Data Visualization

Data visualizations include simple graphs like bar and pie charts, scatter plots, and line graphs, but they can also be more complicated, like network visualizations or 3D models. This guide will walk you through the process of deciding if a data visualization project is a good choice for your research.

If you have questions about data visualization, want to discuss your research, or talk about training opportunities, reach out to the Digital Initiatives and Preservation department at AskDSP@udel.libanswers.com. 

Timeline Planning

For any project, but especially data visualization which requires technical skills, it is important to consider how long each step will take. You may be learning a new software platform, organizing and reformatting your data, creating summary statistics, and more. Factor this into your project timeline -- how long do you have to complete this project? A month, a semester, a year? Knowing this will help you choose an appropriate tool and visualization type. 

Planning Questions

1. What will happen to your data after your visualization project is finished? Consider how you will share and preserve the data. You may keep personal copies, or upload a copy to a data repository for wider use.  
2. How will you prepare the data? This includes cleaning, formatting, changing file types or structure, and more. This step often takes the most time, so factor that into your timeline. See the Working with Data guide for more information on data cleaning. 
3. Who is the visualization for? Consider your audience -- you will make different visualizations for yourself to analyze, experts in your field who are accustomed to data visualizations, or an administrator who just wants the highlights. Keep your intended audience in mind to make your visualization as effective as possible. 

We often think of data as numeric, but it can also be text, images, qualitative, time-based, spatial, and more. The best type of visualization for your research will depend on the data you're using and what you want to show on your visualization. 

The most important thing to remember about data visualization is that they are meant to make data easier to understand. Do not inadvertently make the data harder to understand with a confusing or overly complicated visual. The more you create data visualizations, the better you'll become at clarifying the dataset with the visual. See Making an Effective Data Visualization for examples. 

When choosing your visualization, consider the following:

• What kind of data do you have? 
  • Summary statistics? Time-based? Categorical? GIS or spatial? 
• What format is it in?
  • Is it tabular (spreadsheet) data? Free text? Another format like SQL or R?
• What do you want to show? 
  • Changes over time? Comparisons between a few separate categories? 

A great place to research data visualizations and find ones suitable to your goals is the Data Visualization Catalogue. You can search by function to see those appropriate to your data type or general visualization method.