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NATFOOD GitHub code.R
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### EPIC Oxford Environmental score uncertainty analyses ###
# Last updated: 9th JUne 2023
# This R script supports the analyses for the following paper:
# Scarborough P, Clark M, Cobiac LJ, Papier K, Knuppel A, Lynch J, Harrington RA, Key T, Springmann M. Vegans, vegetarians, fish-eaters and meat-eaters in the UK show discrepant environmental impacts. Nature Food, 2023.
### Description of primary dataset:
## Name: Results_21Mar2022
## Variables:
# mc_run_id {1-1000 indicating which iteration the result is taken from}
# grouping {combined grouping of diet_group and age_group}
# mean_ghgs {Mean for diet + age + sex group for GHG emissions}
# mean_land {Mean for diet + age + sex group for land use}
# mean_watscar {Mean for diet + age + sex group for water scarcity measure}
# mean_eut {Mean for diet + age + sex group for eutrophication}
# mean_ghgs_ch4 {Mean for diet + age + sex group for methane}
# mean_ghgs_n2o {Mean for diet + age + sex group for nitrous oxide}
# mean_bio {Mean for diet + age + sex group for biodiversity measure}
# mean_watuse {Mean for diet + age + sex group for water use}
# mean_acid {Mean for diet + age + sex group for acidification (land-based version of eutrophication)}
# sd_ghgs {SD for diet + age + sex group for GHG emissions}
# sd_land {SD for diet + age + sex group for land use}
# sd_watscar {SD for diet + age + sex group for water scarcity measure}
# sd_eut {SD for diet + age + sex group for eutrophication}
# sd_ghgs_ch4 {SD for diet + age + sex group for methane}
# sd_ghgs_n2o {SD for diet + age + sex group for nitrous oxide}
# sd_bio {SD for diet + age + sex group for biodiversity measure}
# sd_watuse {SD for diet + age + sex group for water use}
# sd_acid {SD for diet + age + sex group for acidification}
# n_participants {number of individuals in each diet + age + sex group}
# sex {female; male}
# diet_group {fish; meat; meat50; meat100; vegan; veggie} NB: meat50 = low meat eaters; meat = medium meat eaters; meat100 = high meat eaters
# age_group {20-29; 30-39; 40-49; 50-59; 60-69; 70-79}
### Description of secondary dataset:
## Name: Results_21Mar2022_nokcaladjust. NB: the date refers to the fact that this uses exactly the same data and runs as
# the primary dataset. The dataset was actually produced in April 2023.
# Variables:
# Identical to the primary dataset, except all results are no longer standardised to 2000kcal
# Install packages
#install.packages("openxlsx")
library("openxlsx")
library("Rcpp")
# Open datasets
dirname <- "ADD HERE"
setwd(dirname)
# agesexdietdata <- "Results_21Mar2022.csv" # For primary analysis
agesexdietdata <- "Results_21Mar2022_nokcaladjust.csv" # For secondary analysis
itdb <- read.csv(agesexdietdata)
#################################################################################################
# #
# DISAGGREGATING CO2 #
# #
#################################################################################################
# Mike C's analyses provide 72k iterations with GHG estimates for CO2e, CH4 and N2O. To estimate CO2, we need to back
# transform the CO2e estimates, using the conversion factors that Mike has used. This is done line by line, so when
# compiled (below) the results will be comparable to how the other gases have been compiled.
# Function to generate CO2 estimates
# NB: In Mike's dataset, the CH4 and N2O data have already been converted to CO2e, so this is simply removing them
co2gen <- function(co2e, ch4, n2o) {
co2e - ch4 - n2o
}
# Apply to the dataset
itdb$mean_ghgs_co2 <- mapply(co2gen,
co2e = itdb$mean_ghgs,
ch4 = itdb$mean_ghgs_ch4,
n2o = itdb$mean_ghgs_n2o)
#################################################################################################
# #
# GENERATING NEW AGGREGATE GHG MEASURES #
# #
#################################################################################################
# First need to reset the CH4 and N2O data, by removing the conversion factors that Mike C applied (that transform them to CO2e)
convert_ch4_MC <- 34
convert_n2o_MC <- 298
# CH4 and N2O untransformed
itdb$mean_ghgs_ch4_nont <- itdb$mean_ghgs_ch4/convert_ch4_MC
itdb$mean_ghgs_n2o_nont <- itdb$mean_ghgs_n2o/convert_n2o_MC
# Generate three new GHG aggregate terms for the paper
co2efun <- function(co2, ch4, n2o, ch4convert, n2oconvert){
co2 + ch4*ch4convert + n2o*n2oconvert
}
itdb$gwp100 <- mapply(co2efun, # This uses IPCC AR6 conversion factors
co2 = itdb$mean_ghgs_co2,
ch4 = itdb$mean_ghgs_ch4_nont,
n2o = itdb$mean_ghgs_n2o_nont,
ch4convert = 27,
n2oconvert = 273)
itdb$gtp100 <- mapply(co2efun,
co2 = itdb$mean_ghgs_co2,
ch4 = itdb$mean_ghgs_ch4_nont,
n2o = itdb$mean_ghgs_n2o_nont,
ch4convert = 11,
n2oconvert = 297)
itdb$gwp20 <- mapply(co2efun,
co2 = itdb$mean_ghgs_co2,
ch4 = itdb$mean_ghgs_ch4_nont,
n2o = itdb$mean_ghgs_n2o_nont,
ch4convert = 86,
n2oconvert = 268)
#################################################################################################
# #
# UNCERTAINTY ANALYSIS FOR BOTH SEXES COMBINED #
# #
#################################################################################################
# The secondary results reported in the paper come from regression analyses, which are shown in the log files shared by
# Keren Papier on 24/01/2022. The log files are available in K:\CPNP\Pete\ENVIRONMENTAL SUSTAINABILITY\OTHER PROJECTS\LEAP\PAPERS\EPIC OXFORD 2
