Package 'DIETCOST'

Title: Calculate the Cost and Environmental Impact of a Ideal Diet
Description: 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>.
Authors: Henrique Bracarense [cre, aut] (ORCID: <https://orcid.org/0009-0001-5964-9969>), Thais Marquezine [aut] (ORCID: <https://orcid.org/0000-0002-9415-5817>), Rafael Claro [aut] (ORCID: <https://orcid.org/0000-0001-9690-575X>)
Maintainer: Henrique Bracarense <[email protected]>
License: MIT + file LICENSE
Version: 1.0.0.0
Built: 2026-06-10 07:33:34 UTC
Source: https://github.com/hbracarense/dietcost

Help Index

Float range

Description

Checks if a numeric variable is within a continuous float range.

Usage

add_float_range(variable, min, max)

Arguments

variable

Numeric variable.
min

Minimum possible value.
max

Maximum possible value.

Value

No return value,only performs a check.

Discrete range

Description

Checks if a variable is within a discrete range.

Usage

add_range(variable, range, message)

Arguments

variable

variable.
range

Allowed range.
message

Message to be printed in case of failure.

Value

No return value, only performs a check.

Food constraint data addition

Description

Adds nutrients constraint data, according to chosen diet, to foods dataframe.

Usage

addConstraintData(filepath, df, diets, max_scale, override_min = NULL)

Arguments

filepath

Path in which the dataset, in .xlsx format, is stored..
df

Foods dataframe.
diets

Chosen diets. Constraint sheets in foods dataset must be of format 'constraints_DIETNAME_diet_foods', then the parameter passed will be DIETNAME. Can be a vector of diets in format c('DIETNAME1','DIETNAME2',...,'DIETNAMEN').
max_scale

Maximum scale.
override_min

If is not null, overrides all minimum values.

Value

Foods dataframe with constraints columns.

Emission data addition

Description

Adds emission data to foods dataframe.

Usage

addEmissionData(filepath, df, emission_cols = NULL)

Arguments

filepath

Path in which the dataset, in .xlsx format, is stored.
df

Foods dataframe.
emission_cols

Optional parameter. Emission column names if standard dataset isn't used.

Value

Food dataframe with emission data.

Food group constraint data addition

Description

Adds serves constraints to food groups dataframe

Usage

addFoodGroupsConstraintData(filepath, df, diets)

Arguments

filepath

Path in which the dataset, in .xlsx format, is stored..
df

Food groups dataframe.
diets

Chosen diets. Constraint sheets in foods dataset must be of format 'constraints_DIETNAME_diet_food_groups', then the parameter passed will be DIETNAME. Can be a vector of diets in format c('DIETNAME1','DIETNAME2',...,'DIETNAMEN').

Value

Food groups dataframe with added constraint data.

Nutrients data addition

Description

Adds nutrients data to foods dataframe.

Usage

addNutrientData(filepath, df)

Arguments

filepath

Path in which the dataset, in .xlsx format, is stored..
df

Foods dataframe.

Value

Foods dataframe with nutrient columns.

Price data addition

Description

Adds price data to foods dataframe.

Usage

addPriceData(filepath, df)

Arguments

filepath

Path in which the dataset, in .xlsx format, is stored..
df

Foods dataframe.

Value

Foods dataframe with added price data.

Calculates grouped results for a Monte Carlo Simulation

Description

Calculates a confidence interval for price and footprints obtained through a Monte Carlo Simulation, grouped by food groups. This function should be employed only if the standard table supplied with this package is utilized. Prints a .xlsx file in the home directory.

Usage

calculateGroupedResults(path_file, report_path, confidence_interval)

Arguments

path_file

A string containing the path to the folder containing the .csv files created in the monteCarlo function.
report_path

A string containing the path to where the report will be saved.
confidence_interval

A float. Must be either 0.01, 0.05 or 0.1.

Value

No R object return, prints an Excel workbook.

Calculates results for a Monte Carlo Simulation

Description

Calculates a confidence interval for several parameters obtained through a Monte Carlo Simulation. This function should be employed only if the standard table supplied with this package is utilized. Prints a .xlsx file in the home directory.

Usage

calculateResults(path_file, report_path, confidence_interval)

Arguments

path_file

A string containing the path to the folder containing the .csv files created in the monteCarlo function.
report_path

A string containing the path to where the report will be saved.
confidence_interval

A float. Must be either 0.01, 0.05 or 0.1.

Value

No R object return, prints an Excel workbook.

Missing value check

Description

Checks if there are any missing values in a given column from the dataset.

Usage

check_function(name, column)

Arguments

name

Column in which missing values will be sought.
column

Column name, in string format.

Value

No return, only performs a check.

