Jackson Two

Category: Data Visualization

  • Dynamic Quantile Map Coloring in Tableau Desktop

    Dynamic Quantile Map Coloring in Tableau Desktop

    Last week at Tableau’s customer conference (TC18) in New Orleans I had the pleasure of speaking in three different sessions, all extremely hands on in Tableau Desktop.  Two of the sessions were focused exclusively on tips and tricks (to make you smarter and faster), so I wanted to take the time to slow down and share with you the how of my favorite mapping tip.  And that tip just so happens to be: how to create dynamic coloring based on quantiles for maps.

    First, a refresher on what quantiles are.  Quantiles are points that you can make within a data distribution to evenly cut it into equal intervals.  The most popular of the quantiles is the quartile, which partitions out data into 0 to 25%, 25 to 50%, 50 to 75%, and 75 to 100%.  We see quartiles all the time with boxplots and it’s something we’re quite comfortable with.  The reason the quantile is valuable is that it lines up all the measurements from smallest to largest and buckets them into groups – so when you use something like color, it no longer represents the actual value of a measurement, but instead the bucket (quantile) that the measurement falls into.  These are particularly useful when measurements are either widely dispersed or very tightly packed.

    Here’s my starting point example – this is a map showing the number of businesses per US county circa 2016.

    The range of number of businesses per county is quite large, going from 1 all the way to about 270k.  And since there is such a wide variety in my data set, it’s hard to understand more nuanced trends or truly answer the question “which areas in the US have more businesses?”

    A good first step would be to normalize by the population to create a per capita measurement.  Here’s the updated visualization – notice that while it’s improved, now I’m running into a new issue – all my color is concentrated around the middle.

    The trend or data story has changed, my eyes are now drawn toward the dark blue in Colorado and Wyoming, but I am still having a hard time drawing distinctions and giving direction on my question of “which areas in the US have the most businesses?”

    So as a final step I can adjust my measurements to percentiles and bucket them into quantiles.  Here’s the same normalized data set now turned into quartiles.

    I now have 4 distinct color buckets and a much richer data display to answer my question.  Furthermore I can make the legend dynamic (leading back to the title of this blog post) by using a parameter.  The process to make the quantiles dynamic involves 3 steps:

    1. Turn your original metric (the normalized per capita in my example) into a percentile by creating a “Percentile” Quick Table Calculation.  Save the percentile calculation for later use.

    2. Determine what quantiles you will allow (I chose between 4 and 10).  Create an integer parameter that matches your specification.

    3. Create a calculated field that will bucket your data into the desired quantile based on the parameter.

    You’ll notice that the Quantile Color calculation depends on the number of quantiles in your parameter and will need to be adjusted if you go above 10.

    Now you have all the pieces in place to make your dynamic quantile color legend.  Here’s a quick animation showing the progression from quartiles to deciles.

    The next time you have data where you’re using color to represent a measure (particularly on a map) and you’re not finding much value in the visual, consider creating static or dynamic quantiles.  You’ll be able to unearth hidden insights and help segment your data to make it easier to focus on the interesting parts.

    And if you’re interested in downloading the workbook you can find it here on my Tableau Public.

     

  • Without Water an Iron Viz feeder

    Without Water an Iron Viz feeder

    Jump directly to the viz

    At the time of writing it is 100°F outside my window in Arizona and climbing.  It’s also August and we’re right in the middle of feeder round 3 for Tableau Public’s Iron Viz contest.  Appropriately timed, the theme for this round is water.  So it’s only fitting that my submission for this round would take into consideration the mashup of these two and form my submission: Without Water, 2 decades of drought & damage in Arizona.

    The Genesis of the Idea

    I’ll start by saying that water is a very tricky topic.  The commonplace of it makes searching for data and a narrative direction challenging.  It’s necessary for sustaining life, so it seems to want to have a story tied directly to humankind – something closely related to water quality, water availability, loss of water – essentially something that impacts humans.  And because it’s so vital, there are actually several organizations and resources doing fantastic things to demonstrate the points above.  Unicef tracks drinking water and sanitation, Our World in Data has a lengthy section devoted to the topic, there’s the Flint Water Study, and the Deepwater Horizon oil spill.

    This realization around the plethora of amazing resources associated with water led me to the conclusion that I would have to get personal and share a story not broadly known.  So what could be more personal than the place I’ve called home for 14 years of my life: Arizona.