# For each environmental outcome, a regression analysis is conducted adjusted for age (and sex, when results are for
# adults). The 'margins' command in Stata is then used to produce a marginal analysis by diet group. See an explanation
# for marginal analyses in Stata here: https://www.stata.com/features/overview/marginal-analysis/
# Fundamentally, it uses the regression output to calculate a result for each diet group assuming average levels of
# all covariates.
# This is equivalent to producing results for the Monte Carlo analysis that are standardised to the EPIC Oxford
# sample. Since all of the food parameter distributions that are combined in the Monte Carlo analysis are
# lognormal and heavily right-skewed, the results of the uncertainty analysis right shifts the estimates by diet group
# (see Combining lognormal distributions.R for details). Therefore, the results of the uncertainty analysis and the
# regressions are not equal, but are conducted using equivalent methods to allow for comparisons between them.
# First, enter the number of participants by age and sex in the full sample (i.e. not disaggregated by diet group)
# Note that in the names below, 2 = 20-29; 3 = 30-39 and so on.
# These are provided in email from Keren Papier 25/01/2022
n.male.2 <- 1421
n.male.3 <- 2818
n.male.4 <- 3285
n.male.5 <- 2374
n.male.6 <- 1981
n.male.7 <- 787
n.female.2 <- 7394
n.female.3 <- 9645
n.female.4 <- 11342
n.female.5 <- 8388
n.female.6 <- 4455
n.female.7 <- 1614
# Check this sums to appropriate number (n = 55,504)
sum(n.male.2,
n.male.3,
n.male.4,
n.male.5,
n.male.6,
n.male.7,
n.female.2,
n.female.3,
n.female.4,
n.female.5,
n.female.6,
n.female.7)
## Collapse onto diet groups
# Here, the mean (weighted by number of participants) for each diet group across all iterations is calculated
# Variable for display of results
dietindex <- c("vegan",
"veggie",
"fish",
"meat50",
"meat",
"meat100")
# Function for standardising results to the EPIC Oxford population
all.adult.standard <- function(male.2,
male.3,
male.4,
male.5,
male.6,
male.7,
female.2,
female.3,
female.4,
female.5,
female.6,
female.7){
sum(male.2*n.male.2, # This produces a weighted mean, that is weighted by the proportion of participants in each group
male.3*n.male.3,
male.4*n.male.4,
male.5*n.male.5,
male.6*n.male.6,
male.7*n.male.7,
female.2*n.female.2,
female.3*n.female.3,
female.4*n.female.4,
female.5*n.female.5,
female.6*n.female.6,
female.7*n.female.7) / sum(n.male.2,
n.male.3,
n.male.4,
n.male.5,
n.male.6,
n.male.7,
n.female.2,
n.female.3,
n.female.4,
n.female.5,
n.female.6,
n.female.7)
}
# Check whether standardising results from the regression works:
all.adult.standard(male.2 = 10.44407,
male.3 = 10.44407 + 0.034442,
male.4 = 10.44407 + 0.2183768,
male.5 = 10.44407 + 0.3909661,
male.6 = 10.44407 + 0.2853161,
male.7 = 10.44407 + 0.1492136,
female.2 = 10.44407 + 0.4991238,
female.3 = 10.44407 + 0.034442 + 0.4991238,
female.4 = 10.44407 + 0.2183768 + 0.4991238,
female.5 = 10.44407 + 0.3909661 + 0.4991238,
female.6 = 10.44407 + 0.2853161 + 0.4991238,
female.7 = 10.44407 + 0.1492136 + 0.4991238) # That recreates the margins value for high meat eaters perfectly.
