R/dataManagement.Pop.R
In GeraldCNelson/nutmod: Calculates nutrient information worldwide
Defines functions
repCons
#' @title "Management of SSP Population Data"
#' @keywords data management, SSP, population data
#' @name dataManagement.Pop.R
#' #Copyright (C) 2015 - 2018 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/.
#' \description{
#' This script reads in cleaned up population data written by dataPrep.SSP.R for the SSP populaton data
#' or the population data set for the African Ag Futures work prepared in dataPrep.UNscenarios.R
#' and does manipulations to prepare it for later use
#' to align the SSP population data with the nutrient requirements age and gender structure data.
#' }
sourceFile <- "dataManagement.Pop.R"
description <- "This script reads in cleaned up population data written by dataPrep.SSP.R for the SSP populaton data or the population data set for the African Ag Futures work prepared in dataPrep.UNscenarios.R and does manipulations to prepare it for later use to align the SSP population data with the nutrient requirements age and gender structure data."
createScriptMetaData()
#' Creates the following files
#' req.EAR.ssp - data/req.EAR.percap.rds
#' req.RDA.vits.ssp - data/req.RDA.vits.percap.rds
#' req.RDA.minrls.ssp - data/req.RDA.minrls.percap.rds
#' req.RDA.macro.ssp - data/req.RDA.macro.percap.rds
#' req.UL.vits.ssp - data/req.UL.vits.percap.rds
#' req.UL.minrls.ssp - data/req.UL.minrls.percap.rds"
#' req.MRVs.ssp - data/req.MRVs.percap.rds" # added March 24, 2017
#' @source \url{https://tntcat.ac.at/SspDb/download/common/SspDb_country_data_2013-06-12.csv.zip}
#Copyright (C) 2015 - 2017 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/.
gdxChoice <- "SSPs"
source("R/nutrientModFunctions.R")
keepYearList <- keyVariable("keepYearList")
# Read in the cleaned up population data ----
dt.pop <- getNewestVersion("dt.SSPPopClean", fileloc("uData"))
#' do this to remove year 0, which was needed for the fish and alcohol calculations
dt.pop <- dt.pop[year %in% keepYearList,]
# Extract lists of the scenarios, regions, and data variables ------------------------------------
# dt.SSP.scen <- data.table::copy(dt.pop)
# # start process of creating a separate list for pregnant and lactating (P/L) women----
# this list is for females who could be pregnant and lactating and have for the most part
# identical nutrient needs if they are not P/L
# Estimate the number of pregnant women and lactating women -----
# the estimate is based on the number of children aged 0 to 4.
#sum boys and girls 0 to 4
# convert rows of age gender groups to columns to make the addition process straightforward
formula.wide <- paste("scenario + region_code.IMPACT159 + year ~ ageGenderCode")
dt.pop.wide <- data.table::dcast(
data = dt.pop,
formula = formula.wide,
value.var = "value")
ageColsToSum <- c("F15_19", "F20_24", "F25_29", "F30_34", "F35_39", "F40_44", "F45_49") #ages for which women can be pregnant or lactating
dt.pop.wide[, F15_49 := rowSums(.SD), .SDcols = ageColsToSum]
dt.pop.wide <- unique(dt.pop.wide)
#pregnant and lactating women are a constant share of kids 0 to 4. This is a *kludge*!!!
