Build menus using USDA data, solve them using the simplex algorithm, scrape recipes from Allrecipes, and do some exploratory NLP.
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README.md

Food for Thought 🍜

This is a working exploration of food and that touches upon a few things: APIs, algorithms, simulations, web scraping, NLP, networks. The 🚧 very much under construction writeup 🚧 walks through the highlights.

The main poriton of this project is a daily menu planning and optimization using foods from the USDA food database and the Rglpk package interface to the GNU linear programming solver to implement an algorithmic solution to a given menu if one exists. This minimizes the cost of each menu while keeping us above the minimum daily nutrient values and below the daily maximum "must restrict" values. If solving isn't possible, we move to swapping foods in and out of the menu. We'll simulate what proportion of menus are solvable with 0 or more swaps.

The latest phase is intended to optimize the tastiness of menus. Menus were scraped from Allrecipes in order to identify which foods tend to co-occur in menus in which portion sizes. The adventure of getting portion sizes from wild recipes is most of what /scrape deals with. I do a little tidy text analysis on these menus to top it off.

Structure

  • api
    • Grab data from the USDA API
    • Tidy the JSON and send that chunk to a database (Postgres, MySQL) such that only one chunk is kept in memory at a time
  • data
    • Raw abbreviated data in ABBREV.csv
      • Normalized in scaled.csv
    • Daily nutrient constraints in all_nut_and_mr_df.csv
  • menu-builder
    • Code for the Shiny app for building a random menu and tweaking it into compliance

Note: a few functions here rely on a personal package collecting a smorgasbord of functions, dobtools, which you can install with devtools::install_github("aedobbyn/dobtools").

  • scripts
    • All scripts are stored in sub-directories here. These are pure scripts and do not contain any assignment of data to variables so that they can easily be loaded without executing any code. Top-level .R scripts here call functions defined in these sub-directories. These are batch sourced in with dobtools::import_scripts().

    • /prep

      • Read in, clean, and standardize data
      • Define daily guidelines for
        • "Must restricts" (macros that have an daily upper limit)
        • "Positive nutrients" (micronutrients that have a daily lower bound)
    • /build: build a random menu without worrying about compliance yet; run in build.R

      • build_menu.R adds random foods (1 serving size per food) until we reach 2300 calories (the daily minimum)
      • test_compliance.R tests for compliance on the three dimensions we care about: must restricts, positives, and calorie content
      • smart_swap.R loops through must restricts; if the daily value of that must restrict is over the max limit, until we're compliant, swap out the "worst offender" that respect and replace it
        • if possible, with a food from our corpus that is < 0.5 standard deviations below the mean per gram on that nutrient
        • else, with a random food
      • adjust_portion_sizes.R is a brute force alternative to a linear programming solution (defined in /solve) that only addresses positive nutrients
        • If the combined amount of a nutrient in our menu is below the minimum, find the food in our menu that is highest in this positive nutrient per gram and increase its amount by 10% until we're above that particular nutrient threshold
        • adjust_portion_sizes_and_square_calories.R does the same while decreasing the total calorie count in the amount that it was increased by the adjustment
      • master_builder.R does all the above while staying above the 2300 calorie minimum
    • /solve: solve a menu, do a single smart swap, and test its compliance

      • solve_it.R uses the GNU lienar programming solver from the Rglpk package to minimize cost (randomly generated for now) while satisfying each nutritional and calorie constraint
        • Takes a menu with either nutrients in nutrient values per 100g of food or raw gram weight of nutrients, as specified by the df_is_per_100g boolean flag, and a dataframe of nutritional constraints
        • Some levers to pull here:
          • Upper and lower bounds for each portion size
          • Should this be solved with only full portion sizes (integer coefficients on the original portion sizes provided)?
          • Should you be told the cost and whether we arrived at a feasible solution?
      • solve_menu.R
        • Take a solution (from solve_it()) and return a menu with the newly solved portion sizes (GmWt_1)
        • Optionally be told which food we've got the most of (largest number of portions) -- something we might want to decrease
      • solve_nutrients.R
        • Take a solution and get the raw values of each of the nutrients in the solved menu
        • Optionally be told which nutrient we've overshot the most on
      • smart_swap_single.R loops through must restricts only once and, if we're uncompliant, uses the swapping mechanism in smart_swap() to find a suitable replacement
    • /score

      • Give a given menu a score on must-restricts, positive nutrients, and a combined measure
      • Also rank_foods() by their score
        • This allows us to build a naive_supermenu.R using build_best_menu()
    • /simulate

      • Two types of simulations here: simulating building and solving random menus given a minimum portion size for all ingredients and simulating scraping some menus and determining what proportion of them are bad URLs (404s)

      • Solving:

        • In simulate_spectrum(), which calls get_status(), we take a range of minimum portion sizes (say from -1 to 1), a number of intervals we want to break that range into, and the number of simulations we want to do at each interval
        • We get back a dataframe with the minimum portion size at each interval and corresponding proportion of menus that could be solved at that min portion size
        • This allows us to graph a curve of how the percent of menus that are solvable changes as we increase the minimum portion size
        • simulate_swap_spectrum() is the same except we give ourselves a user-defined number of single swaps we can do before giving it up as an unsolvable job
      • Scraping:

        • In simulate_scrape() we scrape a random percent of a vector of URLs and determine what proportion of them are Bad URLs
        • In simulate_scrape_on_lst() we take a list of scraped menus (the product of get_recipes() before it has gone through dfize()) and, for a spectrum of percents of those menus, deterimine what porportion of them are bad
        • Then we can graph how the variance of the proportion of bad URLs decreases as we increase our sample size
    • /test

      • dobtools::run_tests() provides a user-directed way to run all or some of the tests in the testthat directory, interactively or non-interactively
      • There is a test script for each of the main script directories: prep, build, score, scrape, simulate, solve
    • Helper functions

      • get_raw_vals() and get_per_g_vals(), which both take dataframes and a dataframe of nutrients, allow us to go back and forth between nutrients per 100g and raw weight of nutrients using the helper functions
      • transpose_menu() makes foods into columns and nutrients into rows, leaving us with something that looks more like our constraint matrix and can be read left to right

Potential future directions include:

  • Glean ideal food group proportions from scraped menus such that we can balance menus by food group better
  • Build three distinct meals per menu (breakfast, lunch, and dinner)
  • Incorporate food flavor profiles to increase flavor consistency within a meal
  • Join in actual food costs
  • Cluster analyses
    • Can we reverse-engineer food groups?

Contributions welcome! 🍻