#MakeoverMonday Week 22 – Internet Usage by Country

This week’s data set demonstrates the number of users per 100 people by country spanning several years.  The original data set and accompanying visualization starts as an interactive map with the ability to animate through the changing values year by year.  Additionally, the interactor can click into a country to see percentage changes or the comparative changes with multiple countries.

Channeling my inner Hans Rosling – I was drawn to play through the animation of the change by year, starting with 1960.  What sort of narrative could I see play out?

Perhaps it was the developer inside of me, but I couldn’t get over the color legend.  For the first 30 years (1960 to 1989) there’s only a few data points, all signifying zero.  Why?  Does this mean that those few countries actually measured this value in those years, or is it just bad data?  Moving past the first 30 years, my mind was starting to try and resolve the rest of usage changes.  However – here again my mind was hurt by the coloration.  The color legend shifts from year to year.  There’s always a green, greenish yellow, yellow, orange, and red.  How am I to ascertain growth or change when I must constantly refer to the legend?  Sure there’s something to say about comparing country to country, but it loses alignment once you start paginating through the years.

Moving past my general take on the visualization – there were certain things I picked up on and wanted to carry forward on my makeover.  The first was the value out of 100 people.  Because I noticed that the color legend was increasing year to year, this meant that overall number of users was increasing.  Similarly, when thinking about comparing the countries, coloration changed, meaning ranks were changing.

I’ll tell you – my mind was originally drawn to the idea of 3 slope charts sitting next to each other.  One representing the first 5 years, then next 5 years, and so on.  Each country as a line.  Well that wasn’t really possible because the data has 1990 to 2000 as the first set of years – so I went down the path of the first 10 years.  It doesn’t tell me much other than something somewhat obvious: internet usage exploded from 1990 to 2000.

Here’s how the full set would have maybe played out:

This is perhaps a bit more interesting, but my mind doesn’t like the 10 year gap between 1990 and 2000, five year gaps from 2000 to 2010, and then annual measurements from 2010 to 2015 (that I didn’t include on this chart).  More to the point, it seems to me that 2000 may be a better starting measurement point.  And it created the inflection point of my narrative.

Looking at this chart – I went ahead and decided my narrative would be to understand not only how much more internet usage there is per country, but to also demonstrate how certain countries have grown throughout the time periods.  I limited the data set to the top 50 in 2015 to eliminate some of the data noise (there were 196 members in the country domain, when I cut it to 100 there were still some 0s in 2000).

To help demonstrate that usage was just overall more prolific, I developed a consistent dimension to block out number of users.  So as you read it – it goes from light gray to blue depending on the value.  The point being that as we get nearer in time, there’s more dark blue, no light gray.

And then I went the route of a bump chart to show how the ranks have changed.  Norway had been at the top of the charts, now it’s Iceland.  When you hover over the lines you can see what happened.  And in some cases it makes sense, a country is already dominating usage, increasing can only go so far.

But there are some amazing stories that can unfold in this data set: check out Andorra.  It went from #33 all the way up to #3.

You can take this visualization and step back into different years and benchmark each country on how prolific internet usage was during the time.  And do direct peer comparatives to boot.

This one deserves time spent focused on the interactivity of the visualization.  That’s part of the reason why it is so dense at first glance.  I’m intentionally trying to get the end user to see 3 things up front: overall internet usage in 2000 (by size and color encoding) and the starting rank of countries, the overall global increase in internet usage (demonstrated by coloration change over the spans), and then who the current usage leader is.

Take some time to play with the visualization here.

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.


#MakeoverMonday Week 21 – Are Britons Drinking Less?

After some botched attempts at reestablishing routine, #MakeoverMonday week 21 got made within the time-boxed week!  I have one pending makeover and an in-progress blog post to talk about Viz Club and the 4 developed during that special time.  But for now, a quick recap of the how and why behind this week’s viz.

This week’s data set was straightforward – aggregated measures sliced by a few dimensions.  And to what I believe is now becoming an obvious trend on how data is published, it included both aggregated and lower dimensions within the same field (read this as “men,” “women,” “all people”).  The structured side of my doesn’t like it and screams for me to exclude from any visualizations, but this week I figured I’d take a different approach.

