shiny/data_InternalFAO.R
In SWS-Methodology/faoswsLoss: Package to perform the computation of food loss FAO food balance sheets
#' Part of the FAO Loss Module
#'
#' @author Alicia English
#'
#'
#'
# ---
# runtime: shiny
# output: html_document
# Color palates #https://www.quackit.com/css/css_color_codes.cfm
# ---
library(faoswsLoss)
library(shiny)
library(shinythemes)
library(rmarkdown)
library(ggplot2)
library(plyr)
library(dplyr)
library(dtplyr)
library(DT)
library(magrittr)
library(data.table)
library(plotly)
library(yaml)
library(rdrop2)
suppressMessages({
library(faosws)
library(faoswsUtil)
library(faoswsFlag)
library(lme4)
library(data.table)
library(magrittr)
library(reshape2)
library(plyr)
library(dplyr)
})
############# Computation Parameters #####################################
savesws <- TRUE
LocalRun <- TRUE # For if you are running the model on a local environment and loading data tables from local fiiles
if(CheckDebug()){
maxYear <- format(Sys.Date(), "%Y")
selectedYear <- as.character(1991:2016)
ReportingYear<- as.character(c(2015))
aggregation <- "geographicaream49"
weights <- "intl_prices"
basketn <- "top2perhead_byCtry"
ComparisonYear <- (c(2005,2016))
gfli_Reporting <- TRUE
gfli_compare <- TRUE
}
#####################
BaseYear = as.character(c(2014,2016)) ## This is not an option to choose after the movement to the SDG base yr
areaVar = "geographicAreaM49"
yearVar = "timePointYears"
itemVar = "measuredItemCPC"
elementVar = "measuredElement"
keys =c(areaVar,yearVar,itemVar)
keys_lower =tolower(keys)
##### Load Data ######
## These two tables are constantly needing to be merged - country groups and food groups
if(CheckDebug()){
message("Not on server, so setting up environment...")
USER <- if_else(.Platform$OS.type == "unix",
Sys.getenv('USER'),
Sys.getenv('USERNAME'))
library(faoswsModules)
settings <- ReadSettings(file = file.path(paste(getwd(),"sws.yml", sep='/')))
SetClientFiles(settings[["certdir"]])
GetTestEnvironment(
baseUrl = settings[["server"]],
token = settings[["token"]]
)
}else if(CheckDebug() & LocalRun){
#Load local last dataset
load("InputData.RData")
}else{
# Remove domain from username
USER <- regmatches(
swsContext.username,
regexpr("(?<=/).+$", swsContext.username, perl = TRUE)
)
options(error = function(){
dump.frames()
filename <- file.path(Sys.getenv("R_SWS_SHARE_PATH"), USER, "PPR")
dir.create(filename, showWarnings = FALSE, recursive = TRUE)
save(last.dump, file = file.path(filename, "last.dump.RData"))
})
}
#### Data In #####
production <- getProductionData(areaVar,itemVar,yearVar,elementVar,selectedYear) # Value_measuredElement_5510
imports <- getImportData(areaVar,itemVar,yearVar, selectedYear)
nutrient_table <- getNutritionData(areaVar,itemVar,yearVar,elementVar,selectedYear, protected = FALSE)
ConvFactor1 <- ReadDatatable('flw_lossperfactors__clone3')
#fbsTree <- ReadDatatable("fbs_tree")
gfli_basket <- ReadDatatable("gfli_basket")
M_aggregates <- ReadDatatable('aggregate_loss_table')
CountryGroup <- ReadDatatable("a2017regionalgroupings_sdg_feb2017")
FAOCrops <- ReadDatatable("fcl2cpc_ver_2_1")
intPrice <- ReadDatatable("int_dollar_prices_all") #int_$_prices_2005
names(production) <- tolower(names(production))
names(imports) <- tolower(names(imports))
names(nutrient_table) <- tolower(names(nutrient_table))
names(intPrice)[names(intPrice) == "measureditemcpc_description"] <- "crop"
production$geographicaream49 <- as.character(production$geographicaream49)
nutrient_table$geographicaream49 <- as.character(nutrient_table$geographicaream49)
production$timepointyears <- as.numeric(production$timepointyears)