ID mismatch check

Description

Checks if a given food has an ID assigned but is absent in another dataset.

Usage

check_id_defined(df1, df2, value)

Arguments

df1

First dataframe.
df2

Second dataframe.
value

Dataset name.

Value

No return, only performs a check.

Food/price mismatch check

Description

Checks if all foods have a price.

Usage

check_match_food_price(df)

Arguments

df

Dataframe.

Value

No return, only performs a check.

Individual/diet mismatch check

Description

Checks if all individuals have a matching diet.

Usage

check_match_individual_diet(df)

Arguments

df

Dataframe.

Value

No return, only performs a check.

Minimum intake food groups check

Description

Looks for a minimum value and sets zero if missing,

Usage

check_min_exists(df, check, col)

Arguments

df

Dataframe.
check

Boolean variable to permit optional food group.
col

Minimum percentage intake column name.

Value

Dataframe.

Applies non-nummeric value check to entire dataframe

Description

Checks if values supposed to be numeric are in fact numeric.

Usage

check_nom_num_df(df)

Arguments

df

Dataframe columns.

Value

No return, only performs a check.

Non-numeric check

Description

Checks if values supposed to be numeric are in fact numeric.

Usage

check_non_num(df)

Arguments

df

Dataframe column.

Value

No return, only performs a check.

Spellcheck

Description

Checks if two datasets have the same spelling in names column.

Usage

check_spelling(df1, df2, condition)

Arguments

df1

First dataframe.
df2

Second dataframe.
condition

Column to be joined.

Value

No return, only performs a check.

Variety check

Description

Checks if varieties are into the allowed range (1,2 or 3).

Usage

check_variety(df)

Arguments

df

Dataframe variety column.

Value

No return, only performs a check.

Linked foods check

Description

Checks if lower bound linked foods serves are lower or equal to higher bound linked foods serves.

Usage

checkLinkedFoods(df, low, high)

Arguments

df

Random meal plan.
low

Vector of lower bound food IDs.
high

Vector of higher bound food IDs.

Value

Differences dataframe.

Optional food groups check

Description

If discretionary foods, alcohol or takeaway are permitted, looks for a minimum value and sets zero if missing,

Usage

checks_optional_food_groups(check, value)

Arguments

check

Boolean variable to permit optional food group.
value

Minimum percentage of energy intake from optional food group.

Value

Minimum percentage of energy intake from optional food group.

All zero difference check

Description

Checks if differences dataframe is all zeroes.

Usage

checkZeroDiff(diff)

Arguments

diff

Differences dataframe

Value

Boolean. TRUE if all zeroes, FALSE otherwise.

Weekly conversion

Description

Converts data from daily to weekly

Usage

converts_dataframe(df, exclusion_cols)

Arguments

df

Dataframe.
exclusion_cols

Columns (non-numerical or percentage) that conversion won't be applied.

Value

Weekly dataframe.

Food group serves conversion

Description

Converts food group serves dataframe to weekly values.

Usage

convertWeeklyFoodGroups(df, diet, individual)

Arguments

df

Food group serves dataframe.
diet

Chosen diet. Must be DIETNAME from 'constraints_DIETNAME_diet_food_groups' sheet in dataset.
individual

Individual whose random meal plan will be created to. Can be one of man, woman, boy or girl.

Value

Converted food group serves dataframe.

Examples

food_groups_wk <- convertWeeklyFoodGroups(DIETCOST::food_groups, 'C', 'man');

Nutrient targets conversion

Description

Converts nutrient targets dataframe to weekly values.

Usage

convertWeeklyNutrientTargets(df, diet, person, nutrient_constraints = NULL)

Arguments

df

Nutrient targets dataframe.
diet

Chosen diet. Must be DIETNAME from 'constraints_DIETNAME_diet_foods' sheet in dataset.
person

Individual whose random meal plan will be created to. Can be one of man, woman, boy or girl.
nutrient_constraints

Optional parameter. Vector of nutrients column names to be used if not all nutrients are to be used as constraints.

Value

Converted nutrient targets dataframe.

Examples

nutrient_targets_wk <- convertWeeklyNutrientTargets(DIETCOST::nutrient_targets, 'C', 'man')

Food data creation

Description

Creates a food data dataframe

Usage

createFoodData(filepath, redmeat_ids)

Arguments

filepath

Path in which the dataset, in .xlsx format, is stored.
redmeat_ids

Vector of redmeat IDs.

Value

Food dataframe.

Food group data creation

Description

Creates and populates a food group data dataframe

Usage

createFoodGroupData(df)

Arguments

df

Foods dataframe.

Value

Food group dataframe.

Nutrients data addition

Description

Adds nutrients data to foods dataframe.

