#WorkoutWednesday Week 24 – Math Musings

The Workout Wednesday for week 24 is a great way to represent where a result for a particular value falls with respect to a broader collection.  I’ve used a spine chart recently on a project where most data was centered around certain points and I wanted to show the range.  Propagating maximums, minimums, averages, quartiles, and (when appropriate) medians can help to profile data very effectively.

So I started off really enjoying where this visualization was going.  Also because the spine chart I made on a recent project was before I even knew the thing I developed had already been named.  (Sad on my part, I should read more!)

My enjoyment turned into caution really quickly once I saw the data set.  There are several ratios in the data set and very few counts/sums of things.  My math brain screams trap!  Especially when we start tiptoeing into the world of what we semantically call “average of all” or “overall average” or something that somehow represents a larger collective (“everybody”).  There is a lot of open-ended interpretation that goes into this particular calculation and when you’re working with pre-computed ratios it gets really tricky really quickly.

Here’s a picture of the underlying data set:

 

Some things to notice right away – the ratios for each response are pre-computed.  The number of responses is different for each institution.  (To simplify this view, I’m on one year and one question).

So the heart of the initial question is this: if I want to compare my results to the overall results, how would I do that?  Now there are probably 2 distinct camps here.  1: take the average of one of the columns and use that to represent the “overall average”.  Let’s be clear on what that is: it is the average pre-computed ratio of a survey.  It is NOT the observed percentage of all individuals surveyed.  That would be option 2: the weighted average.  For the weighted average or to calculate a representation of all respondents we could add up all the qualifying respondents answering ‘agree’ and divide it by the total respondents.

Now we all know this concept of average of an average vs. weighted average can cause issues.  Specifically we’d feel the friction immediately if there were several low-end responses commingled with several higher response capturing entities.  EX: Place A: 2 people out of 2 answered yes (100%) and  Place B: 5 out of 100 answered ‘yes’ (5%).  If we average 100% and 5% we’ll get 52.5%.  But what if we take 7 out of 102, that’s 6.86% – a way different number.  (Intentionally extreme example.)

So my math brain was convinced that the “overall average” or “ratio for all” should be inclusive of the weights of each Institution.  That was fairly easy to compensate for: take each ratio and multiply it by the number of respondents to get raw counts and then add those all back up together.

The next sort of messy thing to deal with was finding the minimums and maximums of these values.  It seems straightforward, but when reviewing the data set and the specifications of what is being displayed there’s caution to throw with regard to level of aggregation and how the data is filtered.  As an example, depending on how the ratios are leveraged, you could end up finding the minimum of 3 differently weighted subjects to a subject group.  You could also probably find the minimum Institution + subject result at the subject level of all the subjects within a group.  Again I think the best bet here is to tread cautiously over the ratios and get into raw counts as quickly as possible.

So what does this all mean?  To me it means tread carefully and ask clear questions about what people are trying to measure.  This is also where I will go the distance and include calculations in tool tips to help demonstrate what the values I am calculating represent.  Ratios are tricky and averaging them is even trickier.  There likely isn’t a perfect way to deal with them and it’s something we all witness consistently throughout our professional lives (how many of us have averaged a pre-computed average handle time?).

Beyond the math tangent – I want to reiterate how great a visualization I think this is.  I also want to highlight that because I went deep-end math on it that I decided to go deep end development different.

The main difference from the development perspective?  Instead of using reference bands, I used a gannt bar as the IQR.  I really like using the bar because it gives users an easier target to hover over.  It also reduce some of the noise of the default labeling that occurs with reference lines.  To create the gannt bar – simply compute the IQR as a calculated field and use it as the size.  You can select one of the percentile points to be the start of the mark.

#WorkoutWednesday Week 23 – American National Parks

I’m now back in full force from an amazing analytics experience at the Alteryx Inspire conference in Las Vegas.  The week was packed with learning, inspiration, and community – things I adore and am honored to be a part of.  Despite the awesome nature of the event, I have to admit I’m happy to be home and keeping up with my workout routine.

So here goes the “how” of this week’s Workout Wednesday week 23.  Specifications and backstory can be found on Andy’s blog here.

Here’s a picture of my final product and my general assessment of what would be required for approach:

Things you can see from the static image that will be required –

  • Y axis grid lines are on specific demarcations with ordinal indicators
  • X-axis also has specific years marked
  • Colors are for specific parks
  • Bump chart of parks is fairly straight forward, will require index() calculation
  • Labels are only on colored lines – tricky

Now here’s the animated version showing how interactivity works

  • Highlight box has specific actions
    • When ‘none’ is selected, defaults to static image
    • When park of specific color is selected, only that park has different coloration and it is labeled
    • When park of unspecified color is selected, only that park has different coloration (black) and it is labeled

Getting started is the easy part here – building the bump chart.  Based on the data set and instructions it’s important to recognize that this is limited to parks of type ‘National Historical Park’ and ‘National Park.’  Here’s the basic bump chart setup:

and the custom sort for the table calculation:

Describing this is pretty straight for – index (rank) each park by the descending sum of recreation visitors every year.  Once you’ve got that setup, flipping the Y-axis to reversed will get you to the basic layout you’re trying to achieve.