imports$timepointyears<- as.numeric(imports$timepointyears)
prod_imports <- merge(production,imports, by= keys_lower, all.x = TRUE)
prod_imports[,prod_imports := rowSums(.SD, na.rm = TRUE), .SDcols=c("value.x","value.y")]
CountryGroup$geographicaream49 <- CountryGroup$m49_code
CountryGroup$country <- CountryGroup$m49_region
FAOCrops$measureditemcpc <- FAOCrops$cpc
FAOCrops[, "crop" := FAOCrops$description]
############Official Losses for the input dataset ##############
#load("~/faoswsLossa/shiny/Soup2Nuts/march22_v2.RData")
##load("~/faoswsLossa/shiny/Soup2Nuts/march22_nostageest.RData")
#Losses <- DataSave[measuredElementSuaFbs== "5126",]
Losses <- getLossData_LossDomain(areaVar,itemVar,yearVar,elementVar,selectedYear,'5126')
Losses[,flagcombination := paste(flagObservationStatus, flagMethod, sep=";")]
#### Protected Data ###
flagValidTableLoss <- as.data.table(flagValidTable)
flagValidTableLoss[flagObservationStatus == "M" & flagMethod== "-","Protected"] <- FALSE
protectedFlag <- flagValidTableLoss[flagValidTableLoss$Protected == TRUE,] %>%
.[, flagCombination := paste(flagObservationStatus, flagMethod, sep = ";")]
Losses[flagcombination %in% protectedFlag$flagCombination,Protected := TRUE,]
names(Losses) <- tolower(names(Losses))
Losses$geographicaream49 <- as.character(Losses$geographicaream49)
names(Losses)[names(Losses) == "measureditemsuafbs"] <- "measureditemcpc"
OfficialLoss <- Losses[protected == TRUE,]
names(OfficialLoss)[names(OfficialLoss) == "value"] <- "loss_per_clean"
OfficialLoss <- merge(OfficialLoss,CountryGroup[,c("geographicaream49","isocode","country"),with=F], by= c("geographicaream49"), all.x = T)
OfficialLoss <- merge(OfficialLoss,FAOCrops[,c("measureditemcpc","crop"),with=F], by= c("measureditemcpc"), all.x = T, allow.cartesian=T)
OfficialLoss[, fsc_location := "SWS_Total"]
OfficialLoss[, tag_datacollection := "FBS/APQ"]
OfficialLoss[, loss_per_clean := loss_per_clean*100]
ConvFactor1 <- rbind(ConvFactor1,OfficialLoss, fill = T)
opt <- as.data.table(cbind( c("m49_code","iso2code","isocode","m49_region","sdgregion_code","sdg_regions","m49_level1_code",
"m49_level1_region","m49_level2_code","m49_level2_region","mdgregions_code","mdgregions_region","ldcs_code","ldcs_region",
"lldcssids_code","lldcssids_region","fao_region","fao_operational_agg", "worldbank_income2018_agg", "sofa_agg"),
c("m49_code","ISO2","ISO3","Country","sdgregion_code","SDG Regions","m49_level1_code",
"Geographic Regions(m49) Level1","m49_level2_region_code","Geographic Regions(m49) Level2","mdgregions_code","MDG Regions","ldcs_code","Least Developed Countries (LDC)",
"lldcssids_code","Land Locked Developing Countries (LLDC)","FAO Operational Region","FAO Operational Coverage", "World Bank Income Groups", "SOFA Aggregations")))
names(opt) <- c("code", "Aggregates")
opt2 <- c("m49_code","sdgregion_code","m49_level1_code",
"m49_level2_code","mdgregions_code","ldcs_code",
"lldcssids_code","fao_operational_agg", "worldbank_income2018_agg","gfli_basket", "sofa_agg","sofa_wu_agg", "basket_sofa_wu")
gfli_basket[foodgroupname %in% c(2905,2911), gfli_basket :='Cereals & Pulses',]
ProdQtySWS <- subset(production,
select = c(keys_lower,"value")) %>% filter(timepointyears >= BaseYear[1] & timepointyears <= BaseYear[2])
Base_Prod <- ProdQtySWS[,qty_avey1y2 := mean(value),by = c("geographicaream49",'measureditemcpc')]
Base_Prod <- Base_Prod[timepointyears == as.numeric(BaseYear[2])-1,]
Base_Prod <- Base_Prod[,c("geographicaream49",'measureditemcpc','qty_avey1y2'),with=F]
f <- list(
family = "Times New Roman",
size = 12,
face="bold",
color = "#7f7f7f"
)
InputData_Out <- ConvFactor1