### LAND USE RESULTS
# Compile results by diet group
for (i in dietindex) {
temp <- NULL
for (j in 1:1000) {
output <- c(j,all.adult.standard(male.2 = itdb$mean_land[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="20-29"],
male.3 = itdb$mean_land[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="30-39"],
male.4 = itdb$mean_land[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="40-49"],
male.5 = itdb$mean_land[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="50-59"],
male.6 = itdb$mean_land[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="60-69"],
male.7 = itdb$mean_land[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="70-79"],
female.2 = itdb$mean_land[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="20-29"],
female.3 = itdb$mean_land[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="30-39"],
female.4 = itdb$mean_land[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="40-49"],
female.5 = itdb$mean_land[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="50-59"],
female.6 = itdb$mean_land[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="60-69"],
female.7 = itdb$mean_land[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="70-79"]))
temp <- rbind(temp,output)
}
assign(paste0("land.",i),temp)
}
## Calculate 95% uncertainty intervals and median
land.UI <- data.frame(diet.group = character(),
median = numeric(),
lowUI = numeric(),
highUI = numeric())
for (i in 1:6){
land.UI[i,] <- c(dietindex[i],
eval(parse(text=paste0("as.numeric(quantile(land.",dietindex[i],"[,2], 0.5))"))),
eval(parse(text=paste0("as.numeric(quantile(land.",dietindex[i],"[,2], 0.025))"))),
eval(parse(text=paste0("as.numeric(quantile(land.",dietindex[i],"[,2], 0.975))"))))
}
### GHG EMISSION RESULTS
## GWP100
# Compile results by diet group
for (i in dietindex) {
temp <- NULL
for (j in 1:1000) {
output <- c(j,all.adult.standard(male.2 = itdb$gwp100[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="20-29"],
male.3 = itdb$gwp100[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="30-39"],
male.4 = itdb$gwp100[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="40-49"],
male.5 = itdb$gwp100[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="50-59"],
male.6 = itdb$gwp100[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="60-69"],
male.7 = itdb$gwp100[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="70-79"],
female.2 = itdb$gwp100[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="20-29"],
female.3 = itdb$gwp100[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="30-39"],
female.4 = itdb$gwp100[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="40-49"],
female.5 = itdb$gwp100[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="50-59"],
female.6 = itdb$gwp100[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="60-69"],
female.7 = itdb$gwp100[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="70-79"]))
temp <- rbind(temp,output)
}
assign(paste0("gwp100.",i),temp)
}
## Calculate 95% uncertainty intervals and median
gwp100.UI <- data.frame(diet.group = character(),
median = numeric(),
lowUI = numeric(),
highUI = numeric())
for (i in 1:6){
gwp100.UI[i,] <- c(dietindex[i],
eval(parse(text=paste0("as.numeric(quantile(gwp100.",dietindex[i],"[,2], 0.5))"))),
eval(parse(text=paste0("as.numeric(quantile(gwp100.",dietindex[i],"[,2], 0.025))"))),
eval(parse(text=paste0("as.numeric(quantile(gwp100.",dietindex[i],"[,2], 0.975))"))))
}
## GWP20
# Compile results by diet group
for (i in dietindex) {
temp <- NULL
for (j in 1:1000) {
output <- c(j,all.adult.standard(male.2 = itdb$gwp20[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="20-29"],
male.3 = itdb$gwp20[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="30-39"],
male.4 = itdb$gwp20[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="40-49"],
male.5 = itdb$gwp20[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="50-59"],
male.6 = itdb$gwp20[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="60-69"],
male.7 = itdb$gwp20[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="70-79"],
female.2 = itdb$gwp20[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="20-29"],
female.3 = itdb$gwp20[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="30-39"],
female.4 = itdb$gwp20[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="40-49"],
female.5 = itdb$gwp20[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="50-59"],
female.6 = itdb$gwp20[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="60-69"],
female.7 = itdb$gwp20[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="70-79"]))
temp <- rbind(temp,output)
}
assign(paste0("gwp20.",i),temp)
}
## Calculate 95% uncertainty intervals and median
gwp20.UI <- data.frame(diet.group = character(),
median = numeric(),
lowUI = numeric(),
highUI = numeric())
for (i in 1:6){
gwp20.UI[i,] <- c(dietindex[i],
eval(parse(text=paste0("as.numeric(quantile(gwp20.",dietindex[i],"[,2], 0.5))"))),
eval(parse(text=paste0("as.numeric(quantile(gwp20.",dietindex[i],"[,2], 0.025))"))),
eval(parse(text=paste0("as.numeric(quantile(gwp20.",dietindex[i],"[,2], 0.975))"))))
}
## GTP100
# Compile results by diet group
for (i in dietindex) {
temp <- NULL
for (j in 1:1000) {
output <- c(j,all.adult.standard(male.2 = itdb$gtp100[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="20-29"],
male.3 = itdb$gtp100[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="30-39"],
male.4 = itdb$gtp100[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="40-49"],
male.5 = itdb$gtp100[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="50-59"],
male.6 = itdb$gtp100[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="60-69"],