share.preg <- 0.2
share.lact <- 0.2
dt.pop.wide[, Preg := (F0_4 + M0_4) * share.preg][,Lact := (F0_4 + M0_4) * share.lact][,nonPL := F15_49 - Preg - Lact]
# data.table::setkey(dt.SSP.scen.wide)
# get rid of temporary column
deleteListCol <- c("F15_49")
dt.pop.wide[,(deleteListCol) := NULL]
# the next step makes F15_49 be only women of that age who are not PL. The sum of F15_49, Preg, Lact is all women in this group
data.table::setnames(dt.pop.wide, old = "nonPL",new = "F15_49")
idVarsPop <- c("scenario", "region_code.IMPACT159", "year" )
measureVarsPop <- names(dt.pop.wide)[!names(dt.pop.wide) %in% idVarsPop]
dt.pop <- data.table::melt(dt.pop.wide,
id.vars = idVarsPop,
variable.name = "ageGenderCode",
measure.vars = measureVarsPop,
value.name = "value",
variable.factor = FALSE)
#change names of age groups; prepend SSP because the DRI requirements have been converted to SSP age groups and the reqsSSP files use these age group names (eg. SSPM15_19)
dt.pop[, ageGenderCode := paste("SSP", ageGenderCode, sep = "")]
data.table::setnames(dt.pop, old = "value",new = "pop.value")
#' Title repCons
#' Short for Representative Consumer
#' @param dt.pop - data table with population data for one scenario (at least for now)
#' @param nutReqName - the short name of a nutrients requirement dataframe, eg. req_EAR.ssp
#' that has been converted to SSP (and UN?) age and gender groups
#' @param common.nut - list of nutrients common to the food nutrient list and the
#' requirements list
#' @param ageRowsToSum - a list of the female age groups that could potentially be pregnant.
#' It is included to so these individual rows can be deleted. They are replaced with
#' preg, lact, and nonPL, which should sum to the total of the list in ageRowsToSum
#' @param region - code for the region over which the operations should be done; e.g., region_code.IMPACT159
#' @return requirements for a representative consumer
#' @export
# repCons generates the age and gender specific nutrient requirements for a representative consumer as they change over time.
repCons <- function(dt.pop, nutReqName) {
dt.nutReq <- getNewestVersion(nutReqName)
common.nut <- names(dt.nutReq)[!names(dt.nutReq) %in% "ageGenderCode"] # get just the names of the nutrients in the nutReqName file
dt.temp <- merge(dt.pop, dt.nutReq, by = "ageGenderCode", all.y = TRUE)
keyValues <- c("scenario", "region_code.IMPACT159", "year", "ageGenderCode","pop.value")
data.table::setkeyv(dt.temp,keyValues)
# multiply the number of people in an age and gender group by the nutritional requirements of that group
dt.temp[, (paste(common.nut, "prod", sep = "_")) :=
lapply(.SD, function(x) x * dt.temp$pop.value), .SDcols = (common.nut)][,(common.nut) := NULL]
keyValues <- c("scenario", "region_code.IMPACT159", "year")
data.table::setkeyv(dt.temp, keyValues)
reqlist <- c(paste(common.nut, "prod", sep = "_"), "pop.value")
# sum the nutritional requirements for all groups
dt.temp[, (paste(reqlist, "sum", sep = "_")) := lapply(.SD, sum), by =
keyValues, .SDcols = c(reqlist)][, c(reqlist) := NULL]
dt.temp.sum <- unique(dt.temp[, c("scenario","region_code.IMPACT159", "year",
paste(reqlist, "sum", sep = "_")), with = FALSE])
sumlist <- paste(common.nut, "prod_sum", sep = "_")
finlist <- paste(common.nut, "fin", sep = "_")
#divide every element in sumlist by pop.value and put in corresponding variable in finlist
# cat("\n\n", names(dt.temp.sum))
dt.temp.sum[,(finlist) := lapply(.SD, "/", dt.temp.sum$pop.value_sum), .SDcols = sumlist]
dt.temp.sum[, c("pop.value_sum", sumlist) := NULL]
# change column names back to just nutrient names
data.table::setnames(dt.temp.sum,old = finlist, new = common.nut)
dt.temp.melt <- data.table::melt(
dt.temp.sum,
id.vars = c("scenario","region_code.IMPACT159", "year"),
measure.vars = common.nut,
variable.name = "nutrient",
variable.factor = FALSE,
value.name = "value"
)
inDT <- dt.temp.sum
outName <- paste(gsub("_ssp","",nutReqName),"percap",sep = "_")
desc <- paste0("Per capita requirement of ", nutReqName, " by specific population scenario")
cleanup(inDT,outName,fileloc("mData"), desc = desc) # changed to uData June 5, 2018; changed to mData Oct 5, 2018 because of combine Un and pop data
temp <- data.table::dcast(
dt.temp.melt,
scenario + region_code.IMPACT159 + nutrient ~ year,
fun = NULL,
value.var = "value")
return(temp)
}
#nutrient requirements are calculated in dataPrep.nutrientRequirements.R.
