In last week’s Makeover Monday recap Andy reminded us that this is a makeover. The intention is to evaluate what is good and what can be improved with a viz and create a new one with those points in mind. People can use makeover Monday for what they what but the intention is to improve upon the selected viz.
I normally try to take that approach but I don’t often document what I like and what can be improved so for the next few weeks I am going to attempt to put my thoughts and approach together here.
The viz this week comes from HowMuch.net and looks at R&D spending across the globe.
I like that the person who created this tried a different approach to displaying the information. They want the reader to focus on the large circles in the middle for the US, China, Japan, and Germany. What I think can be improved is the amount of clutter in the viz. There is a lot going on here between the circles, the map, the flag, and the multiple colors. I think a better approach would be to simplify the viz and draw the attention to the top 5 countries. I don’t think the flag and the country shape add to the story so I would remove them.
I selected a treemap for this week’s makeover. While treemaps may not always be the best option to compare values I think in this case it works because I want to highlight the contribution of the top 5 countries and I don’t want to compare all of the countries against each other.
Overall I think this meets the goal of drawing attention to the top countries.
Last month’s Sports Viz Sunday was the Masters. I created 3 different vizzes using Tableau public.
The first one I created looked at how closely contested the Masters usually is. I’ve always felt that Masters Sunday was the best TV viewing day of the year and looking at the data backed that up. The tournament has only been won by 5 strokes or more 5 times.
Overall I like how this turned out. The one thing I would change is the title. I don’t feel that it gives a good take-a-way of what the viz is about.
The next one I did was on Tiger’s 1997 win. Tiger won by 12 strokes the largest margin of victory (as of this post). I wanted to see round by round how much better Tiger was than the average score for the day. Tiger is known for wearing red and black on Sundays and I used the color scheme in honor of that.
This is a simple viz and the goal was to highlight how good his 2nd and 3rd rounds were in relation to the field average score. There was a Twitter discussion about showing the better score on the bottom of the viz. In golf being under par is good and while it may seem strange to see better on the bottom I think it make sense when you are looking at golf scores. If I was showing tournament position (first place, second place etc.) it makes sense to show them at the top, but, I believe when showing in relation to par at the bottom of the viz makes more sense.
The 3rd viz looked at 1956 Masters where Jackie Burke Jr started Sunday 8 strokes behind Ken Venturi and came back to win by 1 stroke. I wanted to show round by round how well Venturi played for the first 3 rounds and how steady Burke was. I’d like to do a more in depth analysis on this to show how great Burke’s final round was. There were only 2 players under par on Sunday and Bobby Jones said it was the toughest weather conditions the Masters had been played in. This is my favorite of the 3 and hopefully I’ll expand upon this with a more in depth analysis.
I was excited that Cole Nussbaumer Knaflic’s Storytelling with Data current challenge is to create a basic bar chart. She says “The #SWDchallenge this month is to create a basic bar chart. Nothing fancy. No need to stack it or do anything else crazy.” I love a good bar chart and have been known to say “don’t underestimate the power of the bar” more than once.
For this challenge I used data from the 2017 Masters to show which holes had the highest percent of scores over par.
At first, I sorted the data in descending order so the top three were together at the top of the chart. For other data sets I think this works, but, for this I liked keeping the holes ordered by the hole number.
I debated the bar color for the top 3 for a while. I wanted to use the green to tie with the Masters theme. I decided against that because people tend to associate green with good – if I were showing the 3 easiest I would have used that. I tried orange, a maroon-ish red, dark gray, and brown but I didn’t love any of those choices. I had my husband look and he suggested that I color code them in multiple shades. Instead of shutting that down immediately I changed the scheme to show him what it would look like and asked do the top 3 still stand out? When he agreed that it didn’t, I switched it back to a two color scheme and he suggested the purple and I think it pops.
Initially, I labeled the bars and tested out different alignments. I felt that the chart was too busy with the bars labeled. I needed to add the percent over par to the chart so I added it next to the hole name. To do this in Tableau add your measure to the row shelf and change it to discrete.
I don’t have any annotations on this chart and if you aren’t familiar with golf over par may not resonate with you. I am sure some folks would suggest adding text to explain over par but I opted not to because I liked the clean look and felt that my title got the point of the chart across.
To see other entries for this challenge take a look at #swdchallenge on Twitter.
