A cycle plot is used to display cyclical patterns or trends over time, such as days of the week or months, to identify patterns, compare groups, and visualize seasonality in data.

I found this week's data set super interesting, but also super sad in some ways, like people are more and more alone as they age. Or people spending less time with their partners when they are much older, clearly showing when people typically pass away.

Despite the massive setback to women's right in Texas, less Americans are anti-abortion than in 1975. Now maybe if the barbaric dinosaurs in Congress in Texas came into the 21st century, they'd finally realize women are our equals and we shouldn't control them. It's all very, very disgusting. They should be ashamed, but of course they're not. In fact, they are quite proud of what they "achieved".

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

This week at The Data School, I was teaching DS24 about dashboard best practices and how to effectively use containers. We picked a visualization from the wild (this one by Ryan Sleeper) as an example for us to rebuild and to learn about effective use of containers.

With Amy Coney Barrett being sped through the confirmation process of the US Senate before the 2020 Election (as hypocritical as it was) to become the 9th justice on the Supreme Court, there is now a conservative stranglehold on the judicial branch of government.

Justice Barrett deflected all questions about her stance on abortion during the confirmation process and it has raised lots of speculation that Roe v. Wade will be overturned. Yeah, you know, because the government should control a woman's body, yet there is nothing similar for men. Like, why doesn't a man get castrated if he accidentally impregnates a woman? Blasphemy they say; hypocritical I say.

I wonder if any of the anti-abortion Justice or Congresspeople have ever considered their stance if their daughter had an unplanned pregnancy. What is she got raped and pregnant? I bet their tune would change.

Anyway, my political and social views aside, this made me think about abortion rates in America. Since Roe v. Wade, abortion rates in the US have plummeted.

What the data tells us is that Roe v. Wade has actually led to a REDUCTION in abortions. It also shows us the shift towards women waiting later in their lives to have children. 

As part of the training at The Data School, we're often tasked by clients to build industry specific dashboards and/or dashboards that can be used as templates for their organization. Of course what the client can ask for is often much more broad. From a dashboarding perspective, we tend to have the freedom to create what we think works best for their data. This them, in turn, helps create a sort of "brand" for their dashboards internally.

That got me thinking about common use cases for dashboards, dashboards that would likely span industries and companies and could serve as templates for others. In this series, I'll be posting templates that I have been building based on the data and sample dashboards from Excel Dashboard School. 

Let me make it clear that I am in no way criticizing the work they have created. My intent is to build an alternative method for displaying the data as a template in Tableau. The data and templates they have provided are the starting point for my work. I want to thank them for being so kind and sharing their work.

TOPIC

Customer Service Dashboard

RESOURCES

1. Dashboard Overview - https://exceldashboardschool.com/customer-service-dashboard/
2. Data and Interactive Dashboard - https://cdnspeed-exceldashboardsc.netdna-ssl.com/wp-content/uploads/2017/08/080_CSD_final.zip

DASHBOARD TEMPLATE

This week, we're looking at the change in smartphone ownership for tweens and teens between 2015 and 2019.

SOURCE: Morning Brew

What works well?

What could be improved?

What I did

It's TC19 week and Eva has provided a data set about literacy rates around the World for the 1200+ people in Vegas to viz live plus the hundreds more of you that aren't live with us.

Here's the original viz:

What works well?

What could be improved?

What I did

Year 4 of Makeover Monday is here! I can't believe how quickly it's gone. We've made a number of changes for this year which you can read about here. We want to have more visibility into who is participating and how often, so we're also asking people to help us track their work by filling out the simple weekly submission form here. After we get a few weeks in, we'll build a dashboard to share with everyone.

For week 1, we're making over this chart from NHL to Seattle. Granted the chart is from 2013, but it's still worth a makeover. If you want to see what John Barr has done since then, you can find a more recent visualization of NHL attendance from him here.

What works well?

What could be improved?

What I did

Because I'm tend to forget Valentine's Day (I consider it a Hallmark Holiday), it almost passed me that this week included Valentine's Day and I had intended to use another data set for Makeover Monday. Fortunately we have people on Twitter to keep us straight and this tweet changed the theme for this week.

Maybe some love or Valentine's Day dataset for #makeovermonday week 7? We love data. It's our day too. @TriMyData @VizWizBI

— Staticum (@staticum) February 11, 2017

This meant spending my Sunday morning find a new viz and data set. A quick google search turned up this infographic from KarBel Multimedia:


What I like:

• Color choices that match the theme
• Simple title that tells me what I'm about to see
• Proper sourcing
• Nice description that include a question that explains what the viz is about
• Donut chart works well here as it's only 2 slices
• Clear labeling

Chart of the Day recently published an article on Samsung’s Insane Marketing Budget whose primary purpose was to show how Samsung’s budget has increased over time.  The article also pushes the reader towards comparing Samsung to other companies included in the chart.  To do so, COTD presented this bar chart:

Before we makeover this bar chart, let’s examine it.

