Posts by Ann Jackson

2019 Week 2: Order Sales Spread by Region

Happy New Year!  It’s time for my first Workout Wednesday of 2019 and I hope you’re ready for a challenge.  This week I’ve decided to take inspiration from a trick or two from past workouts and combine them with some recent work I’ve been doing.  To be more specific, one thing about Tableau that I like is that you can set independent axes for continuous measures if you’ve got headers on the same shelf (rows or columns).  But what if you wanted to have independent axes and headers aren’t on the same shelf?  The workout this week explores that idea and puts it to the test – I’ll be honest, the jury is still out for me on if this works well with the Superstore data set, but I can absolutely see value in this if we were to have a similar metric with extremely varied ranges.

In addition to exploring how to get over that obstacle, I also wanted to play around with date filtering.  This week you’ll be exposed to a user-friendly calendar that doubles as a filter OR highlighter.  I’ve found that it represents a great way for end users to freely pick a single date, multiple dates, date ranges, random individual dates – pretty much any combination that they would like.  Calendars are also familiar visualizations of dates, so I think it’s quite comforting – and additionally it serves as another layer of information – when there isn’t any data, there isn’t a square for the date.

 

click to view on Tableau Public

Requirements

  • Dashboard size: 1200 x 800, jitterplots must be one sheet, everything else is up to you
  • Create a jitterplot that shows sales by order ID (no need to worry if your jitter isn’t exactly the same as mine)
    • Ensure that each plot behaves as if it has an “independent axis” and spans the extent of data within each region
  • Create a calendar view that can be used as a filter or a highlighter
    • When used as a filter, chosen dates will filter the jitterplot
    • When used as a highlighter, chosen dates will change to a darker color in jitterplot
  • Create a footnote that is responsive to the date selection
  • Create average line & callout that are responsive to date selection
  • Match colors & tooltips please 🙂

Dataset

This week uses the superstore dataset for Tableau 2018.3.  You can get it here at data.world

Attribute

When you publish your solution on Tableau Public make sure to take the time and include a link to the original inspiration.

Week 52: Nobel Laureates, 1901 to Present

Congratulations for making it to the end of 2018 and the last workout of the year!  This week I’ve spiced things up a bit and decided to use a different data set.  The data you’ll be working with is a list of Nobel Prize Laureates from 1901 to 2018 (‘present’ at time of writing).  And I’ve chosen to use the data set to construct a timeline view.

This data set comes from a real life problem I solved recently – a way to work with multiple date columns to construct timelines.  You’ll be forced to work with the data set as-is, no reshaping the data.  And the goal is to display multiple dates as marks of different colors.  And true to form, you’ll also be constructing some custom labels, and be working on creating a drop-down that sorts both alphabetically and by dates.

I hope you’re intrigued!

Click to view interactive version on Tableau Public

Requirements

  • Dashboard size: 1000 x 1200; you choose # of sheets
  • Create a timeline showing birth date, prize date(s), death date or today
    • Death date will be a circle, today will be a gantt bar
    • Assume that each prize is awarded on December 10th of every year
  • Color the dots of the prize dates according to their category
  • Create a line that goes from birth date to death/today depending on the person
  • Construct a label that is beneath each person’s timeline – make sure it only shows up once
    • Include critical birth/death dates
  • Create reference lines for December 1901, the first year of the prize and today (which should be dynamic)
  • Create a legend that acts as a filter
  • Construct sorting for each of the following
    • Alphabetical
    • Birth date newest/oldest
    • Prize date newest/oldest
    • Death date newest/oldest
  • Match formatting & tooltips
    • I’m using Tableau Medium and Tableau Bold this week
    • Colors are from Superfishel Stone

Dataset

This week uses a special Nobel Laureates data set modified from Kaggle (to include more birthdays, and default birth dates to January 1 of the year if unknown).  You can get it here at data.world

Attribute

When you publish your solution on Tableau Public make sure to take the time and include a link to the original inspiration.

Share

After you finish your workout, share on Twitter using the hashtag #WorkoutWednesday2018 and tag @AnnUJackson@LukeStanke@lorna_eden@curtisharris_@RodyZakovich, and @VizWizBI!