Usage

createNutrientTargets(
  filepath,
  allow_alcohol = TRUE,
  allow_discretionary = TRUE,
  allow_takeaway = TRUE,
  alcohol_perc_max = NULL,
  discretionary_perc_max = NULL,
  takeaway_perc_max = NULL
)

Arguments

filepath

Path in which the dataset, in .xlsx format, is stored..
allow_alcohol

Boolean variable checking if alcohol is permitted. Default TRUE.
allow_discretionary

Boolean variable checking if discretionary foods are permitted. Default TRUE.
allow_takeaway

Boolean variable checking if takeaway is permitted. Default TRUE.
alcohol_perc_max

Optional parameter. Defines maximum energy intake derived from alcohol.
discretionary_perc_max

Optional parameter. Defines maximum energy intake derived from discretionary foods.
takeaway_perc_max

Optional parameter. Defines maximum energy intake derived from takeaway.

Value

Nutrient targets dataframe.

Random meal plan

Description

Creates a random meal plan.

Usage

createRandomMeal(
  foods_df,
  targets_df,
  person,
  diet,
  allowed_varieties,
  min_serve_size_difference,
  allow_discretionary = TRUE,
  allow_alcohol = TRUE,
  allow_takeaway = TRUE,
  emission_cols = NULL,
  nutrient_cols = NULL
)

Arguments

foods_df

Foods dataframe.
targets_df

Nutrient targets dataframe.
person

Individual whose random meal plan will be created to. Can be one of man, woman, boy or girl.
diet

Chosen diet. Must be DIETNAME from 'constraints_DIETNAME_diet_foods' sheet in dataset.
allowed_varieties

Permitted food varieties. Can be a vector of the following: 1,2 and/or 3.
min_serve_size_difference

Multiplier to serve difference. A float between 0 and 1.
allow_discretionary

Boolean variable checking if discretionary foods are permitted. Default TRUE.
allow_alcohol

Boolean variable checking if alcohol is permitted. Default TRUE.
allow_takeaway

Boolean variable checking if takeaway is permitted. Default TRUE.
emission_cols

Optional parameter. Emission column names if standard dataset isn't used.
nutrient_cols

Optional parameter. Nutrients column names if standard dataset isn't used.

Value

Random meal plan dataframe.

Examples

foods_df <- createRandomMeal(foods_df = DIETCOST::foods,
                             targets_df = DIETCOST::nutrient_targets,
                             person = 'man', 
                             diet = 'C', 
                             allowed_varieties = c(1,2,3), 
                             min_serve_size_difference = 0.5, 
                             allow_takeaway = TRUE, 
                             allow_alcohol = TRUE, 
                             allow_discretionary = TRUE)

Difference calculator

Description

Calculates difference between values of random meal plan created and targets logged.

Usage

diff_calc(val, min, max)

Arguments

val

Value to be evalueted.
min

Minimum constraint.
max

Maximum constraint.

Value

Difference.

MJ to KJ conversion

Description

Converts energy values in megajoules (MJ) to kilojoules (KJ),

Usage

energy_conversor(df, min, max)

Arguments

df

Dataframe.
min

Minimum energy column name. Default 'energy_mj_min'.
max

Maximum energy column name. Default 'energy_mj_max'.

Value

No return, only performs a check.

Food groups dataset example

Description

A set of data containing commonly available food groups based on a Brazilian typical diet.

Usage

food_groups

Format

A dataframe with 12 rows and 74 columns:

food_group

Food group, i.e. 'Fruit' or 'Vegetable'

