A Fine Slice Of SQL Server
Zhenguo Zhang moves the legend somewhere else:
By default,
ggplot2places the legend outside the plot area (usually on the right). However, sometimes you may want to move the legend inside the plot to save space or improve the layout. This post explores how to achieve this usingtheme()parameters.
Click through to see what options are available.
Comments closedZhenguo Zhang adds some patterns:
Adding patterns to plots is a great way to improve accessibility (making plots colorblind-friendly) and to add an extra dimension of information. The
ggpatternpackage provides a rich set of tools to achieve this inggplot2.
I’m personally not the biggest fan of patterns. I see them as a point of necessity when dealing with grayscale circumstances, such as printing out a chart in an academic journal. But it’s very easy to overdo patterns and end up making a mess of the visual.
But one side note about color vision deficiency and plots: make sure that your plots are monochrome-friendly because somebody probably will try to print out your chart or view it on a grayscale-only device. Or might actually be monochromatic.
Comments closedKieran Healy scratches his head:
I just finished driving a very long way up the side of the country, so I’m kind of tired. But even allowing for that, boy, this way of representing things really is quite confusing. Not being an Apple Sports user I had to look at it for a bit to understand what was happening. But, now that it has given me a headache, I can kind of see why whoever designed this ended up in the undoubtedly bad place they did.
Before I get to why I have some sympathy for the designer, why did I find this representation of these numbers so disorienting? It’s not just just because I’ve been driving for nine hours. John is right to call the picture a “Zero Sum” representation. The design strongly suggests to the viewer that, within each row, we’re looking at each team’s share of a total. Each pair of black and blue lines seem to be vying for control of their whole row, with the longest line being the “winner” in each case.
Click through for the challenge, as well as a trio of attempts to improve the results. The tornado chart at the end is probably what I’d go with if I needed to include all of this on a single chart. H/T R-Bloggers.
Comments closedRuben Van de Voorde embraces the second dimension:
Scatterplots are in a weird place in Power BI reports. They’re incredibly good at their core business: showing how two metrics relate across many things, like products, customers, or suppliers.
But they can miss the landing in a few ways. Sometimes the relationship itself matters but the chart asks the reader to do too much inference: “Why should I care about a product’s Gross Margin % vs. Shipping Weight?” Other times, the reader can’t tell what the dots actually are. A reader asking “What does one dot represent?” is the clearest tell, sometimes followed by “Can’t this be a table instead of these dots?”
Click through for musings about scatterplots, their bubble plot cousins, and what’s available in DAX and Power BI to make them work for you.
Comments closedThomas Williams builds a visual:
Mermaid https://mermaid.ai/ is “Markdown inspired” diagrams as code. With the diagram defined as text, special/proprietary software isn’t needed to create or edit diagrams. The text defining the diagrams can be edited by anyone – so can be kept up-to-date. Diagrams can also be version controlled, like any other code.
This matters because better diagrams improve documentation, and communication.
I’ve used Mermaid a bit. It’s fairly powerful, though can be tricky if you’re used to a more free-flow diagram design.
Comments closedValerie Junk pretties up a visual:
Many Power BI developers view tables and matrix visuals as the enemy. They dislike building them, and often think, “the user is just going to export this to Excel anyway.”
But here’s the thing: tables and matrix visuals have an important business case, and sometimes a well-structured table communicates data far better than any chart would.There’s also something we don’t talk about enough: trust. BI developers often assume users trust our data, but that’s rarely true. Many users have been burned before by incorrect data or unreliable tools. Providing a matrix visual for row-by-row verification is a powerful way to rebuild trust.
That said, a matrix visual that looks like default Power BI formatting isn’t doing you any favors.
And they’re probably going to export it to Excel anyhow. Them’s the breaks.
Comments closedZhenguo Zhang makes a change to the legend:
In
ggplot2, the legend keys (the symbols next to the labels) usually inherit their size and appearance from the layers in the plot. For example, if you have a scatter plot with very small points, the legend keys will also be very small, making them hard to see.
Read on to see how you can change this and make the key size larger (or smaller).
Comments closedYihui Xie makes an announcement:
A few weeks ago I added preliminary support for Typst to knitr. The way it works is simple: if your file has the extension
.Rtyp, knitr will recognize it as a Typst document, knit it, and produce a.typoutput file. The chunk syntax follows the same Markdown-style fenced code block convention:```{r}to start a chunk and```to end it, with inline R expressions written as`r expr`. A minimal example (hello.Rtyp):
Click through for that example, as well as some notes on Typst and HTML.
Comments closedRuben Van de Voorde shares some tips:
The bar chart is one of the oldest statistical graphics we have. William Playfair published the first bar chart for categorical comparison in 1786: a horizontal bar chart of Scotland’s imports and exports with trading partners. Two and a half centuries later, it’s a familiar sight wherever numbers are shown visually: news stories, research reports, business dashboards; bar charts are everywhere. Most people have seen one before and instinctively know how to read them.
Click through for plenty of examples of where bar charts work best, as well as important notes when using them. I’m particularly fond of Cleveland dot plots versus bar charts, but a good bar chart does tell an important story.
Comments closedFollowing on from my previous post on building The Burrito Bot, I want to delve into visualisation of vector embeddings that were generated from the restaurant data pulled from Google Maps.
Those embeddings had 1536 dimensions, each dimension corresponding to an axis within a high dimensional space, with embeddings that have similar meanings grouped together in that high dimensional space.
1536 dimensions…is a lot of dimensions! And for me, a hard concept to get my head around. It all just feels so abstract (to me anyway), I want to see what they actually look like!
Click through for a link to a website that helps with that visualization. It ultimately performs principal component analysis (PCA) to get 1536 (or however many) dimensions down to 3 principal components. It’s not perfect, but it does give us the ability to reason over the data.
Comments closed