# Step 1: Load the food survey dataset
# 'food_coded.csv' contains survey responses about food habits and preferences.
food <- read.csv("food_coded.csv")

# Step 2: Inspect the structure and first few rows of the data
# This helps us understand variable types and spot obvious issues.
str(food)
head(food)

# Step 3: Install and load the skimr package
# skimr provides clean, readable summaries of datasets, including distributions and missing values.
install.packages("skimr")
library(skimr)

# Step 4: Generate a comprehensive summary of the data
# skim() outputs key statistics for numeric and categorical variables
# It shows counts, missing values, means, min/max, and small histograms for numeric variables
skim(food)