    Arizona is a very interesting state, it’s home to the Grand Canyon, several mountain ranges, and of course a significant portion of the Sonoran desert.  This means that in October it can be snowing in the mountains of Flagstaff and a stifling 90°F two hours south in Phoenix.  And, despite the desert, it needs water.  Particularly in the large uninhabited sections of the mountains covered with forests.  Getting to the punchline: since my time in Arizona, the state has been in a long sustained drought.  A drought that’s caused massive wildfires, extreme summer heat, and conversation thread that never steers far from the weather.

    Getting Started

    A quick google search led me to my first major resource: NOAA has a very easy to use data portal for climate data which includes: precipitation, various drought indices, and temperatures – all by month/state/and division.  This served as the initial data set along with the joining of climate division shapefiles maintained by NCEI.  Here’s the first chart I made showing the divisions by their drought index.  This uses the long term Palmer Drought Severity Index and any positive values (non-drought) are zeroed out to focus attention on deficit.

    My next major find was around wildfire data from the Federal Fire Occurrence website.  Knowing that fire is closely associated with drought, it seemed a natural progression to include.  Here’s an early iteration of total acres destroyed by year:

    It’s clear that after 2002 a new normal was established.  Every few years massive fires were taking place.

    And after the combination of these two data sets – the story started developing further – it was a time bound story of the last 20 years.

    Telling the Story

    I headed down the path of breaking out the most relevant drought headlines by year with the idea of creating 20 micro visualizations.  Several more data sources were added (including dust storms, heat related deaths, and water supply/demand).  An early iteration had them in a 4 x 5 grid:

    As the elements started to come together, it was time to share and seek feedback.  Luke Stanke was the first to see and gave me the idea of changing  from a static grid to a scrolling mobile story.  And that’s where things began to lock into place.  Several iterations later and with input from previous Iron Viz winner Curtis Harris – the collection of visualizations was starting to become more precisely tuned to the story.  White space became more defined and charts were sharpened.

    My final pass of feedback included outsourcing to Arizona friends (including Josh Jackson) to ask if it evoked the story we’re all experiencing and it’s what led to the ultimate change in titles from years to pseudo-headlines.

    Wrapping Up

    My one last lingering question: Mobile only or to include a desktop version?  The ultimate choice and deciding factor was to create a medium and version that was optimized for getting to the largest end audience – thus, mobile only.

    WITHOUT WATER

    And now that all leads to the final product.  A mobile only narrative data story highlighting the many facets of drought and it’s consequences for the state of Arizona.  Click on the image to view the interactive version on Tableau Public.

    click to view on Tableau Public

     

     

  • Building Out Your Analytics Brand

    Building Out Your Analytics Brand

    We all know the value of having a brand, whether it’s your personal brand or your organization’s brand, it’s the differentiator that distinguishes you from others.  It’s what makes us trust certain companies, emulate celebrities, and visit trendy places.  A great brand encompasses a wide array of important components – style, voice, preferences, value systems, and more.  So it’s likely not surprising that analytics within an organization or department should also have a brand.

    Having a brand for your analytics and specifically your analytical displays (yes, I mean dashboards) can have a significant impact on your audience.  Oftentimes when I work with clients it’s one of the first things I look for.  Is there a voice to what has been developed?  Is it cohesive?  More often than not, I find that the idea of branding gets deprioritized as other more pressing matters take center stage (I’m blaming you data gathering).  That lack of focus and emphasis also tends to leech out into other areas of analytics – likely there’s a struggle to answer questions or display the “right” data.  People just aren’t satisfied with what you give them.

    The upside?  Having these issues means there’s a wealth of opportunity and one that can begin with a conscious effort to develop a brand.  And an easy way to do this is to tackle the most superficial component: design and presentation.  As I use the word superficial here, don’t worry, I don’t mean the shallowest component from a value perspective, I purely mean it in the sense that it’s the outermost layer and easiest to see.

    Additional bonus of focusing on design?  You’ll start getting entrenched in the audience and naturally become more empathetic to their needs and desires.  And my favorite by-product: your audience starts to get empathetic toward you.  They start to understand constraints in a positive way and contribute in a more productive way.

    To get started, ask this one question:

    “HOW WILL YOU BE LOOKING AT THIS?”

    This question instigates every project I’m on where something is being developed.  How and where this is going to be consumed is a huge priority – knowing something is going to be put into a quarterly PDF report vs. consumed on a phone is going to result in widely different design and style choices.  You should ask this question first – and if nobody has the answer, offer guidance.  Ferreting out the answer to this question will undoubtedly build in some natural constraints.

