A Fine Slice Of SQL Server
It’s important to note that there isn’t a particular finding or insight that needs to be emphasized here. Instead, the goal for this visual is to provide the data in a digestible format, which will be part of a regularly updated report. That way, physicians and researchers can easily monitor any changes in the observations.
I was unsure of the best way to approach this task, so I started sketching.
Click through to get Alex’s thought process while building a chart in Excel.
Comments closedSimon Rowe gives a multi-pie chart a once-over:
This data is from a small company that is looking to get a sense of its sales pipeline across its four regions–North, South, East and West. The data is broken into five categories which relate to the progression of any given opportunity–Prospect, Conversation, Presentation, Agreement and Formal Offer.
The pipeline composition is expected to have minor variations between regions, but the proportion of opportunities at each stage should be consistent overall. Any notable discrepancies should be flagged to management immediately.
Here we run into one of the main issues people have with data visualization: context is critical. A stacked bar chart is often not a good alternative for any cluster of pie charts. In this case it was a very good answer because of the nature of the exact problem to solve. If I need to make a generic report given the same raw data, it would be difficult for me to land on this specific visual.
Comments closedMeagan Longoria uses a custom label:
Notice the label on each column that shows the year and average game length in h:mm format.
These custom labels are available for any bar or column chart – small multiples are not required.
Click through to see how.
Comments closedSteve Jones performs a comparison:
I ran across an interesting post from Rita Fainshtein that looked at the different types of graphs for a set of data. I thought that was interesting, so I ran my own experiment. I found for my data, a line graph was better, but let me know what you think.
My data set was simple, a few players across a few events and their number of kills. I coach volleyball and I’m always trying to present stats in a useful way. Here was the small set I picked.
Something to keep note of is that line charts generally imply time series data: you are looking at some periodic activity and analyzing changes between periods.
The stacked column (or bar) chart tells you two things: the total and the first event. It’s really hard to discern any other events from a stacked chart comparison, as Steve points out.
If you don’t have periodic data or if you’re more focused on general trends than what happened at a specific event, the Cleveland dot plot (available in custom visuals) could plot each of the events, with the Y axis representing player names and the X axis representing number of kills, and each dot being a specific game. This saves a lot of real estate and gives you an idea of how points cluster, especially if you look at a larger number of games or players.
Comments closedMara Pereira trims off the edges:
If you’ve been following my posts, you probably know by now that one of the things that really annoys me in Power BI is not being able to build bar charts with rounded corners, unless you use a custom visual.
However, recently I found out about a trick to make this happen without needing to use custom visuals (special shout out to Federico Pastor who shared this trick with me!).
Read on to see how you can do this. It’s definitely not ideal, but if you absolutely need rounded corners for your bar chart, you can get them this way.
Comments closedErik Svensen does a wardrobe upgrade:
But I want to see how my new theme might look on some old report where I might have used another theme or perhaps some reports other users have created where they have done some individual formatting instead of using the theme.
And did you know – we can do it and see the result for existing report in your workspaces – and we can directly modify the theme and see if we should change a color or a font size or a….
Read on for the process. This is a clever use of the embedding service, that’s for sure.
Comments closedNicola Rennie shows off a new package:
Flowcharts can be a useful way to visualise complex processes. However, I couldn’t find an easy way to create a flowchart in R. There are a few packages for either drawing basic components of flowcharts (like {grid}), packages that are great for visualising complex network data where order doesn’t really matter (like {ggnetwork} and {igraph}), but none of them gave me the control over customisation I was used to with {ggplot2}.
{ggflowchart} tries to fill that gap. The aim of {ggflowchart} is to help R users make simple, good-looking flowcharts, with as little code as possible. It computes a layout, then uses existing {ggplot2} functions to stitch together rectangles, text, and arrows.
It does remind me a bit of Mermaid, though quite early in the process. H/T R-Bloggers.
Comments closedReza Rad shows off the new visual formatter in Power BI Desktop:
Where is the Format visual option in the new Power BI Desktop? There is a simple answer to this question, which I explained in this blog and video. This change applied from March 2023 version of Power BI Desktop, and by the time you read this article, it might be at general availability and the only way to format a visual in the Power BI Desktop.
I do hope they also keep the old way of formatting visuals, as there appear to be fewer clicks involved.
Comments closedSteven Sanderson plots a course:
The code is used to create a Shiny app that allows the user to search for a type of amenity (such as a pharmacy) in a particular city, state, and country, and then display the results on a map. Here is a step-by-step explanation of how the code works.
Click through for notes, the code, and an example of the app in operation.
Comments closedRita Fainshtein shows examples of how over-usage of color makes charts harder to read:
Both graphs convey a message of ranking and grouping into categories.
The categories are shown in both cases in a color-coded manner instead of in a hierarchical format. As graph creators, why do we tend to create graphs with color categories?
1. The fear of being boring, one color seems uninteresting, and here we have both colors and icons. This is an “excellent” attribute for a storyteller.
2. Visually representing a group with similar characteristics makes sense.
But can such graphs tell us anything about groups? Are they easy to understand?
Let’s discuss a few aspects of those cases together:
Click through for the full story, including an alternative to using color as a way to categorize data.
Comments closed