Our MBA students in Data Journalism produced a series of tutorials as their final project in the Low Code course: Transforming data into news stories without programming, taught by Professor Adriano Belisário. This month you can check out some of their work and... Enjoy the adventures with the tutorials they've created. Today you can check out the tutorial made by Ana Soraggi.
One of the issues that arises in the data visualization process is related to the choice of tool to be used, as there are currently several possibilities available. They also vary in the quantity and quality of the resources they offer and in the degree of complexity in their application. Thus, it is possible to build visualizations using the charting tool of a spreadsheet editor, such as Google Spreadsheets, or using vector software, such as Adobe Illustrator, and libraries developed from programming languages, such as Python, R, or JavaScript.
Between these two points, it's possible to find some intermediate tools that are available online. They tend to be intuitive and have good support from developers or the community itself. They also allow for the creation of more complex visualizations than those made by spreadsheet editors, and in some cases, even offer interactive features. So, they can be a good starting point for exploring new possibilities for data visualization.
Even so, choosing which of these tools to start with is a challenge, as there are currently several options available. The best approach is still to learn about and test the features each one offers to understand which is most suitable for your context. This is a very useful exercise since the possibilities for presenting a dataset visually are usually not limited to just one alternative. Thus, familiarity with the features offered by some of these tools can facilitate your selection process when creating this material.
Therefore, the objective of this tutorial is to present the general characteristics that these tools usually offer, starting with a presentation of the workflow for creating a chart in Datawrapper. The issues raised may serve as a starting point for the reader when exploring other available visualization tools. Some of these are listed at the end of this text.
1. Free Resources Available
Datawrapper offers free access or subscription options for individuals or businesses, including more support and resources.
In the free version, you can publish unlimited previews with attribution to the platform and export previews as PNG files or as a link to embed on your webpage. Paid plans offer more features and support. At the bottom of this page, you can compare the differences between the plans.
2. Workflow
To begin working, you need to create an account that will give you access to your workspace. It is on this page that you can create a project and access previously developed projects. The platform offers the possibility of developing visualizations using three main approaches: graphs, maps, and tables.
Charts: the tool offers 19 chart templates that can be customized with adjustments and some attributes, creation of legends and comments, and layout specifications.
Maps: the tool allows the development of 3 types of maps.
Tables: the tool allows you to include charts in tables and add other visual elements, such as colors and icons.
This tutorial focuses on the workflow for developing charts.
When starting a new project, the first step requested by the platform is to... data importFour file format options are offered for the data: copy and paste the data into the indicated field, XSL/CSC, Google Spreadsheet, or access to the dataset via an external link. To test and explore the tool, some sample databases are provided.
The next step involves checking. and description of the data This is so that the tool understands what is a number, date, and text. This step is necessary to ensure that the chart correctly assigns and displays the information and relationships that the dataset provides. At this point, it is possible to make specific adjustments to the data organization, such as changing rows to columns, defining headers, or specifying the correct number and date format.
The third stage consists of visualization construction It begins with defining the type of chart. At this point, it's helpful to run tests to see how your data behaves and identify which chart most effectively demonstrates the relationship between the data you want to highlight. Simply click on the chart types on the left and the visualizations will load.
Once the chart is defined, it's possible to adjust some of its attributes, such as colors and legends, or even the organization and order of the data in the visualization. These customization possibilities vary depending on the visualization chosen previously.
In the notes section, you can insert text that provides context for understanding the relationships presented in the visualization and contributes to good understanding.
Transparency practices. Thus, it is possible to insert a title, description, notes, source and access to the data, authorship of the graph, and a description of the information intended for reading by accessibility tools. Another accessibility feature that the platform provides is related to color checking. Thus, if the colors chosen for visualization make it impossible for a person with some degree of visual impairment to understand, the platform generates an alert in the highlighted field.
Finally, in the third step, the layout tab allows you to make further adjustments to the data presentation, which are most useful if it's applied to a web page. Also regarding layout, you can always test the display behavior on desktop, mobile, and tablet displays in the highlighted field.
Once the visualization is complete, the fourth step allows you to export it as a PNG file or create a link for inclusion on web pages.
Conclusion
Choosing a tool for building a data visualization depends on several factors, ranging from the dataset and the relationships you want to represent to more practical issues, such as the time available to complete the task. Each tool offers its own specificities and limitations, and understanding them helps in the process of developing a data visualization project.
Datawrapper is an interesting tool to start venturing into building more elaborate visualizations, as its workflow is very intuitive and they offer databases to explore its features. Furthermore, it's also possible to find guidance on the tool and knowledge about the data visualization process on the platform itself. Another educational resource they offer is a presentation proposal for conducting training on the tool. However, a potential obstacle is the language, as all the material and the platform are in English.
Finally, here is a selection of other tools for building visualizations:
- a. Flourish
- b. Tableau Public
- c. RawGraphics
- d. Infogram
