R/dataManagement.foodNnuts.R
In GeraldCNelson/nutmod: Calculates nutrient information worldwide
#' @title Calculate nutrient content from food availability
#' @author Gerald C. Nelson, \email{nelson.gerald.c@@gmail.com}
#' @keywords utilities, nutrient data, IMPACT food commodities nutrient lookup
# Intro ---------------------------------------------------------------
#Copyright (C) 2016 Gerald C. Nelson, except where noted
# This program is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the Free
# Software Foundation, either version 3 of the License, or (at your option)
# any later version.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
# or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
# for more details at http://www.gnu.org/licenses/.
#' @name dataManagement.foodNnuts.R
#' @description {
#' Does all the calculations combining food availability with nutrient content.
#' }
source("R/nutrientModFunctions.R")
sourceFile <- "dataManagement.foodNnuts.R"
description <- "Does all the calculations combining food availability with nutrient content."
createScriptMetaData()
# Read in all data first and standardize variable names -----
# Read in IMPACT food data ----------
dt.IMPACTfood <- getNewestVersion("dt.IMPACTfood", fileloc("iData")) # has food availability quantity information, pc income and prices. These don't change with vars and varFort
# budget share and price growth don't depend on suffix
budgetShare(dt.IMPACTfood)
priceGrowth(dt.IMPACTfood)
# get the list of scenarios in the IMPACT data for use below
scenarioListIMPACT <- keyVariable("scenarioListIMPACT")
# read in nutrients data. switch variable determines which nutrient info to include -----
for (switchloop in getSwitchChoice()) {
switch.useCookingRetnValues <- keyVariable("switch.useCookingRetnValues")
switch.fixFish <- keyVariable("switch.fixFish") #get rid of nutrient info for shrimp, tuna, and salmon because they are not currently in the FBS data
if (switchloop == 2) {switch.vars <- TRUE; switch.fortification <- FALSE; suffix = "var"}
if (switchloop == 3) {switch.vars <- TRUE; switch.fortification <- TRUE; suffix = "varFort"}
if (switchloop == 4) {switch.vars <- TRUE; switch.fortification <- FALSE; suffix = "var"}
#dt.nutrients is per 100 gm
#dt is per kg of food
dt <- switches() # is specific to countries if switchloop = 2, 3, or 4. nutrient composition of composites vary
# in .var because the composition of composites changes by country
dt.foodNnuts <- merge(dt.IMPACTfood, dt, by = c("region_code.IMPACT159", "IMPACT_code"), allow.cartesian = TRUE)
#make order the same for all versions of dt.foodNnuts
setcolorder((dt.foodNnuts), neworder = c("scenario", "year", "region_code.IMPACT159", "IMPACT_code", "FoodAvailability", "pcGDPX0", "PCX0", "PWX0",
"foodAvailpDay", "phytate_mg", "calcium_mg", "carbohydrate_g", "energy_kcal", "ethanol_g", "fat_g", "folate_µg",
"ft_acds_mono_unsat_g", "ft_acds_plyunst_g", "ft_acds_tot_sat_g", "iron_mg", "magnesium_mg", "niacin_mg", "phosphorus_mg",
"potassium_g", "protein_g", "riboflavin_mg", "sugar_g", "thiamin_mg", "totalfiber_g", "vit_a_rae_µg", "vit_b12_µg", "vit_b6_mg",
"vit_c_mg", "vit_d_µg", "vit_e_mg", "vit_k_µg", "zinc_mg", "kcals_fat_g", "kcals_protein_g", "kcals_carbohydrate_g",
"kcals_sugar_g", "kcals_ft_acds_tot_sat_g", "kcals_ethanol_g", "food_group_code", "staple_code"))
#gdp per cap and prices removed here.
deleteListCol <- c("pcGDPX0", "FoodAvailability", "PCX0", "PWX0")
dt.foodNnuts[, (deleteListCol) := NULL]
list.minrls <- keyVariable("minerals")
list.vits <- keyVariable("vitamins")
list.macro <- keyVariable("macronutrients")
list.energy <- keyVariable("energy")
#list.other <- c("caffeine_mg", "cholesterol_mg", "phytate_mg", "sugar_g", "ethanol_g")
#' the lists below differ slighly from the keyVariables. Need to fix this up some time.
