematical functions and explore their behavior. In this blog post, we will dive into the syntax of the curve() function, provide a couple of examples to demonstrate its usage, and encourage readers to try it on their own.

Click through for several examples.

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Creating a Calendar View in Power BI

Published 2023-08-16 by Kevin Feasel

Martin Schoombee needs a calendar:

It’s pretty sad that we don’t have a built-in calendar visualization in Power BI, and the custom visuals in the marketplace don’t have everything I need/want for my own internal reporting…so I decided to experiment a little and see how close I could get with the standard graphs that are available.

Read on to see how close Martin could get. It’s actually more calendar-looking than I would have expected, though also frustratingly limited.

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Managing Plot Parameters in R

Published 2023-08-10 by Kevin Feasel

Steven Sanderson switches up a visual:

When it comes to data visualization in R, the par() function is an indispensable tool that often goes overlooked. This function allows you to control various graphical parameters, unleashing a world of customization possibilities for your plots. In this blog post, we’ll demystify the par() function, break down its syntax, and provide you with hands-on examples to help you create stunning visualizations.

Click through to check it out. My loyalties definitely lie with ggplot2 for static visual development in R but it’s definitely not the only way to get images to look the way you want them.

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Adding Text to a Plot in R

Published 2023-08-09 by Kevin Feasel

Steven Sanderson texts up a plot:

As a programmer, you’re well aware of the importance of data visualization. A well-crafted plot can convey complex information with clarity and impact. In R, creating stunning plots is a breeze, especially when you’re armed with the versatile text() function. This little gem allows you to add custom text to your plots, enabling you to annotate and highlight essential details. Let’s dive into the world of text() and uncover its syntax and potential through some hands-on examples.

I’m also a big fan of geom_text_repel() in ggplot2’s ggrepel library. It is by no means perfect but it does do a good job of not overlapping important visual features like plotted lines.

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