By Martin Hadley, Research Technology Specialist at the University of Oxford specializing in data visualization, and course instructor on Interactive Visualization with R for Social Scientists.
Data in a table or spreadsheet is valuable, but as human beings, seeing patterns or being able to summarize tabular data can be challenging. Visualizations allow us to do exactly that – create a visual representation of the data, giving us a chance to see it and interpret complex data efficiently at a glance.
Now, let’s consider the implications of making those visualizations interactive. It might seem that making content and data interactive is more about keeping up with trends, but actually adding interactivity to your visualizations is profoundly powerful. Interactive visualizations enable the following benefits:
1. Multiple questions:
Interactivity allows us to pose multiple questions per visualization – allowing us to switch axes or to add confabulating factors, which could be very useful for survey data, and allowing viewers to break down responses to a specific question by gender, age or perhaps occupation.
2. Focus on detail:
Interactivity allows users to zoom into a visualization – physically selecting an area of interest and blowing up that area of the chart. Hover information is also incredibly useful – if you’re built a choropleth that visualizes the relative populations of different countries, allowing users to hover over a country to get the exact population value is much more useful than simple providing a color gradient legend.
3. User experience:
Interactivity is particularly powerful when you allow users to select points or series in a chart and for a summary of the relevant data to appear. This allows us to build clean looking visualizations that are actually very rich and provide a tool for viewers to explore your data as little or as much as they’re interested.
Interactive visualization is particularly useful for social science as we usually look for variation or trends over time, which are easy to spot in visualizations. Of course, once we do find interesting variation, the next step is to try to explain it. This usually means bringing in additional variables. Interactive data visualization allows you to zoom in on an interesting variation, and then quickly add additional variables in order to explain the pattern you have found. In addition, interactive visualization will also help you identify observations that do not fit the explanation. These would certainly deserve further exploration!
The next cohort of Interactive Visualizations with R for Social Scientists starts on July 9th. Find out more and sign up here.