list.other <- c( "phytate_mg", "sugar_g", "ethanol_g")
#list.fattyAcids <- c("ft_acds_mono_unsat_g", "ft_acds_plyunst_g", "ft_acds_tot_sat_g", "ft_acds_tot_trans_g")
list.fattyAcids <- c("ft_acds_mono_unsat_g", "ft_acds_plyunst_g", "ft_acds_tot_sat_g")
list.tot <- c(list.minrls, list.vits, list.macro, list.other, list.energy, list.fattyAcids)
names.tot <- paste(list.tot, "Q", sep = "_")
dt.foodNnuts[, kcalsPerCommod := foodAvailpDay * energy_kcal]
# set NAs to zero
dt.foodNnuts[, (list.tot) := lapply(.SD, function(x){x[is.na(x)] <- 0; x}), .SDcols = list.tot]
# calculate the total quantity of a nutrient available for each food item as the amount per 100 g * quantity of food available per day
# for Norway in 2010 average daily egg availability was 2.739431e-02, egg content of vitamin d is 17.6 micro grams per kg
# (converted from 100 grams by switches) so vit d availability (vit_d_µg.Q)
# is 2.739431e-02 * 17.6 = 0.482139798
for (j in 1:length(list.tot)) {
data.table::set(dt.foodNnuts, i = NULL, j = names.tot[j], value = dt.foodNnuts[[list.tot[j]]] * dt.foodNnuts[['foodAvailpDay']])
}
# deleteListCol <- c("kcals_fat_g", "kcals_protein_g", "kcals_carbohydrate_g", "kcals_sugar_g", "kcals_ft_acds_tot_sat_g", "kcals_ethanol_g")
# dt.foodNnuts[, (deleteListCol) := NULL]
dt.foodNnuts[, `:=`(
foodQ.sum = sum(foodAvailpDay),
kcalsPerDay_tot = sum(kcalsPerCommod),
kcalsPerDay_carbohydrate = sum(kcals_carbohydrate_g_Q),
kcalsPerDay_fat = sum(kcals_fat_g_Q),
kcalsPerDay_protein = sum(kcals_protein_g_Q),
kcalsPerDay_ethanol = sum(kcals_ethanol_g_Q),
kcalsPerDay_sugar = sum(kcals_sugar_g_Q),
kcalsPerDay_ft_acds_tot_sat = sum(kcals_ft_acds_tot_sat_g_Q)),
by = c("scenario", "year", "region_code.IMPACT159")
]
#cap the amount of ethanol to 100 gm per day. Converted to kcals_
ethanolKcals <- 6.9 # needed for ethanol kcals cap
kcals_ethanol.cap <- 100 * ethanolKcals
dt.foodNnuts[kcalsPerDay_ethanol > kcals_ethanol.cap, kcalsPerDay_ethanol := kcals_ethanol.cap]
dt.foodNnuts[, kcalsPerDay_other := kcalsPerDay_tot - (kcalsPerDay_carbohydrate + kcalsPerDay_fat + kcalsPerDay_protein) ]
#deleteListCol <- c(list.tot, "usda_code", "Ref_Desc", "phytate_source")
# deleteListCol <- list.tot[!list.tot %in% c("kcals_fat_g", "kcals_carbohydrate_g", "kcals_protein_g", "kcals_ethanol_g",
# "kcals_sugar_g", "kcals_ft_acds_tot_sat_g")]
dt.foodNnuts[, (list.tot) := NULL]
# rename [nutrient].Q to just [nutrient]
data.table::setnames(dt.foodNnuts, old = c(names.tot), new = c(list.tot))
dt.foodNnuts <- adjustBioavailability(dt.foodNnuts)
dt.foodNnuts[, scenario := gsub("-", "_", scenario)]
dt.foodNnuts[, scenario := gsub("_REF", "", scenario)]
outName <- paste("dt.foodNnuts", suffix, sep = ".")
inDT <- dt.foodNnuts
inDT <- inDT[scenario %in% c("SSP1_NoCC", "SSP2_HGEM", "SSP2_NoCC","SSP3_NoCC"),]
inDT <- inDT[year %in% c("X2010", "X2030", "X2050"),]
desc <- "Combines dt.IMPACTfood with nutrients and kcalsPerDay. Units are for 1 kg, daily availability. iron and zince are adjusted for bioavailability"
createMissingDir(fileloc("resultsDir")) # checks for the existence of the directory and if its not available, creates it Dec 21, 2018
cleanup(inDT, outName, fileloc("resultsDir"), desc = desc)
#' produce subsets that are more manageable in size -----
#' dt.nutrients.kcals -----
dt.nutrients.kcals <- data.table::copy(dt.foodNnuts)
deleteListCol <- c( "foodAvailpDay", "foodQ.sum", "ft_acds_tot_sat_g", "ft_acds_mono_unsat_g", "ft_acds_plyunst_g",
keyVariable("minerals"), keyVariable("vitamins"), keyVariable("macronutrients"),
"phytate_mg", "sugar_g", "ethanol_g", "energy_kcal")
dt.nutrients.kcals[, (deleteListCol) := NULL]
kcalsSources <- c("kcalsPerDay_other", "kcalsPerDay_carbohydrate", "kcalsPerDay_fat", "kcalsPerDay_protein",
"kcalsPerDay_ethanol", "kcalsPerDay_sugar", "kcalsPerDay_ft_acds_tot_sat")
kcalsShare <- paste0(kcalsSources, "_share")
#' calculate ratio of energy to kcals per day for all items in sumlist
dt.nutrients.kcals[, (kcalsShare) := lapply(.SD, "/", kcalsPerDay_tot/100), .SDcols = (kcalsSources)] # divide by 100 to get to percent
#' keep only results that are for the country, ie, not for individual commodities
deleteListCol <- c( "IMPACT_code", "kcalsPerCommod",
"kcals_fat_g", "kcals_carbohydrate_g", "kcals_protein_g", "kcals_ethanol_g", "kcals_sugar_g", "kcals_ft_acds_tot_sat_g")
dt.nutrients.kcals[, (deleteListCol) := NULL]
dt.nutrients.kcals <- unique(dt.nutrients.kcals)
idVars <- c("scenario", "region_code.IMPACT159", "year")
measureVars <- names(dt.nutrients.kcals)[!names(dt.nutrients.kcals) %in% idVars &
!names(dt.nutrients.kcals) %in% c("food_group_code", "staple_code")]
dt.nutrients.kcals <- data.table::melt(dt.nutrients.kcals,
id.vars = idVars,
measure.vars = measureVars,
variable.name = "nutrient",
value.name = "value",
variable.factor = FALSE)
inDT <- unique(dt.nutrients.kcals)
inDT <- inDT[scenario %in% c("SSP1_NoCC", "SSP2_HGEM", "SSP2_NoCC","SSP3_NoCC"),]
inDT <- inDT[year %in% c("X2010", "X2030", "X2050"),]
outName <- paste("dt.nutrients_kcals", suffix, sep = ".")