The key questions asked related to alcohol consumption frequency by different age and gender combinations (plus those aggregates) – so there was lots of opportunity to compare within those dimensions.  More to that, the original question and how the data was presented begged to rephrase into what became the more direct title (Are Britons Drinking Less?)

The question really informed the visualizations – and more to that point, the phrasing of the original article seemed to dictate to me that this was a “falling measure.”  Meaning it has been declining for years or year-to-year, or now compared to then – you get the idea.

With it being a falling measure and already in percentages, this made the concept of using an “difference from first” table calculation a natural progression.  When using the calculation the first year of the measure would be anchored at zero and subsequent years would be compared to it.  Essentially asking and answering for every year “was it more or less than the first year we asked?”  Here’s the beautiful small multiple:

Here the demographics are set to color, lightest blue being youngest to darkest blue being oldest; red is the ‘all.’  I actually really enjoyed being able to toss the red on there for a comparison and it is really nice to see the natural over/under of the age groups (which mathematically follows if they’re aggregates of the different groups).

One thing I did to add further emphasis was to put positive deltas on size – that is to say to over emphasize (in a very subdued, probably only Ann appreciates the humor behind it way) when it is anti-the trend.  Or more directly stated: draw the readers attention to different points where the percentage response has increased.

Here’s the resultant:

So older demographics are drinking more than they used to and that’s fueled by women.  This becomes more obvious to the point of the original article when looking at the Teetotal groups and seeing many more fat lines.

Here’s the calculation to create the line sizing:

Last up was to make one more view to help sell the message.  I figured a dot plot would mimic champagne bubbles in a very abstract way.  And I also thought open/closed circles in combination with the color encoding would be pleasant for the readers.  Last custom change there was to flip the vertical axis of time to be in reverse.  Time is read top down and you can see it start to push down to the left in some of the different groupings.

If you go the full distance and interact with the dashboard, the last thing I hope you’ll notice and appreciate is the color legend/filter bar at the top.  I hate color legends because they lack utility.  Adding in a treemap version of a legend that does double duty as highlight buttons is my happy medium (and only when I feel like color encoding is not actively communicated enough).

#MakeoverMonday Week 17

After a bit of life prioritization, I’m back in full force on a mission to contribute to Makeover Monday.  To that end, I’m super thrilled to share that I’ve completed my MBA.  I’ve always been an individual destined not to settle for one higher education degree, so having that box checked has felt amazing.

Now on to the Makeover!  This week’s data set was extra special because it was published on the Tableau blog – essentially more incentive to participate and contribute (there’s plenty of innate incentive IMO).

The data was courtesy of LinkedIn and represented 3 years worth of “top skills.”  Here’s my best snapshot of the data: 


This almost perfectly describes the data set, without the added bonus of there being a ‘Global’ skills in the Country dimension as well.  Mixing aggregations or concepts of what people believe can be aggregated, I sighed just a little bit.  I also sighed at seeing some countries are missing 2014 skills and 2016 is truncated to 10 skills each.

So the limitations of the data set meant that there had to be some clever dealing to get around this.  My approach was to take it from a 2016 perspective.  And furthermore to “look back” to 2014 whenever there was any sort of comparison.    I made the decision to eliminate “Global” and any countries without 2014 from the data set.  I find that the data lends itself best to comparison within a given country (my perspective) – so eliminating countries was something I could rationalize.

Probably the only visualization I really cared about was a slope chart.  I thought this would be a good representation of how a skill has gotten hotter (or not).  Here’s that:

Some things I did to jazz it up a bit.  Added a simple boolean expression to color to denote if the rank has improved since 2014.  Added on reference lines for the years to anchor the lines.  I’ve done slope charts different ways, but this one somehow evolved into this approach.  Here’s what the sheet looks like:

Walking through it, starting with the filter shelf.  I’ve got an Action filter on country (based on action filter buttons elsewhere on the dashboard).  Year has been added to context and 2015 eliminated.  Datasource filtered out the countries without 2014 data & global.  Skill is filtered to an LOD for 2016 Rank <> 0.  This ensures I’m only using 2016 skills.  The context filters keep everything looking pretty for the countries.

The year lines are reference lines – all headers are hidden.  There’s a dual axis on rows to have line chart & circle chart.  The second Year in columns is redundant and leftover from an abandoned labeling attempt (but adds nice dual labels automatically to my reference lines).

