Level 2: Hello World

August 25, 2022

Welcome back!

Earlier in Level 1 we selected our players, now we enter into the world of the game. For our next theme we’re saying “Hello World” as we attempt to navigate the game world and leverage data to increase our chances of winning.

Finding our way around a new place can be challenging so any data we have would be invaluable, and from this data we may want to find out:

  • Where are the best items?
  • Where’s the safest place for me to stay?
  • Where can I find the best companions?

There are three challenges to choose from.

Challenge 1: Worlds

Visualising data about our various game WORLDS, with datasets ranging from the landscapes of Minecraft to the depths of ocean warfare in battleship

Pick one of the data sets below, or a data set you've found, and visualise it.

Casual Difficulty - For those new to data visualisation or with limited time available)

Normal Difficulty - A fair size data set that could create multiple data visualisations

Heroic Difficulty - A large data set for those with more time available

Legendary difficulty - Bring your own data or expand on the data provided by bringing new data to the project

For those considering the Legendary difficulty (bring your own data), here are a few ideas to get you started:

  • Do you have a favourite character?
  • Did they appear in multiple match/games or is there one particular match/game that was interesting?
  • Do they have particular strengths that gives them an advantage over others?

Looking for data sets? Check out Sarah Bartlett's Twitter thread for data sources

Stuck for Ideas? Here are some questions you could try to answer:

  • Minecraft: Where can you find the best resources in Minecraft? Are there any potential dangers at these levels?
  • Super Mario Bros: How many enemies must Mario face to save the princess in Super Mario Bros?
  • PUBG: Where are the most dangerous places to land in PUBG’s Erangel map?

Challenge 2: Fonts

Boldly stepping into the world of FONTS, with tools to help you pick the perfect font combo and deploy it in your viz

We’ve put together a blog post on how to use fonts intentionally and challenge you to try it out this month. Read Tina’s post How to Pick the Perfect Font. Also, here are also some examples of good uses of font in visualizations from the Tableau community! Focus on the choices these authors have made to draw your attention by:

  • the style of font(s)
  • the font hierarchies
  • how color has been incorporated

Gaming On by David Borczuk

Horror Films - A Visual Story by Bo McCready

Mobile Data by Ghafar Shah


When creating your visualization, use 1-2 fonts max throughout the entire visualization. In place of using several fonts, use stylistic techniques mentioned in the post above such as (but not limited to): bolding, italicizing, spacing, color, etc. Create a visualization and narrative with a very intentional approach to fonts.

You can use any tools you’d like. A few of our favorites are:

  • For font discovery: Adobe fonts
  • For crafting text: Figma, PowerPoint, or Illustrator

If you are planning to use non-native Tableau Public fonts, the post includes supporting material for different tools to use: How to Pick the Perfect Font

When picking your fonts you may want to consider how accessible they are for your audience, this Medium post from The Readability Group gives a good overview on font accessibility: A Guide to Understanding What Makes a Typeface Accessible gives a great overview and introduction.

Challenge 3: Surviving Battleships!

Preparing data to SURVIVE against CLiam Browns Battleship AI, with steps to follow along the way

C.Liam Brown has built an AI to play Battleship, and it's very effective, but can we learn from previous matches where the safest squares are to give us a better chance of winning?

Special thanks to C.Liam Brown for supplying the data, try a game against the AI: https://cliambrown.com/battleship/play.php

Find the top 10 safest squares in Battleship, where safety is a square occupied by a player’s ship which has the highest win rate by the player. An overview of the full dataset has been provided here: Github/cliambrown, this task works from the battleship_game_squares.csv dataset.

  1. Input the data
  2. Filter ai_mode_id = 3 (the AI Learning mode)
  3. Create columns for the grid row and column for each square
  • e.g. row 1 will include squares 1-10, row 2 includes squares 11-20, etc.
  • and column 1 will contain 1, 11, 21, etc.
  1. Filter for squares that the player occupies, ai_ships == 0
  2. Reduce data set to ai_win, square, games, row, & column
  3. Create column, games won by ai, i.e. ai_win * games
  4. Create an aggregated view summing the ai_games_won and games field by square, row, and column
  5. Calculate the player win rate, i.e. games not won by the ai / games
  6. Sort by the highest player win rate and select the top 10
  7. Output the data

Inspiration from the Tableau community

Your content may be different but focus on the choices the authors have made in presenting the data, what would you do differently? And what aspects would you like to emulate? Here are some vizzes to check out for inspiration.

Pandemic Board Game Map, by Kevin Flerlage

Tableau Minesweeper, by Joshua N.Milligan

Zelda game, by Turner family

Board Game Simulation: Hold on to Your Cats!, by Kelly Gilbert

Game of the Year, by Lisa Rapp

Happy Mario Day, by Amar Singh

Trips in the Middle Earth, by Wendy Shijia

Fast Travel to 1899 (a Red Dead Redemption travel guide), by Tina Covelli

Profiling the Romanian video game players, by Razvan Zamifa

How to Submit

Team GNV

This project focuses a monthly theme that you can participate to challenge either data preparation, data visualization or visual design. Existing datasets on video games will be readily available and comes with difficulty scales to help those newer or with limited time to practice. You can also bring data from your own favorite games too! We love all types of games: card games, board games, video games, party games, game shows, the list goes on!

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