A Fine Slice Of SQL Server
Jameson Marriott fixes a spacing issue:
I recently noticed that ggplot2 spaces date axes literally even when grouped by month. I’ve been using ggplot2 extensively for years and I don’t remember noticing before, so this is not really a big deal, but now that I know it bugs me a lot. Take a look below.
I don’t think I had noticed this before either, though now that Jameson has pointed it out, it certainly is annoying. H/T R-Bloggers.
Comments closedRiqo Chaar get more than 80% of the way there:
The Pareto principle, commonly referred to as the 80/20 rule, is a concept of prioritisation.
It states that for many outcomes, 80% of the outputs are derived from 20% of the inputs. Although this isn’t a universal truth, this pattern has been observed in many different cases. For example, a large proportion (80%) of the revenue a particular business generates may primarily be associated with only a small proportion (20%) of big-selling products. This concept is related to the law of diminishing returns and poses the following question: If, after reaching a certain level of output (80%), significantly more effort is required to achieve further increases in this output, is this additional effort worth it?
In this article, we demonstrate the process (using DAX expressions) of creating a Pareto chart in Power BI.
Read on to see how you can build a chart like this. It’s a very popular style of chart for manufacturing scenarios.
Comments closedChristian Lorentzen makes a swap:
With its newest release 1.1.0, the Python package model-diagnostics got the concept of a plotting backend. Before this release, all plots were constructed with matplotlib. This is still the default. But additionally, the user can now select plotly, if it is installed.
Click through to see how it works.
Comments closedAlex Velez shares some advice:
A tip I regularly share when providing data visualization feedback is to use multiple graphs instead of packing several series into a single chart. Although it is important to be concise, people are often surprised to hear that when it comes to the number of graphs we share, fewer isn’t always better.
Read on for the advice. It makes a lot of sense for several reasons, as Alex shares. One additional reason is that it goes toward Edward Tufte’s argument about information density: multiple smaller visuals allow you to put more relevant information into the same amount of physical space. This makes your visuals more impactful as a result.
Comments closedMauricio 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 closed