desc <- "Kcals and kcals shares from carbohydrates, fat, etc. by country"
cleanup(inDT, outName, fileloc("resultsDir"), desc = desc)
#' dt.nutrients.sum.all ------
dt.nutrients.sum.all <- data.table::copy(dt.foodNnuts)
deleteListCol <- c("energy_kcal")
dt.nutrients.sum.all[, (deleteListCol) := NULL]
# the list.tot values are supposed to be average daily availability
dt.nutrients.sum.all[, (list.tot) := lapply(.SD, sum), .SDcols = (list.tot),
by = c("scenario", "region_code.IMPACT159", "year")]
dt.nutrients.sum.all <- unique(dt.nutrients.sum.all)
measureVars <- c(list.tot, "kcalsPerDay_tot", "kcalsPerDay_carbohydrate", "kcalsPerDay_fat", "kcalsPerDay_protein",
"kcalsPerDay_ethanol", "kcalsPerDay_sugar", "kcalsPerDay_ft_acds_tot_sat", "kcalsPerDay_other")
dt.nutrients.sum.all <- data.table::melt(dt.nutrients.sum.all,
id.vars = c("scenario","region_code.IMPACT159", "year"),
variable.name = "nutrient",
measure.vars = measureVars,
value.name = "value",
variable.factor = FALSE)
dt.nutrients.sum.all <- unique(dt.nutrients.sum.all)
inDT <- dt.nutrients.sum.all
inDT <- inDT[scenario %in% c("SSP1_NoCC", "SSP2_HGEM", "SSP2_NoCC","SSP3_NoCC"),]
inDT <- inDT[year %in% c("X2010", "X2030", "X2050"),]
outName <- paste("dt.nutrients_sum_all", suffix, sep = ".") #this includes phytate. Might want to remove later.
desc <- "Sum of each nutrient from each of the food items"
cleanup(inDT,outName, fileloc("resultsDir"), desc = desc)
#' dt.nutrients.sum.staples
dt.nutrients.sum.staples <- data.table::copy(dt.foodNnuts)
deleteListCol <- c("energy_kcal")
dt.nutrients.sum.staples[, (deleteListCol) := NULL]
deleteListCol <- c("kcalsPerDay_tot", "kcalsPerDay_carbohydrate", "kcalsPerDay_fat", "kcalsPerDay_protein",
"kcalsPerDay_ethanol", "kcalsPerDay_sugar", "kcalsPerDay_ft_acds_tot_sat", "kcalsPerDay_other",
"foodQ.sum")
dt.nutrients.sum.staples[, (deleteListCol) := NULL]
dt.nutrients.sum.staples[, (list.tot) := lapply(.SD, sum), .SDcols = (list.tot),
by = c("scenario", "region_code.IMPACT159", "year", "staple_code")]
deleteListCol <- c("IMPACT_code", "foodAvailpDay", "kcalsPerCommod", "food_group_code")
dt.nutrients.sum.staples[, (deleteListCol) := NULL]
dt.nutrients.sum.staples <- unique(dt.nutrients.sum.staples)
# measureVars <- c(list.tot, "kcalsPerDay_tot", "kcalsPerDay_carbohydrate", "kcalsPerDay_fat", "kcalsPerDay_protein",
# "kcalsPerDay_ethanol", "kcalsPerDay_sugar", "kcalsPerDay_ft_acds_tot_sat", "kcalsPerDay_other")
measureVars <- c(list.tot)
dt.nutrients.sum.staples.long <- data.table::melt(dt.nutrients.sum.staples,
id.vars = c("scenario","region_code.IMPACT159", "year", "staple_code"),
measure.vars = measureVars,
variable.name = "nutrient",
value.name = "value",
variable.factor = FALSE)
inDT <- unique(dt.nutrients.sum.staples.long)
inDT <- inDT[scenario %in% c("SSP1_NoCC", "SSP2_HGEM", "SSP2_NoCC","SSP3_NoCC"),]
inDT <- inDT[year %in% c("X2010", "X2030", "X2050"),]
outName <- paste("dt.nutrients_sum_staples", suffix, sep = ".")
desc <- "Sum of each nutrient from staples and nonstaples"
cleanup(inDT,outName, fileloc("resultsDir"), desc = desc)
#' dt.nutrients_sum_FG
dt.nutrients_sum_FG <- data.table::copy(dt.foodNnuts)
deleteListCol <- c("energy_kcal")
dt.nutrients_sum_FG[, (deleteListCol) := NULL]
dt.nutrients_sum_FG[, (list.tot) := lapply(.SD, sum), .SDcols = (list.tot),
by = c("scenario", "region_code.IMPACT159", "year", "food_group_code")]
deleteListCol <- c("IMPACT_code", "foodAvailpDay", "kcalsPerCommod", "staple_code")
dt.nutrients_sum_FG[, (deleteListCol) := NULL]
dt.nutrients_sum_FG <- unique(dt.nutrients_sum_FG)
dt.nutrients_sum_FG <- data.table::melt(dt.nutrients_sum_FG,
id.vars = c("scenario","region_code.IMPACT159", "year", "food_group_code"),
variable.name = "nutrient",
measure.vars = list.tot,
value.name = "value",
variable.factor = FALSE)
dt.nutrients_sum_FG <- unique(dt.nutrients_sum_FG) #note that this contains phytate
inDT <- dt.nutrients_sum_FG
inDT <- inDT[scenario %in% c("SSP1_NoCC", "SSP2_HGEM", "SSP2_NoCC","SSP3_NoCC"),]
inDT <- inDT[year %in% c("X2010", "X2030", "X2050"),]
outName <- paste("dt.nutrients_sum_FG", suffix, sep = ".")