Just as a note – I made the 2016 LOD with a 2014 LOD to do some cute math for line size – I didn’t like it so abandoned.

Last steps were to add additional context to the “value” of 2016 skills.  So a quick unit chart and word cloud.  One thing I like to do on my word clouds these days is square the values on size.  I find that this makes the visual indicator for size easier to understand.  What’s great about this is that smaller rank is better, so instead of “^2” it became this:

Sometimes math just does you a real solid.

The kicker of this entire data set for me and gained knowledge: Statistical Analysis and Data Mining are hot!  Super hot!  Also really like that User Interface Design and Algorithm Design made it to the top 10 for the United States.  I would tell anyone that a huge component of my job is designing analytical outputs for all types of users and that requires an amount of UX design.  And coincidentally I’m making an algorithm to determine how to eliminate a backlog, all in Tableau.  (basic linear equation)

#MakeoverMonday Week 12 – All About March Madness

This week’s Makeover Monday topic was based on an article attempting to provide analysis into why it is harder for people to correctly pick their March Madness brackets. The original visualization is this guy:

With most Makeover Monday approaches I like to review the inspiration and visualization and let that somewhat decide the direction of my analysis. In this case I found that completely impossible. For my own sake I’m going to try and digest what it is I’m seeing/interpreting.

  • Title indicates that we’re looking at the seeds making the final 4
  • Each year is represented as a discrete value
  • I should be able to infer that “number above column represents sum of Final Four seeds” by the title
  • In the article it says in 2008 that all the seeds which made it to the final 4 were #1 – validates my logical assumption
  • Tracking this down further, I am now thinking each color represents a region – no idea which colors mean what – I take that back, I think they are ranked by the seed value (it looks like the first instance of the best seed rank is always yellow)
  • And then there’s an annotation tacked on % of Final Four teams seeded 7th or lower for two different time periods
    • Does 5.2% from 1985 to 2008 equal: count the blue bars (plus one red bar) with values >=7 – that’s 5 out of (24 * 4) = 5/96 = 5.2%
    • Same logic for the second statistic: 7 out of (8 * 4) = 21.9%

And then there’s the final distraction of the sum of the seed values above each bar.  What does this accomplish?  Am I going to use it to quickly try and calculate an “average seed value” for each year?  Because my math degree didn’t teach me to compute ratios at the speed of thought – it taught me to solve problems by using a combination of algorithms and creative thinking.  It also doesn’t help me with understanding interesting years – the height of the stacked bars does this just fine.

So to me this seems like an article where they’ve decided to take up more real estate and beef up the analysis with a visual display.  It’s not working and I’m sad that it is a “Chart of the Day.”

Now on to what I did and why.  I’ll add a little preface and say that I was VERY compelled to do a repeat of my Big Game Battle visualization, because I really like the idea of using small multiples to represent sports and team flux.  Here’s that display again:

Yes – you have to interact to understand, but once you do it is very clear.  Each line represents a win/loss result for the teams.  They are then bundled together by their regions to see how they progressed into the Superbowl.  In the line chart it is a running sum.  So you can quickly see that the Patriots and Falcons both had very strong seasons.  The 49ers were awful.

So that was my original inspiration, but I didn’t want to do the same thing and I had less time.  So I went a super distilled route of cutting down the idea behind the original article further.  Let’s just focus on seed rank of those in the championship.  To an extent I don’t really think there’s a dramatic story in the final 4 rankings – the “worst” seed that made it there was 11th.  We don’t even know if that team made it further.

In my world I’ve got championship winners vs. losers with position indicating their seed rank.  Color represents the result for the team for the year and for overall visual appeal I’ve made the color ramp.  To help orient the reader, I’ve added min/max ranks (I screwed this up and did pane for winner, should have been table like it is for loser, but it looks nice anyway).  I’ve also added on strategic years to help demonstrate that it’s a timeline.  If you were to interact, you’d see the name of the team and a few more specifics about what it is you’re looking at.

The reality of my takeaway here – a #1 seed usually wins.  Consistently wins, wins in streaks.  And there’s even a fair amount of #1 losers.  If I had to make a recommendation based on 32 years of championships: pick the #1 seeds and stick with them.  Using the original math from the article: 19 out of 32 winners were seed #1 (60%) and 11 out of 32 losers were seed #1 (34%).  Odds of a 1 being in the final 2 across all the years?  47% – And yes, that is said very tongue in cheek.