Also take time to check out Cole’s website and buy the Storytelling with Data book.
Last week the Dick’s Sporting Goods ad with Arnold Palmer popped into my head. If you haven’t seen it before it’s worth the 50 odd seconds.
In the add Palmer says:
“Swing your Swing.
Not some idea of a swing.
Not a swing you saw on TV.
Not the swing you wish you had.
No, swing your swing.
Capable of greatness.
Prized only by you.
Perfect in its imperfection.
Swing your swing.
I know I did”
I see “swing your swing” as be true to yourself in your approach. In golf all that truly matters is the contact with the ball. How you get to that point is where you “swing your swing”. There are methods and instructions that make it easier to square the club at impact, however, do what works for you. Explore your swing. Figure out your tendencies good and bad. Swing your swing. Don’t become so mechanical that you lose sight of you.
Because I have a golf “issue” I immediately applied this my data viz work. My goal in data visualization is to depict data in a manner that clearly communicates the insights in the data. Like golf, there are a number of best practices and methods on how to do this. And like golf there are different approaches to get to the same end point. Take a look at #makeovermonday or #dataforacause and you will see an number of people approach the same data set in a number of different ways. You’ll see things that run the range of simple bar charts to radial charts. This is where people “viz their viz”.
In both golf and data viz your personal style is important, however, if your style trumps your success or ability to communicate effectively you need to refine your style. It is hard to square the club consistently when you come over the top and it is hard to communicate data effectively when you have a pie chart that uses 20 different colors.
So how do you get there?
Practice, practice, practice.
Experiment – #makeovermonday is a great opportunity for this.
Learn from others – be inspired by their work but don’t seek to duplicate someone else’s style.
December 12th this year will be a pretty special day for me – it will be 10 years since my last radiation treatment at Brigham and Women’s.
I had a disease that a lot of other people around the world have and I’m lucky that I had a great team of doctors and nurses and that their treatment plan for me (knock on wood) has worked so far.
To celebrate I created some BANs!
If you don’t have the Big Book of Dashboards yet you are missing out. Steve Wexler, Jeffrey Shaffer, and Andy Cotgreave put together a fantastic resource for Business Dashboards.
I used the BBOD to help solve a request to show both the ranking and magnitude for a 12 week snapshot and also show that by different groups. Chapter 22 in the book walks through this type of scenario.
This is an example of what I created – all of the information below is fake. The chart on the left shows the ranking and controls what is highlighted in the chart on the right. The bar chart about the chart on the right appears when something is selected on the left. This was added to make it easier for the user to compare the volume across the groups.A big thanks to Steve who helped me with this addition!
This design isn’t flashy but it serves a specific purpose and it has been well received by the end users. I will probably adjust the fonts, titles, and colors a bit more but will keep the main design as it is.
I’d love to hear your feedback or ideas on this!
I spent time with a couple of co-workers yesterday going through revisions on an existing dashboard and received valuable constructive feedback. One person said the visuals are great and you built exactly what I asked for but I find it hard to answer questions – I end up having to do the work in Excel.
The thing they struggled with the most was looking at before and after a specific date. They needed the ability to pick the date to pivot off of and the ability to select the number of days back and forward to look at.
This was a good challenge for me and after thinking it over for a while I thought that parameters were exactly what I needed.
My idea was to create a parameter for the X date (the date to look back and forward from) and then create a parameter for the days back and the days forward and use those to limit the dates in the chart and use them to calculate the before and after measures they were looking for.
My Parameters are:
- Selected Date – date to pivot back and forward from (date)
- Days Back – # of days to go back (integer)
- Days Forward – # of days to go forward (integer)
My Calculated fields are:
- Back Date – [Selected Date] – [Days Back]
- Forward Date – [Selected Date] + [Days Forward]
- Custom Date Range – IF [Order Date] >= [Back Date]
AND [Order Date] <= [Forward Date]
THEN [Order Date]
I then added the Custom Date range to the filters to exclude the nulls. To exclude the nulls I added the Custom Date dimension and selected Range of Dates > Special > Non Null
I used these fields to create a line chart to show the trend during the custom date range and added a reference line for the selected date. I also create a metric tile to show the before and after counts and changes in those counts. I added a few other charts to help them see what changed by different dimensions.
