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.

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”).

Makeover Monday 2017 – Week 3 Trump Tweets

**Update (1/20/17) : The original data set had a date formatting snafu resulting in 1307 tweets at the 12:00-12:59 PM (UTC time) hour to be displayed as 00:00-00:59 (aka 12 AM hour).  This affected 4.3% of the original data set visualization and has been corrected.  I have also added a footnote denoting the visualization is in EST.  This affects the shape of the data in both the 4 AM – 8 AM and 4 PM – 8 PM sections.

Rolling right along into week 3’s Makeover Monday.  The data set this week: Donald Trump’s tweets.  The original Buzzfeed viz and article accompanying this analyzed Trump’s retweet activity since his announcement of running for president.  The final viz ended up being what I would best describe as bubble charts of the top users he retweeted during this time:

What’s interesting is that the actual article goes into significant depth on how their team systematically reviewed the tweets.  It’a a bummer that the additional analysis done couldn’t be synthesized into visual form.

My take on the makeover this week was driven completely by the underlying data available.  The TDE provided had the following fields:

Two things stuck out to me with the data.  First: the username being retweeted wasn’t included; second: the entire tweet text was included.  Having full text available just screams for some sort of text analysis.  I got committed at that point to doing something with the text.

My initial idea was to do some sort of sentiment analysis.  Recently I had installed both R-Studio and Python on my PC to try integration with Tableau.  I’d had success with R-Studio (mind you after watching a brief YouTube video), but I hadn’t gotten Python to cooperate (my effort in assisting in this cooperation = 2 out of 10).  I figured since I had both available maybe I should make an attempt.  After marinating on the concept I didn’t feel comfortable adding more sentiment analysis to the fire of American politics.  (On a personal note: I have been politically checked out since the early primaries.)

So instead of doing sentiment analysis, I decided to turn the data more into text mining for mentions and hashtags.  I had done some fiddling with the time component and was digging how the cycle plot/horizon chart were playing out visually.  So it seemed natural to continue on a progression of getting more details out of the bars and times of day.

Note on the time: time is graciously parsed into correct format with the data.  In looking at the original time, I am under the impression it was represented in GMT (+0000).  To adjust for this, I added -5 hours to all of the parsed dates to put it in EST aka Trump time.

So back to text mining.  Post #data16 conference, a colleague of mine was recounting how to use regex to scrub through text.  I walked away from his talk thinking I need to use that next time I have the opportunity.  And what I love about it: NATIVE FUNCTION TO TABLEAU!!  So this was making me sing.  Now I don’t know a ton about regex (lots of notation I have yet to memorize), so I decided to quickly google my way to getting the user handles and hashtags.  These handy results really made this analysis zip along: regexr & regex+twitter.

Everything else came to life pretty quickly.  I knew I wanted to include at least one or two tweets to read through, but I wanted to keep it curated.  I think this was accomplished well and I spent a good deal of time trying out different time combinations just to see what would bubble to the surface.

A final note on aesthetics this week: I’m reading Alberto Cairo’s The Functional Art, and as I mentioned in an earlier post, I’m also participating in his MOOC that starts tomorrow.  I am only 4 chapters in, but Alberto has me taking a few things to heart.  I don’t think it is by coincidence that I decided to push the beauty side of things.  I always strive for elegance, but I strive for it through white space and keeping that “data ink ratio” at a certain point.  But I’m not blind to the different visualizations out there that attract people.  So for once I used a non-white background (yay!).  And I also went for a font that’s well outside of the look of my usual vizzing font.

More than focusing on aesthetics, is of course the function of the viz.  I tried to spend more time thinking about the audience and what they were going to “get” out of it.  I hope that the final product is less of a “visual aid” to my analysis and more of an interactive tool to explore the tweets of the soon to be President.

Full viz available on my Tableau public page.