Makeover Monday Week 10 – Top 500 YouTube Game(r) Channels

We’re officially 10 weeks into Makeover Monday, which is a phenomenal achievement.  This means that I’ve actively participated in recreating 10 different visualizations with data varying from tourism, to Trump, to this week’s Youtube gamers.

First some commentary people may not like to read: the data set was not that great.  There’s one huge reason why it wasn’t great: one of the measures (plus a dimension) was a dependent variable on two independent variables.  And that dependent variable was processed via a pre-built algorithm.  So it would almost make sense to use the resultant dependent variable to enrich other data.

I’m being very abstract right now – here’s the structure of the data set:

Let’s walk through the fields:

  • Rank – this is a component based entirely on the sort chosen by the top (for this view it is by video views, not sure what those random 2 are, I just screencapped the site)
  • SB Score/Rank – this is some sort of ranking value applied to a user based on a propriety algorithm that takes a few variables into consideration
  • SB Score (as a letter grade) – the letter grade expression of the SB score
  • User – the name of the gamer channel
  • Subscribers – the # of channel subscribers
  • Video Views – the # of video views

As best as I can tell through reading the methodology – SB score/rank (the # and the alpha) are influenced in part from the subscribers and video views.  Which means putting these in the same view is really sort of silly.  You’re kind of at a disadvantage if you scatterplot subscribers vs. video views because the score is purportedly more accurate in terms of finding overall value/quality.

There’s also not enough information contained within the data set to amass any new insights on who is the best and why.  What you can do best with this data set is summarization, categorization, and displaying what I consider data set “vitals.”

So this is the approach that I took.  And more to that point, I wanted to make over a very specific chart style that I have seen Alberto Cairo employ a few times throughout my 6 week adventure in his MOOC.

That view: a bar chart sliced through with lines to help understand size of chunks a little bit better.  This guy:

So my energy was focused on that – which only happened after I did a few natural (in my mind) steps in summarizing the data, namely histograms:

Notice here that I’ve leveraged the axis values across all 3 charts (starting with SB grade and through to it’s sibling charts to minimize clutter).  I think this has decent effect, but I admit that the bars aren’t equal width across each bar chart.  That’s not pleasant.

My final two visualizations were to demonstrate magnitude and add more specifics in a visual manner to what was previously a giant text table.

The scatterplot helps to achieve this by displaying the 2 independent variables with the overall “SB grade” encoded on both color and size.  Note: for size I did powers of 2: 2^9, 2^8, 2^7…2^1.  This was a decent exponential effect to break up the sizing in a consistent manner.

The unit chart on the right is to help demonstrate not only the individual members, but display the elite A+ status and the terrible C+, D+, and D statuses.  The color palette used throughout is supposed to highlight these capstones – bright on the edges and random neutrals between.

This is aptly named an exploration because I firmly believe the resultant visualization was built to broadly pluck away at the different channels and get intrigued by the “details.”  In a more real world I would be out hunting for additional data to tag this back to – money, endorsements, average video length, number of videos uploaded, subject matter area, type of ads utilized by the user.  All of these appended to this basic metric aimed at measuring a user’s “influence” would lead down the path of a true analysis.

Makeover Monday Week 9 – Andy’s AMEX

So I started my dream job at the beginning of February.  This means I’ve been spending the month adjusting and tweaking my personal schedule and working on bringing back good habits.  In particular – I’ve missed out on doing daily workouts and consistently blogging about data viz.  Fortunately I’ve been keeping up with the practice component (Makeover Monday, Hackathon, Workout Wednesday), but I wholeheartedly believe in the holistic approach of sharing the thought process behind the viz.  (TL;DR – this was my paragraph of empty excuses)

Moving on then- the thought process behind the makeover.  And what’s even more interesting perhaps is that I can almost post-viz take some of the thoughts that Andy had regarding this week’s visualizations and provide my context.

Based on the original visualization I had an inkling that there wasn’t going to be a ton of data funneling in.  Being an individual who tracks all expenses and has seen them visually represented, I felt like food should represent a larger proportion of expenses.

Andy’s AMEX ’16

For reference, here’s a wonderful donut chart that Mint.com provided me on my top 3 most used credit cards.  I funnel everything I can through credit cards, and food in general takes up a huge portion of spend.

