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The Fundamentals of Data-driven Storytelling
The Fundamentals of Data-driven Storytelling
  • About this course
    • Course Introduction
  • Module 1 - Find
    • 1.1 How to Find Data for Storytelling and journalism
      • Starting with a question
      • Open data portals and platforms
      • Other sources of data
    • 1.2 How to get better data from a Goolge Search
      • Searching for filetypes and formats
      • More on Advanced Search operators
      • Other common Google Search operators
    • 1.3 Sourcing your own data
      • Creating a Google Form for Research
      • Creating a questionnaire with TypeForm
      • Using quizzes and comments as a sources of data
  • Module 2 - Get
    • 2.1 Turning websites and PDFs into machine readable data
      • Scraping data with Tabula
    • 2.2 An introduction to spreadsheet software
      • Google Sheets, Microsoft Excel and Libre Office Calc.
      • Finding your way around a spreadsheet
      • Simple web scraping with Google Sheets
  • Module 3 - Verify
    • 3.1 Can I use this data in my work?
      • Initial steps for verification
      • What do these column headings mean?
  • Module 4 - Clean
    • 4.1 What to do with disorganised data?
      • Why is clean data important?
      • Keep your data organised
      • Cleaning data cheatsheet
  • Module 5 - Analyse
    • 5.1 What is the story within the data?
      • Spreadsheet rows, columns, cells and tabs
        • Spreadsheet formats, forumlas and essential shortcuts
          • Using the VLOOKUP Function
            • Combine Data From Multiple Spreadsheets
    • 5.2 How to turn numbers into stories
  • Module 6 - Visualise
    • 6.1 Ways we visualise data
    • 6.2 Why we visualize Data
    • 6.3 How to visualise data
  • Course Testing & Feedback
    • ⏱️Quick course exam
    • 🎓Extended course exam
    • 📝Survey and feedback
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  1. Module 6 - Visualise

6.3 How to visualise data

Previous6.2 Why we visualize DataNextQuick course exam

Last updated 2 years ago

The process of visualising data is known as visual encoding.

Visual encoding is the process of mapping data variables to visual properties in order to create a visual representation of the data.

These visual properties include;

  • Colour 🟢

  • Line weight —

  • Area ▭

  • Position ↸

  • Height ↕

  • Length ↔

Sound complicated? don't worry! there are many online tools auto-generate the process of visual encoding using templates

  • User only needs to upload the data.

  • Then choose from a selection of different visualisation options.

  • And customise with labels, annotations, colour and images.

Graphic: Alberto Cairo, The Truthful Art: Data, Charts and Maps for Communication

Click the expander below to view our step-by-step visual encoding guide

Visual Encoding Guide | Step-by-step

Visual encoding is a process of mapping data variables to visual properties such as size, shape, color, position, and texture, in order to create a visual representation of the data that is easy to understand and interpret. The following are the steps to conduct visual encoding:

  1. Identify the data: The first step is to identify the data that needs to be visualized. This might involve collecting data from various sources or using data that has already been collected.

  2. Determine the purpose: Determine the purpose of the visualization, including the questions that need to be answered and the audience that will be viewing the visualization.

  3. Select the appropriate visualization technique: Choose the most appropriate visualization technique based on the type and complexity of the data, as well as the purpose of the visualization. This might involve using charts, graphs, maps, or other types of visualizations.

  4. Map data variables to visual properties: Map the data variables to the visual properties selected in the previous step. For example, if using a scatter plot, map the x-axis to one variable and the y-axis to another variable.

  5. Choose appropriate scales: Choose appropriate scales for the visual properties used to encode the data. This might involve selecting appropriate color palettes, size ranges, or axis ranges.

  6. Add contextual elements: Add contextual elements to the visualization to provide additional information or context to the audience. This might include labels, annotations, or other explanatory text.

  7. Refine the visualization: Refine the visualization by adjusting the design, layout, or other elements to ensure that it effectively communicates the desired message or insights to the audience.

  8. Test and validate: Test and validate the visualization with the intended audience to ensure that it effectively communicates the desired message and insights.

By following these steps, data analysts and visualizers can effectively create visualizations that are easy to understand and communicate complex data insights to a wide range of audiences.