#DataResolutions – More than a hashtag

This gem of a blog post appeared on Tableau Public and within my twitter feed earlier this week asking what my #DataResolutions are.  Here was my lofty response:

 


Sound like a ton of goals and setting myself up for failure?  Think again.  At the heart of most of my work with data visualization are 2 concepts: growth and community.  I’ve had the amazing opportunity to co-lead and grow the Phoenix Tableau user group over the past 5+ months.  And one thing I’ve learned along the way: to be a good leader you have to show up.  Regardless of skill level, technical background, formal education, we’re all bound together by our passion for data visualization and data analytics.

To ensure that I communicate my passion, I feel that it’s critical to demonstrate it.  It grows me as a person and stretches me outside of my comfort zone to an extreme.  And it opens up opportunities and doors for me to grow in ways I didn’t know existed.  A great example of this is enrolling in Alberto Cairo and Healther Krause’s MOOC Data Exploration and Storytelling: Finding Stories in Data with Exploratory Analysis and Visualization.  I see drama and story telling as a development area for me personally.  Quite often I think I get very wrapped up in the development of data stories that the final product is a single component being used as my own visual aid.  I’d like the learn how to communicate the entire process within a visualization and guide a reader through.  I also want to be surrounded by 4k peers who have their own passion and opinions.

Moving on to collaborations.  There are 2 collaborations I mentioned above, one surrounding data+women and the other is data mashup.  My intention behind developing out these is to once again grow out of my comfort zone.  Data Mashup is also a great way for me to enforce accountability to Makeover Monday and to develop out my visualization interpretation skills.  The data+women project is still in an incubation phase, but my goal there is to spread some social good.  In our very cerebral world, sometimes it takes a jolt from someone new to be used as fuel for validation and action.  I’m hoping to create some of this magic and get some of the goodness of it from others.

More to come, but one thing is for sure: I can’t fail if I don’t write down what I want to achieve.  The same is true for achievement, unless it’s written down, how can I measure?

Makeover Monday 2017 – Week 2

It’s time for Makeover Monday – Week 2.  This week’s data set was the quarterly sales (by units) of Apple iPhones for the past 10ish years.  The original article accompanying the data indicated that the golden years of Apple may be over.

So let me start by saying – I broke the rules (or rather, the guidelines).  Makeover Monday guidelines indicate that the goal is to improve upon the original visualization and stick to the original data fields.  I may have overlooked that guideline this week in favor of adding a little more context.

When I first approached the data set and dropped it into Tableau, the first thing I immediately noticed was that Q4 always has a dip compared to the other quarters of the year.

This view contradicted all of my existing knowledge of how iPhone releases work.  Typically every year Apple holds a conference around the middle/end of September announcing the “new” iPhone.  That can either be the gap increase (off year, aka the S) or the new generation.  It lines up such that pre-sales and sales come in the weeks shortly following.  And in addition to that I would suspect that sales would stay heightened throughout the holiday season.

This is where I immediately went back to the data to challenge it and I noticed that Apple defines its fiscal year differently.  Specifically October to December (of the previous year) counts as Q1 of the current year.  Essentially Q1 of 2017 is actually 10/1/16 to 12/31/16.  Meaning that in the normalized world thinking about quarters, everything should be adjusted.

Now I was starting to feel much better about how things were looking.  It aligned with my real world expectations.

I still couldn’t help but feel that a significant portion of the story was missing.  In my mind it wasn’t fair to only look at iPhone sales over time without understanding more data points of the smartphone market.  I narrowed it down to overall sales of smartphones and number of smartphone users.  The idea I had was this: have we reached a point where the number of smartphone users is now a majority?  Essentially the Adoption Curve came to my mind – maybe we’ve hit that sweet spot where the Late Majority is now getting in on smartphones.

To validate the theory and keep things simple, I did quick searches for data sets I could bring into the view.  As if through serendipity, the two additional sources I stumbled upon came from the same as the original (statistica.com).  I went ahead and added them into my data set and got to work.