Ann’s ’16 Credit Card Spending

Both of these visualizations leave something to be desired.  I like Andy’s original AMEX one better than the donut I got, but they are both very distilled.  Andy spent a lot on transportation and travel, and apparently I spent a lot on shopping and education.

Getting REALLY specific about the data – there were 110 records (FYI my donut is 477, 209 is food/dining).  Plotting the data quickly over time, there were large gaps of time with no purchases.

Armed with this, I decided to take an approach of piggybacking off the predefined categories to see if throughout time Andy typically has one category that gets a lot of spend, or to see if the spend trends are lumped together.

More to that point, I wanted to show the way the data was dispersed in a daily fashion… so I went down this path.  The largest transaction for each day plotted (using the category on color, amount on size) across the 12 months.  I actually really like this view because I can clearly see the large vehicle purchase in December and you get a better feel for how spread out the card’s utilization was.  (I am guessing my lack of axis label on the day of the month is jarring.)

Also because I hate color legends, this meant I needed to introduce the idea of a color legend via data points elsewhere and led to the first view:

So… I kind of got really interested in utilization frequency and wanted to take it further.  So the next step was to make a barcode chart.  Very similar in concept to what the “top daily spend” is showing, but not limiting the data to only the top daily in this case.

Insights gained here – I get this feeling that Andy may only (or mostly) use his AMEX for meals where he’s out traveling.  Hovering over the points would add more insight to the transaction values.  More than that, we get a feel for what this card is generally swiped for: the 3 categories at the bottom (and FYI I ranked these by sum dollars spent).

Finally – bundling it together in a palatable format – what were the headline transactions for the year?  I wanted to do monthly and have categories, but there wasn’t enough data.  So I opted to go transaction level and keep it top 5 each quarter.  I think there’s novelty here in terms of presentation, but also value in quick rough comparisons of values over each quarter.

And this rounds out the end of the analysis.  Most of the transactions here are centered around travel.  My brain is not sanitized enough to say what you can infer – I have too much generalized knowledge of how Andy’s profession could explain these findings to present from a pure lack of knowledge standpoint.  (TL;DR – I know that Andy travels for his job, was surprised #data16 wasn’t an obvious point within the data set)

So the thought process in general behind the path I took this: I wanted to explore how often Andy spends money in certain categories.  I was intrigued by frequency of usage to see if it could eventually point back to provide the data creator (the guy who bought stuff) some additional aha! moments.

To be more honest – I actually think this is something that I would want for myself.  I in particular would love to plot my Amazon.com transactions and see how that changes throughout the year.  Both in barcode for frequency (imagining Black Friday is heavy) and then to see if I’m utilizing their services any differently.  (I have this feeling that grocery type purchases are on the climb).

Oh – and in terms of asking about colors and fonts: I did go Andy’s blog for inspiration.  I wanted to do a red/blue motif based on the blog, but needed more colors.  So I think I googled “blue color palette” and ended up with this cute starting palette that evolved into having pops of orange and yellow.  Font: I left this to something minimal that I thought Andy would be okay with (Arial Narrow) that would also bode well across all platforms.

Makeover Monday Week 8 – Potatoes in the EU

I’ll say this first – I don’t eat potatoes.  Although potatoes are super tasty, I refuse to have them as part of my diet.  So I was less than thrilled about approaching a week that was pure potato (especially coming off the joy of Valentine’s Day).  Nonetheless – it presented itself with a perfect opportunity for growth and skill testing.  Essentially, if I could make a viz I loved about a vegetable I hate – that would speak to my ability to interpret varying data sets and build out displays.

I’m very pleased with the end results.  I think it has a very Stephen Few-esque approach.  Several small multiples with high and low denoted, color playing throughout as a dual encoder.  And there’s even visual interest in how the data was sorted for data shape.

So how did I arrive there?  It started with the bar chart of annual yield.  I had an idea on color scheme and knew that I wanted to make it more than gray.

This gave perfect opportunity to highlight the minimum and maximum yields.  To see what years different countries production was affected by things like weather and climate.  It’s actually very interesting to see that not too many of the dark bars (max value) are in more recent times.  Seems like agricultural innovation is keeping pace with climate issues.

