With Level of Detail Expressions, calculating year over year change is simple with the 7 steps.

Are you looking for ways to improve how you visualize comparisons between 2 to 3 performance measurements in Tableau? 

In many performance monitoring dashboards, you need to make comparison of actual values to budget or target. In this video, I will show you effective ways of making these comparisons. 

I will also show you how to compare your performance to prior year (PY) to see if performance is improving year over year. 

One thing you'll notice in these examples is the consistent use of color. Make sure your metrics use the same colour throughout your dashboards, reports and worksheets. The colors should be easy to differentiate. 

I have 4 designs that you can apply to make any comparison look better and easier to understand. 

Download the workbook for a bonus 5th chart type that compares these three metrics over time.

In this tip, I show you how to calculate year over year change based on a date selected with a parameter. The use case is to calculate YTD sales depending on the date selected and compare it to the prior YTD sales for the same period.

I struggled to come up with anything interesting this week, but that's ok. I was still able to explore the data a lot and ended up with a simple dashboard. Check out #WatchMeViz and interact with the viz below.

Back in March 2020 when COVID lockdown started in the UK, the streets were amazingly empty, the air got fresher and I saw more and more people outside. You could hear birds chirping on streets you never would have before. And we could ride our bikes right down the middle of the road since there were no cars. The lack of cars was glorious! (COVID isn't of course).

For 2021, #MakeoverMonday gets started with a simple graphic that compares pedestrian and bicycle counter stats for 2019 and 2020 at 31 counters across America. The data is collected by the Rails to Trails Conservancy, and you can learn more about the data here. 

ORIGINAL VISUALIZATION

WHAT WORKS WELL?

Floating bar charts are similar to Gantt bar charts, except they don't use dates or duration for the length of the bar. In this video, I show you how to use floating bar charts for comparing metrics.

Example 1 - Comparing mental health syndromes between women and men

Example 2 - Comparing year over year sales

Data Sources:

1. Mental Health Symptoms - https://data.world/makeovermonday/2020w27-comparing-common-mental-disorder-by-sex
2. Superstore 2020.3 - https://data.world/vizwiz/superstore-20203

Four weeks to go with Makeover Monday 2019. We've had lots of interesting vizzes to makeover and lots of interesting data. This week, I wanted to pick a simple visualization and simple data.

What works well?

What could be improved?

What I did

I'm going back to Workout Wednesday week 16. Why? Because I really struggled with it. I was so close for so long, but couldn't quite get my date calculations correct. After writing them down on paper and building tables in Tableau to verify I had them correct, the rest was pretty straightforward.

This use case is super useful in a business context. I like Workout Wednesday challenges that you can employ later. The most important requirements:

1. Use a date parameter to select a select end date, limit it to all days in 2017.
2. Use a parameter to select the period type (day, week, or month).
3. Use a parameter to select the number of periods to go back (limit from 1 to 12).
4. Create a bar chart that show the total sales for the last complete period. 
5. Add sales for the same period as a label on the end of the bars.
6. Compare the selected period to sales over the same period from the prior year.
7. Add a blue arrow up if sales are up compared to prior year.
8. Add a red arrow down if sales are down compared to prior year.
9. Show the difference in sales over these time periods. Make sure to show no negative signs. The arrows will indicate the change.

For this week’s Tableau Tip Tuesday I show you how to use Level of Detail expressions to get the latest year and prior year and then compare then to create visual alerts.

For week 3 of Makeover Monday, I challenged Andy Cotgreave to not only makeover this graphic below, but also to use only greyscale colours. Let's start by looking at the graph/chart/infographic in question:


This chart is so bad, it's tough to know where to start. Let's start with what works well:

• There's a clear title and subtitle that tell us what the chart is about.
• While I don't use icons often, their use of icons for each type of technology might aid some people in understanding, though I suspect they add them more for decoration.
• There's a clear order to the donuts, from highest to lowest based on the 2016 purchase rate.
• The font is consistent throughout the graph.
• They fit a lot of information in a small space.

UPDATE – 10-Apr-3014:
I received some feedback from both Jonathan Drummey and Joe Mako about this blog post and some of its inaccuracies.  There are a couple of key notes:

1. My intent was to show how you can compare the 7-day averages of two time periods. In this example, I’m calling this a Year over Year calculation, but really it’s a comparison versus 365 days ago. Small, but important distinction.
2. The Superstore Sales data set has days that are missing, so unless you turn on domain padding, you won’t be comparing the prior 365 days that you’re expecting to compare. I’ve updated the post below.
3. Like most things in Tableau, there are many ways to solve the same problem. Joe pointed out some things he would have done differently with the calculations. While neither of us is wrong, note that you should always look for ways to be more efficient.