reqsSSP <- keyVariable("reqsListSSP")
#The list of nutrients for each is common.EAR, common.RDA.vits, common.RDA.minrls, common.RDA.macro, common.UL.vits,
# common.UL.minrls, common.AMDR
common <- keyVariable("commonList")
# code that creates and writes out the nutrient requirements files
# set up workbook wbGeneral -----
wbGeneral <- openxlsx::createWorkbook()
#create a variable, creationInfo, with info on creator, date, model version, etc.
creationInfo <-
("Information on creator, date, model version, etc.")
creationInfo <- rbind(creationInfo,
paste("Creator:", keyVariable("userName")))
creationInfo <- rbind(creationInfo,
paste("Date of file creation:", Sys.Date()))
#creationInfo <- rbind(creationInfo, paste("IMPACT data:", IMPACTfileName))
nutrientFileName <- fileNameList("nutrientFileName")
creationInfo <- rbind(creationInfo,
paste("Nutrient data:", nutrientFileName))
DRIFileName <- fileNameList("DRIFileName")
creationInfo <- rbind(creationInfo,
paste("Nutrient requirements data:", DRIFileName))
SSPdataZip <- fileNameList("SSPdataZip")
creationInfo <- rbind(creationInfo,
paste("SSP.regions data:", SSPdataZip))
openxlsx::addWorksheet(wb = wbGeneral, sheetName = "creation_Info")
# write the creationInfo variable to a worksheet called creation_Info
openxlsx::writeData(
wb = wbGeneral,
x = creationInfo,
sheet = "creation_Info",
startRow = 1, startCol = 1,
rowNames = FALSE, colNames = FALSE
)
#Set up df wbInfoGeneral for common worksheet names and descriptions -----
wbInfoGeneral <-
data.frame(sheet_Name = character(),sheet_Desc = character(),stringsAsFactors = FALSE)
wbInfoGeneral[(nrow(wbInfoGeneral) + 1), ] <-
c("creation_Info","Information on creator, date, model version, etc.")
wbInfoGeneral[(nrow(wbInfoGeneral) + 1), ] <-
c("Sheet names", "Description of sheet contents")
#add a worksheet called IMPACTBaselist to the workbook wb = wbGeneral, with info on the commodities
# in the base set and their nutrients
# dt.nutrients <- getNewestVersion("dt.nutrients.var", fileloc("iData")) # not created until dt.footNnuts.R
# openxlsx::addWorksheet(wb = wbGeneral, sheetName = "IMPACTBaselist")
# #commodityNames <- cbind(nutrients[c("Name","IMPACT_code")])
# openxlsx::writeData(
# wb = wbGeneral,
# x = dt.nutrients,
# sheet = "IMPACTBaselist",
# startRow = 1,
# startCol = 1
# )
# openxlsx::addStyle(
# wb = wbGeneral,
# sheet = "IMPACTBaselist",
# style = numStyle,
# rows = 2:nrow(dt.nutrients),
# cols = 3:(ncol(dt.nutrients)),
# gridExpand = TRUE
# )
# for loop for the nutrient requirements worksheets ----
for (i in 1:length(reqsSSP)) {
dt.temp.internal <- repCons(dt.pop, reqsSSP[i]) #, ageRowsToSum)
data.table::setkeyv(dt.temp.internal, c("nutrient", "region_code.IMPACT159"))
dt.name <- paste(reqsSSP[i], "percap", sep = "_")
#print(dt.name)
openxlsx::addWorksheet(wb = wbGeneral, sheetName = dt.name)
openxlsx::addStyle(
wb = wbGeneral, sheet = dt.name, style = numStyle, rows = 2:nrow(dt.temp.internal),
cols = 3:(ncol(dt.temp.internal)),
gridExpand = TRUE
)
openxlsx::writeData(
wb = wbGeneral,
x = dt.temp.internal,
sheet = dt.name,
