A Fine Slice Of SQL Server
Zhenguo 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).
Leave a CommentYihui 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.
Leave a CommentRuben 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.
Leave a CommentFollowing 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.
Leave a CommentAmy Esselman looks at a chart:
Gantt charts are a popular choice for illustrating the start and duration of events, which is common practice in project management. While useful for representing timelines, these charts can quickly become busy and difficult to interpret, especially when dealing with complex workflows.
Let’s consider an example.
Click through for that example and how you can turn a rather complex-looking chart into something a bit easier to understand and work with.
Leave a CommentRuben Van de Voorde shares some tips:
Line charts plot a metric along an ordered axis. Usually that axis is time, which is why they’re the first thing most people reach for when the X axis is a date. They show direction, speed and rhythm in a way that tables and bar charts don’t. That ordering is the key requirement: for unordered categories like regions or product types, connecting the points implies a sequence that isn’t there, and a bar chart is the clearer choice.
Power BI makes line charts easy to build: pick a date column, pick a measure, maybe split by a category, and you are done. The result is technically correct, but maybe not as clear as it could be. This article walks through the most common ways a line chart falls short and how to fix them.
There’s a lot of solid advice and good examples in here.
Leave a CommentKerry Kolosko digs into data visualization theory:
Power BI development is a relatively straight forward process when managed by one individual start to finish. But when the development process is shared among team members, ways of working need to be established and common work management frameworks such as agile, lean, HCD and UI/UX Design are adopted.
These frameworks can be useful for teams but as always, the rigid adoption and adherence to frameworks can cause project inefficiencies. It took a fair bit of corporate learning to acknowledge that applying Agile methodologies to a construction project and waterfall methodologies to a software project, weren’t effective.
There’s a lot in here around pros and cons of various tooling (like wireframing), visual selection, the grammar of graphics, and what smoothed lines actually represent. Smoothed lines is a bit of a hobby horse for me, as those smoothed lines represent a model of the data rather than the actual data, so if you show me the former, you’d better also show the latter.
Comments closedYihui Xie announces a new package:
Last month, @bastistician opened an issue on the litedown repo pointing out that knitr has a
hook_pngquant()function for compressing PNG plots from code chunks, but litedown lacks such a feature. He included a reasonable workaround—callingsystem2("pngquant", ...)withlitedown::get_context("plot_files")in a chunk at the end of the vignette. It shrank his vignette from 80 KB to 54 KB, which is a 33% reduction. Not bad.The catch, of course, is that it requires pngquant to be installed on the system. For R users, installing a system binary is more friction than it sounds: it is
brew install pngquanton macOS, a separate package manager invocation on Linux, and hunting down a standalone executable on Windows. If you maintain a package that others will build, you are now asking all of them to do this—for every machine they use. By contrast,install.packages("tinyimg")works the same way everywhere, which is the kind of simplicity that makes a tool actually get used.This is why I created tinyimg.
Read on for more details about how tinyimg works, how well it compresses, and how it integrates with litedown.
Comments closedJohannes Kebeck makes an announcement:
When we envisioned Maps in Microsoft Fabric, our goal was to empower any data citizen to analyze data in time and space without any specialized knowledge. Introduced in preview at FabCon Europe 2025, it has since been used by customers across industries creating and sharing map-centric applications. Additional features were added at Ignite 2025, and this week at FabCon Atlanta, Maps in Microsoft Fabric is generally available – along with new capabilities that expand how geospatial data can be modeled, visualized, and operationalized at any scale.
Read on to see what’s new in maps.
Comments closedKenneth Omorodion lives in the now:
For Power BI developers, one very common (and frustrating) issue is when measures spill into future dates on charts especially when working with some time intelligence DAX calculations (e.g. MTD, YTD, etc.), date dimensions that extend beyond current date, and forecast-enabled tables.
In Power BI charts (e.g. line or bar charts), apart from dates with data, measures are also evaluated for every date on the axis, regardless if there is data or not. For example, if my dates table runs to 2026 December, but my data table only have data up to today, when I create a measure that leverages MTD or YTD for example, Power BI will tend to evaluate the measure for all dates that exist in my Dates table, unless I explicitly apply a logic to prevent this behaviour. This behaviour might result in flat lines on charts, misleading trends, and confusion to intended users.
In this article, I will demonstrate some examples of approaches to prevent or manage future dates spillage in Power BI.
Click through for some tips.
Comments closed