My initial idea was this: line plot of iPhone sales vs. overall smartphone sales.  See if directionality was the same.  Place a smaller graph of smartphone users to the side (mainly because it was US only, couldn’t find a free global data set).  And the last viz was going to be a combination of the 3 showing basic “growth” change.  That in my mind would in a very basic way display an answer to my questioning.

I went through a couple of iterations and finally landed on the view below as my final.

I think it sums up the thought process and answers the question I originally asked myself when I approached the data set.  And hopefully I can be pardoned (if even necessary) since the accompanying data added in merely enhanced information at hand and kept with the simplicity of data points available (units and time).

Makeover Monday 2017 – Week 1

It’s officially 2017 – the start of a new year.  As such, this is a great time for anyone in the Tableau universe to make a fresh commitment to participate in the community challenge known as Makeover Monday.

As I jump into this challenge, I’ve made the conscious decision to start with the things I already like doing and to add on each time.  This to me is the way that I’ll be able to stay actively involved and enthusiastic.  Essentially: keep it simple.

For this week’s data set it was obvious that something of a comparative nature needed to be applied.  I started off with a basic dot plot and went from there.

What I ended up with: a slope chart with the slope representing the delta in rank of income by gender, the size of the line representing the annual monetary difference in income, and 3 colors representing categorized multipliers on the wage gap.

I wanted this to be for a phone, so I held to the idea of a single viz.  Interactivity is really limited to tooltips, most other nuance comes from the presentation of the visualization itself.

And I pushed myself to add a little journalistic flare this week.  Not really my style, but I figured I would see where it took me.

How do you add value through data analytics?

I recently read this article that really ignited a lot of thoughts that often swirl around in my mind.  If you were to ask me what my drive is, it’s making data-informed, data-driven decisions.  My mechanism for this is through data visualization.  More broadly than that, it is communicating complex ideas in a visual manner.  Often when you take an idea and paint it into a picture people can connect more deeply to it and it becomes the catalyst for change.

All that being said – I’ve encountered a sobering problem.  Those on the more “analytical” side of the industry sometimes fail to see the value in the communication aspect of data analytics.  They’ve become mired down by the concept that knowing statistical programming languages, database theory, and structured query language are the most important aspects of the process.  While I don’t discount the significance of these tools (and the ability to utilize them correctly), I can’t be completely on board with it.

We’ve all sat in a meeting that is born out of one idea: how do we get better.  We don’t get better by writing the most clever and efficient SQL query.  We get better by talking through and really understanding what it IS we’re trying to measure.  When we say X what do we mean?  How do we define X.  Defining X is the hard part – pulling it out of the database, not as difficult.  If you can get really good at definitions, it becomes intuitive when you start trying to incorporate it into your business initiatives.

As we continue to evolve in the business world, I highly encourage those from both ends of the spectrum to try and meet somewhere in the middle.  We have an unbelievable amount of technical tools at our disposal, yet quite often you step into a business who is still trying to figure out HOW to measure the most basic of metrics.  Let’s stop and consider how this happened and work on achieving excellence and improvement through the marriage of business and technical acumen – with artistry and creativity thrown in there for good measure.

The Float Plot

One of the more interesting aspects of data visualization is how new visualization methods are created.  There are several substantial charts, graphs, and plots out there that visualization artists typically rely on.

As I’ve spent time reading more about data visualization, I started thinking about potential visualizations out there that could be added into the toolkit.  Here’s the first one that I’ve come up with: The Float Plot.

The idea behind the float plot is simple.  Plot one value that has some sort of range of good/acceptable/bad values and use color banding to display where it falls.  It works well with percentage values.

I’ve also made a version that incorporates peers.  Peers could be previous time period values or they could be less important categories.  The version with peers reminds me somewhat of a dot plot, but I particularly appreciate the difference in size to distinguish the important data point.

What’s also great about the Float Plot is that it doesn’t have to take up much space.  It looks great scaled short vertically or narrow horizontally.

Enjoy the visualization on my Tableau public profile here.