Autoplotter Tutorial ❲90% TRENDING❳

She never wrote a ggplot from scratch for exploration again.

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

I’ve structured it like a data analyst’s journey from confusion to insight. Dr. Alia Khan, a marine biologist, stared at a CSV file named coral_bleaching_2025.csv . It had 14 columns: site , temperature , salinity , light_intensity , bleaching_score , date , depth_m , turbidity , nitrates , ph , algae_cover , fish_diversity , treatment , and recovery_days . She never wrote a ggplot from scratch for exploration again

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: I’ve structured it like a data analyst’s journey

data %>% filter(depth_m < 10) %>% auto_plot(by_group = treatment) # separate dashboard per treatment And for Shiny apps:

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 :

autoplotter allowed :