Calculate the Cost and Environmental Impact of a Ideal Diet
Float range
Discrete range
Food constraint data addition
Emission data addition
Food group constraint data addition
Nutrients data addition
Price data addition
Calculates grouped results for a Monte Carlo Simulation
Calculates results for a Monte Carlo Simulation
Missing value check
ID mismatch check
Food/price mismatch check
Individual/diet mismatch check
Minimum intake food groups check
Applies non-nummeric value check to entire dataframe
Non-numeric check
Spellcheck
Variety check
Linked foods check
Optional food groups check
All zero difference check
Weekly conversion
Food group serves conversion
Nutrient targets conversion
Food data creation
Food group data creation
Nutrients data addition
Random meal plan
Difference calculator
MJ to KJ conversion
Single-function food dataframe creation
Single-function food group dataframe creation
General difference calculation
Food group serves calculator
Nutrients values calculator
Percentage values calculator
Join function
Monte Carlo simulation
Single-function Monte Carlo simulation and results export.
Nutrient data application to random meal plan created
Permitted individuals check
Pipe operator
Price/emission data application to random meal plan created
Exportation of Monte Carlo results
Random deletion
Redmeat flag
Suffix removal
Safe sampling
Sauces, protein and discretionary food groups treatment
Standard name check
Starchy vegetables serves addition
Pre-treatment of constraint data
Treatment of food group constraints dataframe
Unique value check
Data upload
Easily perform a Monte Carlo simulation to evaluate the cost and carbon, ecological, and water footprints of a set of ideal diets. Pre-processing tools are also available to quickly treat the data, along with basic statistical features to analyze the simulation results — including the ability to establish confidence intervals for selected parameters, such as nutrients and price/emissions. A 'standard version' of the datasets employed is included as well, allowing users easy access to customization. This package brings to R the 'Python' software initially developed by Vandevijvere, Young, Mackay, Swinburn and Gahegan (2018) <doi:10.1186/s12966-018-0648-6>.