InputData_Out$fsc_location1 = sapply(strsplit(InputData_Out$fsc_location,"/"), '[', 1)
InputData_Out <- merge(InputData_Out,gfli_basket[,c("gfli_basket", "measureditemcpc"),with=F], by= c("measureditemcpc"), all.x = T)
InputData_Out <- merge(InputData_Out,CountryGroup, by= c("geographicaream49","country"), all.x = T)
dataStages <- unique(InputData_Out$fsc_location1)
datatags <- unique(InputData_Out$tag_datacollection)
#Losses <- getLossData_LossDomain(areaVar,itemVar,yearVar,elementVar,selectedYear,'5126')
#load("~/faoswsLossa/shiny/Soup2Nuts/timeSeriesDataToBeImputed_5126_jan3.RData")
#load("~/faoswsLossa/shiny/Soup2Nuts/timeSeriesDataToBeImputed_5126_jan-FFAll.RData") ## all data sources
#oad("~/faoswsLossa/shiny/Soup2Nuts/timeSeriesDataToBeImputed_5126_jan-s2lb.RData") # smoothed data
#load("~/faoswsLossa/shiny/Soup2Nuts/timeSeriesDataToBeImputed_5126_jan_all_markovadj_noProtected.RData")
#load("~/faoswsLossa/shiny/Soup2Nuts/timeSeriesDataToBeImputed_5126_jan_all_markovadj_Protected.RData")
#load("~/faoswsLossa/shiny/Soup2Nuts/timeSeriesDataToBeImputed_5126_jan_all_markov_1.RData")
#load("~/faoswsLossa/shiny/Soup2Nuts/OnlyFarm.RData")
#load("~/faoswsLossa/shiny/Soup2Nuts/Jan18_v2.RData")
timeSeriesDataToBeImputed_5126 <- DataSave[measuredElementSuaFbs == "5126",]
#Losses <- timeSeriesDataToBeImputed_5126 %>% filter(timePointYears <= 2016)
names(Losses) <- tolower(names(Losses))
Losses$geographicaream49 <- as.character(Losses$geographicaream49)
Losses[ ,flagcombo := paste(flagobservationstatus,flagmethod, sep=";")]
names(Losses)[names(Losses) =="value"] <- "value_measuredelement_5126"
names(Losses)[names(Losses) =="measureditemsuafbs"] <- "measureditemcpc"
#intPrice <- merge(intPrice, FAOCrops[,c("measureditemcpc","crop"),with=F], by= c("measureditemcpc"))
# ProdQtySWS <- subset(prod_imports,
# select = c(keys_lower,"prod_imports")) %>% filter(timepointyears >= BaseYear[1] & timepointyears <= BaseYear[2])
Losses_Out <- merge(Losses,unique(FAOCrops[,c("crop", "measureditemcpc"),with=F]), by= c("measureditemcpc"), all.x = T)
Losses_Out <- merge(Losses_Out,gfli_basket[,c("gfli_basket","basket_sofa_wu", "measureditemcpc"),with=F], by= c("measureditemcpc"), all.x = T)
Losses_Out <- merge(Losses_Out,CountryGroup, by= c("geographicaream49"), all.x = T)
Losses_Out <- Losses_Out %>% filter(timepointyears <2016)
Losses_Out <-Losses_Out[!value_measuredelement_5126==1,]
# ## Remove working system packagesto upload to shinyapps ###########
#if(shinyapps){
rm( swsContext.computationParams,swsContext.datasets,swsContext.executionId,swsContext.token,swsContext.userEmail,
swsContext.userId,swsContext.username,swsContext.baseRestUrl,settings,USER)
#rm (token)
detach("package:faoswLosss", unload=TRUE)
detach("package:faosws", unload=TRUE)
detach("package:faoswsUtil", unload=TRUE)
detach("package:faoswsModules", unload=TRUE)
# }
####
save.image("~/faoswsLossa/shiny/Soup2Nuts/data_InternalFAO_marc26.RData")
#drop_upload("~/faoswsLossa/shiny/Soup2Nuts/data_InternalFAO_jan.RData", path ="shiny")
# if(shinyappsA){
# # ###
load(file.path(paste(getwd(),"shiny", "Soup2Nuts", "data_InternalFAO.RData", sep='/')))
settings <- yaml.load_file(file.path(paste(getwd(),"shiny", "Shiny.yml", sep='/')))
rsconnect::setAccountInfo(name = settings$shinyio$name, token= settings$shinyio$token, secret=settings$shinyio$secret)
rsconnect::deployApp(file.path(paste(getwd(),"shiny","Soup2Nuts", sep='/')))
# # ## Remove working system packagesto upload to shinyapps ###########
# # }
#
#
# If external
# remove(Losses_Out)
SWS-Methodology/faoswsLoss documentation built on Dec. 31, 2019, 12:02 p.m.