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April is #TableauTipsMonth, so I thought I would pick something from my backlog and get to it. Today I’ll be writing about a fairly typical scenario:

1. Your data is cyclical through the week
2. Daily numbers vary wildly, so smoothing is necessary
3. You need to compare to the same period from the prior year

This can be handled pretty easily and understandably with a few table calculations. To start, let’s look at daily sales for 2013 in the Superstore Sales data set to illustrate the wild nature of daily data that many of us see.

This data set looks like it might be cyclical, so let’s apply a 7-day average calculation to it. We could do this via the quick table calculations on the pill, but we will want this calculation for later. Right-click on Sales in the Measures pane and choose Create Calculated field. Build this calculation:

Here’s what the chart looks like if I filter Order Date to 2013 on the Filter shelf (I’ve focused in on January):

Since I have filtered the year of Order Date to 2013 by dragging the Order Date pill onto the Filters shelf, Tableau first ran a query that returns only results for 2013 and then it will do the 7-day average calculation. We don’t want that because the first six days of 2013 will be wrong. They are wrong because they don’t contain a full 7-day date range.
To fix this, we need to change the calculation. Notice that I’m not passing a filter to Order Date inside the calculation. By passing the filter in the table calc, Tableau will return all of the data for all years, then the table calc will running after the database query, which will then provide the full date range for the 7-day average to calculate correctly.

And this is what the chart looks like if filter the data via the table calculation (of course you have to remove Order Date from the Filters shelf):

Quite a different story. The lesson here is that you need to understand how Tableau is filtering the data. Anything on the Filters shelf will filter the data before any table calcs are performed.
One thing to note, though, is that this data set does not include all dates. Therefore, we need to turn domain padding on by right clicking on the Order Date pill and checking the “Show Missing Values” option. Notice how there are now gaps in the line; that’s because Tableau is filling in the missing dates for us.

So that covers our 7-day average. To calculate the 7-day average for the same 7-day period 365 days ago, we only have to make a simple adjustment to the 7-day average calculation we’ve already created.

The only change is the start and end period inside the WINDOW_AVG function. Now drag the new measure onto the same axis as the 7-day average.

So now we can see how the 7-day average on an day compared to the 7-day average 365 days ago. Perfect!
Now that we have these two table calcs, we can easily compute the change between the two dates periods with another calculated field:

Drag this new measure onto the Rows shelf. Clean it up a bit, and we now have a nice view that answers a simple question: How are sales performing compared to last year?

Notice that I used an orange-blue color palette for the comparisons to the prior 365 day values. This is a good practice to employ because the colors corresponding with the colors for each line. If the bar is orange, then last year performed better and vice versa.
Download the workbook used in this example here.

UPDATE (May 2, 2013): I have removed 2013 from all charts.

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I posted an article on my Facebook page the other day asking if this radar graphs about artic sea ice volume works.  The comments were mixed.

I think this radar graph is merely ok.  Radar graphs, in general, are hard to read because relationships and trends are not easily discerned.  Some of the issues I see with this graph include:

• It’s difficult to see the entire pattern over time.  I see the pattern is spiraling in, but does it change year by year.  Ask yourself this: How does 1999 compare with 2004?  It takes a lot of work. 
• It’s very hard to follow the ice volume labels around the chart.  I bet you found this as well in the example above.
• The author only included September.  Why?  The data is available by day.  Get the data here.
• Are there any seasonal patterns?  You can’t answer this question.

I created a couple of different views for your consideration in Tableau.  You can download the workbook here.

In my version, I’ve addressed all of the problems I mentioned above.

• In the upper left graph, you can easily see that the artic sea ice volume is trending down.  
• The upper right chart allows you to see two things:
  1. The seasonal patterns
  2. Comparisons by year:  The comparisons across years can be see because the years get darker as the data gets closer to 2013.  You can see the lines get darker as you look down the graph.
• The Daily Volume graph makes the cyclical patterns much, much clearer.

How would you visualize this data?  Do you agree that these line graphs work better than the radar graph?