food_group_id

Numerical code for dood group

man_min_g_C

Weekly minimal current diet intake for males, in grams

man_max_g_C

Weekly maximal current diet intake for males, in grams

man_target_g_C

Weekly target current diet intake for males, in grams

man_min_serve_C

Weekly minimal current diet intake for males, in serves

man_max_serve_C

Weekly maximal current diet intake for males, in serves

man_target_serve_C

Weekly target current diet intake for males, in serves

woman_min_g_C

Weekly minimal current diet intake for females, in grams

woman_max_g_C

Weekly maximal current diet intake for females, in grams

woman_target_g_C

Weekly target current diet intake for females, in grams

woman_min_serve_C

Weekly minimal current diet intake for females, in serves

woman_max_serve_C

Weekly maximal current diet intake for females, in serves

woman_target_serve_C

Weekly target current diet intake for females, in serves

boy_min_g_C

Weekly minimal current diet intake for boys, in grams

boy_max_g_C

Weekly maximal current diet intake for boys, in grams

boy_target_g_C

Weekly target current diet intake for boys, in grams

boy_min_serve_C

Weekly minimal current diet intake for boys, in serves

boy_max_serve_C

Weekly maximal current diet intake for boys, in serves

boy_target_serve_C

Weekly target current diet intake for boys, in serves

girl_min_g_C

Weekly minimal current diet intake for girls, in grams

girl_max_g_C

Weekly maximal current diet intake for girls, in grams

girl_target_g_C

Weekly target current diet intake for girls, in grams

girl_min_serve_C

Weekly minimal current diet intake for girls, in serves

girl_max_serve_C

Weekly maximal current diet intake for girls, in serves

girl_target_serve_C

Weekly target current diet intake for girls, in serves

man_min_g_PF

Weekly minimal EAT-Lancet diet intake for males, in grams

man_max_g_PF

Weekly maximal EAT-Lancet diet intake for males, in grams

man_target_g_PF

Weekly target EAT-Lancet diet intake for males, in grams

man_min_serve_PF

Weekly minimal EAT-Lancet diet intake for males, in serves

man_max_serve_PF

Weekly maximal EAT-Lancet diet intake for males, in serves

man_target_serve_PF

Weekly target EAT-Lancet diet intake for males, in serves

woman_min_g_PF

Weekly minimal EAT-Lancet diet intake for females, in grams

woman_max_g_PF

Weekly maximal EAT-Lancet diet intake for females, in grams

woman_target_g_PF

Weekly target EAT-Lancet diet intake for females, in grams

woman_min_serve_PF

Weekly minimal EAT-Lancet diet intake for females, in serves

woman_max_serve_PF

Weekly maximal EAT-Lancet diet intake for females, in serves

woman_target_serve_PF

Weekly target EAT-Lancet diet intake for females, in serves

boy_min_g_PF

Weekly minimal EAT-Lancet diet intake for boys, in grams

boy_max_g_PF

Weekly maximal EAT-Lancet diet intake for boys, in grams

boy_target_g_PF

Weekly target EAT-Lancet diet intake for boys, in grams

boy_min_serve_PF

Weekly minimal EAT-Lancet diet intake for boys, in serves

boy_max_serve_PF

Weekly maximal EAT-Lancet diet intake for boys, in serves

boy_target_serve_PF

Weekly target EAT-Lancet diet intake for boys, in serves

girl_min_g_PF

Weekly minimal EAT-Lancet diet intake for girls, in grams

girl_max_g_PF

Weekly maximal EAT-Lancet diet intake for girls, in grams

girl_target_g_PF

Weekly target EAT-Lancet diet intake for girls, in grams

girl_min_serve_PF

Weekly minimal EAT-Lancet diet intake for girls, in serves

girl_max_serve_PF

Weekly maximal EAT-Lancet diet intake for girls, in serves

girl_target_serve_PF

Weekly target EAT-Lancet diet intake for girls, in serves

man_min_g_H

Weekly minimal healthy diet intake for males, in grams

man_max_g_H

Weekly maximal healthy diet intake for males, in grams

man_target_g_H

Weekly target healthy diet intake for males, in grams

man_min_serve_H

Weekly minimal healthy diet intake for males, in serves

man_max_serve_H

Weekly maximal healthy diet intake for males, in serves

man_target_serve_H

Weekly target healthy diet intake for males, in serves

woman_min_g_H

Weekly minimal healthy diet intake for females, in grams

woman_max_g_H

Weekly maximal healthy diet intake for females, in grams

woman_target_g_H

Weekly target healthy diet intake for females, in grams

woman_min_serve_H

Weekly minimal healthy diet intake for females, in serves

woman_max_serve_H

Weekly maximal healthy diet intake for females, in serves

woman_target_serve_H

Weekly target healthy diet intake for females, in serves

boy_min_g_H

Weekly minimal healthy diet intake for boys, in grams

boy_max_g_H

Weekly maximal healthy diet intake for boys, in grams

boy_target_g_H

Weekly target healthy diet intake for boys, in grams

boy_min_serve_H

Weekly minimal healthy diet intake for boys, in serves

boy_max_serve_H

Weekly maximal healthy diet intake for boys, in serves

boy_target_serve_H

Weekly target healthy diet intake for boys, in serves

girl_min_g_H

Weekly minimal healthy diet intake for girls, in grams

girl_max_g_H

Weekly maximal healthy diet intake for girls, in grams

girl_target_g_H

Weekly target healthy diet intake for girls, in grams

girl_min_serve_H

Weekly minimal healthy diet intake for girls, in serves

girl_max_serve_H

Weekly maximal healthy diet intake for girls, in serves

girl_target_serve_H

Weekly target healthy diet intake for girls, in serves

Source

Elaborated by authors.

Single-function food dataframe creation

Description

Creates foods dataframe, with emission, nutrients, constraints and price data, in a single function.