Track your progress

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

Week 49: Where Do Sub-Categories Succeed?

Last week I had the honor of attending Tapestry Conference in Miami.  While I was there Jon Schwabish gave a quick 6 minute talk that connected every chart to every other chart.  This along with some of Elijah Meek’s keynote mentioning that data viz is getting more custom and funky got me curious about some neglected chart types.  Combine this with a recent interest in how clustering works in Tableau and you’ve arrived at the genesis for this week’s challenge.  Your goal is to create a Parallel Coordinates chart (click the link if you’re not sure what it is).

This chart is perfect for multivariate analysis and seeing relationships among more than 2 measures (in this case 3).  It can also be useful for finding commonalities among things.  Traditionally I think most people may shy away from implementing this in Tableau because quite often different measures have different scales, so as part of the challenge, you’ll have to figure out how to overcome that obstacle to present a parallel coordinate chart with 3 measures of different magnitudes.

Also to help reinforce some recent challenges using table calculations – you are not allowed to use LODs and must only use table calculations and regular calcs.

Click to view on Tableau Public

Requirements

  • Dashboard size: 1200 x 850; you choose # of sheets
  • Create a parallel coordinate chart that shows Sales, Profit Ratio, and # Customers (CountD Customer Name) per sub-category
  • Do not use LODs, use table calculations (and normal calculated fields)
  • Each sub-category should be positioned based on its value, but all measures should be on one sheet
  • Ensure there is a dark gray vertical line for each measure
  • Label the top and bottom of each vertical line with the measure name and respective minimum or maximum
  • Color the lines based on which measure the sub-categories have the highest value in
  • Colors are based off of the Viridis color palette, which I encourage you to paste into your .TPS file
  • Create a color legend that has a hover action based on the newly defined colors (high sales, high profit ratio, high customers)
  • Match formatting & tooltips

Dataset

This week uses the superstore dataset for Tableau 2018.3.  You can get it here at data.world

Attribute

When you publish your solution on Tableau Public make sure to take the time and include a link to the original inspiration.

Share

After you finish your workout, share on Twitter using the hashtag #WorkoutWednesday2018 and tag @AnnUJackson@LukeStanke@lorna_eden@curtisharris_@RodyZakovich, and @VizWizBI!

Track your progress

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

Week 44: Ready, Set: Let’s Drill Down on Sales!

At time of writing Tableau 2018.3 was just released.  With it comes some brand new features, including the one that’s the focus of this workout: set actions.  Sets have long been a feature in Tableau and are responsible for creating dashboard actions, but set actions are something completely new.  Set actions allow you to interact with your data to determine what data elements are part of the set, essentially sets become more dynamic.

One of the most straightforward uses of set actions is one that was demonstrated during Devs on Stage at TC18 – drilling down on the same sheet.  They showed a video that included drilling down on a treemap from one level of detail to a lower granularity, to a third granularity.

So my challenge for you this week is to learn more about sets and set actions.  You’ll find these to be extremely useful moving forward on drill-filtering on the same sheet – both on maps and when you’re trying to explore more detail.  Not a lot of showy formatting or sophisticated calculations – your only goal is to get comfortable with set actions.

click to view on Tableau Public

Requirements

  • Dashboard size: 1200 x 800; 2 sheets
  • Create a line chart of monthly sales
  • Create way for user to select months and drill/filter the line chart, this should also filter the treemap below
  • Create a treemap of sales by category that has the ability to drill to sub-category and product name upon clicking
  • Create dynamic labels (and tooltip) for the treemap that display based on the level of detail shown (category/sub-category/product name)
  • Match all other formatting, labels, and tooltips
  • Add a region filter for fun

FYI: Set Actions aren’t quite working on Tableau Public, so I encourage you to download the workbook and view in 2018.3 to explore the full interactivity!

Data from this week comes from the Saved Data Source in Tableau 2018 (Sample – Superstore), download here if needed.