startRow = 1, startCol = 1, rowNames = FALSE, colNames = TRUE
)
nutrientList <- paste(unique(dt.temp.internal$nutrient), collapse = ", ")
sheetDesc <- paste("nutrients included: ", nutrientList)
wbInfoGeneral[(nrow(wbInfoGeneral) + 1), ] <-
c(dt.name, sheetDesc)
dt.temp.melt <- data.table::melt(dt.temp.internal,
id.vars = c("region_code.IMPACT159", "nutrient"),
measure.vars = keepYearList,
value.name = "value",
variable.factor = FALSE)
}
#add sheet with info about each of the worksheets
openxlsx::addWorksheet(wb = wbGeneral, sheetName = "sheetInfo")
openxlsx::writeData(
wb = wbGeneral,
x = wbInfoGeneral,
sheet = "sheetInfo",
startRow = 1,
startCol = 1,
rowNames = FALSE,
colNames = FALSE
)
openxlsx::addStyle(
wb = wbGeneral,
sheet = "sheetInfo",
style = textStyle,
rows = 1:nrow(wbInfoGeneral),
cols = 1:(ncol(wbInfoGeneral)),
gridExpand = TRUE
)
openxlsx::setColWidths(wb = wbGeneral,
sheet = "sheetInfo",
cols = 1:2,
widths = 20)
#move sheetInfo worksheet from the last to the first
temp <- 2:length(names(wbGeneral)) - 1
temp <- c(length(names(wbGeneral)), temp)
openxlsx::worksheetOrder(wbGeneral) <- temp
xcelOutFileName <-
paste("results/",gdxChoice, "/nut.requirements.", Sys.Date(), ".xlsx", sep = "")
openxlsx::saveWorkbook(wb = wbGeneral, xcelOutFileName, overwrite = TRUE)
finalizeScriptMetadata(metadataDT, sourceFile)
GeraldCNelson/nutmod documentation built on May 8, 2023, 8:04 a.m.
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Defines functions repCons
#' @title "Management of SSP Population Data"
#' @keywords data management, SSP, population data
#' @name dataManagement.Pop.R
#' #Copyright (C) 2015 - 2018 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/.
#' \description{
#' This script reads in cleaned up population data written by dataPrep.SSP.R for the SSP populaton data
#' or the population data set for the African Ag Futures work prepared in dataPrep.UNscenarios.R
#' and does manipulations to prepare it for later use
#' to align the SSP population data with the nutrient requirements age and gender structure data.
#' }
sourceFile <- "dataManagement.Pop.R"
description <- "This script reads in cleaned up population data written by dataPrep.SSP.R for the SSP populaton data or the population data set for the African Ag Futures work prepared in dataPrep.UNscenarios.R and does manipulations to prepare it for later use to align the SSP population data with the nutrient requirements age and gender structure data."
createScriptMetaData()
#' Creates the following files
#' req.EAR.ssp - data/req.EAR.percap.rds
#' req.RDA.vits.ssp - data/req.RDA.vits.percap.rds
#' req.RDA.minrls.ssp - data/req.RDA.minrls.percap.rds
#' req.RDA.macro.ssp - data/req.RDA.macro.percap.rds
#' req.UL.vits.ssp - data/req.UL.vits.percap.rds
#' req.UL.minrls.ssp - data/req.UL.minrls.percap.rds"
#' req.MRVs.ssp - data/req.MRVs.percap.rds" # added March 24, 2017
#' @source \url{https://tntcat.ac.at/SspDb/download/common/SspDb_country_data_2013-06-12.csv.zip}
#Copyright (C) 2015 - 2017 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/.
