Makeover Monday Week 4 – Ann’s Take

So apparently there’s a competition brewing between the two of us.  I said it on Twitter, but I’ll say it again: the only victory here (for me) is the learning and growth opportunity I receive from developing the makeover and the subsequent review of peer work.  It’s also the victory of having a portfolio of visualizations and something I’ve found to be very interesting: gauging the reactions of others to my vizzes.  In the world of being a data communicator/visualizer, resonating with your consumers (audience) is critical.  That’s probably what I’ve appreciated the most – seeing what gets noticed and what doesn’t.

Okay – now that the whole competition bit is out of the way, it’s time to dig in.  Christopher’s viz this week is very functional, so from an audience perspective I’m not enticed or immediately captured by the data.  I do think the data elements that are captioned out are eye-catching and I like the shape they’re creating.  The map does a good job of orienting me to the Matamata-Piako region via the annotation.  Nice that there are particulars about the geography.  The picture doesn’t add to the overall viz for me, but maybe I’m missing something regarding New Zealand.  There’s no interactivity with the data, it is very WYSIWYG – a static snapshot of data.

Moving to the mechanics and overall analysis.  I would be doing a small injustice to a lot of the chatter on Twitter this week if I didn’t mention that there’s a little bit of concern on how the RTI is being used as a measure.  The box and whisker plots are showing the RTI for domestic or international visitors for each region by year.  The RTI for each region is summed for the year and this makes me uncomfortable.  What’s kind of interesting is theoretically it “doesn’t matter” because if we were to average and divide everything by 12, the data shape would be the same.  BUT (and this is the big “but”) 2016 doesn’t have all the months.  So unfortunately the districts looks like they’ve been awesome except in 2016.  From a visual perspective this can be seen by comparing the companion line chart (RTI by month) below.  This shows a steady increase for international travelers perpetuating year over year.

I feel the same way about the caption stating that it’s RTI is 37,401 in the map.  And I am slightly bummed that it seems like the regions are colored by name, a potential missed opportunity!  Here – if all data points were used, then the coloration of RTI (when summed) would accurately represent which regions are hotter spots overall.

I’ve noticed an overall trend in Christopher’s style: he likes minimal chart labeling and leverages annotations.  The minimal labeling helps by freeing up canvas space between the box plot and the line chart.  There’s now space for the color legend and it couples the two charts together more seamlessly.  I also appreciate the care taken to make an information button explaining what the RTI is.  And as always – I think Christopher has demonstrated multiple times that he likes to help the end user zero in on what’s critical.  Here the critical elements are clearly: 1) Matmata-Piako district is a huge tourist attraction, one that by box plot standards is considered an outlier.  2) Seems like this insanity started in 2011 and keeps getting more prominent.  3) It’s not even the biggest or greatest place, so again “what’s the deal?” 4) It’s in the northern part of New Zealand, and we now have a nugget of geography that will be retained.

I would go so far as to say if asked this trivia question in the future you’d be able to answer it given multiple choice options: “Which northern district in New Zealand boasts the highest amount of tourism although it has a population of only 34k?”

Finishing up here, I’ll end by saying this:  each week of data comes with a new set of challenges and obstacles – this one seems to have had a few new landmines that tripped up most of the community.  I’ll be interested to see if Christopher finds anything within my makeover.

Makeover Monday Week 3 – Ann’s Take on Christopher’s Viz

We’re on week 3 of Makeover Monday and I think we can almost officially call this a habit.  I can’t reiterate enough how awesome it is to have a partner in this mission.  It keeps me engaged and accountable and I am very grateful to Christopher for that.

The spirit of participation was definitely necessary for me this week since the topic of viz was Trump’s tweets.  I won’t bring my political views to the post, but this ranks lower than visualizing sports data in my book.  (sorry!!)

Anyway – on to the viz!  We’ve got a fair amount of whimsy this week.  Right off the bat a line chart is being used to display Donald’s tweets and simultaneously resembles noise.  The placement and choice of photo makes it even more clear to me – he’s throwing more and more loud, erratic tweets out into the universe (hey, that’s what the line chart shows).