male.7 = itdb$gtp100[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="70-79"],
female.2 = itdb$gtp100[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="20-29"],
female.3 = itdb$gtp100[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="30-39"],
female.4 = itdb$gtp100[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="40-49"],
female.5 = itdb$gtp100[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="50-59"],
female.6 = itdb$gtp100[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="60-69"],
female.7 = itdb$gtp100[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="70-79"]))
temp <- rbind(temp,output)
}
assign(paste0("gtp100.",i),temp)
}
## Calculate 95% uncertainty intervals and median
gtp100.UI <- data.frame(diet.group = character(),
median = numeric(),
lowUI = numeric(),
highUI = numeric())
for (i in 1:6){
gtp100.UI[i,] <- c(dietindex[i],
eval(parse(text=paste0("as.numeric(quantile(gtp100.",dietindex[i],"[,2], 0.5))"))),
eval(parse(text=paste0("as.numeric(quantile(gtp100.",dietindex[i],"[,2], 0.025))"))),
eval(parse(text=paste0("as.numeric(quantile(gtp100.",dietindex[i],"[,2], 0.975))"))))
}
### WATER SCARCITY RESULTS
# Compile results by diet group
for (i in dietindex) {
temp <- NULL
for (j in 1:1000) {
output <- c(j,all.adult.standard(male.2 = itdb$mean_watscar[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="20-29"],
male.3 = itdb$mean_watscar[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="30-39"],
male.4 = itdb$mean_watscar[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="40-49"],
male.5 = itdb$mean_watscar[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="50-59"],
male.6 = itdb$mean_watscar[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="60-69"],
male.7 = itdb$mean_watscar[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="70-79"],
female.2 = itdb$mean_watscar[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="20-29"],
female.3 = itdb$mean_watscar[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="30-39"],
female.4 = itdb$mean_watscar[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="40-49"],
female.5 = itdb$mean_watscar[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="50-59"],
female.6 = itdb$mean_watscar[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="60-69"],
female.7 = itdb$mean_watscar[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="70-79"]))
temp <- rbind(temp,output)
}
assign(paste0("watscar.",i),temp)
}
## Calculate 95% uncertainty intervals and median
watscar.UI <- data.frame(diet.group = character(),
median = numeric(),
lowUI = numeric(),
highUI = numeric())
for (i in 1:6){
watscar.UI[i,] <- c(dietindex[i],
eval(parse(text=paste0("as.numeric(quantile(watscar.",dietindex[i],"[,2], 0.5))"))),
eval(parse(text=paste0("as.numeric(quantile(watscar.",dietindex[i],"[,2], 0.025))"))),
eval(parse(text=paste0("as.numeric(quantile(watscar.",dietindex[i],"[,2], 0.975))"))))
}
### WATER USE RESULTS
# Compile results by diet group
for (i in dietindex) {
temp <- NULL
for (j in 1:1000) {
output <- c(j,all.adult.standard(male.2 = itdb$mean_watuse[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="20-29"],
male.3 = itdb$mean_watuse[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="30-39"],
male.4 = itdb$mean_watuse[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="40-49"],
male.5 = itdb$mean_watuse[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="50-59"],
male.6 = itdb$mean_watuse[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="60-69"],
male.7 = itdb$mean_watuse[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="70-79"],
female.2 = itdb$mean_watuse[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="20-29"],
female.3 = itdb$mean_watuse[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="30-39"],
female.4 = itdb$mean_watuse[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="40-49"],
female.5 = itdb$mean_watuse[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="50-59"],
female.6 = itdb$mean_watuse[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="60-69"],
female.7 = itdb$mean_watuse[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="70-79"]))
temp <- rbind(temp,output)
}
assign(paste0("watuse.",i),temp)
}
## Calculate 95% uncertainty intervals and median
watuse.UI <- data.frame(diet.group = character(),
median = numeric(),
lowUI = numeric(),
highUI = numeric())
for (i in 1:6){
watuse.UI[i,] <- c(dietindex[i],
eval(parse(text=paste0("as.numeric(quantile(watuse.",dietindex[i],"[,2], 0.5))"))),
eval(parse(text=paste0("as.numeric(quantile(watuse.",dietindex[i],"[,2], 0.025))"))),
eval(parse(text=paste0("as.numeric(quantile(watuse.",dietindex[i],"[,2], 0.975))"))))
}
### EUTROPHICATION RESULTS
# Compile results by diet group
for (i in dietindex) {
temp <- NULL
for (j in 1:1000) {
output <- c(j,all.adult.standard(male.2 = itdb$mean_eut[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="20-29"],
male.3 = itdb$mean_eut[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="30-39"],
male.4 = itdb$mean_eut[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="40-49"],
male.5 = itdb$mean_eut[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="50-59"],
male.6 = itdb$mean_eut[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="60-69"],
male.7 = itdb$mean_eut[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="70-79"],
female.2 = itdb$mean_eut[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="20-29"],
female.3 = itdb$mean_eut[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="30-39"],
female.4 = itdb$mean_eut[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="40-49"],
