This project involves creating a daily allergy alert list based on school lunch menus, their ingredients, and the data of students with allergies. By doing so, it helps prevent students from unknowingly consuming allergenic foods and reduces the risk of emergency situations.
This code analyzes vegetable waste by grade and week using Bayesian modeling in Stan, with R for data prep, prior estimation, and visualization. It predicts waste probability, compares observed vs. predicted values, and plots trends over weeks and grades.
This Shiny app visualizes Korean crop production seasons using interactive filters for crops, months, and seasons, displaying results with Plotly segments and a data table, built with R, dplyr, and ggplot2.
This code analyzes meal evaluation data and BMI using R, calculating correlations, visualizing distributions with boxplots and word clouds, identifying high-risk students, and assessing program impact on snack/drink consumption with paired t-tests.