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

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

Week 41: Top & Bottom Highlights

This week’s workout is designed for the end user looking for immediate insight – think of an executive stakeholder or someone more drawn to quickly getting numbers and results.  These folks don’t necessarily have the time to look deeper into the details, but are still craving data in context.

To satisfy their requests you’ll be creating a small multiples dashboard that highlights different combinations of dimensions based on what the user selects.  It also has strategically placed labels that give the most pertinent information – total sales for the chart shown and the most recent monthly measurement.

Click to view on Tableau Public

Requirements

  • Dashboard size: 1200 x 900
  • Create small multiples showing monthly sales from 2017 & 2018 by Category and Region
  • Create color highlight to meet these 6 conditions
    • Highlight top category per region (by sales)
    • Highlight bottom category per region
    • Highlight top & bottom category per region
    • Highlight top region + category combination
    • Highlight bottom region + category combination
  • Create a label that shows the Total Sales in the top middle of each small multiple
  • Create a label that shows the most recent sales in the lower right of each small multiple
  • Create a dynamic title that changes based on the highlight chosen
  • Match tooltips and interactivity – mark type matters!
  • Match all formatting

Data from this week comes from the Saved Data Source in Tableau 2018 (Sample – Superstore), download here if needed.

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

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

Week 38: Discovery Dashboard

One of my absolute favorite things about Tableau is that not only is it a fantastic data display tool, but it is amazing at data exploration and discovery.  While you’re in the flow of analysis there are tons of built in features that provide you feedback, awareness, and insight into your data.  Everything from displaying the number of marks on a sheet in the lower left corner (along with the sum of a measure) to exposing the worksheet summary card.  You can customize it to show you so many options and it’s one of the first tools I use when getting hands on with new data.

But what if you want to enhance your end-users’ capabilities and provide them with the same interaction you’re afforded in desktop?  Enter this week’s challenge: the Discovery Dashboard.  Designed off of a scatterplot, it’s goal is to give interactors the same features, insights, and tools that you passively have access to – all from Server and without the necessity of desktop or knowledge of how to develop.

This week your challenge is to recreate this guy (this is animated, so give it a moment!):

click to view on Tableau Public

Requirements

  • Dashboard size: 1100 x 900; 4 sheets tiled
  • Create a scatterplot of Sales vs. Profit with Segment on color
  • Create BAN of total marks, total sales, total profit
    • Include supporting information (min, max, slicing information)
  • Create controls that allow the user to
    • Change how the data is sliced (scattered)
    • Separate out segments to different rows
    • Separate out years to different columns
    • Add or remove reference lines
  • Match titles of each axis
  • Match tooltips
  • BANs should respond to selection inside of the scatterplot
  • Don’t forget the color legend & line formatting!

Data from this week comes from the Saved Data Source in Tableau 2018 (Sample – Superstore), download here if needed.

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

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

Week 35: Superstore Small Multiples

We’re wrapping up our month of Community submissions with a workout submitted by Zen Master Neil Richards.  A great way to end an awesome month facilitated by so many members of the Workout Wednesday community.  We’ve definitely been inspired and challenged by your contributions and impressed with the support you’ve given to those completing your workout.

So without further ado – here’s this week’s brief and background from Neil:

This week’s workout is about recreating a small multiple version of a 2×2 quadrant chart. There may be limited uses for such a chart but I’m a great fan of small multiple charts for visual horizon scanning to compare and contrast patterns in data over dimensions. Since the superstore dataset has 49 states included (no Alaska or Hawaii, but District of Columbia is included), we have a perfect dataset for a 7×7 grid.

I recently created an example of such a chart in a submission for #VizForSocialGood which can be seen here. Your challenge this week is to recreate a similar chart using the (US) Superstore dataset which is supplied with every Tableau installation. We will look at profit ratio across 4 category types (splitting the Technology category into Phones and Other Tech) and display as four proportionally sized squares. We will then highlight the category with the highest profit ratio using the colour of the state’s region. You can see the finished viz below – further explanation is given in the top section.

 

click to view on Tableau Public

 

Requirements:

  • Dashboard size: 1000 x 1300 – 2 sheets
  • Create a view looking at profit across 4 categories
  • Split out phones vs. other tech
  • Ensure the category with the highest profit is highlighted with color
  • Color for the highlight should match the state’s region
  • Create a custom legend for the upper right with a larger version of the overall chart
  • Ensure negative profit ratio is expressed as zero

Data from this week comes from the Saved Data Source in Tableau 2018 (Sample – Superstore), download here if needed.

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

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

Week 34: Building an Interactive Display to Visualize Violence in the DRC

This week we’re visualizing conflict data provided by ACLED.  ACLED, which stands for Armed Conflict Location & Event Data Project, is a “disaggregated conflict collection, analysis and crisis mapping project.”  You’ll notice throughout the Tableau Community this week that there are several partner projects going on – all designed to provide awareness on ACLED’s cause.  Zen Masters Anya A’Hearn and Allan Walker are responsible for orchestrating this massive collaboration campaign.

For Workout Wednesday’s part, we were passed this brief “Create a map for the Democratic Republic of the Congo in which we depict where specific actors have engaged in violence. This way we can begin to understand what locations have seen a multitude of actors engaging in violence.  With the recent Ebola outbreak in the DRC, humanitarian groups are interested in understanding what groups may be most active and in which locations they have engaged in violence.”  This included a specific data set focused on militant groups within the DRC and including a record for each actor engaged in a specific event (and as you can imagine, there’s usually multiple parties involved in a violent event).  It’s important to recognize and point out that this data set is different than ACLED’s traditional event-based set, which is not narrowly limited to the DRC or militant groups.

So the focus and challenge for this workout turned into creating an interactive display that would allow humanitarian groups to explore specific areas and quickly gain insight into recent violent acts.  Some things we kept in mind were ensuring the final work was approachable (and simple) for end users less familiar with Tableau.  I think you’ll find throughout the challenge there’s a pristine level of detail in the design and technical aspects that help promote simplicity.  It’s also intentionally been put into a format that may resemble a dashboard template found in a business/enterprise environment.

click to view interactive version on Tableau Public

 

Requirements

  • Dashboard size: 1200 x 800; 8 sheets (7 tiled, 1 float)
  • Create a map of each event with color & shape depicting year
  • Create BAN of total involved actors
  • Create bar chart of # events by year, including custom legend & highlight on map
  • Create treemap of most involved actors, # events involved in, date of last activity, and viz in tooltip verison of map
  • Create a section showing most recent event and militants involved
  • Mechanics of Interactivity
    • Users should be able to select marks on the map using either the rectangle, radial tool, or lasso; users shouldn’t be able to re-position map, or exclude points
    • Filtering on the map should affect the charts on right
    • The viz in tooltip displays each event for actor, and also reflects filtering of the larger map and includes labels of locations
    • When a recent even includes more than 200 characters in the description, it should have an ellipse (…) display in the sheet space, however the tooltip should include the full description
    • Date range at top should filter appropriately
  • Additional inclusions
    • Hover-over information button
    • Logo for ACLED sourced from here 
    • Icon for mouse selection – you can freely use this icon I own the rights to without attribution here
  • And don’t forget to match all other tooltip requirements
  • Pay attention to the titles, particularly the overall dashboard title section
  • White space matters on this one – for those who have wanted to flex their skills at padding and layout containers: now is your chance

Data from this week was provided by ACLED and can be downloaded at data.world

After you finish your workout, share on Twitter using the hashtag #WorkoutWednesday and tag @AnnUJackson, @LukeStanke, @RodyZakovich, and @ACLEDINFO.  Please be sure to include the additional hashtag #ACLEDconflictviz.

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

 

Week 30: Statistical Signals in Superstore Sales

This week’s workout is all about finding signals or patterns in data, specifically using run charts (aka line charts).  Variance over time is one of the most common ways to understand a process and often when we’re visualizing the data we use data summarization tools like the mean (average) or median to help aid in what we’re seeing.  Quite often there’s a story that unfolds during our visual analysis – these points are all low, look at this trending section, this single measurement is way different than the others.

These visual tests are good on their own, but they can lead to imprecise interpretation, focusing on detail that may not indicate a pattern, or the inability to communicate findings in a rational and logical way to others.  Fortunately for us there’s an easy way to combat that from happening – enter statistical tests and control charts!

Control charts using their most basic definition are a way to determine if a process is stable (aka “under control”) and typically uses different types of statistical tests to programmatically identify any signals or patterns based on rules placed on the data set.  I was introduced to them from a process engineering and industrial engineering perspective and I would say from my experience they work most effectively when a process isn’t constantly changing (the most popular example: a screw factory always makes 1 inch screws, anything outside of 1 inch would indicate a problem in the manufacturing process).

So your challenge this week is to recreate 3 different statistical tests using Superstore data.  (Play along with me a little bit and assume Superstore doesn’t invest anything in changing monthly sales).  And remember – these are just signals within your data – it’s always important to validate these signals to determine if there’s a legitimate reason behind them.

 

click to view on Tableau Public

Requirements

  • Dashboard size: 1100 x 800; 3 sheets, tiled
  • Create a run chart of monthly sales (without date drilling)
  • Create a middle line that is either the mean or median
  • Create +3 SD and -3 SD lines from the middle line
  • Build out the 3 statistical tests – match for test causes an orange dot to be plotted on run chart
    • more than 3 SD from the middle line
    • 3 consecutive points trending in the same direction positively or negatively (ex: point 1 > point 2 > point 3)
    • 3 consecutive points above or below the middle line
  • Create a corresponding chart below run chart with indicators of the test
    • Yellow dot if it’s part of the pattern that corresponds with the test
    • Orange dot if it’s a signal (aka meets the test criteria, should match the run chart)
  • Interactivity
    • In line chart, tooltip for orange dots should indicate ‘signal’
    • In symbol chart, tooltip should indicate
      • ‘part of test pattern’ = gold dot
      • ‘meets test criteria’ = orange dot
    • Match hovering on the run chart or the symbol chart
      • Specifically look at hovering over an orange dot, label appears in run chart
  • Test Description – match the text, this should change with the test selected
  • Match all other tooltips, descriptions, and filters

Data from this week comes from the Saved Data Source in Tableau 2018 (Sample – Superstore), download here if needed.

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.

Week 27: How do current year sales compare to previous?

For this week’s workout I’ve decided to keep things simple and focus on creating one powerful alternative visualization for comparison over time.  I think we all tend to get stuck in a bar chart rut and struggle to find ways to spice things up.  I tend to do this a lot, especially when I am comparing large chunks of time.  It is so easy to build out a bar chart with years or months on labels that go in sequential order.  (And let’s be honest, there’s really nothing wrong with that!).  But sometimes the sequential ordering of years prevents us from doing multiple comparisons at the same time.  Bar charts can also get messy when there are tons of dimensions slicing up our view.  Bar charts love vertical space.

So instead of a bar chart, this week we’re building out dot plots!  Dot plots have the advantage of using position instead of length for data comparisons.  They can make it easier when looking at multiple comparative data points to spot trends or patterns.  They become very powerful if what your audience cares about is something like a specific measurement compared to all other recordings of that measurement.  You’ll notice that the visualization forces you to focus on the color representing “current year” but you can’t mentally map a trend of sales by year.

To help make the dot plot shine, I’ve found that adding a horizontal line works really well.  The simple act of the horizontal line makes cognition quicker – grounded by the line scanning left to right becomes automatic.  I’ve also spiced up the labels, colors, and tooltips to ensure that you keep strengthening your skills.

click to view on Tableau Public

 

Requirements

  • Dashboard size: 1250 x 900, 2 sheets, tiled
  • Each dot represents annual sales by subcategory
  • Color of dots:
    • Most recent year below average = pink
    • Most recent year above average = blue
    • Not most recent year = gray
  • Calculation for most recent year should work if data updates and 2018 gets added in (don’t hard code 2017!)
  • Match tooltip language – don’t hard code the number of years, build a calculation!
  • Match formatting: specifically horizontal line, banding, reference line
  • Don’t forget about the legend

I’ve added on a year filter so you can see what the tooltips and colors look like – I will be checking to see what happens if I filter out 2017!

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.