female.5 = itdb$mean_eut[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="50-59"],
female.6 = itdb$mean_eut[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="60-69"],
female.7 = itdb$mean_eut[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="70-79"]))
temp <- rbind(temp,output)
}
assign(paste0("eut.",i),temp)
}
## Calculate 95% uncertainty intervals and median
eut.UI <- data.frame(diet.group = character(),
median = numeric(),
lowUI = numeric(),
highUI = numeric())
for (i in 1:6){
eut.UI[i,] <- c(dietindex[i],
eval(parse(text=paste0("as.numeric(quantile(eut.",dietindex[i],"[,2], 0.5))"))),
eval(parse(text=paste0("as.numeric(quantile(eut.",dietindex[i],"[,2], 0.025))"))),
eval(parse(text=paste0("as.numeric(quantile(eut.",dietindex[i],"[,2], 0.975))"))))
}
### CARBON DIOXIDE RESULTS
# Compile results by diet group
for (i in dietindex) {
temp <- NULL
for (j in 1:1000) {
output <- c(j,all.adult.standard(male.2 = itdb$mean_ghgs_co2[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="20-29"],
male.3 = itdb$mean_ghgs_co2[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="30-39"],
male.4 = itdb$mean_ghgs_co2[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="40-49"],
male.5 = itdb$mean_ghgs_co2[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="50-59"],
male.6 = itdb$mean_ghgs_co2[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="60-69"],
male.7 = itdb$mean_ghgs_co2[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="70-79"],
female.2 = itdb$mean_ghgs_co2[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="20-29"],
female.3 = itdb$mean_ghgs_co2[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="30-39"],
female.4 = itdb$mean_ghgs_co2[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="40-49"],
female.5 = itdb$mean_ghgs_co2[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="50-59"],
female.6 = itdb$mean_ghgs_co2[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="60-69"],
female.7 = itdb$mean_ghgs_co2[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="70-79"]))
temp <- rbind(temp,output)
}
assign(paste0("co2.",i),temp)
}
## Calculate 95% uncertainty intervals and median
co2.UI <- data.frame(diet.group = character(),
median = numeric(),
lowUI = numeric(),
highUI = numeric())
for (i in 1:6){
co2.UI[i,] <- c(dietindex[i],
eval(parse(text=paste0("as.numeric(quantile(co2.",dietindex[i],"[,2], 0.5))"))),
eval(parse(text=paste0("as.numeric(quantile(co2.",dietindex[i],"[,2], 0.025))"))),
eval(parse(text=paste0("as.numeric(quantile(co2.",dietindex[i],"[,2], 0.975))"))))
}
### METHANE RESULTS
# NB: ALL RESULTS MULTIPLIED BY 1000 TO CONVERT TO G/D
# Compile results by diet group
for (i in dietindex) {
temp <- NULL
for (j in 1:1000) {
output <- c(j,all.adult.standard(male.2 = 1000*itdb$mean_ghgs_ch4_nont[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="20-29"],
male.3 = 1000*itdb$mean_ghgs_ch4_nont[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="30-39"],
male.4 = 1000*itdb$mean_ghgs_ch4_nont[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="40-49"],
male.5 = 1000*itdb$mean_ghgs_ch4_nont[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="50-59"],
male.6 = 1000*itdb$mean_ghgs_ch4_nont[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="60-69"],
male.7 = 1000*itdb$mean_ghgs_ch4_nont[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="70-79"],
female.2 = 1000*itdb$mean_ghgs_ch4_nont[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="20-29"],
female.3 = 1000*itdb$mean_ghgs_ch4_nont[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="30-39"],
female.4 = 1000*itdb$mean_ghgs_ch4_nont[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="40-49"],
female.5 = 1000*itdb$mean_ghgs_ch4_nont[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="50-59"],
female.6 = 1000*itdb$mean_ghgs_ch4_nont[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="60-69"],
female.7 = 1000*itdb$mean_ghgs_ch4_nont[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="70-79"]))
temp <- rbind(temp,output)
}
assign(paste0("ch4.",i),temp)
}
## Calculate 95% uncertainty intervals and median
ch4.UI <- data.frame(diet.group = character(),
median = numeric(),
lowUI = numeric(),
highUI = numeric())
for (i in 1:6){
ch4.UI[i,] <- c(dietindex[i],
eval(parse(text=paste0("as.numeric(quantile(ch4.",dietindex[i],"[,2], 0.5))"))),
eval(parse(text=paste0("as.numeric(quantile(ch4.",dietindex[i],"[,2], 0.025))"))),
eval(parse(text=paste0("as.numeric(quantile(ch4.",dietindex[i],"[,2], 0.975))"))))
}
### NITROUS OXIDE RESULTS
# NB: ALL RESULTS MULTIPLIED BY 1000 TO CONVERT TO G/D
# Compile results by diet group
for (i in dietindex) {
temp <- NULL
for (j in 1:1000) {
output <- c(j,all.adult.standard(male.2 = 1000*itdb$mean_ghgs_n2o_nont[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="20-29"],
male.3 = 1000*itdb$mean_ghgs_n2o_nont[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="30-39"],
male.4 = 1000*itdb$mean_ghgs_n2o_nont[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="40-49"],
male.5 = 1000*itdb$mean_ghgs_n2o_nont[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="50-59"],
male.6 = 1000*itdb$mean_ghgs_n2o_nont[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="60-69"],
male.7 = 1000*itdb$mean_ghgs_n2o_nont[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="70-79"],
female.2 = 1000*itdb$mean_ghgs_n2o_nont[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="20-29"],
female.3 = 1000*itdb$mean_ghgs_n2o_nont[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="30-39"],
female.4 = 1000*itdb$mean_ghgs_n2o_nont[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="40-49"],