1. It’s pretty straight forward to compare the years within a single company, but the emphasis is on time, which means a line chart should be the natural first choice.
2. But wait.  The years are in reverse chronological order, so turn your head 90 degrees to the left and look at the chart.  Without referring to the legend, one would likely assume that the most recent year was at the bottom of each set of bars.
3. The bars are colored by year, which makes the comparisons across companies for a given year possible, but not easy.  It’s easier to compare the years if they’re grouped together.
4. Each bar uses gradient coloring and shadowing.  Why?  What value does this add? 
5. They’re using red and green bars together.  They’re not thinking about the color-blind folks out there.  They should be using natural, color-blind friendly colors.

To address these issues, I’ve created the following viz using Tableau.  Download a free trial here.

With this viz, you can now see:

1. The trend for each company.  It’s much easier to see the large increase in Samsung’s budget, while all of the others in the view are relatively flat.
2. I thought it was important to emphasize the difference between Samsung and the other companies, thus the five small line charts, which all clearly show how much more Samsung is spending.

How would you do this differently?  Download the data here and the workbook here.

Are Premier League referees getting worse? It sure seems that way when I watch games.  And despite the opinion of Sir Alex Ferguson, the decisions do not even out.

What makes it even tougher is when the players dive all over the place with no intent other than to con the referee.  Is anyone with me on starting a campaign for retroactive suspensions for diving?  That would stop all of the diving immediately!  Guaranteed!

Ashley Young has been near criminal in two recent matches.  He’s even been mentioned for a position on Great Britain's Olympic diving team.  Refer to the video of Man U vs. Villa and Man U vs. QPR for some of the finest acting in years.  He really ought to be ashamed of himself.

But it’s not only the PKs that are called; it’s those that are not called as well.  Refer to the highlights, if you can find them, of the Man U vs. Fulham game on 26 March 2012.  The game was at Old Trafford, it was late in the game, there was a CLEAR penalty against Man U and, of course, it wasn’t called.

I’m not saying there’s a bias towards the “bigger clubs”, but looking at penalty kick data sure leans me in that direction.  All you need to do is filter the viz below to only include teams that finished 1-4 and you’ll see what I mean.

The top four teams get way more PKs called in their favor than the rest of the clubs AND they significantly more PKs called at home.  I simply can’t believe that referees are NOT intimidated by Fergie and Old Trafford?

Click on the Manchester United logo to focus on them.  Look at those startling trends that appear at the bottom.  Intimidation at its finest!

If you want to see the details behind the charts, go to the Team PK Stats tab. 

• Look at how many PKs the top four teams have gotten over the year, both overall and at home
• Now compare that to the relegated teams (18-20)

Maybe it’s because the top teams have the ball more and create more situations where a PK could be called. But still, should the numbers be so overwhelming?

We have one of those TVs in the elevators at work that flashes headlines. While these have kept me a bit more informed about current events, they’ve been detrimental to elevator conversations. 

So I’m riding the elevator the other day, talking to no one, and a headline appears about the incredible decline in teen pregnancy rate.  I thought this would be a perfect opportunity to “show” the results in Tableau.

The trend data comes from the Guttmacher Institute and the state-level data come from The National Campaign to Prevent Teen and Unplanned Pregnancy.  Additional context comes from the CDC.

I used a couple of techniques in Tableau that I will explain after the viz.  But first, some notes from explaining the situation.

• Despite legalized aborting in 1973, the significant increases in pregnancy rates in the the 1980s and early 1990s are explained by increased birthrates but stable abortion rates.(Guttmacher)
• Almost all of the decline in the pregnancy rate between 1995 and 2002 among 18–19-year-olds was attributable to increased contraceptive use. (Guttmacher)
• Among women aged 15–17, about one-quarter of the decline during the same period was attributable to reduced sexual activity and three-quarters to increased contraceptive use. (Guttmacher)
• The teen birth rate in the United States declined during 1991--2009 to its lowest level in the nearly 70 years. (CDC)

When looking at pregnancy rates what stuck out to me immediately is the clear dividing line between the north and the south.  According to the CDC, teen birth rates in the United States have declined but remain high, especially among black and Hispanic teens and in southern states.  Perhaps the higher rates are explained by race, but I wonder if the rates can be partly explained by the religious stigmas that are associated with abortion in the south.  Or perhaps sex education programs are not as strongly emphasized.  I haven’t found data to support my theories, but having lived here for almost 15 years, I notice what’s going on around me.