Usage

foodData(
  filepath = filepath,
  redmeat_ids,
  diets,
  max_scale,
  emission_cols = NULL,
  override_min = NULL
)

Arguments

filepath

Path in which the dataset, in .xlsx format, is stored..
redmeat_ids

Vector of unique food IDs that are redmeat.
diets

Chosen diets. Constraint sheets in foods dataset must be of format 'constraints_DIETNAME_diet_foods', then the parameter passed will be DIETNAME. Can be a vector of diets in format c('DIETNAME1','DIETNAME2',...,'DIETNAMEN').
max_scale

Maximum scale. Default is two.
emission_cols

Optional parameter. Emission column names if standard dataset isn't used.
override_min

If is not null, overrides all minimum values

Value

Foods dataframe.

Single-function food group dataframe creation

Description

Creates food groups dataframe, with constraints data, in a single function.

Usage

foodGroupData(filepath, df_foods, diets)

Arguments

filepath

Path in which the dataset, in .xlsx format, is stored.
df_foods

Foods dataframe.
diets

Chosen diets. Constraint sheets in foods dataset must be of format 'constraints_DIETNAME_diet_food_groups', then the parameter passed will be DIETNAME. Can be a vector of diets in format c('DIETNAME1','DIETNAME2',...,'DIETNAMEN').

Value

Food groups dataframe.

Foods dataset example

Description

A set of data containing commonly available foods based on a Brazilian typical diet.

Usage

foods

Format

A dataframe with 99 rows and 45 columns:

food_group

Food group, i.e. 'Fruit' or 'Vegetable'

food_group_id

Numerical code for dood group

food_name

Food name, i.e. LEMON

food_id

Unique numerical food id

variety

Variety. Must be 1, 2 or 3

redmeat

Boolean redmeat identifier

CF_gCO2eq

Carbon footprint

WF_l

Water footprint

EF_g_m2

Ecological footprint

serve_size_C

Serve size for current diet, in grams

man_min_C

Minimal current diet intake for males, in grams

woman_min_C

Minimal current diet intake for females, in grams

boy_min_C

Minimal current diet intake for boys, in grams

girl_min_C

Minimal current diet intake for girls, in grams

man_max_C

Maximal current diet intake for males, in grams

woman_max_C

Maximal current diet intake for females, in grams

boy_max_C

Maximal current diet intake for boys, in grams

girl_max_C

Maximal current diet intake for girls, in grams

serve_size_PF

Serve size for EAT-Lancet diet, in grams

man_min_PF

Minimal EAT-Lancet diet intake for males, in grams

woman_min_PF

Minimal EAT-Lancet diet intake for females, in grams

boy_min_PF

Minimal EAT-Lancet diet intake for boys, in grams

girl_min_PF

Minimal EAT-Lancet diet intake for girls, in grams

man_max_PF

Maximal EAT-Lancet diet intake for males, in grams

woman_max_PF

Maximal EAT-Lancet diet intake for females, in grams

boy_max_PF

Maximal EAT-Lancet diet intake for boys, in grams

girl_max_PF

Maximal EAT-Lancet diet intake for girls, in grams

serve_size_H

Serve size for healthy diet, in grams

man_min_H

Minimal healthy diet intake for males, in grams

woman_min_H

Minimal healthy diet intake for females, in grams

boy_min_H

Minimal healthy diet intake for boys, in grams

girl_min_H

Minimal healthy diet intake for girls, in grams

man_max_H

Maximal healthy diet intake for males, in grams

woman_max_H

Maximal healthy diet intake for females, in grams

boy_max_H

Maximal healthy diet intake for boys, in grams

girl_max_H

Maximal healthy diet intake for girls, in grams

energy_kj_g

Energy content of food, in kJ/g

fat_g

Fat content of food per grams

sat_fat_g

Saturated fat content of food per grams

CHO_g

Carbohydrates content of food per grams

sugars_g

Sugars content of food per grams

fibre_g

Fibre content of food per grams

protein_g

Protein content of food per grams

sodium_mg

Sodium content of food per miligrams

price

Price of food per 100g

Source

Elaborated by authors.

General difference calculation

Description

Applies difference calculation to entire dataset.

Usage

getDifference(df_target, df_nutrients, merge_col)

Arguments

df_target

Constraints dataframe.
df_nutrients

Nutrients/serves from random meal plan dataframe.
merge_col

Column to join both dataframes.

Value

Differences dataframe.

Food group serves calculator

Description

Calculates total food group serves of random meal plan.

Usage

getFoodGroupServes(df)

Arguments

df

Random meal plan.

Value

Food group serves dataframe.

Nutrients values calculator

Description

Calculates nutritional value of meal plan.

