A Fine Slice Of SQL Server
Line charts are another common chart type in reporting. They show time series or continuous data along an x-axis (typically). Like bar charts, they are easy to read and understand for most people. There are different variants and formatting for line charts to use them effectively.
Click through for templates and plenty of great examples.
Comments closedKurt Buhler builds a series of charts from the Power BI bar chart visual:
These are only a few examples; there are many more. Knowing how to create these different bar chart variants is important to address these use-cases. While many of these visuals can be created custom in Deneb or from SVGs, you can also create them in the core visuals. However, it’s important to know the limitations of the core visuals in Power BI, and when this “Macguyvering” of the core visual formatting settings goes too far.
Kurt gives us a template (in pbib format) and explains various options we have around customizing visuals, not just taking what’s built in.
Comments closedMike Cisneros goes bar-hopping:
Let’s take a closer look at that extra-tall bar chart. I came across a graph like this one when working with a recent client. They created a visual to compare the quarterly sales revenues for each of 25 different sales associates against their individual sales targets.
Mike then gives us two routes to improving the quality of this visual, one which maintains the bar chart style and one which replaces it with a Cleveland dot plot.
Comments closedPeter Baranovskiy breaks it down:
There are multiple tutorials available online on writing modular Shiny apps. So why one more? Well, when I just started with building modular apps myself, these didn’t do much for me. So I really only learned how to write modules when I had an opportunity to team up with an experienced R Shiny developer. The reason I guess is that Shiny modules is an advanced topic, and you typically get to writing modules only when you finally need to scale your apps – and keep opportunities for further scaling open. This typically means when your app goes into production. By then you probably have already developed multiple apps, and switching over to a way of thinking required to write modules may be challenging. If you don’t know what modules are, I recommend starting here and then coming back to this post. Otherwise, read on.
So, I decided to try a different approach and instead of building a simple modular app from scratch, to go in the opposite direction by breaking down a complex real-life app into modules. Here’s the app’s original non-modular code. Note a single
app.Rfile that contains the entire app.static_assets.Rincludes some object definitions which I moved to a separate file for convenience.calgary_crime_data_prep.Ris not part of the app; it is a data retrieval and cleaning script executed once a month with cron. Running the script each time the app launches would have made it extremely slow and would use way too much bandwidth, as the script downloads and processes 150+ Mb of data on each run.
Read on for the reasoning behind using modules, as well as Peter’s notes on the process.
Comments closedMeagan Longoria has an airing of grievances:
I recently created a treemap in Power BI for a Workout Wednesday challenge. Originally, I had set out to make a different treemap, but I ran into some limitations with the visual. I ended up with the treemap below, which isn’t bad, but it made me realize that the treemap is in need of some improvements to make it really useful. So I decided to share my thoughts here.
Read on for Meagan’s thoughts on the existing treemap visual. I agree with all of Meagan’s points and would love to see this visual be updated.
Comments closedRita Fainshtein talks treemaps:
The main challenge arises from the difficulty in comparing the areas of the segments within the chart.
Consequently, pie charts and similar graphs become challenging to interpret.
Colors for categories don’t make it easy either since the brain tries to figure out how the color relates to the category, which in the example here just happens to be a random choice.
Let’s see if the Treemap helps and make it easier.
Rita notes that treemaps work well in a specific niche: hierarchical, categorical data. But within their niche, they work really well, which is more than you can say about pie charts…
Comments closedPeter Baranovskiy doesn’t want bog standard but is okay with mostly standard:
There are multiple ways to style or theme a Shiny app. A high-level overview is available in the Mastering Shiny book by Hadley Wickham. Here I’ll show the easiest way to do this. If you need to build an entirely – or mostly – new Shiny theme (e.g. a corporate theme), this post is probably not for you. In that case
bslibmay be the best starting point. Otherwise, if you are generally happy with a pre-made theme and just want to tweak some of its elements, read on.This post is based on an actual app, so that you can see how it all works. Here’s the app’s source.
Click through to see what kinds of changes you can make without a major overhaul.
Comments closedAmy Esselman only needs one Y axis:
You may be confused and overwhelmed at first. Dual-axis graphs like this are inherently challenging. Whether you call them dual-axis graphs, combo charts, or secondary y-axis graphs, they always demand extra effort from a reader to figure out which data series to read against which vertical axis.
Click through for a variety of ways to improve a busy dual-axis chart.
Comments closedMy long-running catalogue of Data Visualisation Resources has for many years been the most-popular, most-visited, and most-referenced content on my website. For the last couple of years, though, it has been a little stagnant with my limited time preventing the frequent updates it needed.
Having recently completed the migration of my website to a new host and undertaken a wide-spread redesign and restructure, it felt an opportune moment to roll up my sleeves and belatedly spend some time pruning the catalogue of out-dated references and introduce all the new ones that I’d encountered, and bookmarked, but not yet added.
Click through for that, as well as the Chartmaker Directory, which gives you an idea of which visuals are available in which products, as well as examples to see them in action.
Comments closedLeila Etaati shows off some new functionality:
One of the interesting features of the New Card visual is the capability to add images to the Card visual.
It added more context to the car visual.
In this blog post, I will show some data related to cancer in New Zealand based on gender and cancer type.
Read on for the example.
Comments closed