This blog post from Matt Stiles made me think not only the number of pregnancies, but also the rate.  The rate gives you a much more accurate comparison across states.

You might notice that I have three maps, one for the continental US and then one for each of Alaska and Hawaii.  This is done so that the map isn’t so zoomed out when looking at all of the states in one map.  Tableau does not come with a map like this so I:

1. Created a single map of all states,
2. Zoomed in on the continent,
3. Pinned the map, and
4. Hid the zoom controls.

I then:

1. Duplicated the map twice,
2. Changed the zoom to Alaska and Hawaii respectively, and
3. Placed all three maps on a dashboard.

The reason I duplicated the maps instead of filtering each of the maps is because the color scale would not be accurately represented on any of the maps.  I want all states to use the same scale, therefore all states are actually on all of the maps.

I added a subtle feature you may not notice.  As you change the statistic from the drop down on the upper left, the title for the color legend changes dynamically.  Tableau doesn’t allow you to expose information from the viz in titles for the Size and Color cards like it does for captions, titles, tooltips, etc.  Here’s the technique I used to work around this limitation:

1. Create a calculated field for a label based on the statistic selected (which is a parameter)

2. Create a blank worksheet and place this calculated field on the Level of Detail shelf
3. Updated the title of the worksheet to expose this field

4. Format the worksheet so that the rows and columns are as small as possible and the gridlines are removed
5. Place the worksheet on the dashboard above the color legend
6. Change the Fit to Entire View
7. Show the title

That’s it.  I now have a dynamic title for the color legend.

As we approach kickoff of the Arsenal vs. Wigan game this afternoon, I thought I’d take a quick look back at the Gunners’ season and when the data says the season may have turned around.

It’s well documented that Arsenal got off to one of the club’s worst starts ever, but gradually they have come back and are currently in third place.  With five games remaining, their magic number over Spurs and Newcastle stands at 11 and 8 over Chelsea.

“Magic number” is a phrase we use in the States, particularly with baseball, “to indicate how close a front-running team is to clinching a season title (or third place and a Champions League automatic qualifying spot in Arsenal’s case). It represents the total of additional wins by the front-running team or additional losses (or any combination thereof) by the rival team after which it is mathematically impossible for the rival team to capture the title in the remaining games.”

Many people point to the goal below by Bacary Sagna when Arsenal were down 0-2 against Spurs on 26-Feb as the turning point in the season.  (Pardon the poor quality; it’s all I could find on YouTube.)  I must say that I love the way Sagna took the goal, followed the ball into the goal, picked it up, ran it to half field and all but said “I’m sick of this!”  The entire attitude of the team seemed to shift on this one goal.  I get the chills every time I watch it.

Again, I have no doubt about the attitude shift at this single moment, particularly after back-to-back poor results against AC Milan (Champions League) and Sunderland (FA Cup).  But looking at the data for the EPL only, one particular match stands out as the turnaround point..the 7-1 thrashing of Blackburn, whom Arsenal somehow managed to lose to during their early season swoon.

Below you will see four chart, all which highlight this 7-1 game.

1. Points Trend – the upward trend of full-point matches clearly starts with the Blackburn match
2. Points vs. Possible – Here, you want to see a flat green line, which indicates full points are taken from the match.  Look at that long run of results that starts with the Blackburn match.
3. Table position – What an ugly start to the season!  There was a recovery for a few weeks 1/3 of the way into the season, but another string of poor results began with the 0-1 loss to Manchester City.  But the run of results beginning with the Blackburn match, combined with a significant dip in form by Spurs, has pole vaulted the Gunners into 3rd place.
4. Score vs. Result – This is a simple scatterplot. The darker the bubble, the more results for that score.  Excluding the 2-8 drubbing at the hands of Manchester Units, Arsenal have lost every match, bar a 0-2 defeat by only one goals.  This indicates that they have been in every match, but haven’t been able to score late to turn the result around.  But look at our wins, the scores have been tremendous with a but more than a 4-1 average score.

Touch wood that Arsenal will hold on to third place, and please, please, please RVP, sign a new contract!

Yesterday I wrote about being a hypocrite and walked through several alternatives to pie charts.  Today I am sharing a Tableau Public workbook that I created based on comments to the last post.

I received questions about how the different charts would look for different time periods, so I’ve added:

1. A year filter
2. A parameter that allows you to pick a date format (e.g., view the data by Year, Year/Month, etc.)

You can download the workbook from the viz below or here.