Autoplotter Tutorial -

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 |

Alia whispered: “This would have taken me 3 hours.” But defaults weren’t perfect. The site names were long, and points overlapped. autoplotter tutorial

auto_notes(data) <- "Temperature above 29°C drives bleaching, mitigated by shading treatment." Those notes appeared in the report’s appendix. Alia had to re-run the same plots weekly as new data arrived. autoplotter worked inside dplyr pipelines: Her PI said: “Can you send me all

Her final discovery plot:

auto_scatter(data, x = temperature, y = bleaching_score, color = treatment) + geom_smooth(method = "lm", se = FALSE) + labs(title = "Bleaching increases with temperature, worse in control") Still one line of code for the plot, but now custom. Her PI said: “Can you send me all the key relationships by tomorrow?” point_alpha = 0.6

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 :