Last year, data journalists from the BBC overhauled the process by which they produced graphics for the BBC News website. They expanded their capabilities in R (using the ggplot2 package) for complex data analysis and they came up with a “graphics cookbook” that codified hard-earned knowledge for other team members to profit from.
This approach did not magically turn the other members of their team into coding wizards but it did familiarize people with R so that there was much less resistance and greater shared learning when online tutorials on R were launched later.
Learn in much greater detail how the BBC Visual and Data Journalism Team transformed their graphics and data visualization capabilities in this report from Medium.com.
ggplot2 gives you far more control and creativity than a chart tool and allows you to go beyond a limited number of graphics. Working with scripts saves a huge amount of time and effort, in particular when working with data that needs updating regularly, with reproducibility a key requirement of our workflow.
In short, it was a game changer, so we quickly turned our attention to how best manage this newly-discovered power.
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The focus on creating a reproducible workflow means we can create as many charts as possible completely in R, without having to open them up in a different program to add finishing touches, and collecting all our knowledge in one place made it easy to spread to team members less comfortable with using R.
Is R a solution to all our charting needs? No. We don’t use it for interactive graphics, for which the JavaScript library D3 is better suited, and sometimes it is much less work to tweak an annotation separately using software like Illustrator than directly in R. But for static charts we’ve found R and ggplot2 to be very useful.
Most important, perhaps, was the team work: we made exponential gains in knowledge by pooling our efforts and sharing our skills. Because developing our use of R was not one person’s sole responsibility, but rather shared among several people on the data team experimenting in parallel, our collected knowledge grew much faster than it would otherwise have done.