    Let’s take a relatively easy scenario and assume that we’re building out self-service, interactive dashboards that will be consumed via web browser on a computer.  From that simple statement, we can derive a universe that would best fit the scenario.

    • View on computer browser: decent amount of real estate, can figure out optimum resolution
    • Self-service: people are expecting filters, flexibility in the data display
    • Interactive: not all the answers are immediately summarized, there’s an opportunity for supporting detail
    • Content repository: if everything’s in one place, we can leverage repetition and create continuity to drive the displays

    Given the bullet points, here’s a simple template you can start a conversation with:

    a simple dashboard template

    On it’s own it may not look like much, but it can easily become the focal point of a conversation.  It can even be handed out for folks to print out and manually sketch in their data elements.

    Here are some basics that the template helps to express:

    As you’re viewing do you find yourself visualizing different charts and information in each of the areas?  I know I am.  There’s something very settling about removing the requirement of thinking through a visual layout and instead filling in the empty boxes.

    Now let’s make an alternative version of the template:

    same template, different layout and purpose

    With a few simple adjustments, we’ve change the purpose of the dashboard.  Instead of summary and aggregated metrics, the purpose has become exploration.  And without adding in any charts or data elements, the audience can immediately get a sense of where majority focus should go.

    And beyond changing the purpose, we’ve kept the trust built up from the first template.  Title placement is expected, the amount of filters and their “where” is already imprinted on you.  Glancing back, you probably bypassed the header all together and went straight to the data section.  That’s the benefit of consistency.

    Let’s now do one final step and place the two templates side by side:

     

     

     

     

     

     

     

    Seeing the pair of dashboards next to each other gives you a sense of possibility.  You can start to imagine a whole portfolio of dashboards – one that evokes organized, thoughtful, clear, purposeful.  It’s established the brand and voice we were chasing.  It’s also given us a strong sense of what belongs and what doesn’t.

    If you find yourself struggling with adoption, stakeholder value, or direction – take a step back and focus on building a brand through design and presentation.  The time invested in this exercise is sure to yield positive results and necessary constraints – it will even help to sharpen existing analytical displays you may have already developed.

  • Building an Interactive Visual Resume using Tableau

    Building an Interactive Visual Resume using Tableau

    click to interact on Tableau Public

    In the age of the connected professional world it’s important to distinguish and differentiate yourself.  When it comes to the visual analytics space, a great way to do that is an interactive resume.  Building out a resume in Tableau and posting it on Tableau Public allows prospective employers to get firsthand insight into your skills and style – it also provides an opportunity for you to share your professional experience in a public format.

    Making an interactive resume in Tableau is relatively simple – what turns out to be more complex is how you decide to organize your design.  With so many skills, achievements, and facts competing for attention, it’s important for you to decide what’s most important.  How do you want your resume to be received?

    In making my own resume, my focus was on my professional proficiency across larger analytics domains, strength in specific analytics skills, and experience in different in industries.  I limited each of these components to my personal top 5, so that it is clear to the audience what areas hold the most interest for me (and I’m most skilled in).

    Additionally, I also wanted to spend a significant amount of real estate highlighting my community participation.  After plotting a gantt chart of my education and work experience, I realized that the last two years are jam packed with speaking engagements and activities that would be dwarfed on a traditional timeline.  To compensate for this, I decided to explode the last two years into its own timeline in the bottom dot plot.  This allowed for color encoding of significant milestones and additional detail on each event.

    The other two components of the resume serve importance as well.  I’ve chosen to demonstrate experience in terms of years (a traditional metric to demonstrate expertise) with the highest level of certification or professional attainment denoted along each bar.  And finally, including a traditional timeline of my education and work experience.  The “where” of my work experience is less important than the “what,” so significant detail was spent adding role responsibilities and accomplishments.

    Once you’ve decided how you want to draw attention to your resume, it’s time to build out the right data structure to support it.  To build out a gantt chart of different professional roles a simple table with the type of record, name of the role, start date, end date, company, flag for if it’s current role, and a few sentences of detail should suffice.

    This table structure also works well for the years of experience and community involvement sections.

    You may also want to make a separate table for the different skills or proficiencies that you want to highlight.  I chose to make a rigid structured table with dimensions for the rank of each result, ensuring I wouldn’t have to sort the data over each category (passion, expertise, industry) once I was in Tableau.

