A Fine Slice Of SQL Server
Mauricio Vargas Sepúlveda makes some tea:
Tetley tea boxes feature the following caffeine meter:
In R we can replicate this meter using ggplot2.
I enjoy this kind of thing because, even though the end result doesn’t look exactly like the chart on the tea box, it’s close enough to appreciate the effort. H/T R-Bloggers.
Comments closedErik Svensen figures out a way:
Power BI has 5 different ways of positioning data labels in a bar chart – Auto, Inside end, Outside end, Inside center and inside base.
Depending on the distribution of your data this can lead to labels are being hidden, cut off or positioned a way that looks strange for the user.
Read on for a clever solution to a weakness in Power BI’s formatting capabilities.
Comments closedElena Drakulevska takes us through some of the accessibility features in Power BI:
As data professionals, we’re constantly striving to create reports that effectively communicate insights to our audience. Did you know that within Power BI, there are hidden accessibility features that can enhance the usability for all users—with minimal effort on your part? Today, we’re digging into this overlooked aspect of Power BI and exploring how these features can elevate your data visualization projects.
Click through for three features.
Comments closedKurt Buhler digs into scatter plots:
In many reports, it is common to find large tables or matrixes with a lot of information. Business users come to these reports to get the details they need to answer questions, make decisions, and take actions. However, these detailed tables can be inefficient, requiring users sort, drilldown, and filter them to find actionable data.
Consider the following example of a matrix to analyze gaps in year-over-year growth by product, and dissect those by region. In this scenario, the intended purpose of the report is for a sales team to analyze why we did not hit our revenue growth target.
Read on to learn what you can do with a scatter plot, either natively in Power BI or via custom visual.
Comments closedRhian Davies consults the map:
I love playing with spatial data. Perhaps because I enjoy exploring the outdoors, or because I spend hours playing Geoguessr, or maybe it’s just because maps are pretty but there’s nothing more fun than tinkering with location data.
However, reading in spatial data, especially large data sets can sometimes be a pain. Here are some simple things to consider when working in spatial data in R and breaking large data sets into more manageable chunks.
Click through for three tips when dealing with spatial data. The code is in R but the tips make sense in any language.
Comments closedDan Oehm notes another brick in the wall:
If you’re looking for something a little different,
ggbrickcreates a ‘waffle’ style chart with the aesthetic of a brick wall. The usage is similar togeom_colwhere you supply counts as the height of the bar and afillfor a stacked bar. Each whole brick represents 1 unit. Two half bricks equal one whole brick.It has been available on Git for a while, but recently I’ve made some changes and it now has CRAN’s tick of approval.
Click through to see how you can use it. This style of waffle chart, in the right scenario, can be quite useful, providing a high-level view and also giving you some idea of fine-grained magnitudes. H/T R-Bloggers.
Comments closedWhen you’re visualizing categorical data, sorting the bars in your chart is usually a straightforward task. Or is it?
In most cases, you probably take the category with the largest value and stick that in the prime spot, the leftmost slot on the horizontal axis. Then, you proceed from left to right in descending order of value. Easy peasy.
But it’s not always that simple, as Mike points out.
Comments closedSteven Sanderson builds a plot:
In the realm of machine learning, visualizing model predictions is essential for understanding the performance and behavior of our algorithms. When it comes to regression tasks, plotting predictions alongside actual values provides valuable insights into how well our model is capturing the underlying patterns in the data. With the
plot_regression_predictions()function in tidyAML, this process becomes seamless and informative.
Read on to see how the function works and the kind of result you can expect from it.
Comments closedKurt Buhler has some new digs:
Format strings greatly improve the usefulness of your model, particularly when you apply custom and dynamic format strings creatively. However, there are considerations to keep in mind.
It is important that the values in a report display for users as they expect. Effective formatting of DAX measures and table columns improves your model, as values are easier to read and interpret. While formatting may seem simple at first glance, the flexibility of DAX and format strings in Power BI can create many opportunities for more creative and efficient tables or visuals.
Click through to understand why proper formatting of measures is so important on dashboards, and also congratulate Kurt on becoming an Italian. At least, I assume citizenship conveys when you do work for Marco & Alberto.
Comments closedSebastian Sauer shows off some code. There’s pretty much nothing to excerpt here, as it’s all code and images, so the gist of what Sebastian shows off is how to use the vjust parameter on ggplot2 labels to force where those labels show up.