female.5 = 1000*itdb$mean_ghgs_n2o_nont[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="50-59"],
female.6 = 1000*itdb$mean_ghgs_n2o_nont[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="60-69"],
female.7 = 1000*itdb$mean_ghgs_n2o_nont[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="70-79"]))
temp <- rbind(temp,output)
}
assign(paste0("n2o.",i),temp)
}
## Calculate 95% uncertainty intervals and median
n2o.UI <- data.frame(diet.group = character(),
median = numeric(),
lowUI = numeric(),
highUI = numeric())
for (i in 1:6){
n2o.UI[i,] <- c(dietindex[i],
eval(parse(text=paste0("as.numeric(quantile(n2o.",dietindex[i],"[,2], 0.5))"))),
eval(parse(text=paste0("as.numeric(quantile(n2o.",dietindex[i],"[,2], 0.025))"))),
eval(parse(text=paste0("as.numeric(quantile(n2o.",dietindex[i],"[,2], 0.975))"))))
}
### BIODIVERSITY RESULTS
# Compile results by diet group
for (i in dietindex) {
temp <- NULL
for (j in 1:1000) {
output <- c(j,all.adult.standard(male.2 = itdb$mean_bio[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="20-29"],
male.3 = itdb$mean_bio[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="30-39"],
male.4 = itdb$mean_bio[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="40-49"],
male.5 = itdb$mean_bio[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="50-59"],
male.6 = itdb$mean_bio[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="60-69"],
male.7 = itdb$mean_bio[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="70-79"],
female.2 = itdb$mean_bio[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="20-29"],
female.3 = itdb$mean_bio[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="30-39"],
female.4 = itdb$mean_bio[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="40-49"],
female.5 = itdb$mean_bio[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="50-59"],
female.6 = itdb$mean_bio[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="60-69"],
female.7 = itdb$mean_bio[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="70-79"]))
temp <- rbind(temp,output)
}
assign(paste0("bio.",i),temp)
}
## Calculate 95% uncertainty intervals and median
bio.UI <- data.frame(diet.group = character(),
median = numeric(),
lowUI = numeric(),
highUI = numeric())
for (i in 1:6){
bio.UI[i,] <- c(dietindex[i],
eval(parse(text=paste0("as.numeric(quantile(bio.",dietindex[i],"[,2], 0.5))"))),
eval(parse(text=paste0("as.numeric(quantile(bio.",dietindex[i],"[,2], 0.025))"))),
eval(parse(text=paste0("as.numeric(quantile(bio.",dietindex[i],"[,2], 0.975))"))))
}
# Extract results for all adults
extractlist <- list("GWP100" = gwp100.UI,
"GWP20" = gwp20.UI,
"GTP100" = gtp100.UI,
"land" = land.UI,
"watscar" = watscar.UI,
"watuse" = watuse.UI,
"eutro" = eut.UI,
"bio" = bio.UI,
"carbon dioxide" = co2.UI,
"methane" = ch4.UI,
"nitrous oxide" = n2o.UI)
# write.xlsx(extractlist, file = "ADD HERE")
#################################################################################################
# #
# UNCERTAINTY ANALYSIS DISAGGREGATED BY SEX #
# #
#################################################################################################
# For the results disaggregated by sex, the results are only age-standardised to the male or female sample,
# and then results are compiled for men and women separately.
# Functions for standardising results to the EPIC Oxford population
male.standard <- function(male.2,
male.3,
male.4,
male.5,
male.6,
male.7){
sum(male.2*n.male.2, # This produces a weighted mean, that is weighted by the proportion of participants in each group
male.3*n.male.3,
male.4*n.male.4,
male.5*n.male.5,
male.6*n.male.6,
male.7*n.male.7) / sum(n.male.2,
n.male.3,
n.male.4,
n.male.5,
n.male.6,
n.male.7)
}
female.standard <- function(female.2,
female.3,
female.4,
female.5,
female.6,
female.7){
sum(female.2*n.female.2,
female.3*n.female.3,
female.4*n.female.4,
female.5*n.female.5,
female.6*n.female.6,
female.7*n.female.7) / sum(n.female.2,
n.female.3,
n.female.4,
n.female.5,
n.female.6,
n.female.7)
}
# Check whether standardising results from the regression works:
male.standard(male.2 = 10.26184,
male.3 = 10.26184 - 0.008085,
male.4 = 10.26184 + 0.1217943,
male.5 = 10.26184 + 0.2743658,
male.6 = 10.26184 + 0.2567864,
male.7 = 10.26184 + 0.2623666) # That recreates the margins value for high meat eaters perfectly.
female.standard(female.2 = 11.03044,
female.3 = 11.03044 + 0.0327147,
female.4 = 11.03044 + 0.2304997,
female.5 = 11.03044 + 0.409631,
female.6 = 11.03044 + 0.2864531,
female.7 = 11.03044 + 0.0751358) # That recreates the margins value for high meat eaters perfectly.
## Collapse onto diet and sex groups
### LAND USE
# Compile results by diet group
for (i in dietindex) {
temp <- NULL
for (j in 1:1000) {
output <- c(j,male.standard(male.2 = itdb$mean_land[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="20-29"],
male.3 = itdb$mean_land[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="30-39"],
male.4 = itdb$mean_land[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="40-49"],
male.5 = itdb$mean_land[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="50-59"],
male.6 = itdb$mean_land[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="60-69"],
male.7 = itdb$mean_land[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="70-79"]))
temp <- rbind(temp,output)
}
assign(paste0("land.male.",i),temp)
}
for (i in dietindex) {
temp <- NULL
for (j in 1:1000) {
output <- c(j,female.standard(female.2 = itdb$mean_land[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="20-29"],
female.3 = itdb$mean_land[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="30-39"],