    Here’s the table:

    That’s it for data structure, leaving style (including chart choices) as the last piece of the puzzle.  Remember, this is going to be a representation of you in the digital domain, how do you want to be portrayed?  I am known for my clean, minimalist style, so I chose to keep the design in this voice.  Typical to my style, I purposely bubble up the most important information and display it in a visual format with supporting detail (often text) in the tooltip.  Each word and label is chosen with great care.  It’s not by mistake that the audience is seeing the name of my education (and not the institution) and the labels of each proficiency.  In a world where impressions must happen instantaneously, it’s critical to know what things should have a lasting impact.

    I also chose colors in a very specific manner, the bright teal is my default highlight color, drawing the eyes in to certain areas.  However, I’ve also chosen to use a much darker gray (near black) as an opposite highlight in the bottom section.  My goal with the dark “major milestones” is to entice the audience to interact and find out what major means.

    The final product from my perspective represents a polished, intentional design, where the data-ink ratio has been maximized and the heart of my professional ambitions and goals are most prominent.

    Now that you’ve got the tools – go forth and build a resume.  I’m curious to know what choices you will make to focus attention and how you’ll present yourself from a styling perspective.  Will it be colorful and less serious, will you focus on your employment history or skills?  Much like other visualizations whatever choices you make, ensure they are intentional.

  • Blending Visualizations of Different Sizes

    Blending Visualizations of Different Sizes

    One of my favorite visualizations is the sparkline – I always appreciated how they are described by Edward Tufte “data-intense, design-simple, word-sized graphics.”  Meaning the chart gets right to the point: conveying a high amount of information without sacrificing real estate.  I’ve found this approach works really well when trying to convey different levels of information (detail and summary) or perhaps different metrics around a common topic.

    I recently built out a Report Card for Human Resources that aims to do just that.  Use a cohort of visualizations to communicate an overall subject area and then repeat the concept to combine 4 subject/metric areas.  Take a look at the final dashboard below.

    click to view on Tableau Public

    The dashboard covers one broad topic – Human Resources.  Within it there are 4 sub-topics: number of employees, key demographics, salary information, and tenure.  As your eyes scan through the dashboard, they likely stopped at the large call outs in each box.  You’ve got your at-a-glance metrics that start to bring awareness to the topic.

    But the magic of this dashboard lies in the collection of charts surrounding the call outs.  Context has been added to surround each metric.  Let’s go through each quadrant and unpack the business questions we may have.

    1. How many active employees do we have?
    2. How many new employees have we been hiring?
    3. How many employees are in each department?
    4. What’s the employee to leadership ratio?

    The first visualization (1) is likely the one a member of management would want.  It’s the soundbite and tidbit of information they’re looking for.  But once that question is asked and answered, the rest of the charts become important to knowing the health of that number.  If it’s a growing company, the conversation could unfold into detail found in chart 2 – “okay we’re at 1500 employees, what’s our hiring trend?”  The same concept could be repeated for the other charts – with chart 4 being useful for where there might be opportunity for restructuring, adding management, or checking up on employee satisfaction.

    The next quadrant focuses specificly on employee demographics.  And the inclusion of it after employee count is intentional.  It’s more contextual information building from the initial headcount number.

    1. Do we have gender equity?
    2. What is the gender distribution?
    3. How does the inclusion of education level affect our gender distribution?

    Again, we’re getting the first question answered quickly (1) – do we have gender equity?  Nope – we don’t.  So just how far off are we, that’s answered just to the right (2).  The second chart is still a bit summarized, we can see the percentages for each gender, but it’s so rolled up that we’d be pressed to figure out how or where the opportunity for improvement might be.  This is where the final chart (3) helps to fill in gaps.  With this particular organization, there could be knowledge that there’s gender disparity based on levels of education.  We don’t get the answers to all the questions we have, but we are starting to narrow down focus immensely.  We could go investigate a potentially obvious conclusion and try to substantiate it (this company hires more men without any college experience).

    The next quadrant introduces salary – a topic everyone cares about.

    1. What’s the average salary of one of our employees?
    2. Are we promoting our employees?  (A potential influence to #1)
    3. What’s the true distribution of salaries within our organization?