Usage

getNutrients(df, nutrient_cols = NULL)

Arguments

df

Random meal plan.
nutrient_cols

Optional parameter. Vector of nutrients column names to be used if nutrients are different from standard dataset.

Value

Nutrients dataframe.

Percentage values calculator

Description

Calculates percentage nutrient values.

Usage

getPerc(df_nutri, df_meal)

Arguments

df_nutri

Nutrient constraints dataframe.
df_meal

Random meal plan

Value

Percentage dataframe.

Join function

Description

Safely performs a left join between two dataframes.

Usage

join_function(df1, df2, condition)

Arguments

df1

First dataframe.
df2

Second dataframe.
condition

Column in which the two datframes will be joined. Can be a single string or a vector.

Value

Dataframe.

Monte Carlo simulation

Description

Creates a Monte Carlo simulation to a given number of iterations. A hit meal consists of one that returnz zero difference between nutrient targets and random meal plan, food groups serves and respects lower linked foods serves lower or equal to higher linked foods serves, if existent.

Usage

monteCarlo(
  dir_path,
  iterations,
  foods_df,
  nutrient_targets_df,
  food_group_targets_df,
  person,
  diet,
  allowed_varieties,
  min_serve_size_difference,
  allow_discretionary = TRUE,
  allow_alcohol = TRUE,
  allow_takeaway = TRUE,
  emission_cols = NULL,
  nutrient_cols = NULL,
  nutrient_constraints = NULL,
  linked_low_1 = NULL,
  linked_high_1 = NULL,
  linked_low_2 = NULL,
  linked_high_2 = NULL
)

Arguments

dir_path

A string containing the path where a directory will be created.
iterations

Number of iterations. Integer.
foods_df

Foods dataframe.
nutrient_targets_df

Nutrient constraints dataframe.
food_group_targets_df

Food group serves dataframe.
person

Individual whose random meal plan will be created to. Can be one of man, woman, boy or girl.
diet

Chosen diet. Must be DIETNAME from 'constraints_DIETNAME_diet_foods' sheet in dataset.
allowed_varieties

Permitted food varieties. Can be a vector of the following: 1,2 and/or 3.
min_serve_size_difference

Multiplier to serve difference. A float between 0 and 1.
allow_discretionary

Boolean variable checking if discretionary foods are permitted. Default TRUE.
allow_alcohol

Boolean variable checking if alcohol is permitted. Default TRUE.
allow_takeaway

Boolean variable checking if takeaway is permitted. Default TRUE.
emission_cols

Optional parameter. Emission column names if standard dataset isn't used.
nutrient_cols

Optional parameter. Nutrients column names if standard dataset isn't used.
nutrient_constraints

Optional parameter. Vector of nutrients column names to be used if not all nutrients are to be used as constraints.
linked_low_1

Optional parameter. Vector of lower bound food IDs.
linked_high_1

Optional parameter. Vector of higher bound food IDs.
linked_low_2

Optional parameter. Vector of lower bound food IDs.
linked_high_2

Optional parameter. Vector of higher bound food IDs.

Value

List of dataframes, containing results of simulation.

Single-function Monte Carlo simulation and results export.

Description

Runs Monte Carlo Simulation and prints results, in .xlsx format, in a single funtion.

Usage

monteCarloSimulation(
  dir_path,
  iterations,
  foods_df,
  nutrient_targets_df,
  food_group_targets_df,
  person,
  diet,
  allowed_varieties,
  min_serve_size_difference,
  allow_discretionary = TRUE,
  allow_alcohol = TRUE,
  allow_takeaway = TRUE,
  emission_cols = NULL,
  nutrient_cols = NULL,
  nutrient_constraints = NULL,
  linked_low_1 = NULL,
  linked_high_1 = NULL,
  linked_low_2 = NULL,
  linked_high_2 = NULL
)

Arguments

dir_path

A string containing the path where a directory will be created. This same path will hold the reports Excel workbook.
iterations

Number of iterations. Integer.
foods_df

Foods dataframe.
nutrient_targets_df

Nutrient constraints dataframe.
food_group_targets_df

Food group serves dataframe.
person

Individual whose random meal plan will be created to. Can be one of man, woman, boy or girl.
diet

Chosen diet. Must be DIETNAME from 'constraints_DIETNAME_diet_foods' sheet in dataset.
allowed_varieties

Permitted food varieties. Can be a vector of the following: 1,2 and/or 3.
min_serve_size_difference

Multiplier to serve difference. A float between 0 and 1.
allow_discretionary

Boolean variable checking if discretionary foods are permitted. Default TRUE.
allow_alcohol

Boolean variable checking if alcohol is permitted. Default TRUE.
allow_takeaway

Boolean variable checking if takeaway is permitted. Default TRUE.
emission_cols

Optional parameter. Emission column names if standard dataset isn't used.
nutrient_cols

Optional parameter. Nutrients column names if standard dataset isn't used.
nutrient_constraints

Optional parameter. Vector of nutrients column names to be used if not all nutrients are to be used as constraints.
linked_low_1

Optional parameter. Vector of lower bound food IDs.
linked_high_1

Optional parameter. Vector of higher bound food IDs.
linked_low_2

Optional parameter. Vector of lower bound food IDs.
linked_high_2

Optional parameter. Vector of higher bound food IDs.