Now – the grid lines and the y-axis header.  Perhaps I’ve been at this game too long, but anytime I notice custom grid lines I immediately think of reference lines.  Adding constant reference lines gives ultimate flexibility in what they’re labelled with and how they’re displayed.  So each of the rank grid lines are reference lines.  You can add the ‘Rank’ header to the axis by creating an ad-hoc calculation of a text string called ‘Rank.’  A quick note on this: if you add dimensions and measures to your sheet be prepared to double check and modify your table calculations.  Sometimes dimensions get incorporated when it wasn’t intended.

Now on to the most challenging part of this visualization: the coloration and labels.  I’ll start by saying there are probably several ways to complete this task and this represents my approach (not necessarily the most efficient one):

First up: making colors for specific parks called out:

(probably should have just used the Grouping functionality, but I’m a fast typer)

Then making a parameter to allow for highlighting:

(you’ll notice here that I had the right subset of parks, this is because I made the Park Type a data source filter and later an extract filter – thus removing them from the domain)

Once the parameter is made, build in functionality for that:

And then I set a calculation to dynamically flip between the two calculations depending on what the parameter was set to.

Looking back on this: I didn’t need the third calculation, it’s exactly the same functionality as the second one.  In fact as I write this, I tested it using the second calculation only and it functions just fine.  I think the over-build speaks to my thought process.

  1. First let’s isolate and color the specific parks
  2. Let’s make all the others a certain color
  3. Adding in the parameter functionality, I need the colors to be there if it is set to ‘(None)’
  4. Otherwise I need it to be black
  5. And just for kicks, let’s ensure that when the parameter is set to ‘(None)’ that I really want it to be the colors I’ve specified in the first calc
  6. Otherwise I want the functionality to follow calc 2

Here’s the last bit of logic to get the labels on the lines.  Essentially I know we’re going to want to label the end point and because of functionality I’m going to have to require all labels to be visible and determine which ones actually have values for the label.  PS: I’m really happy to use that match color functionality on this viz.

And the label setting:

That wraps up the build for this week’s workout with the last components being to add in additional components to the tooltip and to stylize.  A great workout that demonstrates the compelling nature of interactive visualization and the always compelling bump chart.

Interact with the full visualization here on my Tableau Public.

Workout Wednesday Week 21 – Part 1 (My approach to existing structure)

This week’s Workout Wednesday had us taking NCAA data and developing a single chart that showed the cumulative progression of a basketball game.  More specifically a line chart where the X axis is countdown of time and the Y axis is current score.  There’s some additional detail in the form of the size of each dot representing 1, 2, or 3 points.  (see cover photo)

Here’s what the underlying data set looks like:

Comparing the data structure to the image and what needs to be produced my brain started to hurt.  Some things I noticed right away:

  • Teams are in separate columns
  • Score is consolidated into one column and only displayed when it changes
  • Time amount is in 20 minute increments and resets each half
  • Flavor text (detail) is in separate columns (the team columns)
  • Event ID restarts each half, seriously.

My mind doesn’t like that there’s a team dimension that’s not in the same column.  It doesn’t like the restarting time either.  It really doesn’t like the way the score is done.  These aren’t numbers I can aggregate together, they are raw outputs that are in a string format.

Nonetheless, my goal for the Workout was to take what I had in that structure and see if I could make the viz.  What I don’t know is this: did Andy do it the same way?

My approach:

First I needed to get the X axis working.  I’ve done a good bit of work with time so I knew a few things needed to happen.  The first part was to convert what was in MM:SS to seconds.  I did this in my mind to change the data to a continuous axis that I could format into MM:SS format.  Here’s the calculation:

I cheated and didn’t write my calculated field for longevity.  I saw that there was a dropped digit in the data and compensated by breaking it up into two parts.  Probably a more holistic way to do this would be to say if it is of length 4 then append a 0 to the string and then go about the same process.  Here’s the described results showing the domain:

Validation check: the time goes from 0 to 20 minutes (0 to 20*60 seconds aka 1200 seconds).  We’re good.

Next I needed to format that time into MM:SS continuous format.  I took that calculation from Jonathan Drummey.  I’ve used this more than once, so my google search is appropriately ‘Jonathan Drummey time formatting.’  So the resultant time ‘measure’ was almost there, but I wasn’t taking into consideration the +20 minutes for the first half and that the time axis was full game duration.  So here’s the two calculations that I made (first is +20 mins, then the formatting):

At this point I felt like I was kind of getting somewhere – almost to the point of making the line chart, but I needed to break apart the teams.  For that bit I leveraged the fact that the individual team fields only have details in them when that team scores.  Here’s the calc:

I still don’t have a lot going on – at best I have a dot plot where I can draw out the event ID and start plotting the individual points.

