VizWiz

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Showing posts with label small multiples. Show all posts

September 26, 2022

#MakeoverMonday Week 39 - Average Internet Speeds Across Europe

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My goal this week was to correct my failures from last week, so I focused almost exclusively on building the starburst/sunburst chart. As promised to the viewers, I will also record a separate tip video for how to build them.

Here's the WatchMeViz and the viz is below that.


April 12, 2021

#MakeoverMonday 2021 Week 15 - Fouls Called by NBA Referees

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The original viz for this week was so good that I struggled to come up with something different. In the end, I wanted to learn by recreating the original. Check out #WatchMeViz and interact with the viz below.



April 5, 2021

#MakeoverMonday 2021 Week 14 - Multiclass Classification of Dry Beans

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February 2, 2021

How to Create a Trellis Chart in Seven Steps

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Trellis charts are a version of a small multiples chart that organizes the dimensions in the view across a grid. In this tip, I show you how to create a trellis chart that will work even when you are missing data.

Trellis / Panel Chart Calculations

//Columns
(index()-1)%(round(sqrt(size())))

//Rows
int( (index()-1)/(round(sqrt(size()))))

Steps

  1. Create the column and row calcs
  2. Create a custom date
  3. Place custom date on columns, measure on rows and dimension on detail
  4. Place column on columns, row on rows, and custom date on detail
  5. Ensure dimension is above date on detail shelf
  6. Change custom date on columns to ATTR
  7. Configure table calcs
    1. Check both the Dimension and the date field date in order
    2. Ensure the dimension is above the date field in the Specific Dimensions section
    3. Set "At the level" to the dimension

Follow along in this video. Enjoy!

February 1, 2021

#MakeoverMonday Week 5 - Renewables vs Fossil Fuels in Europe

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I found this week's data set super interesting. There was so much to explore. I could probably have gone for hours trying different charts and making lots of comparisons. Here's the video from the Watch Me Viz session.



ORIGINAL CHART




WHAT WORKS WELL?

Overall, I think this is a very good chart. 

  • The colors are perfect for the topic.
  • I like the labels on the ends of the lines.
  • The tooltips are very responsive and color-coded to match the line.
  • The title and subtitle are informative and give good context.
  • The slightly lighter shading of the axes labels make the chart stand out more.


WHAT COULD BE IMPROVED?

  • Make the dashed lines solid.
  • Format the percentages in the tooltip to one decimal place.


MY VERSION

Click on the image or here for the interactive version.

October 29, 2020

How to Create Time Series Tile Grid Maps

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Unlike traditional maps, tile grid maps allow you to allocate equal space to each geographical area. We've probably all seen hex maps of the United States. Tile Grid Maps are similar, except they are squares with each block being the same shape and size.

In this video, I show you first how to create the tile grid map, then how to overlay time series data. I then show you three different visualization types for the time series. You could easily create bar charts as well.

Enjoy!

October 1, 2020

#WorkoutWednesday 2020 Week 32: COVID-19 New Case Trends

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As I continue to catch up on Workout Wednesday challenges I've missed, I'm picking and choosing those that looking interesting at first glance (and that I think are doable in a short amount of time). Week 32 was one I knew I could get done quickly since it was very similar to 2017 week 7. Actually, since the columns and rows were provided to use already via a CASE statement, the trellis part of the exercise required no calculations and no work.

The toughest part was normalizing the calculation within each State. The key here is to think about each State individually, find the highest value, and then compare all of the other dates to that value. I did this by putting each State and date into a row, creating a calculation to get the largest value, then computing the necessary ratio. Once I had that, it was simple to turn it into a chart.

To get the State names in the middle of each chart, think about how you can count half of the day in each chart. Then take that value and add it to the first date and you'll get a single date that's halfway across the date range. This solution will be flexible for any new data that would be included.

Some difference between mine and Luke's

  1. I didn't filter the dates; I decided to use the entire data set, but keep in mind that I did this several weeks after Luke posted the challenge.
  2. I created a mobile version as well.

And with that, here's my completed #WorkoutWednesday for week 32 2020.


June 19, 2020

How to Get Your Data from Strava to Tableau

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UPDATE: If you prefer video tutorials, there is now one on my YouTube channel here. The steps to get the links into the Simple Mass Downloader are done more efficiently in the video. There are several steps removed from the Excel and link uploads that are listed in the blog.



For a while now, I've been using this process for getting my data from Strava into Tableau like this:
  1. Download the data from Strava
  2. Prep it with Alteryx
  3. Visualize it in Tableau

In this post, I will outline the steps for getting your data from Strava into Tableau. I have also created a template with some sample vizzes for you to use. Download it from Tableau Public here.

I started revamping the process after wanting to create a small multiple view of all of my activities like this:


When I first started creating these, I took a process based on R and converted it into a workflow in Alteryx. This worked great, but there were two main drawbacks:

  1. My Alteryx workflow was a mess (I tinkered with it every month).
  2. Each of the routes would be adjusted to fit perfectly in a square.

This second problem meant that the routes were not scaled correct according to the curvature of the Earth. I then saw that Andy Cotgreave reached out to Ken Flerlage for help. They've written about their process here. Great! However, the calculations on this process make rendering the maps in Tableau very, very slow. Like REALLY slow.

That's where Alteryx comes in. I pushed all of the data processing into Alteryx, export each route as a single spatial object and every renders super fast. So if you're interested in getting all of your data out of Strava and visualizing it in Tableau, this process is for you. It does require Alteryx, but you could replicate the process in R.