desc <- "Sum of each nutrient from each food group"
cleanup(inDT, outName, fileloc("resultsDir"), desc = desc)
#' dt.KcalShare.nonstaple
dt.KcalShare.nonstaple <- data.table::copy(dt.foodNnuts)
keepListCol <- c("scenario", "region_code.IMPACT159", "year", "IMPACT_code", "kcalsPerCommod", "kcalsPerDay_tot", "staple_code",
"kcalsPerDay_carbohydrate", "kcalsPerDay_fat", "kcalsPerDay_protein", "kcalsPerDay_other", "kcalsPerDay_ethanol", "kcalsPerDay_sugar", "kcalsPerDay_ft_acds_tot_sat")
dt.KcalShare.nonstaple[, setdiff(names(dt.KcalShare.nonstaple), keepListCol) := NULL]
dt.KcalShare.nonstaple <- unique(dt.KcalShare.nonstaple)
dt.KcalShare.nonstaple[,value := sum(kcalsPerCommod) / kcalsPerDay_tot, by = c("scenario", "region_code.IMPACT159", "year", "staple_code")]
deleteListCol <- c("IMPACT_code", "kcalsPerCommod", "kcalsPerDay_tot")
dt.KcalShare.nonstaple[, (deleteListCol) := NULL]
dt.KcalShare.nonstaple <- unique(dt.KcalShare.nonstaple)
dt.KcalShare.nonstaple <- dt.KcalShare.nonstaple[staple_code %in% "nonstaple",]
dt.KcalShare.nonstaple <- unique(dt.KcalShare.nonstaple)
keepListCol <- c("scenario", "region_code.IMPACT159", "year", "value")
# convert to percent
dt.KcalShare.nonstaple[,value := value * 100]
inDT <- dt.KcalShare.nonstaple
inDT <- inDT[scenario %in% c("SSP1_NoCC", "SSP2_HGEM", "SSP2_NoCC","SSP3_NoCC"),]
inDT <- inDT[year %in% c("X2010", "X2030", "X2050"),]
outName <- paste("dt.KcalShare_nonstaple", suffix, sep = ".")
desc <- "Share of kcals from nonstaples in value column; other Kcal data included"
cleanup(inDT, outName, fileloc("resultsDir"), desc = desc)
# # dt.nutrients.nonstapleShare, not currently used (Feb 2018) so commented out ------
# dt.nut.nonstaple.share <- data.table::copy(dt.nutrients.sum.staples.long)
# shareFormula <- "scenario + region_code.IMPACT159 + nutrient + year ~ staple_code"
# dt.nut.nonstaple.share.wide <- data.table::dcast(data = dt.nut.nonstaple.share,
# formula = shareFormula,
# value.var = "value")
# dt.nut.nonstaple.share.wide[, value := 100 * nonstaple/(nonstaple + staple) ][is.na(value), value := 0]
#
# dt.nutrients.nonstapleShare <- dt.nut.nonstaple.share.wide[,c("nonstaple", "staple") := NULL]
#' dt.foodAvail.foodGroup -----
#sum and convert to grams. Note that the food group alcohol results are for the grams in the total beverage, not ethanol.
dt.foodAvail.foodGroup <- data.table::copy(dt.foodNnuts)
keepListCol <- c("scenario", "region_code.IMPACT159", "IMPACT_code", "year", "food_group_code", "foodAvailpDay")
dt.foodAvail.foodGroup[, setdiff(names(dt.foodAvail.foodGroup), keepListCol) := NULL]
dt.foodAvail.foodGroup <- dt.foodAvail.foodGroup[, foodavail.foodgroup.sum := sum(foodAvailpDay) * 1000,
by = c("scenario", "region_code.IMPACT159", "year", "food_group_code")]
deleteListCol <- c("IMPACT_code", "foodAvailpDay")
dt.foodAvail.foodGroup[, (deleteListCol) := NULL]
dt.foodAvail.foodGroup <- unique(dt.foodAvail.foodGroup)
data.table::setnames(dt.foodAvail.foodGroup, old = "foodavail.foodgroup.sum", new = "value")
dt.foodAvail.foodGroup <- unique(dt.foodAvail.foodGroup)
inDT <- dt.foodAvail.foodGroup
inDT <- inDT[scenario %in% c("SSP1_NoCC", "SSP2_HGEM", "SSP2_NoCC","SSP3_NoCC"),]
inDT <- inDT[year %in% c("X2010", "X2030", "X2050"),]
outName <- paste("dt.foodAvail_foodGroup", suffix, sep = ".")
desc <- "Sum of food available by food group, kgs per day"
cleanup(inDT, outName, fileloc("resultsDir"), desc = desc)
}
finalizeScriptMetadata(metadataDT, sourceFile)
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#' @title Calculate nutrient content from food availability
#' @author Gerald C. Nelson, \email{nelson.gerald.c@@gmail.com}
#' @keywords utilities, nutrient data, IMPACT food commodities nutrient lookup
# Intro ---------------------------------------------------------------
#Copyright (C) 2016 Gerald C. Nelson, except where noted
# This program is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the Free
# Software Foundation, either version 3 of the License, or (at your option)
# any later version.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
# or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
# for more details at http://www.gnu.org/licenses/.
#' @name dataManagement.foodNnuts.R
#' @description {
#' Does all the calculations combining food availability with nutrient content.
