This is the first of hopefully many Pokemon dashboards. Originally, I put the cart before the horse a bit and instantly wanted to do some really complicated probability dashboards. Turns out I’m not good enough at math and database structures to figure out what needs to happen to do that yet. I still want to, so if any Pokemon statisticians out there want to help, I’d be much obliged.
In the mean time, I thought I’d whet my appetite with a basic Pokemon dashboard. On this dashboard, you can use the buttons at the top to select the type of Pokemon you want to see. You can use the dropdowns below the buttons to choose which metrics you want to see. Your options are Attack, Defense, Speed, and HP (Hit Points). I’ve put reference lines at the averages so that we have kind of a quadrant chart going on. Explore it yourself:
Yesterday, we published an awesome blog post on the Tableau Public blog by the always hilarious Ryan Robitaille. When describing the importance of effective visual communication he joked “it would be a shame for your detailed Pokemon analysis to go unnoticed.”
I chuckled at that for a good, long while. And then I asked myself, “Wait… why haven’t I done a detailed Pokemon analysis yet?” I was able to find a couple of examples of Pokemon dashboards in Tableau, but nothing that knocked my socks off. It almost seems silly that there hasn’t been tons of Pokemon dashboards, considering that it is a game based entirely on math and statistics. And there just happens to be TONS of data out there for it.
So, I have been exploring some ideas using a variety of different Pokemon datasets I found. The very first thing I thought to do was import all the sprites as shapes so that I could make an awesome scatter plot of attack points versus defense points. There’s just one problem…there are 718 Pokemon. Anyone who has ever really dealt with custom shapes in Tableau before probably looks like this right now:
Tableau doesn’t make it super easy to assign shapes to things. Really, the only dialog you have is this box:
There isn’t a list view. There actually isn’t any way of viewing the names of the images at all. All you can do is look at the tiny little image and try to match it up with the appropriate Pokemon on the left. As you can imagine, that would take FOREVER. Especially with 718 Pokemon. Especially since I really have no idea what 567 of those Pokemon look like.
Luckily, I realized something today that is going to really simplify this entire process. When Tableau brings custom shape files into the above dialog, it does so alphabetically! So, since my sprites are already numbered, they should all come into Tableau perfectly, right? I can totally just hit “assign shapes” and everything will work out! Please?
Not quite. I ran into a couple snags. First, despite the fact that all of my Pokemon sprites were already named by their numbers, the way alphabetical sorting works makes anything start with a 1 go before anything with a 2 and so on. So instead of going 1,2,3 the order is 1,10,100,11, etc. Luckily, this is really only a problem for the first 100 Pokemon, so I went through the files and added zeros so that the order would be preserved.
The second problem is that the Pokemon are in alphabetical order by name. But that’s easily fixed by sorting the names. However, it’s a little weird. I have a field for number, which I can use to put the names in order. However, when you sort by a field you have to aggregate it somehow. It’s a little strange. Luckily, sorting by either minimum or maximum basically just takes that one number anyway.
(As I’m writing this, I’m just now realizing I probably just could’ve put # on shape and saved me that last step. Silly me!)
Once I got those issues out of the way, assigning my sprites was as easily as clicking “Assign Palette”! So, the moral of the story here is if you are going to be using a crap ton of shape files, name them in a way that matches up with the dimension you are assigning to them and it’ll be WAAAAYYYYY easier to deal with.
