This week's data set was pretty simple. We had 21 baseball seasons and a column for each pitch type. Pivoting the metrics made it much easier to work with for me as I could then split the view by pitch type.

During #WatchMeViz (below), I create a trellis view, showed how to create groups, sets, set actions, sparklines, LODs, custom number formatting, creating a mobile view, and more.

Thank you for tuning in. Here's the video and below is my visualization.

Using variance as a measure in bar charts helps you:

1. Add context - You can answer the question “compared to what?”
2. Make decisions - You can answer the question "what should we do next?"
3. Alert you to areas that need focus

In this video, I show you 10 ways to add variance to bar charts. Get the workbook here.

Data HAS to be consumed quickly to be understood and to make quick decisions. A "typical" profit and loss statement is on a spreadsheet. However, spreadsheets are notorious for (1) errors and (2) being very difficult to spot areas of concern.

That's where dashboards come into play. Back in July 2017 I proposed a new way to visualize a Profit & Loss Statement (P&L). The dashboard has been very well received and put into practice at many organizations. They've reported back to me how useful it has been. 

But then I got thinking about an even easier way to visualize the data. I felt like the previous version was a bit chart heavy. So, while I've been creating templates for other areas of a business (HR, Social Media, Customer Service), I decided to also focus on a new version of a P&L statement with KPIs. This dashboard is based on a Finance Dashboard example from Excel Dashboard School.

You can download the data I used to build this template here. Click on the image below to interact with and download the workbook.

What works well?

What could be improved?

What I did

1. Match my colors.
2. The user should be able to choose the level of detail they want for the date.
3. The date axis format should not change, irrespective of the date level chosen.
4. The user should be able to pick from the list of dimensions shown.
5. The dimensions should be sorted from upper left to lower right based on the sales in the most recent time period.
6. Match my tooltips. Note that they change based on the options the user selects. Pay attention to the date formats in particular.
7. The title should update dynamically based on the date level and dimension selected.
8. The end of each line should be labeled to the right of the last point. 
9. There should be a little circle on the end of each line.
10. Each section of the trellis should include a label for the value of the dimension for that section. E.g., California should be on the upper left when you selected quarters by state.
11. The dimension labels should be centered in each section.
12. There should be no gridlines, but the zero line should be included.
13. Each row should should have a light divider between them.
14. My final view is 900x700.

• Using a line chart to portray a time series rarely is a bad choice
• Nice simple labeling of the axes
• Title tells me what I'm looking at
• Red line on the grey background works well
• The y-axis is truncated, but the chart maintains a nice 3x2 ratio so that the line trends aren't too distorted.

Today I attended an expo at the Devon & Somerset Fire Services with Tom Brown and Robin Kennedy. In preparation for the event I built a few dashboards for us to showcase. A friend of ours over at Surrey County Council pointed me to a dataset about fire incidents in the Greater Manchester area for 2012/13. The beauty of this dataset was that it included the locations of the fires and lots and lots of details about the fires.

Tom asked me to build something, so I started exploring by building lots and lots of visualisations to see if a story stuck out. And one did! When I created a heatmap of fires by month and day, November 4th and 5th jumped right out. That seemed a bit strange to me. I think of those dates as near elections, but here in the UK it the celebration of Guy Fawkes Day and there are lots and lots of fireworks. And, duh, fireworks = fires.

I also noticed some outliers at the beginning of March, which I still haven’t been able to explain, April Fools Day had a lot of false alarms (shocker!), and August 5th and October 27th were near bank holidays, which probably means fires from BBQs.

Yet another fun couple of hours exploring data with Tableau. It was a lot of fun showing these off to potential customers today and also seeing a bit more about the sales side of the company. Great learning indeed!

I’ve written before about making the ends of sparklines actionable here and here and created a video here. Each of these uses table calcs to add the dot on the end of the lines. The problem, though, is the method falls apart when the end date on all of the lines isn’t the same. Yes, there are other workarounds with table calcs, but they are overly complicated.

I’ve been wanted to look at this again with level of detail expressions, thinking there had to be a simpler way to create and maintain them, and a way to overcome the drawback listed above. The video below walks through two level of detail expression:

1. Create a filter using a LOD calc to dynamically return just the latest month in the data set
   
2. Return the value of the end of EACH line of a series of sparklines

This technique could easily be parameterized, but for this example, I stick to a single dimension. Enjoy!

This week I begin what will hopefully be a series of videos and tips about Level of Detail (LOD) expressions in Tableau. The more I teach them, the more I learn about them. Many of the example I’ll show will come from the Tableau Community and/or adjusted to expand on the exercise. Essentially, I hope to turn this into a set of use cases for LOD expressions.

This week I show you how to calculate the number of days each month that were profitable. This started with read this post on the Tableau blog. I’ve extended their example to include sparklines, an area chart and using a parameter. Enjoy!

The CDC publishes an annual report on Health in the United States and included in the report is a “Chartbook”.  It’s 574 pages long, but you can skip to page 32 for the start of the charts.  There are some quite horrendous charts, especially the pie charts, that you will get a kick out of. 

You can download the data on CDC Wonder.  Once you create your query, you get a spreadsheet of the results, a map, and a bar chart.  The bar chart is particularly poor and only allows you to pick two dimensions.

I have downloaded the data and produced an interactive dashboard via Tableau Public.  Within this dashboard you can filter by Gender, Age, State and Disease.  In the end, I have included all of the views from CDC Wonder, plus much more. 

Some observations:

• The infection rate for the total US has continued to climb for all diseases combined.  This is largely due to Chlamydia.
• Syphilis infection rates declined from 1996-2001, but have continued to climb since.  Particularly concerning is the rate in Washington, DC.
• In fact, Washington, DC has the highest infection rate for all three diseases.
• Alaska’s overall infection rate in twice the national average, with the Chlamydia rate 86% higher than the national average.  This is definitely worth looking into.
• The overall infection rate for females is more than double that for males.
• Females between the ages of 15-24 are most likely to get infection, while males are most likely between the ages of 20-24.

There are many more observations and insights to be gleaned from this dashboard.  It is considerably quicker to identify outliers and trends with a simple dashboard like this than with CDC Wonder.  Imagine how much more useful the “Chartbook” would be if the CDC used Tableau.

What other observations can you make?