Value

No R object return. Prints an Excel workbook.

Nutrients dataset example

Description

A set of data containing nutrient weekly targets based on a Brazilian typical diet.

Usage

nutrient_targets

Format

A dataframe with 12 rows and 48 columns:

individual

Person whose nutrient targets will be provided: man, woman, boy or girl

diet

Diet whose nutrient targets will be provided: current (C), EAT-Lancet (PF) or healthy (H)

energy_kj_min

Minimal weekly intake of energy, in kJ/g

energy_kj_max

Maximal weekly intake of energy, in kJ/g

fat_grams_min

Minimal weekly intake of fat in grams

fat_grams_max

Maximal weekly intake of fat in grams

sat_fat_grams_min

Minimal weekly intake of saturated fat in grams

sat_fat_grams_max

Maximal weekly intake of saturated fat in grams

CHO_gram_mins

Minimal weekly intake of carbohydrates in grams

CHO_gram_max

Maximal weekly intake of carbohydrates in grams

sugars_grams_min

Minimal weekly intake of sugars in grams

sugars_grams_max

Maximal weekly intake of sugars in grams

fibre_grams_min

Minimal weekly intake of fibre in grams

fibre_grams_max

Maximal weekly intake of fibre in grams

protein_grams_min

Minimal weekly intake of protein in grams

protein_grams_max

Maximal weekly intake of protein in grams

sodium_mgrams_min

Minimal weekly intake of sodium in grams

sodium_mgrams_max

Maximal weekly intake of sodium in grams

protein_perc_min

Minimal weekly intake of protein in percentage

protein_perc_max

Maximal weekly intake of protein in percentage

sat_fat_perc_min

Minimal weekly intake of saturated fat in percentage

sat_fat_perc_max

Maximal weekly intake of saturated fat in percentage

fat_perc_min

Minimal weekly intake of fat in percentage

fat_perc_max

Maximal weekly intake of fat in percentage

CHO_perc_mins

Minimal weekly intake of carbohydrates in percentage

CHO_perc_max

Maximal weekly intake of carbohydrates in percentage

redmeat_grams_min

Minimal weekly intake of red meat in grams

redmeat_grams_max

Maximal weekly intake of red meat in grams

fruit_serves_min

Minimal weekly intake of Fruit in serves

fruit_serves_max

Maximal weekly intake of Fruit in serves

starchy_veg_serves_min

Minimal weekly intake of Starchy vegetables in serves

starchy_veg_serves_max

Maximal weekly intake of Starchy vegetables in serves

veg_serves_min

Minimal weekly intake of Vegetables in serves

veg_serves_max

Maximal weekly intake of Vegetables in serves

dairy_serves_min

Minimal weekly intake of Dairy in serves

dairy_serves_max

Maximal weekly intake of Dairy in serves

grain_serves_min

Minimal weekly intake of Grains in serves

grain_serves_max

Maximal weekly intake of Grains in serves

protein_serves_min

Minimal weekly intake of Protein in serves

protein_serves_max

Maximal weekly intake of Protein in serves

sugars_perc_mins

Minimal weekly intake of sugars in percentage

sugars_perc_max

Maximal weekly intake of sugars in percentage

alcohol_perc_mins

Minimal weekly intake of Alcohol in percentage

alcohol_perc_max

Maximal weekly intake of Alcohol in percentage

discretionary_perc_mins

Minimal weekly intake of Discretionary foods in percentage

discretionary_perc_max

Maximal weekly intake of Discretionary foods in percentage

takeaway_perc_mins

Minimal weekly intake of Takeaway in percentage

takeaway_perc_max

Maximal weekly intake of Takeaway foods in percentage

Source

Elaborated by authors.

Nutrient data application to random meal plan created

Description

Applies nutrient data calculation to random meal plan generated.

Usage

nutrientDataCalculation(df, nutrient_cols = NULL)

Arguments

df

Random meal plan.
nutrient_cols

Optional parameter. Nutrient column names if standard dataset isn't used.

Value

Random meal plan with nutrients calculated.