#' }
source("R/nutrientModFunctions.R")
sourceFile <- "dataManagement.foodNnuts.R"
description <- "Does all the calculations combining food availability with nutrient content."
createScriptMetaData()
# Read in all data first and standardize variable names -----
# Read in IMPACT food data ----------
dt.IMPACTfood <- getNewestVersion("dt.IMPACTfood", fileloc("iData")) # has food availability quantity information, pc income and prices. These don't change with vars and varFort
# budget share and price growth don't depend on suffix
budgetShare(dt.IMPACTfood)
priceGrowth(dt.IMPACTfood)
# get the list of scenarios in the IMPACT data for use below
scenarioListIMPACT <- keyVariable("scenarioListIMPACT")
# read in nutrients data. switch variable determines which nutrient info to include -----
for (switchloop in getSwitchChoice()) {
switch.useCookingRetnValues <- keyVariable("switch.useCookingRetnValues")
switch.fixFish <- keyVariable("switch.fixFish") #get rid of nutrient info for shrimp, tuna, and salmon because they are not currently in the FBS data
if (switchloop == 2) {switch.vars <- TRUE; switch.fortification <- FALSE; suffix = "var"}
if (switchloop == 3) {switch.vars <- TRUE; switch.fortification <- TRUE; suffix = "varFort"}
if (switchloop == 4) {switch.vars <- TRUE; switch.fortification <- FALSE; suffix = "var"}
#dt.nutrients is per 100 gm
#dt is per kg of food
dt <- switches() # is specific to countries if switchloop = 2, 3, or 4. nutrient composition of composites vary
# in .var because the composition of composites changes by country
dt.foodNnuts <- merge(dt.IMPACTfood, dt, by = c("region_code.IMPACT159", "IMPACT_code"), allow.cartesian = TRUE)
#make order the same for all versions of dt.foodNnuts
setcolorder((dt.foodNnuts), neworder = c("scenario", "year", "region_code.IMPACT159", "IMPACT_code", "FoodAvailability", "pcGDPX0", "PCX0", "PWX0",
"foodAvailpDay", "phytate_mg", "calcium_mg", "carbohydrate_g", "energy_kcal", "ethanol_g", "fat_g", "folate_µg",
"ft_acds_mono_unsat_g", "ft_acds_plyunst_g", "ft_acds_tot_sat_g", "iron_mg", "magnesium_mg", "niacin_mg", "phosphorus_mg",
"potassium_g", "protein_g", "riboflavin_mg", "sugar_g", "thiamin_mg", "totalfiber_g", "vit_a_rae_µg", "vit_b12_µg", "vit_b6_mg",
"vit_c_mg", "vit_d_µg", "vit_e_mg", "vit_k_µg", "zinc_mg", "kcals_fat_g", "kcals_protein_g", "kcals_carbohydrate_g",
"kcals_sugar_g", "kcals_ft_acds_tot_sat_g", "kcals_ethanol_g", "food_group_code", "staple_code"))
#gdp per cap and prices removed here.
deleteListCol <- c("pcGDPX0", "FoodAvailability", "PCX0", "PWX0")
dt.foodNnuts[, (deleteListCol) := NULL]
list.minrls <- keyVariable("minerals")
list.vits <- keyVariable("vitamins")
list.macro <- keyVariable("macronutrients")
list.energy <- keyVariable("energy")
#list.other <- c("caffeine_mg", "cholesterol_mg", "phytate_mg", "sugar_g", "ethanol_g")
#' the lists below differ slighly from the keyVariables. Need to fix this up some time.