gdxChoice <- "SSPs"
source("R/nutrientModFunctions.R")
keepYearList <- keyVariable("keepYearList")
# Read in the cleaned up population data ----
dt.pop <- getNewestVersion("dt.SSPPopClean", fileloc("uData"))
#' do this to remove year 0, which was needed for the fish and alcohol calculations
dt.pop <- dt.pop[year %in% keepYearList,]
# Extract lists of the scenarios, regions, and data variables ------------------------------------
# dt.SSP.scen <- data.table::copy(dt.pop)
# # start process of creating a separate list for pregnant and lactating (P/L) women----
# this list is for females who could be pregnant and lactating and have for the most part
# identical nutrient needs if they are not P/L
# Estimate the number of pregnant women and lactating women -----
# the estimate is based on the number of children aged 0 to 4.
#sum boys and girls 0 to 4
# convert rows of age gender groups to columns to make the addition process straightforward
formula.wide <- paste("scenario + region_code.IMPACT159 + year ~ ageGenderCode")
dt.pop.wide <- data.table::dcast(
data = dt.pop,
formula = formula.wide,
value.var = "value")
ageColsToSum <- c("F15_19", "F20_24", "F25_29", "F30_34", "F35_39", "F40_44", "F45_49") #ages for which women can be pregnant or lactating
dt.pop.wide[, F15_49 := rowSums(.SD), .SDcols = ageColsToSum]
dt.pop.wide <- unique(dt.pop.wide)
#pregnant and lactating women are a constant share of kids 0 to 4. This is a *kludge*!!!
share.preg <- 0.2
share.lact <- 0.2
dt.pop.wide[, Preg := (F0_4 + M0_4) * share.preg][,Lact := (F0_4 + M0_4) * share.lact][,nonPL := F15_49 - Preg - Lact]
# data.table::setkey(dt.SSP.scen.wide)
# get rid of temporary column
deleteListCol <- c("F15_49")
dt.pop.wide[,(deleteListCol) := NULL]
# the next step makes F15_49 be only women of that age who are not PL. The sum of F15_49, Preg, Lact is all women in this group
data.table::setnames(dt.pop.wide, old = "nonPL",new = "F15_49")
idVarsPop <- c("scenario", "region_code.IMPACT159", "year" )
measureVarsPop <- names(dt.pop.wide)[!names(dt.pop.wide) %in% idVarsPop]
dt.pop <- data.table::melt(dt.pop.wide,
id.vars = idVarsPop,
variable.name = "ageGenderCode",
measure.vars = measureVarsPop,
value.name = "value",
variable.factor = FALSE)
#change names of age groups; prepend SSP because the DRI requirements have been converted to SSP age groups and the reqsSSP files use these age group names (eg. SSPM15_19)
dt.pop[, ageGenderCode := paste("SSP", ageGenderCode, sep = "")]
data.table::setnames(dt.pop, old = "value",new = "pop.value")
#' Title repCons
#' Short for Representative Consumer
#' @param dt.pop - data table with population data for one scenario (at least for now)
#' @param nutReqName - the short name of a nutrients requirement dataframe, eg. req_EAR.ssp
#' that has been converted to SSP (and UN?) age and gender groups
#' @param common.nut - list of nutrients common to the food nutrient list and the
#' requirements list
#' @param ageRowsToSum - a list of the female age groups that could potentially be pregnant.
#' It is included to so these individual rows can be deleted. They are replaced with
#' preg, lact, and nonPL, which should sum to the total of the list in ageRowsToSum
#' @param region - code for the region over which the operations should be done; e.g., region_code.IMPACT159
#' @return requirements for a representative consumer
#' @export
# repCons generates the age and gender specific nutrient requirements for a representative consumer as they change over time.