female.4 = itdb$mean_land[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="40-49"],
female.5 = itdb$mean_land[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="50-59"],
female.6 = itdb$mean_land[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="60-69"],
female.7 = itdb$mean_land[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="70-79"]))
temp <- rbind(temp,output)
}
assign(paste0("land.female.",i),temp)
}
## Calculate 95% uncertainty intervals and median
land.male.UI <- data.frame(diet.group = character(),
median = numeric(),
lowUI = numeric(),
highUI = numeric())
land.female.UI <- data.frame(diet.group = character(),
median = numeric(),
lowUI = numeric(),
highUI = numeric())
for (i in 1:6){
land.male.UI[i,] <- c(dietindex[i],
eval(parse(text=paste0("as.numeric(quantile(land.male.",dietindex[i],"[,2], 0.5))"))),
eval(parse(text=paste0("as.numeric(quantile(land.male.",dietindex[i],"[,2], 0.025))"))),
eval(parse(text=paste0("as.numeric(quantile(land.male.",dietindex[i],"[,2], 0.975))"))))
land.female.UI[i,] <- c(dietindex[i],
eval(parse(text=paste0("as.numeric(quantile(land.female.",dietindex[i],"[,2], 0.5))"))),
eval(parse(text=paste0("as.numeric(quantile(land.female.",dietindex[i],"[,2], 0.025))"))),
eval(parse(text=paste0("as.numeric(quantile(land.female.",dietindex[i],"[,2], 0.975))"))))
}
### GREENHOUSE GAS EMISSIONS
# Compile results by diet group
for (i in dietindex) {
temp <- NULL
for (j in 1:1000) {
output <- c(j,male.standard(male.2 = itdb$mean_ghgs[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="20-29"],
male.3 = itdb$mean_ghgs[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="30-39"],
male.4 = itdb$mean_ghgs[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="40-49"],
male.5 = itdb$mean_ghgs[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="50-59"],
male.6 = itdb$mean_ghgs[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="60-69"],
male.7 = itdb$mean_ghgs[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="70-79"]))
temp <- rbind(temp,output)
}
assign(paste0("ghg.male.",i),temp)
}
for (i in dietindex) {
temp <- NULL
for (j in 1:1000) {
output <- c(j,female.standard(female.2 = itdb$mean_ghgs[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="20-29"],
female.3 = itdb$mean_ghgs[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="30-39"],
female.4 = itdb$mean_ghgs[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="40-49"],
female.5 = itdb$mean_ghgs[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="50-59"],
female.6 = itdb$mean_ghgs[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="60-69"],
female.7 = itdb$mean_ghgs[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="70-79"]))
temp <- rbind(temp,output)
}
assign(paste0("ghg.female.",i),temp)
}
## Calculate 95% uncertainty intervals and median
ghg.male.UI <- data.frame(diet.group = character(),
median = numeric(),
lowUI = numeric(),
highUI = numeric())
ghg.female.UI <- data.frame(diet.group = character(),
median = numeric(),
lowUI = numeric(),
highUI = numeric())
for (i in 1:6){
ghg.male.UI[i,] <- c(dietindex[i],
eval(parse(text=paste0("as.numeric(quantile(ghg.male.",dietindex[i],"[,2], 0.5))"))),
eval(parse(text=paste0("as.numeric(quantile(ghg.male.",dietindex[i],"[,2], 0.025))"))),
eval(parse(text=paste0("as.numeric(quantile(ghg.male.",dietindex[i],"[,2], 0.975))"))))
ghg.female.UI[i,] <- c(dietindex[i],
eval(parse(text=paste0("as.numeric(quantile(ghg.female.",dietindex[i],"[,2], 0.5))"))),
eval(parse(text=paste0("as.numeric(quantile(ghg.female.",dietindex[i],"[,2], 0.025))"))),
eval(parse(text=paste0("as.numeric(quantile(ghg.female.",dietindex[i],"[,2], 0.975))"))))
}
### WATER SCARCITY
# Compile results by diet group
for (i in dietindex) {
temp <- NULL
for (j in 1:1000) {
output <- c(j,male.standard(male.2 = itdb$mean_watscar[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="20-29"],
male.3 = itdb$mean_watscar[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="30-39"],
male.4 = itdb$mean_watscar[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="40-49"],
male.5 = itdb$mean_watscar[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="50-59"],
male.6 = itdb$mean_watscar[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="60-69"],
male.7 = itdb$mean_watscar[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="70-79"]))
temp <- rbind(temp,output)
}
assign(paste0("watscar.male.",i),temp)
}
for (i in dietindex) {
temp <- NULL
for (j in 1:1000) {
output <- c(j,female.standard(female.2 = itdb$mean_watscar[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="20-29"],
female.3 = itdb$mean_watscar[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="30-39"],
female.4 = itdb$mean_watscar[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="40-49"],
female.5 = itdb$mean_watscar[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="50-59"],
female.6 = itdb$mean_watscar[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="60-69"],
female.7 = itdb$mean_watscar[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="70-79"]))
temp <- rbind(temp,output)
}
assign(paste0("watscar.female.",i),temp)
}
## Calculate 95% uncertainty intervals and median
watscar.male.UI <- data.frame(diet.group = character(),
median = numeric(),
lowUI = numeric(),
highUI = numeric())
watscar.female.UI <- data.frame(diet.group = character(),
median = numeric(),
lowUI = numeric(),
highUI = numeric())
for (i in 1:6){
watscar.male.UI[i,] <- c(dietindex[i],
eval(parse(text=paste0("as.numeric(quantile(watscar.male.",dietindex[i],"[,2], 0.5))"))),
eval(parse(text=paste0("as.numeric(quantile(watscar.male.",dietindex[i],"[,2], 0.025))"))),