list.other <- c( "phytate_mg", "sugar_g", "ethanol_g")
#list.fattyAcids <- c("ft_acds_mono_unsat_g", "ft_acds_plyunst_g", "ft_acds_tot_sat_g", "ft_acds_tot_trans_g")
list.fattyAcids <- c("ft_acds_mono_unsat_g", "ft_acds_plyunst_g", "ft_acds_tot_sat_g")
list.tot <- c(list.minrls, list.vits, list.macro, list.other, list.energy, list.fattyAcids)
names.tot <- paste(list.tot, "Q", sep = "_")
dt.foodNnuts[, kcalsPerCommod := foodAvailpDay * energy_kcal]
# set NAs to zero
dt.foodNnuts[, (list.tot) := lapply(.SD, function(x){x[is.na(x)] <- 0; x}), .SDcols = list.tot]
# calculate the total quantity of a nutrient available for each food item as the amount per 100 g * quantity of food available per day
# for Norway in 2010 average daily egg availability was 2.739431e-02, egg content of vitamin d is 17.6 micro grams per kg
# (converted from 100 grams by switches) so vit d availability (vit_d_µg.Q)
# is 2.739431e-02 * 17.6 = 0.482139798
for (j in 1:length(list.tot)) {
data.table::set(dt.foodNnuts, i = NULL, j = names.tot[j], value = dt.foodNnuts[[list.tot[j]]] * dt.foodNnuts[['foodAvailpDay']])
}
# deleteListCol <- c("kcals_fat_g", "kcals_protein_g", "kcals_carbohydrate_g", "kcals_sugar_g", "kcals_ft_acds_tot_sat_g", "kcals_ethanol_g")
# dt.foodNnuts[, (deleteListCol) := NULL]
dt.foodNnuts[, `:=`(
foodQ.sum = sum(foodAvailpDay),
kcalsPerDay_tot = sum(kcalsPerCommod),
kcalsPerDay_carbohydrate = sum(kcals_carbohydrate_g_Q),
kcalsPerDay_fat = sum(kcals_fat_g_Q),
kcalsPerDay_protein = sum(kcals_protein_g_Q),
kcalsPerDay_ethanol = sum(kcals_ethanol_g_Q),
kcalsPerDay_sugar = sum(kcals_sugar_g_Q),
kcalsPerDay_ft_acds_tot_sat = sum(kcals_ft_acds_tot_sat_g_Q)),
by = c("scenario", "year", "region_code.IMPACT159")
]
#cap the amount of ethanol to 100 gm per day. Converted to kcals_
ethanolKcals <- 6.9 # needed for ethanol kcals cap
kcals_ethanol.cap <- 100 * ethanolKcals
dt.foodNnuts[kcalsPerDay_ethanol > kcals_ethanol.cap, kcalsPerDay_ethanol := kcals_ethanol.cap]
dt.foodNnuts[, kcalsPerDay_other := kcalsPerDay_tot - (kcalsPerDay_carbohydrate + kcalsPerDay_fat + kcalsPerDay_protein) ]
#deleteListCol <- c(list.tot, "usda_code", "Ref_Desc", "phytate_source")
# deleteListCol <- list.tot[!list.tot %in% c("kcals_fat_g", "kcals_carbohydrate_g", "kcals_protein_g", "kcals_ethanol_g",
# "kcals_sugar_g", "kcals_ft_acds_tot_sat_g")]
dt.foodNnuts[, (list.tot) := NULL]
# rename [nutrient].Q to just [nutrient]
data.table::setnames(dt.foodNnuts, old = c(names.tot), new = c(list.tot))
dt.foodNnuts <- adjustBioavailability(dt.foodNnuts)
dt.foodNnuts[, scenario := gsub("-", "_", scenario)]
dt.foodNnuts[, scenario := gsub("_REF", "", scenario)]
outName <- paste("dt.foodNnuts", suffix, sep = ".")
inDT <- dt.foodNnuts
inDT <- inDT[scenario %in% c("SSP1_NoCC", "SSP2_HGEM", "SSP2_NoCC","SSP3_NoCC"),]
inDT <- inDT[year %in% c("X2010", "X2030", "X2050"),]
desc <- "Combines dt.IMPACTfood with nutrients and kcalsPerDay. Units are for 1 kg, daily availability. iron and zince are adjusted for bioavailability"
createMissingDir(fileloc("resultsDir")) # checks for the existence of the directory and if its not available, creates it Dec 21, 2018
cleanup(inDT, outName, fileloc("resultsDir"), desc = desc)
#' produce subsets that are more manageable in size -----
#' dt.nutrients.kcals -----
dt.nutrients.kcals <- data.table::copy(dt.foodNnuts)
deleteListCol <- c( "foodAvailpDay", "foodQ.sum", "ft_acds_tot_sat_g", "ft_acds_mono_unsat_g", "ft_acds_plyunst_g",
keyVariable("minerals"), keyVariable("vitamins"), keyVariable("macronutrients"),
"phytate_mg", "sugar_g", "ethanol_g", "energy_kcal")
dt.nutrients.kcals[, (deleteListCol) := NULL]
kcalsSources <- c("kcalsPerDay_other", "kcalsPerDay_carbohydrate", "kcalsPerDay_fat", "kcalsPerDay_protein",
"kcalsPerDay_ethanol", "kcalsPerDay_sugar", "kcalsPerDay_ft_acds_tot_sat")
kcalsShare <- paste0(kcalsSources, "_share")
#' calculate ratio of energy to kcals per day for all items in sumlist
dt.nutrients.kcals[, (kcalsShare) := lapply(.SD, "/", kcalsPerDay_tot/100), .SDcols = (kcalsSources)] # divide by 100 to get to percent
#' keep only results that are for the country, ie, not for individual commodities
deleteListCol <- c( "IMPACT_code", "kcalsPerCommod",
"kcals_fat_g", "kcals_carbohydrate_g", "kcals_protein_g", "kcals_ethanol_g", "kcals_sugar_g", "kcals_ft_acds_tot_sat_g")
dt.nutrients.kcals[, (deleteListCol) := NULL]
dt.nutrients.kcals <- unique(dt.nutrients.kcals)
idVars <- c("scenario", "region_code.IMPACT159", "year")
measureVars <- names(dt.nutrients.kcals)[!names(dt.nutrients.kcals) %in% idVars &
!names(dt.nutrients.kcals) %in% c("food_group_code", "staple_code")]
dt.nutrients.kcals <- data.table::melt(dt.nutrients.kcals,
id.vars = idVars,
measure.vars = measureVars,
variable.name = "nutrient",
value.name = "value",
variable.factor = FALSE)
inDT <- unique(dt.nutrients.kcals)
inDT <- inDT[scenario %in% c("SSP1_NoCC", "SSP2_HGEM", "SSP2_NoCC","SSP3_NoCC"),]
inDT <- inDT[year %in% c("X2010", "X2030", "X2050"),]
outName <- paste("dt.nutrients_kcals", suffix, sep = ".")