R Package Documentation
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#' Part of the FAO Loss Module
#'
#' @author Alicia English
#'
#'
#'
# ---
# runtime: shiny
# output: html_document
# Color palates #https://www.quackit.com/css/css_color_codes.cfm
# ---
library(faoswsLoss)
library(shiny)
library(shinythemes)
library(rmarkdown)
library(ggplot2)
library(plyr)
library(dplyr)
library(dtplyr)
library(DT)
library(magrittr)
library(data.table)
library(plotly)
library(yaml)
library(rdrop2)
suppressMessages({
library(faosws)
library(faoswsUtil)
library(faoswsFlag)
library(lme4)
library(data.table)
library(magrittr)
library(reshape2)
library(plyr)
library(dplyr)
})
############# Computation Parameters #####################################
savesws <- TRUE
LocalRun <- TRUE # For if you are running the model on a local environment and loading data tables from local fiiles
if(CheckDebug()){
maxYear <- format(Sys.Date(), "%Y")
selectedYear <- as.character(1991:2016)
ReportingYear<- as.character(c(2015))
aggregation <- "geographicaream49"
weights <- "intl_prices"
basketn <- "top2perhead_byCtry"
ComparisonYear <- (c(2005,2016))
gfli_Reporting <- TRUE
gfli_compare <- TRUE
}
#####################
BaseYear = as.character(c(2014,2016)) ## This is not an option to choose after the movement to the SDG base yr
areaVar = "geographicAreaM49"
yearVar = "timePointYears"
itemVar = "measuredItemCPC"
elementVar = "measuredElement"
keys =c(areaVar,yearVar,itemVar)
keys_lower =tolower(keys)
##### Load Data ######
## These two tables are constantly needing to be merged - country groups and food groups
if(CheckDebug()){
message("Not on server, so setting up environment...")
USER <- if_else(.Platform$OS.type == "unix",
Sys.getenv('USER'),
Sys.getenv('USERNAME'))
library(faoswsModules)
settings <- ReadSettings(file = file.path(paste(getwd(),"sws.yml", sep='/')))
SetClientFiles(settings[["certdir"]])
GetTestEnvironment(
baseUrl = settings[["server"]],
token = settings[["token"]]
)
}else if(CheckDebug() & LocalRun){
#Load local last dataset
load("InputData.RData")
}else{
# Remove domain from username
USER <- regmatches(
swsContext.username,
regexpr("(?<=/).+$", swsContext.username, perl = TRUE)
)
options(error = function(){
dump.frames()
filename <- file.path(Sys.getenv("R_SWS_SHARE_PATH"), USER, "PPR")
dir.create(filename, showWarnings = FALSE, recursive = TRUE)
save(last.dump, file = file.path(filename, "last.dump.RData"))
})
}
#### Data In #####
production <- getProductionData(areaVar,itemVar,yearVar,elementVar,selectedYear) # Value_measuredElement_5510
imports <- getImportData(areaVar,itemVar,yearVar, selectedYear)
nutrient_table <- getNutritionData(areaVar,itemVar,yearVar,elementVar,selectedYear, protected = FALSE)
ConvFactor1 <- ReadDatatable('flw_lossperfactors__clone3')
#fbsTree <- ReadDatatable("fbs_tree")
gfli_basket <- ReadDatatable("gfli_basket")
M_aggregates <- ReadDatatable('aggregate_loss_table')
CountryGroup <- ReadDatatable("a2017regionalgroupings_sdg_feb2017")
FAOCrops <- ReadDatatable("fcl2cpc_ver_2_1")
intPrice <- ReadDatatable("int_dollar_prices_all") #int_$_prices_2005
names(production) <- tolower(names(production))
names(imports) <- tolower(names(imports))
names(nutrient_table) <- tolower(names(nutrient_table))
names(intPrice)[names(intPrice) == "measureditemcpc_description"] <- "crop"
production$geographicaream49 <- as.character(production$geographicaream49)
nutrient_table$geographicaream49 <- as.character(nutrient_table$geographicaream49)
production$timepointyears <- as.numeric(production$timepointyears)
imports$timepointyears<- as.numeric(imports$timepointyears)