Custom shapes aren’t as scary as I once thought! But, the experience could still definitely be improved. Here’s an idea from the Community site about labeling the icons with their file names, which would definitely make things way easier. So now that I have my super awesome shapes in place, I can get cracking at making something cool. Here’s a sneak peak of my shapes in action:
I’m excited for tomorrow, when I will hold an hour-long Tableau Public training here at JAWS Camp. Learning how to use a new piece of software is already hard enough, so I like to use datasets that are more on the fun side, to make things a little more interesting. I also like to avoid datasets that are too specialized (e.g. economic datasets) because I don’t want to have to spend too much time explaining what all the data is. For this training, I was inspired by this cool map that Jezebel published about the top girl’s names by state since 1910. The gif action is cool, but I really wished I could’ve paused it and explored things for a second. The whole dataset from the SSA actually contains way more than just the top name in each state; it has every name with more than 5 occurrences in a state. That dataset ends up being over 5.5 million rows; too big for Tableau Public (but it is fun stuff to play with), so I filtered the data down to just the top names for boys and girls. I whipped up a little dashboard on the flight over yesterday that is pretty fun to play with:
The first line chart at the top shows us which years had the least/most variation in top names. Click on a point and the map will filter to that year. Look how many Lisa’s there were in 1965!
Looks like there is more variation in boys names than girls names.
Letting the pages shelf flip through the years is hypnotizing. After it played once during my presentation, people kept shouting “Again! Again!” just because it’s fun to watch. Obviously, Jezebel’s gif tactic made sense!
Happy October, dear readers! If you still haven’t picked a Halloween costume, don’t fret! I’ve made a little dashboard based on data I scraped from Buycostumes.com via Import.io. You can use it to explore costumes that are “sexy” versus “spooky”. And once you decide what you are going to be for Halloween, you can fill out my costume survey, which I will turn into a viz sometime next week!
Sexy costumes seem to be on average cheaper than other costumes. I guess there is less fabric?
I’m surprised there aren’t as many spooky costumes, seeing that zombies and vampires are both really popular in pop culture these days.
I was too lazy to make a separate category, but it does seem like the majority of costumes these days are based on characters from pop culture. I hope to answer this with my super non-scientific study about what people are wearing for Halloween!
If you haven’t seen it already, I made a little application to find author profiles on Tableau Public. I was pretty happy with the way it turned out. It’s crazy how a few simple shapes and some custom fonts can really make a dashboard look a lot more organized. A few people have asked me how I did some of the custom styling on the dashboard, so I thought I’d make a little tutorial here.
I use GIMP for most of my design work, mostly because I was a poor college student when I was first teaching myself graphics and couldn’t afford Photoshop, and now I’m used to it. It’s a great tool, being free and all, but I know there’s a bit of a learning curve. But, everything I made for this dashboard was totally easy and beginner friendly.
I designed the basic sheets and dashboard interaction before I touched any images. It may look complicated but when you remove all the custom images, my dashboard is really just 2 tables and 4 sheets. The images of the featured authors are laid out in a table with their pictures set as custom shapes. The other table just a text table with a wildcard quickfilter on the field for “display name.” The name, description, profile link, and featured viz are all each their own sheet being filtered to only show information for the last person clicked on. If you take away all the images, this what you are left with.
I wanted away to separate sections but at the same time make everything fit together a little more, so that’s how the module idea came about. Plus, all the rounded rectangles reminded me of pills on the column/row shelves so I thought it looked mildly Tableau looking. As you can see in the image above, I had already been working a particular color scheme in mind. It’s a bright, fun palette I found on colourlovers called “Ocean Five”
I had also decided that I wanted the titles to have a more decorative font, so I used one called “Qlassik” that I found on dafont.com. Since I had already basically placed things on the dashboard, I knew about what sizes I needed all the shapes to be. I used SnagIt to measure the size each of the boxes needed to be so that I could make my shapes fit nicely.
Making the shapes is super easy. This was the process:
Create a new file that is the dimensions I need the shape to be.
Use the paint bucket to fill it with the desired color. Use the text tool to add the title.
In the Menu go to Filters>Decor>Round Corners…
Uncheck “Add drop-shadow”
You now how a nice little container to float your sheets on top of.
Repeat the process a couple times and there you have it! If you like easy design tips like this, you are going to love October on the Tableau Public website because it is design month! I know that I’m excited to share all my design tips with you and I can’t wait to hear some of yours!