desc <- "Kcals and kcals shares from carbohydrates, fat, etc. by country"
cleanup(inDT, outName, fileloc("resultsDir"), desc = desc)
#' dt.nutrients.sum.all ------
dt.nutrients.sum.all <- data.table::copy(dt.foodNnuts)
deleteListCol <- c("energy_kcal")
dt.nutrients.sum.all[, (deleteListCol) := NULL]
# the list.tot values are supposed to be average daily availability
dt.nutrients.sum.all[, (list.tot) := lapply(.SD, sum), .SDcols = (list.tot),
by = c("scenario", "region_code.IMPACT159", "year")]
dt.nutrients.sum.all <- unique(dt.nutrients.sum.all)
measureVars <- c(list.tot, "kcalsPerDay_tot", "kcalsPerDay_carbohydrate", "kcalsPerDay_fat", "kcalsPerDay_protein",
"kcalsPerDay_ethanol", "kcalsPerDay_sugar", "kcalsPerDay_ft_acds_tot_sat", "kcalsPerDay_other")
dt.nutrients.sum.all <- data.table::melt(dt.nutrients.sum.all,
id.vars = c("scenario","region_code.IMPACT159", "year"),
variable.name = "nutrient",
measure.vars = measureVars,
value.name = "value",
variable.factor = FALSE)
dt.nutrients.sum.all <- unique(dt.nutrients.sum.all)
inDT <- dt.nutrients.sum.all
inDT <- inDT[scenario %in% c("SSP1_NoCC", "SSP2_HGEM", "SSP2_NoCC","SSP3_NoCC"),]
inDT <- inDT[year %in% c("X2010", "X2030", "X2050"),]
outName <- paste("dt.nutrients_sum_all", suffix, sep = ".") #this includes phytate. Might want to remove later.
desc <- "Sum of each nutrient from each of the food items"
cleanup(inDT,outName, fileloc("resultsDir"), desc = desc)
#' dt.nutrients.sum.staples
dt.nutrients.sum.staples <- data.table::copy(dt.foodNnuts)
deleteListCol <- c("energy_kcal")
dt.nutrients.sum.staples[, (deleteListCol) := NULL]
deleteListCol <- c("kcalsPerDay_tot", "kcalsPerDay_carbohydrate", "kcalsPerDay_fat", "kcalsPerDay_protein",
"kcalsPerDay_ethanol", "kcalsPerDay_sugar", "kcalsPerDay_ft_acds_tot_sat", "kcalsPerDay_other",
"foodQ.sum")
dt.nutrients.sum.staples[, (deleteListCol) := NULL]
dt.nutrients.sum.staples[, (list.tot) := lapply(.SD, sum), .SDcols = (list.tot),
by = c("scenario", "region_code.IMPACT159", "year", "staple_code")]
deleteListCol <- c("IMPACT_code", "foodAvailpDay", "kcalsPerCommod", "food_group_code")
dt.nutrients.sum.staples[, (deleteListCol) := NULL]
dt.nutrients.sum.staples <- unique(dt.nutrients.sum.staples)
# measureVars <- c(list.tot, "kcalsPerDay_tot", "kcalsPerDay_carbohydrate", "kcalsPerDay_fat", "kcalsPerDay_protein",
# "kcalsPerDay_ethanol", "kcalsPerDay_sugar", "kcalsPerDay_ft_acds_tot_sat", "kcalsPerDay_other")
measureVars <- c(list.tot)
dt.nutrients.sum.staples.long <- data.table::melt(dt.nutrients.sum.staples,
id.vars = c("scenario","region_code.IMPACT159", "year", "staple_code"),
measure.vars = measureVars,
variable.name = "nutrient",
value.name = "value",
variable.factor = FALSE)
inDT <- unique(dt.nutrients.sum.staples.long)
inDT <- inDT[scenario %in% c("SSP1_NoCC", "SSP2_HGEM", "SSP2_NoCC","SSP3_NoCC"),]
inDT <- inDT[year %in% c("X2010", "X2030", "X2050"),]
outName <- paste("dt.nutrients_sum_staples", suffix, sep = ".")
desc <- "Sum of each nutrient from staples and nonstaples"
cleanup(inDT,outName, fileloc("resultsDir"), desc = desc)
#' dt.nutrients_sum_FG
dt.nutrients_sum_FG <- data.table::copy(dt.foodNnuts)
deleteListCol <- c("energy_kcal")
dt.nutrients_sum_FG[, (deleteListCol) := NULL]
dt.nutrients_sum_FG[, (list.tot) := lapply(.SD, sum), .SDcols = (list.tot),
by = c("scenario", "region_code.IMPACT159", "year", "food_group_code")]
deleteListCol <- c("IMPACT_code", "foodAvailpDay", "kcalsPerCommod", "staple_code")
dt.nutrients_sum_FG[, (deleteListCol) := NULL]
dt.nutrients_sum_FG <- unique(dt.nutrients_sum_FG)
dt.nutrients_sum_FG <- data.table::melt(dt.nutrients_sum_FG,
id.vars = c("scenario","region_code.IMPACT159", "year", "food_group_code"),
variable.name = "nutrient",
measure.vars = list.tot,
value.name = "value",
variable.factor = FALSE)
dt.nutrients_sum_FG <- unique(dt.nutrients_sum_FG) #note that this contains phytate
inDT <- dt.nutrients_sum_FG
inDT <- inDT[scenario %in% c("SSP1_NoCC", "SSP2_HGEM", "SSP2_NoCC","SSP3_NoCC"),]
inDT <- inDT[year %in% c("X2010", "X2030", "X2050"),]
outName <- paste("dt.nutrients_sum_FG", suffix, sep = ".")
