A Fine Slice Of SQL Server
Steven Sanderson performs a comparison:
Before we dive into the code, let’s briefly understand what a Bland-Altman plot is. It’s a graphical method to visualize the agreement between two measurement techniques, often used in fields like medicine or any domain with comparative measurements. The plot displays the differences between two measurements (Y-axis) against their means (X-axis).
Click through to see how this works and how you can interpret the results.
Comments closedSteven Sanderson builds a scree plot:
A scree plot is a line plot that shows the eigenvalues or variance explained by each principal component (PC) in a Principal Component Analysis (PCA). It is a useful tool for determining the number of PCs to retain in a PCA model.
In this blog post, we will show you how to create a scree plot in base R. We will use the
irisdataset as an example.
Read on to learn more about the plot, as well as examples of how to create scree plots.
Comments closedSteven Sanderson creates a bubble chart:
Bubble charts are a great way to visualize data with three dimensions. The size of the bubbles represents a third variable, which can be used to show the importance of that variable or to identify relationships between the three variables.
To create a bubble chart in R using
ggplot2, you will need to use thegeom_point()function. This function will plot points on your chart, and you can use the size aesthetic to control the size of the points.
Click through for two examples, one which is a pretty good outcome for using a bubble chart, and one which exposes the key weakness of bubble charts.
Comments closedSteven Sanderson builds a Pareto chart:
A Pareto chart is a type of bar chart that shows the frequency of different categories in a dataset, ordered by frequency from highest to lowest. It is often used to identify the most common problems or causes of a problem, so that resources can be focused on addressing them.
To create a Pareto chart in R, we can use the
qccpackage. Theqccpackage provides a number of functions for quality control, including thepareto.chart()function for creating Pareto charts.
Manufacturing companies love Pareto charts
Comments closedSteven Sanderson flattens the legend:
Creating a horizontal legend in base R can be a useful skill when you want to label multiple categories in a plot without taking up too much vertical space. In this blog post, we’ll explore various methods to create horizontal legends in R and provide examples with clear explanations.
Read on for two demos, one with a single legend and one which creates two legends. I’m not so sure about how valuable the latter is (because you’re splitting valuable information into two places, losing some of the glanceability of a chart along the way), but it is interesting that you can do it.
Comments closedMike Cisneros debugs a visual:
Looking at the chart, you might think, “For such a simple chart, why am I so confused?” It’s a bar chart with one data series, a title, a subtitle, and more info at the bottom. But the real challenge isn’t the design, layout, or labeling. It’s the assumptions made before creating the chart. For example, the chart uses the acronym “ADS” without explaining it. Does everyone know it means Average Daily Sales? And does “Gap Analysis” make sense to coffee shop owners?
One additional thing that I would point out is that column charts tend to imply time series, which adds even more to the confusion, as the presentation makes it look like you’re seeing the comparison of Mellow Bean versus its competition over seven time periods. Bar charts, meanwhile, tend to imply static data, so the move to a bar chart makes sense.
Comments closedBefore diving into code examples, let’s understand the basics. In R, legends are essential for explaining the meaning of different elements in your plot, such as colors, lines, or shapes. Legends help your audience interpret the data effectively.
In most cases, R’s base plotting system provides you with control over the legend’s size. The key functions we’ll explore are
legend()andguides(). We’ll also delve into how to modify legend size in popular plotting packages likeggplot2.
Click through for those demonstrations.
Comments closedMarco Russo and Alberto Ferrari perform some formatting:
If you want to build a report where the user can choose what measure to show, you have two features available in Power BI: field parameters and calculation groups. There are pros and cons to either technique – however, we are not about to talk about those. We narrow our scenario down to a specific requirement: we want to change the color of the value depending on the measure selected.
For example, suppose we let users choose between Sales Amount, Margin, or Total Cost. In that case, we might provide visual feedback about the measure selected through different colors: black for Sales Amount, green for Margin, and red for Total Cost.
Click through for that example, though I will say that the color choices are hard to differentiate if you have protanopia and even more difficult if you have deuteranopia, so about 2% of the male population would struggle with interpreting this measure. People with protanomaly and deuteranomaly (about 6% of men) wouldn’t have too much difficulty with this particular color pairing.
Comments closedSeth Bauer has an announcement:
The Power BI Tips Theme Generator tool already allows you to easily interact with, and adjust, all the visual properties, wireframes, etc… How could we possibly make Power BI Theme building an effortless experience? We start with building it all for you, then letting you adjust it!
The all new Gallery feature represents a significant leap forward in simplifying the theming process for all. This feature is especially for the business users! But, it also opens up exciting opportunities for the Power BI community to contribute in the future as well.
Read on to see how this works.
Comments closedSteven Sanderson has radar love:
Radar charts, also known as spider, web, polar, or star plots, are a useful way to visualize multivariate data. In R, we can create radar charts using the fmsb library. Here are several examples of how to create radar charts in R using the fmsb library:
Radar charts are a guilty pleasure of mine. They are rarely the right choice, but when they are, I love it so much.
Comments closed