    The design pattern is obvious at this point – convey most important single number quickly, and then dive into context, drivers, and supporting detail.  I personally like the inclusion of the histogram with a boxplot, a simple way to apply statistics to an easily skewed metric.  Even in comparing the average number to the visual median, we can see that there are some top heavy salaries contributing to the number.  And what’s even more interesting about the inclusion of the histogram is the frequency of salaries around the $25k mark.  I would take away from this section the knowledge of $78k, but also the visual spread of how we arrive at that number.  The inclusion of (2) here serves mostly for a form of context.  Here it could be that the organization has an initiative to promote internally and thus goes hand-in-hand with salary changes.

    And finally our last section – focused closely on retention.

    1. What’s our average employee tenure?
    2. How much attrition/turnover do we have monthly?
    3. How much seniority is there in our staff?

    After this final quadrant, we’ve got a snapshot of what a typical employee looks like at this organization.  We know their likely salary, how long they’ve been with the company, some ideas on where they’re staffed, and a guess at gender.  We can also start to fill in some gaps around employee satisfaction – seems like there was some high turnover during the summer months.

    And let’s not forget – this dashboard can come more to life by the inclusion of a few action filters.  We’ve put down the groundwork of how we want to measure the health of our team, now it’s time to use these to drive deeper and more meaningful questions and analysis.

    I hope this helps to demonstrate how the inclusion of visualizations of varying sizes can be combined to tell a very rich and contextual data story – perfect for understanding a large subject area with contextual indicators and answers to trailing questions included.

  • The Shape of Shakespeare’s Sonnets | #IronViz Books & Literature

    The Shape of Shakespeare’s Sonnets | #IronViz Books & Literature

    Jump directly to the viz

    If it’s springtime that can only mean that it’s time to begin the feeder rounds for Tableau’s Iron Viz contest.  The kick-off global theme for the first feeder is books & literature, a massive topic with lots of room for interpretation.  So without further delay, I’m excited to share my submission: The Shape of Shakespeare’s Sonnets.

    The genesis of the idea

    The idea came after a rocky start and abandoned initial idea.  My initial idea was to approach the topic with a meta-analysis or focus on the overall topic (‘books’) and to avoid focusing on a single book.  I found a wonderful list of NYT non-fiction best sellers lists, but was uninspired after spending a significant amount of time consuming and prepping the data.  So I switched mid-stream and decided to keep the parameters of a meta-analysis, but change to a body of literature that a meta-analysis could be performed on.  I landed on Shakespeare’s Sonnets for several reasons:

    • Rigid structure – great for identifying patterns
    • 154 divides evenly for small multiples (11×14 grid)
    • Concepts of rhyme and sentiment could easily be analyzed
    • More passionate subject: themes of love, death, wanting, beauty, time
    • Open source text, should be easy to find
    • Focus on my strengths: data density, abstract design, minimalism
    Getting Started

    I wasn’t disappointed with my google search, it took me about 5 minutes to locate a fantastic CSV containing all of the Sonnets (and more) in a nice relational format.  There were some criteria necessary for the data set to be usable – namely each line of the sonnet needed to be a record.  After that point, I knew I could explode and reshape the data as necessary to get to a final analysis.

    Prepping & Analyzing the Data

    The strong structuring of the sonnets meant that counting things like number of characters and number of words would yield interesting results.  And that was the first data preparation moment.  Using Alteryx I expanded out line into columns for individual words.  Those were then transposed back into rows and affixed to the original data set.  Why?  This would allow for quick character counting in Tableau, repeated dimensions (like line, sonnet number), and dimensions for the word number in each line.

    I also extracted out all the unique words, counted their frequency, and exported them to a CSV for sentiment analysis.  Sentiment analysis is a way to score words/phrases/text to determine the intention/sentiment/attitude of the words.  For the sake of this analysis, I chose to go with a negative/positive scoring system.  Using Python and the nltk package, each word’s score was processed (with VADER).  VADER is optimized for social media, but I found the results fit well with the words within the sonnets.

    The same process was completed for each sonnet line to get a more aggregated/overall sentiment score.  Again, Alteryx was the key to extracting the data in the format I needed to quickly run it through a quick Python script.

    Here’s the entire Alteryx workflow for the project:

    The major components
    • Start with original data set (poem_lines.csv)
      • filter to Sonnets
      • Text to column for line rows
      • Isolate words, aggregate and export to new CSV (sonnetwords.csv)
      • Isolate lines, export to new CSV (sonnetlines)
      • Join swordscore to transformed data set
      • Join slinescore to transformed data set
      • Export as XLSX for Tableau consumption (sonnets2.xlsx)
    Python snippet
    make sure you download nltk leixcons after importing; thanks to Brit Cava for code inspiration

    The Python code is heavily inspired by a blog post from Brit Cava in December 2016.  Blog posts like hers are critically important, they help enable others within the community do deeper analysis and build new skills.

