Introduction of Data

data <- read.csv("diet_recommendations_dataset.csv", header = TRUE, sep = ",")
str(data)

Pre-processing

# Select columns
data_selected <- data[, c('Age', 'Gender', 'BMI', 'Disease_Type', 'Severity',
                          'Daily_Caloric_Intake', 'Cholesterol_mg.dL', 
                          'Blood_Pressure_mmHg', 'Preferred_Cuisine', 
                          'Weekly_Exercise_Hours', 'Dietary_Nutrient_Imbalance_Score', 
                           'Diet_Recommendation')]

# Removing rows that contain NA values
data_needed <- na.omit(data_selected)

# Import packages
library(ggplot2)
library(dplyr)

General exploration

Gender vs Disease Type by Proportion

Gender vs BMI

BMI vs Daily Caloric Intake

BMI vs Weekly Exercise Hours

BMI vs Nutrient Imbalance Score

Exploration of Severity

Interpretation

  • Top 2 highest numbers of patients across 3 scales of severity
  • They both heavily rely on frequent use of spiciness, sauces and strong flavors, that’s the key spirit to complete the cuisine.

Severity and Nutrient Imbalance Score

Exploring Diet Recommendation

Blood Pressure vs Diet Recommendation

BMI vs Diet Recommendation

Reference Link