Oh man guys, I had some fun with this one. What you are about to feast your eyes on is data scraped from Etsy using IFTTT, which is quickly becoming my favorite set-it-and-forget-it data tool. I scraped anything tagged “Hello Kitty” from August 14th to September 17th.
I didn’t expect any crazy revelations in the data besides that there is A LOT of Hello Kitty crap on Etsy. And most of it is under $5 it seems! I really wanted to do this because I knew it would be a blast to design. And oh man it was.
I followed the instructions to add colors to my preferences file. Originally, I had this in as a “regular” palette, meaning that it was meant for discrete data. But, it looks pretty continuous to me, and all my data was continuous, so I made it an ordered diverging palette! Weee! Isn’t this fun already?!
Next, I wanted the text to be just as adorable as the colors. I switched pretty much everything to Comic Sans (I know… boo! hiss! but John Maeda said it was ok at his TCC13 Keynote!). I also downloaded a sweet font off of dafont.com called “Loveness Three”
I grabbed a vector image of Hello Kitty and blew it up really big to make the header. I felt it was a particular stroke of genius to put the hover help menu (my signature trick!) in her bow. How nifty!
I’ll go into more detail about the design work behind this viz next month, since it will be Design month at Tableau Public! Without further ado, here’s a dashboard with more hot pink than you will ever see on Tableau Public ever again:
Here to help with all of your session-picking needs!
I’m here at TCC13 doing my duties as a Content Guru! As such, I’ve got the low-down on all the breakout sessions and have rounded up the sessions I think will be the most valuable to Tableau Public publishers.
08:30am-09:30am: How to Create a Viz that Demands Attention with Anya A’Hearn, Kelly Martin, Ryan Sleeper, Ramon Martinez and Ben Jones, Woodrow Wilson A 09:45am-10:45am: Rapid-Fire Tips & Ticks with Kelly Martin, Anya A’Hearn, Ray Randall, Michael Kovner and Dan Hom, Woodrow Wilson A 09:45am-10:45am: Making Flow Happen: Dashboard that Persuade, Inform & Engage with Jeff Pettiross, Chesapeake 1-3 11:00am-12:00pm: Actions Get Reactions with Ray Randall and Guilherme Bronner, Maryland B 11:00am-12:00pm: Let’s Talk About Text Baby: Do More with Your Titles, Labels, Tooltips with James Baker, Cheasapeake 1-3 11:00am-12:00pm: Extreme Viz Makeover with Jock Mackinlay, Erin Easter, and Ben Jones, Woodrow Wilson A 04:00pm-05:00pm: Rapid-Fire Tips & Tricks with Ryan Sleeper, Craig Bloodworth, Mike Klacyznski, Jewel Loree, and Dan Hom, Woodrow Wilson A
09:45am-10:45am: The Art & Science of the Zen Masters, Jesse Gebhardt and Ryan Robataille, Woodrow Wilson A 09:45am-10:45am: Visual Storytelling in the Age of Data, Robert Kosara, Chesapeake A-C 11:00am-12:00pm: Using Design & Emotion to Create an Impactful Data Visualization, Anya A’Hearn, National Harbor 6-7 03:00pm-04:00pm: Hands-On Visual Best Practices, Amanda Pype and Molly Monsey, Woodrow Wilson A 03:00pm-04:00pm: Making Art with Tableau: How I Made the Kraken, Andy Cotgreave, Chesapeake 7-9 03:00pm-05:15pm: Building Better Dashboards, Michael Carpenter and Alexandria Skrivanich, Maryland C
04:15pm-05:15pm: Effective Data Storytelling with Tableau, Robert Kosara, Chesapeake 4-6
09:45am-10:45am: 3rd Annual Iron Viz Championship, Woodrow Wilson A 11:00am-12:00pm: Advanced Social Media with Tableau, Mike Klacynski, Chesapeake 7-9 03:30pm-06:30pm: Fanalytics: Feed Your Passion for Data, Ben Jones, Adam McCann, Craig Bloodworth, Ramon Martinez, National Harbor 4
I’ll be floating around the different answers desks, as well as presenting in the second Rapid-Fire Tips & Tricks sessions and co-hosting Iron Viz. If you see me, don’t forget to hi!
