I am a beginner in data science and I am confused about whether I should spend time learning base R plotting functions or jump straight into ggplot2. Everyone says ggplot2 is better for publication-quality graphics, but is it harder to learn? Which approach is more efficient for creating complex multi-panel charts and customized themes in modern data analysis workflows?
3 answers
The fundamental difference lies in the "Grammar of Graphics" philosophy. Base R follows a "canvas" approach where you add elements sequentially, which can get messy if you want to change something later. ggplot2, however, works with layers. This makes it incredibly easy to map variables to aesthetics like color or size. For multi-panel plots, facet_wrap() and facet_grid() are game-changers that would take dozens of lines of code in base R. While the initial learning curve for the + syntax is steeper, the long-term efficiency for creating professional, reproducible themes is far superior in ggplot2.
While ggplot2 is great for static plots, do you think it's still the best choice if the end goal is an interactive dashboard? Or would you suggest moving towards something like plotly or Shiny directly to avoid learning multiple plotting syntaxes?
Base R is still useful for quick exploratory histograms using hist(), but for anything shared with stakeholders, I strictly use ggplot2 for its theming.
Exactly, Barbara. I also find that ggthemes provides pre-built styles like the "Economist" or "Wall Street Journal" look, which saves so much time during the final reporting phase.
Christopher, that's the beauty of the R ecosystem! If you know ggplot2, you can literally wrap your plot in ggplotly() from the plotly library, and it becomes interactive instantly. Learning the ggplot2 syntax provides the foundation for almost all high-end visualization in R, including what you'll eventually use inside Shiny apps for professional business intelligence reporting.