After that I was hooked on this idea of sets of 3.  So I knew I wanted to replicate a small multiple in a different way using the same sort order.  That’s where Total Yield came in.  I’ve been pondering this one in the shower on the legitimacy of adding up annual ratios for an overall yield.  My brain says it’s fine because the size of the country doesn’t change.  But my vulnerable brain part says that someone may take issue with it.  I’d love for a potato farming expert to come along and tell me if that’s a silly thing to add up.  I see the value in doing a straight total comparison of the years.  Because although there’s fluctuation in the yield annually, we have a normalized way to show how much each countries produces irrespective of total land size.

Next was the dot plot of the current year.  This actually started out its life as a KPI indicator of up or down from previous, but it was too much for the visual.  I felt the idea of the dot plot of current year would do more justice to “right now” understanding.  Especially because you can do some additional visual comparison to its flanks and see more insight.

And then rinse/repeat for the right side.  This is really where things get super interesting.  The amount of variability in pricing for each country, both by average and current year.  Also – 2013 was a great year for potatoes.

The Flow of Human Migration

Today I decided to take a bit of a detour while working on a potential project for #VizForSocialGood.  I was focused on a data set provided by UNICEF that showed the number of migrants from different areas/regions/countries to destination regions/countries.  I’m pretty sure it is the direct companion to a chord diagram that UNICEF published as part of their Uprooted report.

As I was working through the data, I wanted to take it and start at the same place.  Focus on migration globally and then narrow the focus in on children affected by migration.

Needless to say – I got side tracked.  I started by wanting to make paths on maps showing the movement of migrants.  I haven’t really done this very often, so I figured this would be a great data set to play with.  Once I set that up, it quickly divulged into something else.

I wasn’t satisfied with the density of the data.  The clarity of how it was displayed wasn’t there for me.  So I decided to take an abstract take on the same concept.  As if by fate I had received Chart Chooser cards in the mail earlier and Josh and I were reviewing them.  We were having a conversation about the various uses of each chart and brainstorming on how it could be incorporated into our next Tableau user group (I really do eat, drink, and breathe this stuff).

Anyway – one of the charts we were talking about was the sankey diagram.  So it was already on my mind and I’d seen it accomplished multiple times in Tableau.  It was time to dive in and see how this abstraction would apply to the geospatial.

I started with Chris Love’s basic tutorial of how to set up a sankey.  It’s a really straightforward read that explains all the concepts required to make this work.  Here’s the quick how-to in my paraphrased words.

  1. Duplicate your data via a Union, identify the original and the copy (Which is great because I had already done this for the pathing)  As I understand it from Chris’s write-up this let’s us ‘stretch out’ the data so to speak.
  2. Once the data is stretched out, it’s filled in by manipulating the binning feature in Tableau.  My interpretation would be that the bins ‘kind of’ act like dimensions (labeled out by individual integers).  This becomes useful in creating individual points that eventually turn into the line (curve).
  3. Next there are ranking functions made to determine the starting and end points of the curves.
  4. Finally the curve is built using a mathematical function called a sigmoid function.  This is basically an asymptotic function that goes from -1 to 1 and has a middle area with a slope of ~1.
  5. After the curve is developed, the points are plotted.  This is where the ranking is set up to determine the leftmost and rightmost points.  Chris’s original specifications had the ranking straightforward for each of the dimensions.  My final viz is a riff on this.
  6. The last steps are to switch the chart to a line chart and then build out the width (size) of the line based on the measure you used in the ranking (percent of total) calculation.

So I did all those steps and ended up with exactly what was described – a sankey diagram.  A brilliant one too, I could quickly switch the origin dimension to different levels (major area, region, country) and do similar work on the destination side.  This is what ultimately led me to the final viz I made.

So while adjusting the table calculations, I came to one view that I really enjoyed.  The ranking pretty much “broke” for the initial starting point (everything was at 1), but the destination was right.  What this did for the viz was take everything from a single point and then create roots outward.  Initial setup had this going from left to right – but it was quite obvious that it looked like tree roots.  So I flipped it all.

I’ll admit – this is mostly a fun data shaping/vizzing exercise.  You can definitely gain insights through the way it is deployed (take a look at Latin America & Caribbean).

