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Yesterday evening I gave a talk about data visualization to Periodic Tables, a Science Cafe run by Misha Angrist. It was a lot of fun! Amongst other things, I made an animation of the NOAA Daily Sea Surface Temperature Graph from the other week. Here it is:
Here’s the static graph.
Global mean sea surface temperature 1981-2024
And because the hardy perennial of whether, for the sake of honesty and not Lying With Graphs, you should always have your y-axis go to zero also came up, I made a zero-baseline version of the average temperature graph.
Mean global sea surface temperature with a zero baseline on the y-axis
I’ve added these to the Github repo. In making the animation, I found a nice little wrinkle that let me put a ticking version of the year in the title even though year is not the frame_along driving the transition_reveal() that makes the animation. If I get a chance I’ll write this up separately.
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Continue reading: Daily Average Sea Surface Temperature Animation
Key Points and Long-Term Implications on Data Visualization in R
The main subject of the text is a discussion on the value and techniques of data visualization using R language, with a specific example from the field of climate studies – sea surface temperature. This poses significant implications for the future of data presentation and analysis, not just in the realm of climate science, but across the board.
Data Visualization as a Tool for Honest Representation
The author delved into the importance of not “Lying with Graphs”, emphasizing the concept of maintaining transparency and honesty when using data visualization. The debate of using a zero baseline on the y-axis became an example of this situation. A zero baseline provides a genuine comparison point, ensuring data representation remains accurate and not misleading. Therefore, considering how data are graphed is crucial.
This implies that any person or institution using graphs to illustrate data, must always consider their ethical obligation to the viewers and maintain integrity by presenting data accurately.
Animation in Graphs as a Means of Improved Data Visualization
In the piece, the author created a new and dynamic model of data representation by integrating animation into the graphical representation of sea surface temperature data. This technique allows researchers or analysts to demonstrate how certain values evolve over time clearly and efficiently. It adds a new dimension to the usual static nature of graphs, improving audiences’ understanding and engagement.
As such, it is recommended that experts using R explore the potential of using animation in their statistical representations not only for clarity and comprehensibility but also to increase engagement.
Future Opportunities Opened by R
The use of R for climate study, like creating sea surface temperature graphs, signifies that the programming language’s applications can extend to other scientific domains. By documenting and sharing these experiences on platforms like Github, the R users’ community can grow and learn. Opportunities to create more sophisticated visualizations or to develop new analysis approaches may emerge.
Therefore, it is suggested to continue promoting these collaborations and knowledge sharing through online communities and platforms.