    Bringing it all together

    Part of my vision was the provoke patterns, have a highly dense data display, and use an 11×14 grid.  My first iteration actually started with mini bar charts for number of characters in each word.  The visual this produced was what ultimately led to the path of including word sentiment.

    height = word length, bars are in word order

    This eventually changed to circles, which led to the progression of adding a bar to represent the word count of each individual line.  The size of the words at this point became somewhat of a disruption on the micro-scale, so sentiment was distilled down into 3 colors: negative, neutral, or positive.  The sentiment of the entire line instead has a gradient spectrum (same color endpoints for negative/positive).  Sentiment score for each word was reserved for a viz in tool tip – which provides inspiration for the name of the project.

    Sonnet 72, line 2

    Each component is easy to see and repeated in macro format at the bottom – it also gives the end user an easy way to read each Sonnet from start to finish.

    designed to show the progression of abstraction

    And there you have it – a grand scale visualization showing the sentiment behind all 154 of Shakespeare’s Sonnets.  Spend some time reciting poetry, exploring the patterns, and finding the meaning behind this famous body of literature.

    Closing words: thank you to Luke Stanke for being a constant source of motivation, feedback, and friendship.  And to Josh Jackson for helping me battle through the creative process.

    The Shape of Shakespeare’s Sonnets

    click to interact at Tableau Public

     

     

     

  • Dying Out, Bee Colony Loss in US | #MakeoverMonday Week 18

    Dying Out, Bee Colony Loss in US | #MakeoverMonday Week 18

    Week 18 of Makeover Monday tackles the issue of the declining bee population in the United States.  Data was provided by BeeInformed and the re-visualization is in conjunction with Viz for Social Good.  Unfamiliar with a few of the terms – check out their websites to learn what Makeover Monday and Viz for Social Good are all about.

    The original visualization is a filled map showing the annual percentage of bee colony loss for the United States.  Each state (and DC) are filled with a gradient color from blue (low loss) to orange (high loss).  The accompanying data set for the makeover included historical data back to 2010/11.

    Original visualization | Bee Informed

    Looking at the data my goal was to capitalize on some of the same concepts presented in the original visualization, but add more analytical value by including the dimension of time.  The key component I was aiming to understand was that there’s annual colony loss, but how “bad” is the loss.  The critical “compared to what” question.

    My Requirements
    • Keep the map theme – good way to demonstrate data
    • Add in time dimension
    • Keep color as an indicator of performance (good/bad indicator) – clarify how color was used
    • Provide more context for audience
    • Switch to tile map for skill building
    • Key question: where are bees struggling to survive
    • Secondary question: which states (if any) have improved

    Building out the tile map and beginning to add the time series was pretty simple.  I downloaded the hexmap template provided by Matt Chambers.  I did a bit of tweaking to the file to change where Washington D.C. was located.  Original file has it off to the side, I decided to place it in-line with the continental US to clean up the final look.

    Well documented through the Tableau Community – the next step was to take the two data sources (bees + map) and blend them together.  Part of that process includes setting up the relationship between the two data sources and then adding them both to a single view:

    setting up the relationship between data sources
    visual cues – MM18 extract is primary data source, hexmap secondary

    To change to a line chart and start down the path of showing a metric (in our case annual bee colony loss) over time – a few minor tweaks:

    • Column/Row become discrete (why: so we can have continuous axes inside of our rows & columns)
    • Add on continuous fields for time & metric

    This to me was a big improvement over the original visualization (because of the addition of time).  But it still needs a bit of work to clearly explain where good and bad are.  This brought me back to a concept I worked on during Week 17 – using the background of a chart as an indicator of performance.

    forest land consumption

    In week 17 I looked at the annual consumption of carbon, forest land, and crop land by the top 10 world economies compared to the global footprint.  Background color indicates whether the country’s footprint is above/below the current global metric.  I particularly appreciate this view because you get the benefit of the aggregate and immediate feedback with the nice detail of trend.

    This led me down the path of ranking each of the states (plus DC) to determine which state had experienced the most colony loss between the years of the data (2010/11 and 2016/17).  You’d get a sense of where the biggest issues were and where hope is sprouting.

