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Category: Visualization

Building an App with Streamlit

Published 2023-11-28 by Kevin Feasel

Riqo Chaar demonstrates Streamlit:

Off-the-shelf solutions for interactive data app development such as Microsoft Power BI are great – they allow users to easily develop data apps using a GUI. However, Power BI’s ease of use comes at the expense of reduced functionality. This is where programming languages such as Python, JavaScript or C# shine – you can practically code anything you like!

This blog will focus on Streamlit as a means of building interactive data apps. Streamlit is an open-source Python library that enables rapid creation of web apps (including, but not limited to, data apps) with minimal code. It acts as an intermediary between the easy-to-use, but functionally-limited characteristics of Power BI and the functionally-enhanced, but difficult-to-use characteristics of other programming tools such as JavaScript or C#.

I’ve grown to like Streamlit a lot. It’s really simple to put together a good-looking page, similar to Shiny in R.

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Fixing a Double Donut Chart

Published 2023-11-21 by Kevin Feasel

Amy Esselman drags a donut chart:

Here we have not one, but two donuts! It takes a while to figure out what this data represents. Based on the title, it appears that we’re meant to compare the categories of the donuts across the two charts. With the sections in different places, this is rather challenging. 

One may argue that the colors and donut form make the data more visually interesting; however, this often hinders our ability to understand the data. Let’s look at some strategies to make this information easier to comprehend.

Read on for advice around converting two donut charts into something humans can more easily interpret.

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Simulating a Bivariate Normal Distribution in R

Published 2023-11-10 by Kevin Feasel

Steven Sanderson isn’t content with a univariate normal distribution:

Imagine two variables, like height and weight, that exhibit a joint distribution. The bivariate normal distribution captures the relationship between these variables, describing how their values tend to cluster around certain means and how they vary together. It’s like a two-dimensional bell curve, where the peak represents the most likely combination of values for both variables.

Click through to learn a bit more about bivariate normal distributions, including ways to plot one and show its density function.

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Plotting a Cumulative Distribution Function in R

Published 2023-11-09 by Kevin Feasel

Steven Sanderson builds a plot:

Before delving into the world of R programming, let’s first grasp the fundamental concept of a CDF. Imagine a group of students eagerly awaiting their exam results. The CDF for their scores would depict the probability of encountering a student with a score less than or equal to a specific value. For instance, if the CDF indicates a value of 0.7 at 80%, it implies that there’s a 70% chance of finding a student with a score of 80 or lower.

Read on to see how you can calculate this in a dataset and then plot the CDF.

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Log-Log Plots in R

Published 2023-10-31 by Kevin Feasel

Steven Sanderson thinks in percentages:

A log-log plot is a type of graph where both the x-axis and y-axis are in logarithmic scales. This is particularly useful when dealing with data that spans several orders of magnitude. By taking the logarithm of the data, we can compress large values and reveal patterns that might be hidden on a linear scale.

Let’s start with a simple example using base R.

Read on to see how you can create these plots and what you can do to customize them.

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Building a Bland-Altman Plot in R

Published 2023-10-27 by Kevin Feasel

Steven Sanderson performs a comparison:

Before we dive into the code, let’s briefly understand what a Bland-Altman plot is. It’s a graphical method to visualize the agreement between two measurement techniques, often used in fields like medicine or any domain with comparative measurements. The plot displays the differences between two measurements (Y-axis) against their means (X-axis).

Click through to see how this works and how you can interpret the results.

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Scree Plots in R

Published 2023-10-25 by Kevin Feasel

Steven Sanderson builds a scree plot:

A scree plot is a line plot that shows the eigenvalues or variance explained by each principal component (PC) in a Principal Component Analysis (PCA). It is a useful tool for determining the number of PCs to retain in a PCA model.

In this blog post, we will show you how to create a scree plot in base R. We will use the iris dataset as an example.

Read on to learn more about the plot, as well as examples of how to create scree plots.

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Bubble Charts in ggplot2

Published 2023-10-24 by Kevin Feasel

Steven Sanderson creates a bubble chart:

Bubble charts are a great way to visualize data with three dimensions. The size of the bubbles represents a third variable, which can be used to show the importance of that variable or to identify relationships between the three variables.

To create a bubble chart in R using ggplot2, you will need to use the geom_point() function. This function will plot points on your chart, and you can use the size aesthetic to control the size of the points.

Click through for two examples, one which is a pretty good outcome for using a bubble chart, and one which exposes the key weakness of bubble charts.

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