Custom chart templates aren’t a new feature, but I’m not sure how widely known they are. In a guest post, Bill Dean briefly recommended using these to create a non-standard Excel chart, The Bullet Graph. Another use-case is to create what I call a “clean-slate-template.” This is a chart template that incorporates many best practices and allows you—the creator—to focus on the strategic use of color and words while saving time on formatting.
This is nice because it eliminates the need to click-click-click on every chart, removing the same things over and over.
WVPlotswas originally a catch-all package of
ggplot2visualizations that we at Win-Vector tended to use repeatedly, and wanted to turn into “one-liners.” A consequence of this is that the older visualizations had our preferred color schemes hard-coded in. More recent additions to the package sometimes had palette or color controls, but not in a consistent way. Making color controls more consistent has been a “todo” for a while—one that I’d been putting off. A recent request from user Brice Richard (thanks Brice!) has pushed me to finally make the changes.
Click through to see what’s changed and for an example vignette.
So I’ll just leave these here. Yes, these images are all Power BI. They are all clickable links to interactive pages.
It’s fun to click through and gives you some ideas of what Power BI can do.
Now that we can apply custom format strings to fields and measures in Power BI in the September 2019 release, I thought it would be useful to provide some examples of what’s possible with this very flexible new feature because the existing documentation for VBA isn’t easy to make sense of. In fact there’s so much to say I’m going to have to write a series of blog posts to cover everything! In this first post I’m going to look at formatting numbers.
When you need an exact number, a thousands separator goes a long way.
That was ok, but we should try to make the images more aesthetically pleasing using the magick package. We make each image transparent with the image_transparent() function. We can also make the resulting image a specific color with image_colorize().
I then saved the images using the image_write() function. I manually re-uploaded them to GH.
This was a great example of where laying icons on a map works.
I presumed the graph would depict cancellation rates for a set of products, with “Tier 2 with Promotion” at the top, representing the highest cancellation rate. When we get to the data, though, that’s not the case. Rather, the graph shows the inverse metric (retention rate) with Tier 2 + Promo as the bottom line with the lowest retention rate. Eventually I figured this out—but only because I spent time studying the data to make this determination!
Click through for the initial visual as well as a couple of alternatives.
The projection from three coordinates to a 2D plane is achieved by adding the following two measures. Be sure to adjust the column references and what-if parameter names at the top to correspond to your own data.
Here’s my “Ortho x” measure. The initial six bold values are what you’d need to adjust to your own data and parameter names.
David lays out a face, which is pretty neat.
It is all about giving that little bit more insights which can be done in many ways. For example, you want to show the sales by product category, which you’ve put in a bar chart. Looking at these bars, you might be interested in the number of manufactures involved in these sales amounts for product category. You can create a stacked barchart representing the different manufacturers in a legend. Or you can use another chart in your report to represent the top 5 products, which will interact with the sales over time chart. But both options will use additional space on your report canvas and look a bit messy, which can distract the users of where it is all about.
Marc is wrapping up the series and it’s worth the read.
If you are new to ggraph, a short description follows: It is an extension of ggplot2 that implement an extended grammar for relational data (e.g. trees and networks). It provides a huge variety of geoms for drawing nodes and edges, along with an assortment of layouts making it possible to produce a very wide range of network visualization types. It is to my knowledge the most feature packed network visualization framework available in R (and potentially in other languages as well), all building on top of the familiar ggplot2 API. If you want to learn more I invite you to browse the new pkgdown website that has been made available.
It looks really nice.
In the below report you can easily click on a country on the left side to move to another page. When it comes to interactivity it is all done right! On the right top you can also filter on order date. Let’s say we apply a filter to only see the sales up to the end of 2013. This results in a sales amount of nearly $ 319K.
Now, Germany gets my attention. I want to see more and decide to navigate to the other page by clicking on Germany. Ending up at the other page, I see that the sales amount changes back to $2.3M. In other words, my filter is gone!
Much of the difference between adequacy and excellence with visualization is in this kind of polish.