    To accomplish this I ended up using Alteryx to create a rank.  The big driver behind creating a rank pre-visualization was to replicate the same rank number across the years.  The background color for the final visualization is made by creating constant value bar charts for each year.  So having a constant number for each state based off of a calculation from 2010 vs. 2016 would be much easier to develop with.

    notice the bar chart marks card; Record ID is the rank

     

    Here’s my final Alteryx workflow.  Essentially I took the primary data set, split it up into 2010 and 2016, joined it back, calculated the difference between them, corrected for a few missing data points, sorted them from greatest decline in bee colony loss to smallest, applied a rank, joined back all the data, and then exported it as a .hyper file.

    definitely a quick & dirty workflow

    This workflow developed in less than 10 minutes eliminated the need for me to do at least one table calculation and brought me closer to my overall vision quickly and painlessly.

    Final touches were to be a little descriptive to eliminate the need for a color legend and to provide a first-time reader areas to focus on.  And picking the right color palette and title.  Color always leads my design – so I settled on the gold early on, but it took a few iterations to evoke the feeling of “dying out” from the color range.

    tones of brown to keep theme of loss, gold indicates more hope

    And here’s the final visualization again, with link to interactive version in Tableau Public.

    click to interact on Tableau Public
  • Workout Wednesday Week 17: Step, Jump, or Linear?

    Workout Wednesday Week 17: Step, Jump, or Linear?

    What better way to celebrate the release of step lines and jump lines in Tableau Desktop with a workout aimed at doing them the hard way?

    click to view on Tableau Public

    Using alternative line charts can be a great way to have more meaningful visual displays of not-so-continuous information.  Or continuous information where it may not be best to display the shortest distance between two points in a linear way (traditional line charts).

    Step line and jump line charts are most useful for something with few fluctuations in value, an expected value, or something that isn’t consistently measured.

    The workout this week is very straight forward – explore the different types of line charts (step lines, jump lines, and linear/normal lines).  Don’t use the new built in features of 2018.1 (beta or release, depending on when you’re reading) found by clicking on the Path shelf.  Instead use other functions or features to create the charts.

    The tricky parts about this week’s workout will be the step lines.  Pay special attention to the stop and start of the lines and where the tooltips display information.  You are not allowed to duplicate the data or create a “path ID” field.  Everything you do should be accomplished using a single connection of Superstore and no funny business.

    Tiny additional element of creating the ability to flip through the chart types.

    Requirements:

    • Dashboard size 1000 x 800
    • Displaying sales by month for each Category
    • Create a button that flips through each chart type
    • Match step line chart exactly, including tooltip, start/stop of lines, colors, labels
    • Match jump line chart exactly, including axes, labels, tooltips
    • Match normal line chart exactly, including axes, labels tooltips

    This week uses the superstore dataset.  You can get it here at data.world

    After you finish your workout, share on Twitter using the hashtag #WorkoutWednesday and tag @AnnUJackson, @LukeStanke, and @RodyZakovich.  (Tag @VizWizBI too – he would REALLY love to see your work!)

    Also, don’t forget to track your progress using this Workout Wednesday form.

  • Workout Wednesday 14 | Guest Post | Frequency Matrix

    Workout Wednesday 14 | Guest Post | Frequency Matrix

    Earlier in the month Luke Stanke asked if I would write a guest post and workout.  As someone who completed all 52 workouts in 2017, the answer was obviously YES!

    This week I thought I’d take heavy influence from a neat little chart made to accompany Makeover Monday (w36y2017) – the Frequency Matrix.

    I call it a Frequency Matrix, you can call it what you will – the intention is this: use color to represent the frequency (intensity) of two things.  So for this week you’ll be creating a Frequency Matrix showing the number of orders within pairs of sub-categories.

    click to view on Tableau Public

    Primary question of the visualization: Which sub-categories are often ordered together?
    Secondary question of the visualization: How much on average is spent per order for the sub-categories.
    Tertiary question: Which sub-category combination causes the most average spend per order?

    Requirements
    • Use sub-categories
    • Dashboard size is 1000 x 900; tiled; 1 sheet
    • Distinctly count the number of orders that have purchases from both sub-categories
    • Sort the categories from highest to lowest frequency
    • White out when the sub-category matches and include the number of orders
    • Calculate the average sales per order for each sub-category
    • Identify in the tooltip the highest average spend per sub-category (see Phones & Tables)
    • If it’s the highest average spend for both sub-categories, identify with a dot in the square
    • Match formatting & tooltips – special emphasis on tooltip verbiage

    This week uses the superstore dataset.  You can get it here at data.world

    After you finish your workout, share on Twitter using the hashtag #WorkoutWednesday and tag @AnnUJackson, @LukeStanke, and @RodyZakovich.  (Tag @VizWizBI too – he would REALLY love to see your work!)