TOOLS REQUIRED

  • A Strava account - NOTE: Make sure you are logged in to Strava throughout these steps.
  • Alteryx Designer
  • Tableau Desktop
  • Simple Mass Downloader Chrome extension

GET THE DATA FROM STRAVA

Step 1 - Login to Strava, click on your profile icon on the upper right, choose Settings.


Step 2 - On the My Profile page, click on My Account.


Step 3 - Scroll to the bottom and click on the Get Started button in the Download or Delete Your Account section.


Step 4 - In step 2, choose Request Your Archive. Don't do anything else on this page.




Step 5 - Check your email in a few minutes. When it arrives, click on the Download Archive button in the email.



Step 6 - Unzip the file that downloads. Open the folder that's created and you should see something like this.

The activities folder contains all of the raw files, but these won't all be in the same format, which means they're useless. The ONLY file we need is activities.csv. Open the CSV in Excel.

Step 7 - The only column we need in the file is the Activity ID column.


Delete all columns except Activity ID.


Step 8 - Insert a column to the left of Activity ID. I name it URL, but call it whatever you prefer. Then in cell A2 enter this formula: 

=CONCAT("https://www.strava.com/activities/",B2,"/export_gpx")

Then copy it down for all rows. This create a link to each activity in GPX format. If some of the activities don't have location data, don't worry about it. Those will simply not download in the process.

Choose the URL column header (the "A" above URL), choose Copy, then Paste Special => Values. Then delete column B (the column with the activity ids).



DOWNLOAD EACH ACTIVITY

Step 1 - Add the Simple Mass Downloader Chrome extension.


A new tab will open with a tutorial if you're interested to learn how it works.

Step 2 - Click on the extension button and you should see this screen.



Step 3 - Click on the hamburger on the right and choose Import URLs from local file.



Step 4 - Import the activities.csv file and the URLs will upload. This loads all of the files into the queue on the Download List tab. Choose the Select All box and they should all be highlighted.



Step 5 - Click on Start Selected and you'll see a bunch of files start downloading very quickly. Again, some of them will error out if the activity doesn't have location data. The files that download will have a green check next to them.



Phew! That might seem like a lot, but once you do it a couple times, the process is really quick.

Fantastic...all of the files are now downloaded. Onto Alteryx we go. 

ALTERYX PROCESS

Step 1 - Download the Strava Route Maps workflow from the Alteryx Gallery and open it in Alteryx Designer. If you can't download it from the Gallery, I have it on Google Drive here.


The Strava icon is a simple macro that will import all of the GPX files in the Directory you specify when you run the app. 

The workflow is split into four parts:

  1. Import the data, strip out the parts we need, then create points and lines.
  2. Normalize the data based on the calcs from Ken Flerlage, turn them into lines (one mark for each route rather than hundreds of point for each route) and export as a Tableau extract.
  3. For each point, calculate the distance, climb, etc. and extract all of the points.
  4. Take each of the points and turn them into lines.

Neither part 3 nor part 4 above normalize the data into squares. These are simply all of the data for each route.

Step 2 - Run the workflow as an App by clicking on the magic wand next to the Run button.


Step 3 - Choose the folder that contains all of the GPX files from the Simple Mass Downloader output. And click Finish.


This will generate three Tableau extracts that will load into the same directory as the GPX files. The workflow extracts them as TDE because Hyper files don't support polygons. Even though these aren't polygons, it makes me more comfortable that Tableau will read the files correctly.

Success!


Click the Clear button and then the OK button. Otherwise all three files will open in Tableau, which we don't want.

We're all done with Alteryx. Onto Tableau.


TABLEAU PROCESS

I've created a template for you to get started with. Download it from my Tableau Public profile here. Once you have done that, follow these steps.

Step 1- Open Tableau and right-click on each data source and pick Edit Data Source.


Step 2 - Click on the carrot next to the data source name and choose Edit Connection.


Step 3 - Navigate to the data source that downloaded from the Alteryx workflow. It will have the same name.



Step 4 - Go back to your worksheets and everything should update automatically. If not, right-click on the data source name and choose Refresh.

Step 5 - Customize the views as you see fit. That's it!

Here are the vizzes that I have included in the template.










October 22, 2018

Makeover Monday: Historical Major League Baseball Beer Prices

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It's #TC18 and we're hosting a Makeover Monday Live with 1000 people. I thought it would be fun to use (1) a simple data set and (2) my very first Makeover Monday viz. We decided to use this viz so that people could see how practicing week after week can improve their skills like it has mine.


What works well?

  • The title is clear and tells the reader what the data is about.
  • The user can sort the data based on their preference.
  • The placement of the sort options encourages interaction.
  • The rank helps show where a team falls amongst the league.
  • The color of the bars goes with the beer theme.

What could be improved?

  • The data source is not listed.
  • Having so labels on the end of every bar makes the viz too busy.
  • The beer mug icons are completely unnecessary.
  • The font looks very small.

What did I do?

  • The new data set has data for 2013-2018 (except 2017), so I wanted to make sure I looked at the data over time.
  • Made the title more descriptive so that the user (hopefully) understands what the line represents.
  • I borrowed several techniques I learned from Workout Wednesday week 41:
    • Shading those that have increased prices vs. 2013 with a red background
    • Labeling the top middle with the team and the latest price
    • Labeling the end of each line; in WW the labels were all placed on the lower-right of each pane, but I didn't like how it looked in this case
  • Ordered the teams from highest to lowest based on the latest price
  • Organized the team in a trellis format so they fit nicely into a 6x5 grid
  • Included the data source. my name, and the inspiration for the design

And here's my Makeover Monday week 41. Click on the image for the interactive version. I can't wait to see what everyone creates at MM Live!

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