Moving through the viz, there’s a word cloud to get tweets to show up.  I like this concept.  Clicking on a word, I’m greeted with a full list of accompanying tweets.  Simplistic by design, but easy to let time slip by looking through tweets.

I clicked on ‘ever’ – and got a whole list of tweets that start with “Did you ever” and “Do you ever.”  And I now seriously believe that this is a word that Trump uses quite frequently for emphasis in his vocabulary.

I like the position Christopher took on this.  Display a little bit of how verbose Donald Trump can be, but allow maximum flexibility for someone to explore the data through guided keywords.

Now on to the workbook, which I am delighted to do this week because I now know that I’m being mocked regarding the arrow comments and this week Christopher has gone the path of using the arrow shape as a shape and not a picture.

Does this mean he’s becoming more structured?  Is there anything floating in this dashboard?  Alas, there are several floating objects, which makes me realize that I need to explore float for real.

Digging deeper, what I’m really intrigued by is how the words were extracted from the entire tweet text.  Looks like this was done in Alteryx and added in.  So Christopher, we’re dying to know a little bit of detail on the Alteryx data processing that occurred!

As someone who is actively interested in learning more about Alteryx and all of it’s power – please pass on to us some of this wisdom!

I’m keeping this post short, but I want to pause and comment on evolution of style.  I can already see how Christopher’s style and creativity is shifting and changing as the weeks go on.  I’ve experienced this myself (so maybe Christopher you’re going through the same thing) – but participating in these week after week is becoming easier and I am feeling more comfortable creating what I want out of it.  The paralysis of living up to the community is dying down and I am able to get down to vizzing much faster.

I can’t wait for next week (and to move on from this week)!


Makeover Monday Week 2 – Ann’s Take on Christopher’s Viz

This week’s Makeover Monday data set was the quarterly number of iPhone units sold by Apple dating back from FY Q3 2007 to Q4 2016.   The original viz and article bundled with the data set hints that Apple is struggling in more recent times.

As always, my goal in these retrospectives is the same: first approach as an interactor, someone who hasn’t been exposed to the data.  And along with that first blush interaction, document my thoughts/feelings and process.

The viz starts off very direct and journalistic.  Did Steve Jobs’ death make a difference?  The difference to “what” is described along additional text throughout the visualization.  Reading through leads me to the conclusion that it is iPhone sales and toward the bottom that’s confirmed through the more specific question.

The death date of Steve Jobs is clearly marked and the pattern of data nicely changes in conjunction.  Quarters are beginning to dip lower and lower below the X axis – what I am interpreting as zero.  If pressed to answer the question, I’d say “Yes” there is clearly some sort of impact of Jobs’ death on the fluctuation of quarterly iPhone sales.  But… that’s not where things end.

My analyst side is getting comfortable as I spend more time interacting with the visualization and I realize that Q1 isn’t displayed for any of the years.   And spending time in the tooltips I notice that the length of the lollipops must be based on the direct change in sales of units.  This seems to be making more recent years much more dramatic than when I look at the accompanying percentage changes.  A good example of this would be Q3 2010 & Q4 2010 compared to Q1 2016 & Q2 2016.  The percentage change between the first pair is 68% net positive with an increase of 5.7 million units.  The percentage change between the second pair is -31.5% (half the 2010 variance) and 23.59 million units.  So my brain is interpreting half the variance, but my eyes are seeing a bar 4 times as long.    Depending on which measure I’m using as my indicator, there’s two competing stories here.  We’ll leave it to Apple executives for the ultimate decision.

Now diving into the technical side of things aka the workbook itself.  The first thing I notice: we’ve got the arrow again!  Is this Christopher’s signature style?  Adding a floating image version of shapes?  It is definitely a handy trick I intend on stealing.  Next I notice the care taken to make a custom color palette.  I like how it is put into action in conjunction with the gray background.  The Apple aesthetic is being captured.  The last thing I notice – the line of Jobs’ death date is a sheet!  This is blowing my mind.  Once again my structured mind would be churning on how to make this part of the overall underlying viz and here Christopher has shifted into a different dimension by simply floating the line on top.  It’s these moments in our collaboration that I can feel my brain sweating.