    Also, don’t forget to track your progress using this Workout Wednesday form.

    Hints & Detail
    • You may not want to use the WDC
    • Purple is from hue circle
    • You’ll be using both LODs and Table Calculations
    • I won’t be offended if you change the order of the sub-category labels in the tooltips
    • Dot is ●
    • Have fun!
  • Who Gets an Olympic Medal | #MakeoverMonday Week 7

    Who Gets an Olympic Medal | #MakeoverMonday Week 7

    At the time of writing the 2018 Winter Olympic Games are in full force, so it seems only natural that the #MakeoverMonday topic for Week 7 of this year is record level results of Winter Games medal wins.

    I have to say that I was particularly excited to dive into this data set.  Here’s what a few rows of data look like:

    I always find with this level of data there are so many interesting things that can be done that it gets really hard to focus.  The trouble is that all of the rows are interesting, so as a creator I’m immediately drawn to organizing “all the data” and want put to it ALL on display.  And that’s where the first 20 minutes of my development were headed.

    I’d started with a concept of showing all the medals and more specifically showing the addition of new sports over time.   As I was building, the result was quite clearly going to be a giant poster form viz.  Not what I was going for.

    To move past that my mind shifted to female sports at the Winter Olympics.  And if you look through the data set you’ll see there are some interesting points.  Specifically that it took about 60 years for women to get to a similar number of events/medals as men.  (yellow = men, purple = women, gray = mixed)

    I spent some time stuck on this – thinking through how I could segment by different sports and to extract out some of the noise of the different years and come up with a slope chart.  Ultimately I found myself disappointed with all of these pursuits – so my thoughts shifted.

    So I switched gears and stumbled on this chart:

    Which as you look through it is REALLY interesting.  I had just watched the Opening Ceremonies and knew there were 91 delegations (countries) represented in 2018.  To know that in 2014 the number was probably similar, yet only 26 reached a podium seemed to be a sticking point in my mind.

    So – that led to a quick adventure over to http://www.olympic.org to add context to the number of countries represented at the games over the years.  They actually have really nice summary pages for each set of games that made gathering data simple.  Here’s a snapshot of 1980 – Lake Placid:

    Using the ribbon of information at the bottom I went about collecting and enriching the data set.  Because what was missing from our original #MakeoverMonday data was the NULLs.

    Sufficiently enriched I was able to come up with a calculation for the percentage of delegations medalling at each set of games.  Of course I suspected that this would not be close to 100%, only by virtue of knowing that we’ve got 91 delegations in 2018.  Here’s the chart:

    So – now the story is unfolding, but I wanted to take it a few steps further.  My main beef: I want to also see how many additional delegations are bringing athletes to the games.  Specifically at the first data point I’d think that it was a small number of countries because the game were new. Essentially the opportunity for medalling would perhaps be greater.  Hence settling on what ended up being my final submission for the week:

    click to view on Tableau Public

    What are you looking at?  Medals are clearly parsed out into Gold, Silver, and Bronze.  Each bar represents a Winter Games.  The width of the bar is the # of countries/delegations, the height of the bar is the % of countries who medalled in that respective color.  I concede in this that eliminating the dimensionality of medals may have made for a more consolidated view, but I selfishly wanted to use the different colors.

    Here’s the non-medalled version:

    Less abstracted, more analytically presented:

    Ultimately for the sake of the exercise I went with continuous bar sizing representing the number of delegations at each Winter Games.  And my “why” is because this isn’t often seen and within the confines of this visualization it would be a great usage.  Explaining this aloud should facilitate easy cognition.  The wider bars means more countries participating (reinforced by our general knowledge of the games).  And then the height of the bars can cleanly represent the percentage of those getting medals.  Plus – per usual – the tooltip divulges all this in well articulated detail.  (++ bars allow for chronology of time)

    I’m quite pleased with this one.  Maybe because I am the designer, but I was delighted with the final representation both from a visual perspective and an analytical presentation perspective.  There is a certain amount of salience in having both the bars gets larger over time (and repeating that 3 times) and the colors of the medals being represented within a single worksheet.