In this video, I talk about Streamlit, a great Python library for building data applications quickly. We discuss what data applications are, get an idea of how Streamlit compares to other code-first data visualization techniques, and start building a demo application. I also toss in a lengthy sidebar on Python virtual environments because of how important they are.
Streamlit certainly has its foibles—many of which I’ll cover in the series—but I like it a lot as a simple way of building data applications.
Power BI reports have a theme that specifies the default colors, fonts, and visual styles. In Power BI Desktop, you can choose to use a built-in theme, start with a built-in theme and customize it, or create your own theme.
Creating your own theme involves specifying formatting options in a JSON file and importing it into your report. This post will focus on the theme colors, but there are lots of other options that can be specified in a theme, including structural colors, fonts, and page and visual formatting options.
Read on to learn more about the three primary sets of colors you can specify.
The main criticism people have when it comes to ggplot2 is the static nature of the charts it has to offer. Truth be told, it will never be an interactive visualization king like Highcharts, but it doesn’t mean animation is out of the picture.
Meet R gganimate – a natural extension of ggplot2 that allows you to visualize your data change through time or some other variable, and then render and export the chart as a set of PNGs, or a single GIF/MP4.
Click through to learn more about it. I remembered the original gganimate and was going to say, “Wow, I hadn’t heard of that library in forever.” But it turns out that Thomas Lin Pedersen built a newer version of the library and has added in quite a bit of functionality since the last time I looked. H/T R-Bloggers.
Recently, I wrote an article, Rank and Sort Data Based on Multiple Columns in Power BI Using DAX. However, it is very common for business users to request the ability to dynamically view the Top N and Bottom N values of a measure, like Total Sales, on the same visual. This requirement is simple to implement on either the Top or Bottom N options. But, the challenge is when we need to represent the two options on the same chart simultaneously.
Have you ever found yourself staring at a graph or slide, wondering what the creator was trying to convey? Perhaps you’ve sat through a presentation, only to be left scratching your head, unsure of what to do next. Don’t put your audience in this same uncomfortable position. Instead, connect the dots for them to make it clear what the point is and what action they should take. When you fail to explicitly state the purpose of your communication, you run the risk of the important insight being lost, or someone arriving at the wrong conclusion.
Read on for an example of comparing resource plans to actual allocations and moving from showing data to telling a story.
A simple, yet effective way to set your colour palette in R using ggplot library.
Click through for the demonstration. Tomaz keeps the text very light in this post, so I’ll do a little vamping of my own. Creating a custom palette is neat, but do make sure that your custom palette works for users with color vision deficiency (CVD). Taking Tomaz’s bar chart into Coblis (an amazing tool I continue to use quite regularly), here’s what it looks like for people with protanopia—that is, no red cones in their eyes:
It’s not awful, particularly because Tomaz changed the fill but not the border color, so you get a funky striation effect.
But the real kicker is if you switch to the monochromatic option in Coblis.
Granted, I know of exactly one person with monochromacy, so if you want to be fair, this isn’t one I’d check for on a webpage. But the large majority of technical books have grayscale images because it saves money on printing, so if this were your sweet-looking color scheme and you’re adding the image into a book, readers would need to focus particularly hard on the bars to figure anything out.
Effective reports and dashboards should enable users to quickly answer their data questions so that they can focus on their primary business tasks and responsibilities. To help you design effective reports, we introduce the3-30-300 rule for information design.The 3-30-300 rule is a straightforward and practical approach for you to produce efficient report layouts by structuring reports in a functionally hierarchical way. This rule concisely paraphrases the visual information-seeking mantra from Ben Schneidermann (1996). To make it easier to understand for Power BI developers, we express this rule with respect to approximately how long it should take users to get certain information or perform certain tasks in a report.
It’s a clever mnemonic and Kurt does a good job of showing how you could implement it.
Of late, I’ve been falling down a bunch of geospatial rabbit holes. One thing has remained true in each of them: it’s really hard to debug what you can’t see.
There are ways to visualize these. Some more-integrated SQL development environments like pgAdmin recognize and plot columns of geometry type. There’s also the option of standing up a webserver to render out raster and/or vector tiles with something like Leaflet. Unfortunately, I don’t love either solution. I like psql, vim, and the shell, and I don’t want to do some query testing here and copy others into and out of pgAdmin over and over; I’m actually using Leaflet and vector tiles already, but restarting the whole server just to start debugging a modified query is a bit much in feedback loop time.
Calculating cumulative percentage or percentage per group for each time can sometimes be a task with a slight twist. Let’s check this with ggplot2 and tidyverse.
Click through for three separate ways of doing this.
There are plenty of ways to visualize data. There’s PowerBI, Tableu, and a plethora of other options. What about taking the results of a SQL query and creating a graph in PowerShell? Probably not ideal, but is it possible? Let’s see what this might look like.
The thought occurred to me more out of curiosity than it being something I’d use. Admittedly, I’m not proficient enough in PowerShell to quickly build something from scratch. To get an idea of how it might look, I took this as an opportunity to outsource most of the work to Microsoft Copilot to see if I would get anything useful.
If you want to get fancy, I’d recommend Plotly, which has support for the best .NET language (F#) and you can also use it with those other .NET languages (C#, Powershell). There’s no explicit quickstart for Powershell but you can Powershell-itize the C# code pretty easily.
