Final Project

 Recipe Analyzer Package

For my final project, I decided to create a Recipe Analyzer Package. The functions included in my package are designed to help users analyze nutritional information, scale recipes based on servings, and generate shopping lists from recipes.

Description: 

Package: RecipeAnalyzer

Type: Package

Title: Analyze and Scale Recipes

Version: 1.0

Date: 2024-04-25

Author: Elisa Hardie

Maintainer: ehardie@usf.edu

Description: Provides functions to analyze nutritional content, scale recipes, and generate shopping lists.

License: GNU General Public License

Imports:

  Depends: R (>= 4.0)

The first function, `analyze_nutrition`, calculates the nutritional information for a given recipe. This includes total calories, fat, carbohydrates, and protein.

#Analyze Nutrition function (with example values)

analyze_nutrition <- function(recipe) {

  

  # Example nutritional values per unit (could be expanded to use a real database)

  nutrition_data <- data.frame(

    ingredient = c("flour", "sugar", "butter"),

    calories_per_unit = c(100, 120, 150),

    fat_per_unit = c(0.5, 0, 11),

    carbs_per_unit = c(22, 30, 0),

    protein_per_unit = c(3, 0, 0.1)

  )

  

  # Join recipe with nutritional data

  detailed_recipe <- merge(recipe, nutrition_data, by = "ingredient")

  

  # Calculate total nutrition

  detailed_recipe$total_calories <- detailed_recipe$quantity * detailed_recipe$calories_per_unit

  detailed_recipe$total_fat <- detailed_recipe$quantity * detailed_recipe$fat_per_unit

  detailed_recipe$total_carbs <- detailed_recipe$quantity * detailed_recipe$carbs_per_unit

  detailed_recipe$total_protein <- detailed_recipe$quantity * detailed_recipe$protein_per_unit

  

  # Sum up nutritional values

  total_nutrition <- data.frame(

    total_calories = sum(detailed_recipe$total_calories),

    total_fat = sum(detailed_recipe$total_fat),

    total_carbs = sum(detailed_recipe$total_carbs),

    total_protein = sum(detailed_recipe$total_protein)

  )

  

  return(total_nutrition)

}

# Example recipe data

recipe <- data.frame(

  ingredient = c("flour", "sugar", "butter"),

  quantity = c(2, 1, 0.5),

  unit = c("cup", "cup", "cup")

)

# Calculate nutrition information

nutrition_info <- analyze_nutrition(recipe)

print(nutrition_info)

The second function, `scale_recipe`, is designed to adjust the quantities of ingredients based on the desired number of servings. This is particularly useful when recipes need to be scaled up, such as when you prep meals for the week.

#Scale Recipe function

scale_recipe <- function(recipe, original_servings, desired_servings) {

  # Calculate the scaling factor

  scaling_factor <- desired_servings / original_servings

  

  # Scale the quantities in the recipe

  recipe$scaled_quantity <- recipe$quantity * scaling_factor

  

  return(recipe[, c("ingredient", "scaled_quantity", "unit")])

}

# Example recipe data

recipe <- data.frame(

  ingredient = c("flour", "sugar", "butter"),

  quantity = c(2, 1, 0.5),

  unit = c("cup", "cup", "cup")

)

# Scale the recipe from 4 servings to 8 servings

scaled_recipe <- scale_recipe(recipe, original_servings = 4, desired_servings = 8)

print(scaled_recipe)

The third function, 'generate_shopping_list', returns a simplified shopping list showing the total amount of each ingredient needed.

generate_shopping_list <- function(scaled_recipe) {

  

  # Aggregate quantities by ingredient and unit

  shopping_list <- aggregate(scaled_quantity ~ ingredient + unit, data = scaled_recipe, FUN = sum)

  

  return(shopping_list)

}

# Example scaled recipe data

scaled_recipe <- data.frame(

  ingredient = c("flour", "sugar", "butter", "flour"),

  scaled_quantity = c(4, 2, 1, 2),

  unit = c("cup", "cup", "cup", "cup")

)

# Generate shopping list

shopping_list <- generate_shopping_list(scaled_recipe)

print(shopping_list)

Namespace:

export(analyze_nutrition)

export(scale_recipe)

export(generate_shopping_list)