Our Recommended Tools

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When creating this book, we aimed to identify the most essential data visualization tasks that beginners are likely to face, and the digital toolkit needed to complete those tasks. In the prior section we listed ten factors that influenced our tool recommendations, such as being easy-to-learn, free or affordable, with powerful capacity. In this section, we have listed all of the tools featured in this book, with recommended uses and references to chapters where they appear, as shown in Table 1.1. Your data visualization projects may only require you to use only a small number of these, or perhaps even just one tool. But it’s important to be aware of the different types of tools, because you may not realize how they can help you if don’t know that they exist.

Table 1.1: Recommended Tools and Uses, with Chapter References
Tools	Collect	Clean	Chart	Geocode	Map	Table	Code	Transform
Google Sheets spreadsheet/charts	Ch2	Ch4	Ch6	Ch2		Ch8
LibreOffice Calc spreadsheet/charts	Ch2
Airtable relational database	Ch2
Tabula PDF table extractor		Ch4
OpenRefine data cleaner		Ch4
Datawrapper charts/maps/tables			Ch6	Ch7	Ch7	Ch8
Tableau Public charts/maps/tables			Ch6		Ch7	Ch8
Chart.js code templates			Ch11
Highcharts code templates			Ch11
Google My Maps simple map maker				Ch7	Ch7
Leaflet map code templates					Ch12
GitHub edit & host code							Ch10
GitHub Desktop & code editor							Ch10
GeoJson.io edit & draw geodata								Ch13
Mapshaper edit & join geodata								Ch13
Map Warper georeference images								Ch13

If this list initially looks overwhelming, don’t worry! Newer users can complete most of the twelve introductory-level chapters in this book with only two easy-to-learn tools. Begin with Google Sheets for spreadsheets and basic charts, then move up to Datawrapper for more advanced charts and maps. You can create amazing data visualizations with just these two tools. Also, they play nicely together, as Datawrapper allows you to directly import and update data from Google Sheets.

In addition to the tools featured in Table 1.1, you’ll also see many more useful add-ons and assistants mentioned in the text, including ColorBrewer to select map colors, the Geocoding by SmartMonkey add-on for Google Sheets, and the W3Schools TryIt iframe page. Also, consider enhancing your web security by installing the free Privacy Badger browser extension from the Electronic Frontier Foundation to view and exercise some control over who’s tracking you, and also review the EFF’s Surveillance Self-Defense Guide.

We often make compromises about tools that excel in some criteria but not others. For example, the tool most frequently featured in our book’s tutorials is Google Sheets, because it’s easy-to-learn, free, and powerful. But Google Sheets is not open-source, and some people express concerns about giving Google too much access to their information. To address the latter point, one way to make this compromise more palatable is to create a specific Google account to your data visualization work from your private life.

Finally, we recognize that digital tools are continually changing and evolving. Some tools we only discovered because someone mentioned or tweeted about it while we were writing this book. As time goes by, we expect that some tools will no longer be available, and we also anticipate discovering newer ones that do a better job of telling our data stories. If you’d like to recommend a tool that’s not currently on our list, contact the authors and tell us how it rates on the ten factors that guide our selection process above.