ggplot(data, aes(temperature, bleaching_score)) + geom_point(aes(color = fish_diversity > 6), alpha = 0.7) + geom_smooth(method = "lm", se = FALSE, aes(group = fish_diversity > 6)) + labs(title = "High fish diversity buffers thermal bleaching") Saved as Figure_2.png and submitted to Coral Reefs journal. | Function | Use case | |----------|----------| | auto_plot(df) | Interactive EDA dashboard | | auto_scatter(df, x, y, color) | Smart scatter with defaults | | auto_report(df) | Export a full exploration document | | auto_shiny(df) | Launch a custom Shiny explorer | | auto_notes(df) <- "text" | Attach metadata to plots |
auto_shiny(data) # launches a Shiny app with dropdowns for x/y/facet Using auto_plot() , Alia noticed something unexpected: In sites with fish_diversity > 6 , the temperature ~ bleaching_score slope was nearly flat. She never would have thought to facet by that without the automated exploration. autoplotter tutorial
She never wrote a ggplot from scratch for exploration again. She never wrote a ggplot from scratch for exploration again
data %>% filter(depth_m < 10) %>% auto_plot(by_group = treatment) # separate dashboard per treatment And for Shiny apps: % filter(depth_m <
auto_plot(data, point_alpha = 0.6, boxplot_fill = "skyblue", theme_use = "minimal", max_cat_levels = 10) # ignore high-cardinality columns For even more control, she used :
Alia whispered: “This would have taken me 3 hours.” But defaults weren’t perfect. The site names were long, and points overlapped.