I urge you to check out Christopher’s full visualization on his Tableau Public profile.


Makeover Monday Week 1 – Ann’s Take on Christopher’s Viz

Christopher and I have both challenged ourselves to be part of the elusive 100% club that makes up the Tableau community’s social project: Makeover Monday.  I’ve personally read through the attestations from several of my peers within the community on how their involvement has positively influenced their work and shaped them on their data artist journey.

To add further depth to the social project, Christopher and I thought it would be a good idea to take a look at each other’s Makeover Monday work and give our general impressions and opinions.  As I stated it during our recent phone call, the idea was to first take on the role of an end user or interactor.  Walk through findings and perceptions.  Then dive in from the perspective of a Tableau developer (or better said: fellow data artist) and see some of the technical components and how the visualization was made.

Now that the background is out of the way, it’s time for me to dive in to the viz.  The first thing I immediately appreciate is that this seems like the viz is set up for data discovery.  There’s a little bit of guidance in terms of what the subject matter is (and I happen to know since I vizzed it too), but other than that – it’s really up to the end user to explore the data points and understand what’s going on.  I fully appreciate that all data points are plotted because there’s a lot of good data to ingest.

My eyes were immediately drawn to the smaller (more zoomed in) Above $300k scatterplot.  I liked how quickly I could see the spread of gender within these jobs.  I was struck with the thought of “Really?  Only 2 female occupations above $300k?”  And I could immediately answer that by determining what they are.  After some additional investigation, my mind pondered to the idea that really most of these jobs in this section were very specialized and had few employed individuals.  I found myself justifying (if I can honestly say that as a feminist) that maybe the data for the 26 female Neurosurgeons was skewed a bit downward because the male number of the same occupation was 142 and we ARE talking averages.

Moving on, the next striking part within the viz was the massive number of female office workers, dwarfing any other occupation gender combo represented.  That data point caused me to trail back the jobs from largest number of individuals back toward 0,0.  I found myself nodding at the titles and thinking “I’m not really surprised by that # of individuals.”  Reinforcing some of the general gender norms I’ve grown up on.

Last I took a look at the Below $70k section.  It seems to me that it is filled with tons of female occupations and doing some investigation, comparing the occupations between male and female seemed to always lead to female workers being paid less.

Now for the technical take:  I dove right into this workbook.  The first thing I wanted to know was about the arrows.  I’m a very structured developer and so I wanted to see what the arrows were made from.  They turned out to be floating images.  Doing some additional digging, I realized that they were the arrows that come bundled with Tableau (shapes folder).  I chuckled to myself thinking that if I were going to do the same thing I probably would have made a sheet with the shape set to a calculated field or perhaps MIN(Number of Records).  Great reminder to myself: keep things simple.

Next: I had suspected that the large scatterplot had clustering applied to it.  So I went into the main sheet and validated.  This led me to see that the created clusters were actually used as filters for the two mini-scatterplots (also floated!) in the viz.  The last sheet to look at: the gender legend sheet.  Let me say this: I appreciated having this immensely.  (Mini-tangent) I tend to get confused with the gender shapes and having them reinforced with the legend and in some of the smaller vizzes with pink and blue was helpful to my mind.  Using double encoding on a data point (pink = female, plus shape = female) adds integrity and trust to my visual inspection process.

Summing it all up.  Things I really liked and appreciated: double encoding, built for data discovery, no enforced or projected point-of-view, subtle chunking of the data.  What’s still lingering on my mind?  If we had demographics dropped on the clusters and added narrative language to describe them, what would they be called?  Would Cluster 1 (dark blue, high income, low # of individuals) be best described as: Australian men have the market cornered on high-paying, highly specialized medical jobs.  And oh by the way, good luck marrying one because there’s only like 50 on the continent?

All-in-all a great start to Makeover Monday 2017.  And what’s more interesting, the giant juxtaposition between my approach and Christopher’s.

I encourage you to check out the full viz on Christopher’s Tableau Public.