repCons <- function(dt.pop, nutReqName) {
dt.nutReq <- getNewestVersion(nutReqName)
common.nut <- names(dt.nutReq)[!names(dt.nutReq) %in% "ageGenderCode"] # get just the names of the nutrients in the nutReqName file
dt.temp <- merge(dt.pop, dt.nutReq, by = "ageGenderCode", all.y = TRUE)
keyValues <- c("scenario", "region_code.IMPACT159", "year", "ageGenderCode","pop.value")
data.table::setkeyv(dt.temp,keyValues)
# multiply the number of people in an age and gender group by the nutritional requirements of that group
dt.temp[, (paste(common.nut, "prod", sep = "_")) :=
lapply(.SD, function(x) x * dt.temp$pop.value), .SDcols = (common.nut)][,(common.nut) := NULL]
keyValues <- c("scenario", "region_code.IMPACT159", "year")
data.table::setkeyv(dt.temp, keyValues)
reqlist <- c(paste(common.nut, "prod", sep = "_"), "pop.value")
# sum the nutritional requirements for all groups
dt.temp[, (paste(reqlist, "sum", sep = "_")) := lapply(.SD, sum), by =
keyValues, .SDcols = c(reqlist)][, c(reqlist) := NULL]
dt.temp.sum <- unique(dt.temp[, c("scenario","region_code.IMPACT159", "year",
paste(reqlist, "sum", sep = "_")), with = FALSE])
sumlist <- paste(common.nut, "prod_sum", sep = "_")
finlist <- paste(common.nut, "fin", sep = "_")
#divide every element in sumlist by pop.value and put in corresponding variable in finlist
# cat("\n\n", names(dt.temp.sum))
dt.temp.sum[,(finlist) := lapply(.SD, "/", dt.temp.sum$pop.value_sum), .SDcols = sumlist]
dt.temp.sum[, c("pop.value_sum", sumlist) := NULL]
# change column names back to just nutrient names
data.table::setnames(dt.temp.sum,old = finlist, new = common.nut)
dt.temp.melt <- data.table::melt(
dt.temp.sum,
id.vars = c("scenario","region_code.IMPACT159", "year"),
measure.vars = common.nut,
variable.name = "nutrient",
variable.factor = FALSE,
value.name = "value"
)
inDT <- dt.temp.sum
outName <- paste(gsub("_ssp","",nutReqName),"percap",sep = "_")
desc <- paste0("Per capita requirement of ", nutReqName, " by specific population scenario")
cleanup(inDT,outName,fileloc("mData"), desc = desc) # changed to uData June 5, 2018; changed to mData Oct 5, 2018 because of combine Un and pop data
temp <- data.table::dcast(
dt.temp.melt,
scenario + region_code.IMPACT159 + nutrient ~ year,
fun = NULL,
value.var = "value")
return(temp)
}
#nutrient requirements are calculated in dataPrep.nutrientRequirements.R.
reqsSSP <- keyVariable("reqsListSSP")
#The list of nutrients for each is common.EAR, common.RDA.vits, common.RDA.minrls, common.RDA.macro, common.UL.vits,
# common.UL.minrls, common.AMDR
common <- keyVariable("commonList")
# code that creates and writes out the nutrient requirements files
# set up workbook wbGeneral -----
wbGeneral <- openxlsx::createWorkbook()
#create a variable, creationInfo, with info on creator, date, model version, etc.
creationInfo <-
("Information on creator, date, model version, etc.")
creationInfo <- rbind(creationInfo,
paste("Creator:", keyVariable("userName")))
creationInfo <- rbind(creationInfo,
paste("Date of file creation:", Sys.Date()))
#creationInfo <- rbind(creationInfo, paste("IMPACT data:", IMPACTfileName))
nutrientFileName <- fileNameList("nutrientFileName")
creationInfo <- rbind(creationInfo,
paste("Nutrient data:", nutrientFileName))
DRIFileName <- fileNameList("DRIFileName")
creationInfo <- rbind(creationInfo,
paste("Nutrient requirements data:", DRIFileName))
SSPdataZip <- fileNameList("SSPdataZip")
creationInfo <- rbind(creationInfo,
paste("SSP.regions data:", SSPdataZip))
openxlsx::addWorksheet(wb = wbGeneral, sheetName = "creation_Info")
# write the creationInfo variable to a worksheet called creation_Info
openxlsx::writeData(
wb = wbGeneral,
x = creationInfo,
sheet = "creation_Info",
startRow = 1, startCol = 1,
rowNames = FALSE, colNames = FALSE
)
#Set up df wbInfoGeneral for common worksheet names and descriptions -----
wbInfoGeneral <-
data.frame(sheet_Name = character(),sheet_Desc = character(),stringsAsFactors = FALSE)
wbInfoGeneral[(nrow(wbInfoGeneral) + 1), ] <-
c("creation_Info","Information on creator, date, model version, etc.")