I love the flexibility with parameters!
I wanted to see if I could figure this out without Googling how to do this. I’d love to know if there is an easier way to do this or if I over engineered the solution.
I haven’t touched a club since the first weekend in December and it looks like it will be a couple of more weeks until the snow is finally gone (I hope). Can’t wait to get out there!
I was an early Tableau adopter in work and tried to push my end users out of their Excel data dump comfort zone. I would correct people who referred to Tableau as Excel on steroids on a regular basis. I was excited about giving my users a visual representation of their data. But, I kept getting asked to add table view of the data. I would add these views, but, I wouldn’t make them as pretty as the dashboards in hopes that people would use the dashboards instead. But that wasn’t the case, when I looked at Tableau server the most viewed sheets were the boring pivot table views.
I didn’t give up and kept plowing ahead and improving my Tableau skills. I’ve been trying to learn as much as I can about colors, charts, telling stories, LOD calculations, parameters and all the other things that go along with Tableau. I started to convert more users to the visual side and away from the table views. When someone wanted to know how to export the raw data my canned response was what do you need it for. I wanted to make the dashboard helpful for them. I felt like I was making some progress.
My goal with Tableau was to make it easier for the end users to quickly see what they need and allow them to interact and customize the dashboards but I felt like I was missing something – was I really giving them what they needed? Was I giving a solution without really knowing what they needed?
To help me solve that question I attended a Design Thinking Bootcamp class at General Assembly. The class was helpful and gave me a number of ideas on how I can change my approach.
Here are a few of the concepts that stood out to me:
- ask open ended questions to the end user
- silence is fine – if you ask a question and the user stops to think – let them think don’t interject other ideas or options
- pay attention to work-arounds – these are areas the need isn’t being met
- after you understand the need develop your point of view with the user and their need (not solution). someone who can’t reach the top shelf doesn’t need a ladder they need the item on the top shelf.
- get the topic and ideas out on paper – make this a free flowing exercise – no judgements!
- prototype – shouldn’t be a finalized version. It should be used to communicate the idea. be prepared to scrap it and start over – we don’t always get it right on the first go round.
- build your story – who is the user, what is the challenge they face, how does that challenge impact them, what is the solution, how does it meet the need
It was helpful for me to be reminded that you need to fully understand the need and develop a lot of ideas around the need before jumping to a solution.
This week’s exercise looked at Valentine’s day spending in the US. I liked the original viz – the color scheme seemed appropriate for the topic. I liked the images and felt the size and images conveyed what they were intended.
After setting up the data set I started creating the calculated fields I needed:
- The first was to create a date field from the Year field in the dataset – DATE(“02″+ “/” + “14” + “/” + STR([Year])) .
- I then created a couple of measures fields for the % Buying and the Avg Net Spend – IF [Metric] = ‘Percent Buying’ THEN [Measure] END and IF [Metric] = ‘Net Average Spend’ THEN [Measure] END
I wanted a custom shape for whatever I ended up creating – I found a free clip art heart online bought that into PowerPoint did a couple of updates to it and saved it to my custom shapes file.
After creating a few different views I decided to keep it simple and focus on what people were buying for Valentine’s day from 2010 – 2016. I tried line charts and bar charts with the hearts and then thought this may be a good time to give a bump chart a try.
Matt Chambers has a great post on his site that walks you through how to create a bump chart and I used that as a refresher. In order to get the bump chart to work I had to create a couple of more calculated measures:
- Rank for the % Buying – RANK_UNIQUE(SUM([% Buying]))
- Prior Year Ranking – LOOKUP([Rank % Buying],-1)
- Difference From Prior Year – IF [Rank % Buying] > [Prior Year Ranking] THEN ‘down from the prior year’
ELSEIF [Rank % Buying] = [Prior Year Ranking] THEN ‘the same as the prior year’
ELSEIF [Rank % Buying] < [Prior Year Ranking] THEN ‘up from the prior year’
I wanted the prior year and difference from prior year for the tooltip.
After getting the bump chart working I tested out a couple of different color schemes and found the purple to be a bit easier on the eyes than the red I had intended on using.
There is a lot more I could have done with the dataset this week, but, overall I’m pretty happy with what I created. The color scheme is different for me and I was happy with the custom shape and the bump chart. For the next few months I’m going to experiment more with the design side.