eval(parse(text=paste0("as.numeric(quantile(watscar.male.",dietindex[i],"[,2], 0.975))"))))
watscar.female.UI[i,] <- c(dietindex[i],
eval(parse(text=paste0("as.numeric(quantile(watscar.female.",dietindex[i],"[,2], 0.5))"))),
eval(parse(text=paste0("as.numeric(quantile(watscar.female.",dietindex[i],"[,2], 0.025))"))),
eval(parse(text=paste0("as.numeric(quantile(watscar.female.",dietindex[i],"[,2], 0.975))"))))
}
### WATER USE
# Compile results by diet group
for (i in dietindex) {
temp <- NULL
for (j in 1:1000) {
output <- c(j,male.standard(male.2 = itdb$mean_watuse[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="20-29"],
male.3 = itdb$mean_watuse[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="30-39"],
male.4 = itdb$mean_watuse[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="40-49"],
male.5 = itdb$mean_watuse[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="50-59"],
male.6 = itdb$mean_watuse[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="60-69"],
male.7 = itdb$mean_watuse[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="70-79"]))
temp <- rbind(temp,output)
}
assign(paste0("watuse.male.",i),temp)
}
for (i in dietindex) {
temp <- NULL
for (j in 1:1000) {
output <- c(j,female.standard(female.2 = itdb$mean_watuse[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="20-29"],
female.3 = itdb$mean_watuse[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="30-39"],
female.4 = itdb$mean_watuse[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="40-49"],
female.5 = itdb$mean_watuse[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="50-59"],
female.6 = itdb$mean_watuse[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="60-69"],
female.7 = itdb$mean_watuse[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="70-79"]))
temp <- rbind(temp,output)
}
assign(paste0("watuse.female.",i),temp)
}
## Calculate 95% uncertainty intervals and median
watuse.male.UI <- data.frame(diet.group = character(),
median = numeric(),
lowUI = numeric(),
highUI = numeric())
watuse.female.UI <- data.frame(diet.group = character(),
median = numeric(),
lowUI = numeric(),
highUI = numeric())
for (i in 1:6){
watuse.male.UI[i,] <- c(dietindex[i],
eval(parse(text=paste0("as.numeric(quantile(watuse.male.",dietindex[i],"[,2], 0.5))"))),
eval(parse(text=paste0("as.numeric(quantile(watuse.male.",dietindex[i],"[,2], 0.025))"))),
eval(parse(text=paste0("as.numeric(quantile(watuse.male.",dietindex[i],"[,2], 0.975))"))))
watuse.female.UI[i,] <- c(dietindex[i],
eval(parse(text=paste0("as.numeric(quantile(watuse.female.",dietindex[i],"[,2], 0.5))"))),
eval(parse(text=paste0("as.numeric(quantile(watuse.female.",dietindex[i],"[,2], 0.025))"))),
eval(parse(text=paste0("as.numeric(quantile(watuse.female.",dietindex[i],"[,2], 0.975))"))))
}
### EUTROPHICATION
# Compile results by diet group
for (i in dietindex) {
temp <- NULL
for (j in 1:1000) {
output <- c(j,male.standard(male.2 = itdb$mean_eut[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="20-29"],
male.3 = itdb$mean_eut[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="30-39"],
male.4 = itdb$mean_eut[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="40-49"],
male.5 = itdb$mean_eut[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="50-59"],
male.6 = itdb$mean_eut[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="60-69"],
male.7 = itdb$mean_eut[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="70-79"]))
temp <- rbind(temp,output)
}
assign(paste0("eut.male.",i),temp)
}
for (i in dietindex) {
temp <- NULL
for (j in 1:1000) {
output <- c(j,female.standard(female.2 = itdb$mean_eut[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="20-29"],
female.3 = itdb$mean_eut[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="30-39"],
female.4 = itdb$mean_eut[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="40-49"],
female.5 = itdb$mean_eut[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="50-59"],
female.6 = itdb$mean_eut[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="60-69"],
female.7 = itdb$mean_eut[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="female"&itdb$age_group=="70-79"]))
temp <- rbind(temp,output)
}
assign(paste0("eut.female.",i),temp)
}
## Calculate 95% uncertainty intervals and median
eut.male.UI <- data.frame(diet.group = character(),
median = numeric(),
lowUI = numeric(),
highUI = numeric())
eut.female.UI <- data.frame(diet.group = character(),
median = numeric(),
lowUI = numeric(),
highUI = numeric())
for (i in 1:6){
eut.male.UI[i,] <- c(dietindex[i],
eval(parse(text=paste0("as.numeric(quantile(eut.male.",dietindex[i],"[,2], 0.5))"))),
eval(parse(text=paste0("as.numeric(quantile(eut.male.",dietindex[i],"[,2], 0.025))"))),
eval(parse(text=paste0("as.numeric(quantile(eut.male.",dietindex[i],"[,2], 0.975))"))))
eut.female.UI[i,] <- c(dietindex[i],
eval(parse(text=paste0("as.numeric(quantile(eut.female.",dietindex[i],"[,2], 0.5))"))),
eval(parse(text=paste0("as.numeric(quantile(eut.female.",dietindex[i],"[,2], 0.025))"))),
eval(parse(text=paste0("as.numeric(quantile(eut.female.",dietindex[i],"[,2], 0.975))"))))
}
### METHANE
# Compile results by diet group
for (i in dietindex) {
temp <- NULL
for (j in 1:1000) {
output <- c(j,male.standard(male.2 = itdb$mean_ghgs_ch4[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="20-29"],
male.3 = itdb$mean_ghgs_ch4[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="30-39"],
male.4 = itdb$mean_ghgs_ch4[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="40-49"],
male.5 = itdb$mean_ghgs_ch4[itdb$mc_run_id==j&itdb$diet_group==i&itdb$sex=="male"&itdb$age_group=="50-59"],