A floating bar chart is similar to a Gantt chart, except it shows the range of two data points instead of two dates.

In this tip, I take you through the 5 most common date functions in Tableau:

1. DATEPART
2. DATENAME
3. DATETRUNC
4. DATEADD
5. DATEDIFF

By the end of this video you will understand when to use them to meet your use case. Click here for the cheat sheet I created for these date calculations.

Cycle plots are exceptionally useful for analyzing seasonality in time series data. In this video, I walk you through two examples that show the limitations of traditional time series analysis and two examples of how cycle plots overcome these problems.  Download the workbook here.

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 couldn't find too much to do with this week's data set, so I ended up with some simple BANs and line charts that take the original and reorganize them a bit to make them more clear.

Resources:

1. Data set - https://data.world/makeovermonday/2021w32
2. Chart Guide - https://chart.guide/
3. Final Viz - https://bit.ly/mm2021w32

In a recent Makeover Monday #WatchMeViz, I showed how to create a bar chart to compare two measures and then add a candlestick chart as well to show the difference between the two measures. It's actually quite simple; it requires some knowledge of:

1. How Measure Names and Measure Values work together
2. How to create a combined axis chart
3. How to create a dual axis chart
4. How to create a Gantt chart

#MakeoverMonday week 44 is another #Viz5 initiative. The topic this week is access to the internet and mobile phones by gender and country.

First, sorry about the video cutting out at the very end. My mistake.

In this video, I first review the initial visualization and talk about what works and what does. In the end, I went with a quadrant chart, which is a scatter plot with broken up into four quadrants. The viz focuses on only two of the quadrants to highlight the significant difference in the number of countries where women have more access to men for both technologies vs. the opposite.

I showed several methods for visualizing the data:

1. Side-by-Side Bar
2. Bar in bar
3. Bar Graph vs. Reference Line
4. Barbell
5. Peas in a pod
6. Floating bar chart
7. Slope graph (terrible choice)
8. Ranked slope graph (even worse choice)
9. Histograms
10. Box plot
11. Scatter plot

Resources:

1. Final workbook - LINK
2. Data set - https://data.world/makeovermonday/2020w44
3. Country and region information (Be careful joining this as some country names don't match. You'll want to using data blending and alias the country names to match.) - https://data.world/vizwiz/country-region-codes
4. Chart Guide - https://chart.guide/
5. Interactive chart chooser - https://depictdatastudio.com/charts/ 

This week Eva provided a simple data set and a simple viz from the International Organisation of Vine and Wine.

What works well?

What could be improved?

My Goals

This week Rody challenged us to create a tabular view that has three columns, only one of which is colored and the user needs to be able to sort by a given year and metric. Find the full requirements here.

I had done something like this previously for the color. What tripped me up was the sorting calculation. It's not overly complicated. The only hint I'll provide is that you can't sort the subcategory by this calculation. Click on the image below for the interactive version.

Last week I highlighted a really nice line chart from Business Insider over on my Data Viz Done Right site. As a learning exercise I wanted to recreate their chart while also incorporating the feedback I suggested. I highly recommend this process to anyone wanting to learn and practice; take a viz you like and try to rebuild it in Tableau.

Since the Warriors won the NBA title last night, I've updated the viz with the latest data. Simple, fun, effective visualisation. And the Warriors are pretty damn good!

Most week's when we're starting the Makeover Monday process, we have a pretty crappy viz to work with. Eva has mixed things up on us this week by picking a viz that is already quite good. This makes the process of making it over even tougher.

This week, she chose this viz from The Guardian about the jobs that are most likely to be replaced by automation:


What works well?

• The overall design is super simple to understand.
• The gaps are evident by using distinct colors for the two categories.
• Including references to the data source
• Clean fonts
• Grey background bars help make the blue lines pop
• Axis labels only include the % sign on zero
• Sorting is easy to understand even though it isn't stated
• Long scrolling view suits this design

For Makeover Monday week 48, we look at this visualisation from Business Insider about wealth inequality in the US.


What works well?

• Line chart is an appropriate chart choice as we're comparing two values over time
• Title is clear and simple
• Good sourcing and footnotes
• Tells a simple story effectively

Another quick Makeagain Monday as I get ready to head to the States. This morning, I look at this visualisation from Pablo Sáenz de Tejada, who was in the first cohort of The Data School.


What would I change?

• The red/green color scale might be tough for color blind folks.
• I'm confused by the colors on the ends of the bar because those don't represent the same thing as the color of the middle bar.
• There are too many controls for me at the top. Might this be better as a static image with a singular story? Pablo chose to make it interactive, which is perfectly fine. I simply might choose to do different. Neither is better.
• There are a LOTS of cities in this. I would show the top and bottom 10 for simplicity. Again, personal preference.

I was reading through feedly this morning and saw this great viz by The Economist.

I really like this simplicity of the viz, yet the detail and insight it provides. In particular, I like the Gantt chart style they used to compare 2000 to 2013. One of the best way to learn is to recreate charts you find and like.

For my version, I used data from the CDC about motor vehicle deaths by state in the US. Overall I went with a similar Gantt bar style to compare the change in the years. I made these additional enhancements:

• Removed the line that makes them look like candlesticks
  
• Muted the gridlines
• Moved the labels next to the bars
• Colour-coded the bars to show whether each state has increased of decreased
• Moved the United States average to the top to make it easier to compare to

Which version do you prefer? What else would you do differently? You can click on the image below to download the workbook from Tableau Public.

Anyone that knows me knows that I despise the Dallas Cowboys and, in particular, their golden boy Tony Romo. As a lifelong Eagles fan, I’ve been indoctrinated into the hatred for anything associated with that ugly blue star. It drives me nuts to hear how much all of the NFL pundits love Tony Romo. He’s never won anything and chokes in the playoffs every time they make them.

So when I saw this article by FiveThirtyEight, it caught my attention. Was I not giving Romo his due? Does that matter anyway? In the article, the author looks at a metric they call WOWY (or With or Without You). In its most basic sense, this metric measures the impact that a particular player has on their team by measuring the Elo rating when that players plays and when they do not. For this piece, they considered quarterbacks that were the primary QB for at least 50 games and missed at least 20 games. They then pared that down to the top 10 based on what they called the WOWY ∆ ELO.

The result is this table:


The table clearly shows Tony Romo as the 3rd most important player to their team based on this metric. Ok fine. But is there more to the story? Can this simple table be made more intuitive for the readers to understand?

I created the barbell chart below. This view makes it much easier to see the difference between the With and Without You metrics. I also added a metric to the view that calculates the difference between the two. I then created a drop down to allow you, the reader, to sort by the metric you find most interesting. In essence, I’ve turned this simple table into four stories:

1. WOWY ∆ ELO - This metric shows Romo as the 3rd most missed player in NFL history when he’s out injured. 
2. Games With - Sorting the chart by the Games With Elo rating, suddenly Romo is only 5th on this list, yet he’s ahead of Peyton Manning. This view also shows just how amazing John Elway was when he played. Elway’s Elo rating is nearly 50% higher than the second best.
3. Games Without - Interesting…the teams that Rex Grossman played for actually performed better without him in the lineup. Clearly he was quite terrible as an NFL quarterback. You can also see Romo down in 6th position; the Cowboys are definitely much worse without him.
4. Difference - I added this metric to show the variation between the With and Without You values. Now Romo is back in the 3rd position, and look at that gap for John Elway…wow! 

Give it a play for yourself. Do you see anything else interesting?