After the creation of the curvy (onion shape), it was a “what to add next” free for all.  I had wrestled with the names of the destination countries to try and get something reasonable, but couldn’t figure out how to display them in proximity with the lines.  No matter – the idea of a word cloud seemed kind of interesting.  You’d get the same concept of the different chord sizes passed on again and see a ton of data on where people are migrating.  This also led to some natural interactivity of clicking on a country code to see its corresponding chords above.

Finally to add more visual context a simple breakdown of the major regions origin to destinations.  To tell the story a bit further.  The story points for me: most migrants move within their same region, except for Latin America/Caribbean.

Makeover Monday 2017 – Week 4 New Zealand Tourism

This week’s Makeover was addressing Domestic and International tourism trend in New Zealand.  No commentary provided with the data set, the original was just 2 charts left to the user to interpret.  See Eva’s tweet for the originals:

Going back to basics this week with what I like and dislike about it:

  • Titles are clear, bar chart isn’t too busy (like)
  • Not too many grid lines (like)
  • It’s easy to see the shape of the data and seasonality (like)
  • The scales are different between International & Domestic (dislike)
  • 3 years for easy comparison (like)
  • Eva chose this to promote her home country (like)

I think this was a good data set for week 4.  No data story to rewrite, special attention was made by Eva to mitigate data misinterpretation, and she added on a bonus of geospatial data for New Zealand.

My process really began with the geospatial part.  I haven’t yet had a chance to work with geospatial particulars developed and appended to data sets.  My experience in this has been limited to using Tableau’s functionality to manually add in latitude and longitude for unclear/missing/invalid data points.

So as I got started, I had no idea how to use the data.  There were a few fields that certainly pointed me in the right direction.  The first was “Point Order.”  Immediately I figured that needed to be used on “path” to determine where each data point fell.  That got me to this really cute outlined version of NZ (which looks like an upside down boot):

So I knew something additional needed to be done to get to a filled map.  That’s when I discovered the “PolygonNumber” field.  Throwing that onto detail, changing my marks to polygon and voila – New Zealand.  Here’s a Google image result for a comparison:

Eva did a great job trying to explain how NZ is broken up in terms of regions/territories/areas, but I have to admit I got a little lost.  I think what’s clear from the two pictures is I took the most granular approach to dissecting the country.

I’m super thrilled that I got this hands on opportunity to implement.  Geospatial is one of those areas of analytics that everyone wants to go and by including this – I feel much more equipped for challenges in the future.

Next up was the top viz: I’ve been wanting to try out a barbell/DNA chart for a long time.  I’ve made them in the past, but nothing that’s landed in a final viz.  I felt like there was an opportunity to try this out with the data set based on the original charts.  I quickly threw that together (using Andy Kriebel’s video tutorial) and really enjoyed the pattern that emerged.

The shape of the data really is what kicked off the path that the final viz ended up taking on.  I liked the stratification of domestic vs. international and wanted to carry that throughout.  This is also where I chose a colors.

The bottom left chart started out it’s life as a slope chart.  I originally did the first data point vs. the last data point (January 2008 vs. April 2016) for both of the types of tourism.  It came out to be VERY misleading – international had plummeted.  When I switched over to an annual aggregation, the story was much different.

International NZ tourism is failing!
Things look less scary and International is improving!

Good lesson in making sure to holistically look at the data.  Not to go super macro and get it wrong.  Find the right level of aggregation that keeps the message intact.

The last viz was really taking the geospatial component and adding in the tourism part.  I am on a small multiples kick and loved the novelty of having NZ on there more than once.  Knowing that I could repeat the colors again by doing a dual axis map got me sold.

All that was left was to add in interactivity.  Interactivity that originally was based on the barbell and line chart for the maps, but wasn’t quite clear.  I HATE filter drop downs for something that is going to be a static presentation (Twitter picture), so I wanted to come up with a way to give the user a filter option for the maps (because the shading does change over time), but have it be less tied to the static companion vizzes.  This is where I decided to make a nice filter sheet of the years and drop a nice diverging color gradient to add a little more beauty.  I’m really pleased with how that turned out.

My last little cute moment is the data sourcing.  The URLs are gigantic and cluttered the viz.  So instead I made a basic sheet with URL actions to quickly get to both data sets.

A fun week and one that I topped off by spelling Tourism wrong in the initial Tweet (haha).  Have to keep things fun and not super serious.

Full dashboard here.