It’s Fall and school is starting all over the country. I put together a little viz to help current college students see what they are up against this semester and current high school seniors pick a school for themselves. This one is limited to the Pac12, but I’ll probably throw together the same thing for all the Division I schools.
If you toggle between professors rated “Hot” versus “Not Hot” you’ll notice a significant increase in the percentage of positive ratings for Hot professors. Perhaps students are a little more forgiving when their professor is good looking?
I was a little surprised that the hottest professors in the Pac12 are at Stanford University. Perhaps a more academically focused student body would correlate with a stronger attraction to professor types?
I was also surprised that my alma mater, Arizona State University, had as many positive reviews as they did. I feel like when I went there, ratemyprofessors reviews I had read about my teachers were overwhelmingly negative. Perhaps I took hard classes?
Which led to a viz that at the time looked like this:
The original viz was created by a mysterious group (for which I have no contact information so please stop asking!) called the “Pornhub Stats Division.” What you are looking at is the average duration on the website Pornhub and the top 3 searches in each state.The data was so intriguing, but it was apparent that whoever made this was new to Tableau and was missing some of the finishing touches. I wanted to give it a quick facelift so that it the information would be more digestable, so I did. I liked what they did with the formatting of the bar chart on the original. It matched their logo and it really popped. So, I went ahead and changed the map to a dark map (From the menu: Map>Map Options>Style>Dark) and changed the dashboard formatting to a black background (Dashboard>Format>Shading>Default). I also got rid of the table at the side since I didn’t feel it added much. I used duration to color the states and had the top search display as text. I also cleaned up the tool tips a bit. If I had been feeling like spending a little more time on it, I would’ve floated Alaska and Hawaii under the U.S. so that you could easily see them too. If I had felt like spending a lot more time, I would’ve reformatted the data so that you could click on a search term and see all the states that had that in their top 3 searches. But, it felt kind of weird to have it say certain words all over my monitor at work. Anyway, this is how it turned out.
The tweets were (and still are) pouring in. I was getting random emails from friends and colleagues about the viz. It started gaining thousands of views an hour. I permalinked the original behind mine, as to not steal the original author’s thunder. Now both vizzes are pushing 300k views. It was definitely the top viewed viz last week.
A couple of thoughts:
I was interested in how most of the discussion was about “What do people like in MY state?” Not only does the internet love porn, it’s also fairly narcissistic, it seems.
I was surprised with how many times I had to tell people to hover over Utah to see what they are into. The idea of interacting with a viz is still not intuitive to people.
A lot of people wondered how I felt being associated with Pornhub. To me, data is data and interesting data is interesting. And really, nothing makes me more sad than an interesting story being covered up by poor design. All I set out to do was make the pornmap readable. They incorporated some of my changes and got hundreds of times more views to that data, so I think I accomplished that.
If I was Pornhub, this is how I would follow up:
Data on average visit duration based on search. Do people who watch certain things last longer? I’d add variance as well, so we could see if there is a wider range for some topics.
Reformat the data in the original viz so that you could click on a search term and see how many states have it in their top 3. They had the keywords spread across 3 columns: 1st, 2nd, and 3rd. Instead all of the words should be listed under one column with the rank listed in a separate column. There would be three rows for each state. This is pretty much a perfect use case of the Tableau Reshaping Tool Excel Add-in.
Add variance to the duration times in the original viz. There were lots of complaints that the data isn’t really telling you much, which it isn’t.
Worldwide data would be a no-brainer.
Mapping where uploads come from and where the most active users are.
I’d love to see if there was an increase in some of the more specialized searches like Anita Queen and Smoking. Google Trends suggests that there might be.
In closing, what did last week teach us, boys and girls? As the good folks at Avenue Q so eloquently put it: