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Hey! The winter olympics kick off this week, and in the spirit of gold medals here are three quick charts that look at the history of Winter Olympic Success All Time Ranks Word maps really interest me and with the countries abbreviations being so common I thought this was a perfect use case for one. You can fit a lot of obvious info into each cell. Here the colours represent the rank of the country and the size is the % of medals won that year  Sport Dominance It's hard to remember year over year which countries are the favorites in each event. Here's a simple text table that shows the # of all medals won by country for each event.  Host Country Performance Lastly - does hosting make a significant difference in medal count? Checkout all the host countries medal totals over time. The answer, as with any great stats question, is "kind of" 
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Key Takeaways: - Don't force an Area chart. Line charts are often the more legible option. - Help your consumers with consistent reference marks - The most powerful aspect of an area chart is it's ability to give a total, but this can distract from the individual variables. Area charts emphasize the combined performance of a cohort rather than it's individual pieces. If that is your key takeaway to an audience, then an Area chart can be put to good use. However Area Charts often put too much onus on the end users while offering some pretty subtle weaknesses which make it, in my opinion, one of the more challenging charts to effectively use. Here’s an area chart of total volume of alcohol sold in Canada from 2002-2016  It’s showing the growth of three common alcoholic beverage types (+ other which is coolers, ciders etc) from 2002 – 2016. At a glance we can tell that total consumption has increased since 2002, having plateaued a bit between 2009 and 2014. We can see that Other is quite obviously the lowest type and beer is the highest. At a glance, that may be the only things we can say for certain based on this one view. Let’s quickly compare this to a line chart showing the same information.  The line chart, in this circumstance, drives your attention to some of the big stories. Wines have closed a big gap on spirits. We can also see there's been a slight uptick in Other, and Beer isn't growing much at all. This offers a much deeper analysis than the Area chart. You can also see that all sectors are growing, which leads you to conclude that the industry is growing. When to actually use Area Charts Area charts start to add value if you're forced to compare many variables. Take a look at the beer by province chart below. It's showing a lot while being just shy of too busy. You can quickly see that: Ontario has 65% of Beer sales British Columbia, Quebec and Ontario make up 75% of Beer Sales It also shows the relative insignificance of size from the maritimes and territories (despite our best efforts).  This becomes total chaos on a line chart, which has a bunch of clutter, shows the smaller provinces as dwarfed by the bigger players and, overall, is a big mess. . Let's take the scenario one step further. Let's now compare Provincial Sales of all alcohol side by side  The area charts, side by side, give a lot of interesting information. They all use the same scale and with the reference lines following from one to the other it's easy to have a dialogue with this chart. Look at the jump for Quebec Wine compared to Saskatchewan who drops off completely.
How about the higher variability of "other" which sees significantly more peaks and troughs than the other types. What's most interesting is the amount of information you can convey with this in a relatively small space. The impact of each mark means that you can get more with less to tell the story. Most importantly is what this doesn't tell. There's no way to compare total volume by type from province to province. You don't know year of year growth in any of these provinces. The Area chart forces a certain narrative, and it's necessary to determine if the chart matches the message (which is a great rule of thumb). Area charts are so frequently included in my rough sketches of dashboards and then fail to make the final cut. They unfortunately don't have a wide range of use cases that can't be better done by line or stacked bar charts. But if you can find the right place to intuitively use one they can be a powerful accent in your analysis. I've created a full dashboard if you're interested in diving into this information further. It is best viewed on my Tableau Public Site One of my customers said to me the other day "You data scientists are all the same, you hate Pie Charts". And we do. Rightfully so. They are an executive's favorite terrible method to display data. "I want to see sales by province in a pie chart" ... but that's 10 values (and 13 if you want territories - which the BA was vague about) it will be barely legible. Oh well. You're told to make it happen anyway. But this generalization of a data scientists hate for Pie Charts. It caught me off guard. It got me thinking. Could you make an executive dashboard with only pie charts. The ultimate corner office dashboard. Could you? Yes. Should you? If you really have to ask, then get out. Feel free to find me on twitter and let me know your favorite Pie Chart use! @OffTheChartsC The Data I built out a totally fictitious pie franchise based in Central/Western Canada. The dataset is an aggregation of sales and profits for the last 12 months. My Pie franchise keeps things simple. We sell the five best flavours of Pie: Cherry, Lemon (meringue), Pumpkin, Blueberry and Apple. Sales are sporadic (almost totally random, oddly) across the provinces, months and types. There's also Target sales that are tracked provincially and by flavour, so that the VP can hammer on someone if sales trail off. View 1 - Executive Summary. First thing we need is an overall view of which type of Pie is meeting it's Target. And by how much. To do this I made two calculated fields. One was Target minus Sales for when Sales were less Target. The other was Target minus Sales for when Sales were greater than Target. Basically creating separate fields for above and below target. Then I added target itself, put measure values on as the pie slice, and measure names as the colour. Due to the separate fields for below and above only one or the other would show, along with Target. The last little thing was ordering the measures correctly so that "Above" would trend to the right and "Below" to the left. The final result: 
 
Last week we looked at some static data comparing Trump to Obama's twitter tone.
Today I'm excited to show a dynamic option that allows you to search specific terms and compare the tones of the last two US presidents. If you're interested in getting one of your own, check out our products page! 
Sentiment analysis is still an emerging concept that is far from fully refined. It takes in strings of text and evaluates word by word to discover the overall sentiment and subjectivity. Due to the nature of human language there are obvious fail points, especially considering sarcasm. At an individual level it could give the sentence "No, I'm not willing to die yet" a very negative score (due to the words no, not and die) when it may be the opposite. But, on an aggregate level, it's not too bad. The occasional miss calculations usually become anomalies in the grand scheme. Below is a look at the last 3200 tweets written by former US President Obama and current US President Trump. I've run these against sentiment analysis and the results - as well as how their users interact with them - is interesting. Also, to order your own twitter sentiment analysis - See our new products page First let's look at total counts by sentiment and subjectivity  First interesting thing is that in the last 3200 tweets, Obama has never written directly to a user - anyone holding out hope for a reply should bury that dream. Sensitivity: Obama has 3% of all personal tweets as showing a negative sentiment. Trump comes in higher at 11%, having twice as many negative tweets over a smaller sum. Also of interest is that Trump overall is more positive than Obama as well. Overall, Trump shows far more emotion in his tweeting with 42% of tweets registering an emotion compared to Obama's 31%. Subjectivity: Obama registers half his tweets as objective, with Neutral being the next highest and only sending a subjective tweet 19% of the time. Trump is a bit more spread out in his objectivity, but he does tweet subjectively less than he does Neutral and Objective. But where it really gets interesting is how followers react to each tweet category.  Sentiment: Obama's very small negative bucket registers the best, which is surprising. If you consider the president's role, these tweet's are typically denouncements or condemnations. His Neutral and Positive tweets do about the same.
Trump's followers don't go for the neutral tweets. Both the positive and negative tweets do way better. His twitter blows up when he shows emotion, which potentially explains why we see so much of it. Subjectivity: Obama's twitter base go for don't touch his neutral tweets. They, seemingly, like the facts or an opinion, but nothing that flaunts the line. And it's significant, on average when he is down the middle, he gets 2K less retweets per post. For Trump, similar to his sentiment, people love his subjectivity. "He tells it like it is" well that's because it gets results. He averages an extra four thousand retweets more when he's subjective vs objective. To see how the full charts look, or to get one for you or your company register, click here
Key Findings
Key Findings
The City of Fredericton released their total demand and revenue statistics for all of the municipal recreation facilities. Divided by facility type and use. Here is an interesting comparison in how much is earned and when. Click any item to drive the rest of the dashboard. 
ACOA has played a leading role in creating development opportunities for the Atlantic Canadian provinces. Part of this is through funding awarded to projects that are expected to generate growth opportunities in a reason. Since 1995 this data has been recorded and is publicly available here. But below is all the data summarized into a fully drillable dashboard. Click a year, client or province to filter the rest of the information.
Through their open data policy the City of Fredericton have captured all reported motor vehicle accidents in a spreadsheet made available on the city's site. Below is a look at all of those motor vehicle accidents, broken down by type, time of occurrence and where they took place.
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