wbInfoGeneral[(nrow(wbInfoGeneral) + 1), ] <-
c("Sheet names", "Description of sheet contents")
#add a worksheet called IMPACTBaselist to the workbook wb = wbGeneral, with info on the commodities
# in the base set and their nutrients
# dt.nutrients <- getNewestVersion("dt.nutrients.var", fileloc("iData")) # not created until dt.footNnuts.R
# openxlsx::addWorksheet(wb = wbGeneral, sheetName = "IMPACTBaselist")
# #commodityNames <- cbind(nutrients[c("Name","IMPACT_code")])
# openxlsx::writeData(
# wb = wbGeneral,
# x = dt.nutrients,
# sheet = "IMPACTBaselist",
# startRow = 1,
# startCol = 1
# )
# openxlsx::addStyle(
# wb = wbGeneral,
# sheet = "IMPACTBaselist",
# style = numStyle,
# rows = 2:nrow(dt.nutrients),
# cols = 3:(ncol(dt.nutrients)),
# gridExpand = TRUE
# )
# for loop for the nutrient requirements worksheets ----
for (i in 1:length(reqsSSP)) {
dt.temp.internal <- repCons(dt.pop, reqsSSP[i]) #, ageRowsToSum)
data.table::setkeyv(dt.temp.internal, c("nutrient", "region_code.IMPACT159"))
dt.name <- paste(reqsSSP[i], "percap", sep = "_")
#print(dt.name)
openxlsx::addWorksheet(wb = wbGeneral, sheetName = dt.name)
openxlsx::addStyle(
wb = wbGeneral, sheet = dt.name, style = numStyle, rows = 2:nrow(dt.temp.internal),
cols = 3:(ncol(dt.temp.internal)),
gridExpand = TRUE
)
openxlsx::writeData(
wb = wbGeneral,
x = dt.temp.internal,
sheet = dt.name,
startRow = 1, startCol = 1, rowNames = FALSE, colNames = TRUE
)
nutrientList <- paste(unique(dt.temp.internal$nutrient), collapse = ", ")
sheetDesc <- paste("nutrients included: ", nutrientList)
wbInfoGeneral[(nrow(wbInfoGeneral) + 1), ] <-
c(dt.name, sheetDesc)
dt.temp.melt <- data.table::melt(dt.temp.internal,
id.vars = c("region_code.IMPACT159", "nutrient"),
measure.vars = keepYearList,
value.name = "value",
variable.factor = FALSE)
}
#add sheet with info about each of the worksheets
openxlsx::addWorksheet(wb = wbGeneral, sheetName = "sheetInfo")
openxlsx::writeData(
wb = wbGeneral,
x = wbInfoGeneral,
sheet = "sheetInfo",
startRow = 1,
startCol = 1,
rowNames = FALSE,
colNames = FALSE
)
openxlsx::addStyle(
wb = wbGeneral,
sheet = "sheetInfo",
style = textStyle,
rows = 1:nrow(wbInfoGeneral),
cols = 1:(ncol(wbInfoGeneral)),
gridExpand = TRUE
)
openxlsx::setColWidths(wb = wbGeneral,
sheet = "sheetInfo",
cols = 1:2,
widths = 20)
#move sheetInfo worksheet from the last to the first
temp <- 2:length(names(wbGeneral)) - 1
temp <- c(length(names(wbGeneral)), temp)
openxlsx::worksheetOrder(wbGeneral) <- temp
xcelOutFileName <-
paste("results/",gdxChoice, "/nut.requirements.", Sys.Date(), ".xlsx", sep = "")
openxlsx::saveWorkbook(wb = wbGeneral, xcelOutFileName, overwrite = TRUE)
finalizeScriptMetadata(metadataDT, sourceFile)
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