So to get the score was relatively easy.  I also did this in a custom to the data set kind of way with 3 calculations – find the left score, find the right score, then tag the scores to the teams.

Throwing that on rows, here’s the viz:

All the events are out of order and this is really difficult to understand.  To get closer to the view I did a few things all at once:

  • Reverse the time axis
  • Add Sum of the Team Score to the path
  • Put a combined half + event field on detail (since event restarts per half)

Also – I tried Event & Half separately and my lines weren’t connected (broken at half time; so creating a derived combined field proved useful at connecting the line for me)

Here’s that viz:

It’s looking really good.  Next steps are to get the dots to represent the ball sizes.

One of my last calculations:

That got dropped on size on a duplicated and synchronized “Team Score.”  To get the pesky null to not display from the legend was a simple right click and ‘hide.’  I also had to sort the Ball Size dimensions to align with the perceived sizing.  Also the line size was made super skinny.

Now some cool things happened because of how I did this:  I could leverage the right and left scores for tooltips.  I could also leverage them in the titling of the overall scores UNC = {MAX([LeftScore]}.

Probably the last component was counting the number of baskets (within the scope of making it a single returned value in a title per the specs of the ask).  Those were repeated LODs:

And thankfully the final component of the over sized scores on the last marks could be accomplished by the ‘Always Show’ option.

Now I profess this may not be the most efficient way to develop the result, heck here’s what my final sheet looks like:

All that being said: I definitely accomplished the task.

In Part 2 of this series, I’ll be dissecting how Andy approached it.  We obviously did something different because it seems like he may have used the Attribute function (saw some * in tooltips).  My final viz has all data points and no asterisks ex: 22:03 remaining UNC.  Looking at that part, mine has each individual point and the score at each instantaneous spot, his drops the score.  Could it be that he tiptoed around the data structure in a very different way?

I encourage you to download the workbook and review what I did via Tableau Public.

 

Synergy through Action

This has been an amazing week for me.  On the personal side of things my ship is sailing in the right direction.  It’s amazing what the new year can do to clarify values and vision.

Getting to the specifics of why I’m calling this post “Synergy through Action.”  That’s the best way for me to describe how my participation in this week’s Tableau and data visualization community offerings have influenced me.

It all actually started on Saturday.  I woke up and spent the morning working on a VizforSocialGood project, specifically a map to represent the multiple locations connected to the February 2017 Women in Data Science conference.  I’d been called out on Twitter (thanks Chloe) and felt compelled to participate.  The kick of passion I received after submitting my viz propelled me into the right mind space to tackle 2 papers toward my MBA.

Things continued to hold steady on Sunday where I took on the #MakeoverMonday task of Donald Trump’s tweets.  I have to imagine that the joy from accomplishment was the huge motivator here.  Otherwise I can easily imagine myself hitting a wall.  Or perhaps it gets easier as time goes on?  Who knows, but I finished that viz feeling really great about where the week was headed.

Monday – Alberto Cairo and Heather Krause’s MOOC was finally open!  Thankfully I had the day off to soak it all in.  This kept my brain churning.  And by Wednesday I was ready for a workout!

So now that I’ve described my week – what’s the synergy in action part?  Well I took all the thoughts from the social good project, workout Wednesday, and the sage wisdom from the MOOC this week to hit on something much closer to home.

I wound up creating a visualization (in the vein of) the #WorkoutWednesday redo offered up.  What’s it of?  Graduation rates of specific demographics for every county in Arizona for the past 10ish years.  Stylized into small multiples using at smattering of slick tricks I was required to use to complete the workout.

Here’s the viz – although admittedly it is designed more as a static view (not quite an infographic).

 

And to sum it all up: this could be the start of yet another spectacular thing.  Bringing my passion to the local community that I live in – but more on a widespread level (in the words of Dan Murray, user groups are for “Tableau zealots”).

#WorkoutWednesday Week 1

Another great community activity is Workout Wednesday hosted by Andy Kriebel and Emma Whyte.  According to Andy it’s “designed to test your knoweldge of Tableau and help you kick on in your development.”  They’re alternating odd vs. even weeks.

Here’s the first task in a visual nutshell (using Superstore data set):

I’m happy to say that I was able to complete the task.  What was the most interesting part?  To get the dots on the single lines I ended up redoing a field that had a secondary table calculation and using some built in functions.  Those functions were RUNNING_SUM() and TOTAL().  The dots continued to be tricky, but I resolved to using AND logic within my IF statement and leveraging LOOKUP().

I also did a micro upgrade.  The instructions indicated that the red should highlight the “most current year.”  When interacting with the viz on the original blog, I noticed that only 2015 was red and the title was static.  So I added in logic to highlight the most recent year and added the dynamic change to the title as well.

Full viz on my Tableau Public page.