Permitted individuals check

Description

Checks if logged individuals are one or all of the following: man, woman, boy or girl.

Usage

permitted_individuals(df)

Arguments

df

Variable.

Value

No R object return, performs only a check.

Price/emission data application to random meal plan created

Description

Applies price and emission data calculation to random meal plan generated.

Usage

priceEmissionData(df, emission_cols = NULL)

Arguments

df

Random meal plan.
emission_cols

Optional parameter. Emission column names if standard dataset isn't used.

Value

Random meal plan with price and emissions calculated.

Exportation of Monte Carlo results

Description

Exports, in .xlsx format, the results of Monte Carlo simulation.

Usage

printResults(file_path, results, person, diet, allowed_varieties, iterations)

Arguments

file_path

A string containing the path where the file will be saved.
results

List of results
person

Individual whose random meal plan will be created to. Can be one of man, woman, boy or girl.
diet

Chosen diet. Must be DIETNAME from 'constraints_DIETNAME_diet_foods' sheet in dataset.
allowed_varieties

Permitted food varieties. Can be a vector of the following: 1,2 and/or 3.
iterations

Number of iterations. Integer.

Value

No R object return, prints a Excel workbook.

Random deletion

Description

Randomly deletes a food.

Usage

random_plan(df, column, condition)

Arguments

df

Dataframe.
column

Column from which decision about removal of values will be made.
condition

Condition that, if is true, will enable radom removal.

Value

Random meal plan

Redmeat flag

Description

Sets a boolean redmeat flag column in dataset.

Usage

redmeat_check(id, redmeat_ids)

Arguments

id

Food group id column in dataframe.
redmeat_ids

Vector of unique food IDs that are redmeat.

Value

No R object return, performs only a check.

Suffix removal

Description

Removes one of two suffixes from column names

Usage

remove_suffix(vector, suffix_1, suffix_2)

Arguments

vector

Vector of column names
suffix_1

First suffix to be removed.
suffix_2

Second suffix to be removed.

Value

Vector of column names without suffixes.

Safe sampling

Description

Safely extracts a random unitary sample from a vector.

Usage

sample_safe(x)

Arguments

x

Vector.

Value

Random sample.

Examples

intake <- DIETCOST::sample_safe(c(10,25,37,52,100));

Sauces, protein and discretionary food groups treatment

Description

Treats above said food name groups to the format used in the package.

Usage

sauces_protein_discretionary_change(group)

Arguments

group

Food group column in dataframe.

Value

Treated dataframe.

Standard name check

Description

Checks if variable names are the standard defined into DIETCOST R standard table.

Usage

standard_name_check(df, ...)

Arguments

df

Dataframe.
...

Any number of strings.

Value

No R object return, performs only a check.

Examples

standard_name_check(DIETCOST::foods, 'food_id', 'food_name')

Starchy vegetables serves addition

Description

Adds minimum and maximum serves of starchy vegetables.

Usage

starchy_fill(df, starchy_name, serve_identifier, max_identifier)

Arguments

df

Dataframe.
starchy_name

Starchy vegetables food group name. Default 'Starchy vegetables'.
serve_identifier

Serve column identifier. Default 'serve'.
max_identifier

Max column identifier. Default 'max'.

Value

Food group dataframe with starchy vegetable minimum and maximum serves columns added.

Pre-treatment of constraint data

Description

Pre-treatment of constraints dataframe.

Usage

treat_df(df, min_identifier, max_identifier, suffix, max_scale, override_min)

Arguments

df

Dataframe to be treated.
min_identifier

Minimum value column identifier. 'Min' in standard dataset.
max_identifier

Maximum value column identifier. 'Max' in standard dataset.
suffix

Suffix to be added to column name.
max_scale

Maximum scale. Default is two.
override_min

If is not null, overrides all minimum values.

Value

Treated dataframe.

Treatment of food group constraints dataframe

Description

Converts weekly food group serves to daily and adds diet suffix to column names.

Usage

treat_groups_df(df, suffix)

Arguments

df

Dataframe.
suffix

Suffix to be added to column.

Value

Treated food group dataframe.

Unique value check

Description

Checks if there are non-unique values in dataset.

Usage

unique_values(value, df, value_col, value_name)

Arguments

value

Column from which an unique vector will be formed.
df

Dataframe in which lies the column to be checked.
value_col

Name of the column to be checked, in string format.
value_name

Name of the variable tested.

Value

No R object return, performs only a check.

Data upload

Description

Safely uploads data to be processed in DIETCOST software.

Usage

upload_data(filepath, sheet)

Arguments

filepath

The filepath in which the dataset, in .xlsx format, is saved.
sheet

The sheet of the .xlsx to be read.

Value

The dataframe generated by the file which was read.