desc <- "Sum of each nutrient from each food group"
cleanup(inDT, outName, fileloc("resultsDir"), desc = desc)
#' dt.KcalShare.nonstaple
dt.KcalShare.nonstaple <- data.table::copy(dt.foodNnuts)
keepListCol <- c("scenario", "region_code.IMPACT159", "year", "IMPACT_code", "kcalsPerCommod", "kcalsPerDay_tot", "staple_code",
"kcalsPerDay_carbohydrate", "kcalsPerDay_fat", "kcalsPerDay_protein", "kcalsPerDay_other", "kcalsPerDay_ethanol", "kcalsPerDay_sugar", "kcalsPerDay_ft_acds_tot_sat")
dt.KcalShare.nonstaple[, setdiff(names(dt.KcalShare.nonstaple), keepListCol) := NULL]
dt.KcalShare.nonstaple <- unique(dt.KcalShare.nonstaple)
dt.KcalShare.nonstaple[,value := sum(kcalsPerCommod) / kcalsPerDay_tot, by = c("scenario", "region_code.IMPACT159", "year", "staple_code")]
deleteListCol <- c("IMPACT_code", "kcalsPerCommod", "kcalsPerDay_tot")
dt.KcalShare.nonstaple[, (deleteListCol) := NULL]
dt.KcalShare.nonstaple <- unique(dt.KcalShare.nonstaple)
dt.KcalShare.nonstaple <- dt.KcalShare.nonstaple[staple_code %in% "nonstaple",]
dt.KcalShare.nonstaple <- unique(dt.KcalShare.nonstaple)
keepListCol <- c("scenario", "region_code.IMPACT159", "year", "value")
# convert to percent
dt.KcalShare.nonstaple[,value := value * 100]
inDT <- dt.KcalShare.nonstaple
inDT <- inDT[scenario %in% c("SSP1_NoCC", "SSP2_HGEM", "SSP2_NoCC","SSP3_NoCC"),]
inDT <- inDT[year %in% c("X2010", "X2030", "X2050"),]
outName <- paste("dt.KcalShare_nonstaple", suffix, sep = ".")
desc <- "Share of kcals from nonstaples in value column; other Kcal data included"
cleanup(inDT, outName, fileloc("resultsDir"), desc = desc)
# # dt.nutrients.nonstapleShare, not currently used (Feb 2018) so commented out ------
# dt.nut.nonstaple.share <- data.table::copy(dt.nutrients.sum.staples.long)
# shareFormula <- "scenario + region_code.IMPACT159 + nutrient + year ~ staple_code"
# dt.nut.nonstaple.share.wide <- data.table::dcast(data = dt.nut.nonstaple.share,
# formula = shareFormula,
# value.var = "value")
# dt.nut.nonstaple.share.wide[, value := 100 * nonstaple/(nonstaple + staple) ][is.na(value), value := 0]
#
# dt.nutrients.nonstapleShare <- dt.nut.nonstaple.share.wide[,c("nonstaple", "staple") := NULL]
#' dt.foodAvail.foodGroup -----
#sum and convert to grams. Note that the food group alcohol results are for the grams in the total beverage, not ethanol.
dt.foodAvail.foodGroup <- data.table::copy(dt.foodNnuts)
keepListCol <- c("scenario", "region_code.IMPACT159", "IMPACT_code", "year", "food_group_code", "foodAvailpDay")
dt.foodAvail.foodGroup[, setdiff(names(dt.foodAvail.foodGroup), keepListCol) := NULL]
dt.foodAvail.foodGroup <- dt.foodAvail.foodGroup[, foodavail.foodgroup.sum := sum(foodAvailpDay) * 1000,
by = c("scenario", "region_code.IMPACT159", "year", "food_group_code")]
deleteListCol <- c("IMPACT_code", "foodAvailpDay")
dt.foodAvail.foodGroup[, (deleteListCol) := NULL]
dt.foodAvail.foodGroup <- unique(dt.foodAvail.foodGroup)
data.table::setnames(dt.foodAvail.foodGroup, old = "foodavail.foodgroup.sum", new = "value")
dt.foodAvail.foodGroup <- unique(dt.foodAvail.foodGroup)
inDT <- dt.foodAvail.foodGroup
inDT <- inDT[scenario %in% c("SSP1_NoCC", "SSP2_HGEM", "SSP2_NoCC","SSP3_NoCC"),]
inDT <- inDT[year %in% c("X2010", "X2030", "X2050"),]
outName <- paste("dt.foodAvail_foodGroup", suffix, sep = ".")
desc <- "Sum of food available by food group, kgs per day"
cleanup(inDT, outName, fileloc("resultsDir"), desc = desc)
}
finalizeScriptMetadata(metadataDT, sourceFile)
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