prod_imports <- merge(production,imports, by= keys_lower, all.x = TRUE)
prod_imports[,prod_imports := rowSums(.SD, na.rm = TRUE), .SDcols=c("value.x","value.y")]
CountryGroup$geographicaream49 <- CountryGroup$m49_code
CountryGroup$country <- CountryGroup$m49_region
FAOCrops$measureditemcpc <- FAOCrops$cpc
FAOCrops[, "crop" := FAOCrops$description]
############Official Losses for the input dataset ##############
#load("~/faoswsLossa/shiny/Soup2Nuts/march22_v2.RData")
##load("~/faoswsLossa/shiny/Soup2Nuts/march22_nostageest.RData")
#Losses <- DataSave[measuredElementSuaFbs== "5126",]
Losses <- getLossData_LossDomain(areaVar,itemVar,yearVar,elementVar,selectedYear,'5126')
Losses[,flagcombination := paste(flagObservationStatus, flagMethod, sep=";")]
#### Protected Data ###
flagValidTableLoss <- as.data.table(flagValidTable)
flagValidTableLoss[flagObservationStatus == "M" & flagMethod== "-","Protected"] <- FALSE
protectedFlag <- flagValidTableLoss[flagValidTableLoss$Protected == TRUE,] %>%
.[, flagCombination := paste(flagObservationStatus, flagMethod, sep = ";")]
Losses[flagcombination %in% protectedFlag$flagCombination,Protected := TRUE,]
names(Losses) <- tolower(names(Losses))
Losses$geographicaream49 <- as.character(Losses$geographicaream49)
names(Losses)[names(Losses) == "measureditemsuafbs"] <- "measureditemcpc"
OfficialLoss <- Losses[protected == TRUE,]
names(OfficialLoss)[names(OfficialLoss) == "value"] <- "loss_per_clean"
OfficialLoss <- merge(OfficialLoss,CountryGroup[,c("geographicaream49","isocode","country"),with=F], by= c("geographicaream49"), all.x = T)
OfficialLoss <- merge(OfficialLoss,FAOCrops[,c("measureditemcpc","crop"),with=F], by= c("measureditemcpc"), all.x = T, allow.cartesian=T)
OfficialLoss[, fsc_location := "SWS_Total"]
OfficialLoss[, tag_datacollection := "FBS/APQ"]
OfficialLoss[, loss_per_clean := loss_per_clean*100]
ConvFactor1 <- rbind(ConvFactor1,OfficialLoss, fill = T)
opt <- as.data.table(cbind( c("m49_code","iso2code","isocode","m49_region","sdgregion_code","sdg_regions","m49_level1_code",
"m49_level1_region","m49_level2_code","m49_level2_region","mdgregions_code","mdgregions_region","ldcs_code","ldcs_region",
"lldcssids_code","lldcssids_region","fao_region","fao_operational_agg", "worldbank_income2018_agg", "sofa_agg"),
c("m49_code","ISO2","ISO3","Country","sdgregion_code","SDG Regions","m49_level1_code",
"Geographic Regions(m49) Level1","m49_level2_region_code","Geographic Regions(m49) Level2","mdgregions_code","MDG Regions","ldcs_code","Least Developed Countries (LDC)",
"lldcssids_code","Land Locked Developing Countries (LLDC)","FAO Operational Region","FAO Operational Coverage", "World Bank Income Groups", "SOFA Aggregations")))
names(opt) <- c("code", "Aggregates")
opt2 <- c("m49_code","sdgregion_code","m49_level1_code",
"m49_level2_code","mdgregions_code","ldcs_code",
"lldcssids_code","fao_operational_agg", "worldbank_income2018_agg","gfli_basket", "sofa_agg","sofa_wu_agg", "basket_sofa_wu")
gfli_basket[foodgroupname %in% c(2905,2911), gfli_basket :='Cereals & Pulses',]
ProdQtySWS <- subset(production,
select = c(keys_lower,"value")) %>% filter(timepointyears >= BaseYear[1] & timepointyears <= BaseYear[2])
Base_Prod <- ProdQtySWS[,qty_avey1y2 := mean(value),by = c("geographicaream49",'measureditemcpc')]
Base_Prod <- Base_Prod[timepointyears == as.numeric(BaseYear[2])-1,]
Base_Prod <- Base_Prod[,c("geographicaream49",'measureditemcpc','qty_avey1y2'),with=F]
f <- list(
family = "Times New Roman",
size = 12,
face="bold",
color = "#7f7f7f"
)
InputData_Out <- ConvFactor1
InputData_Out$fsc_location1 = sapply(strsplit(InputData_Out$fsc_location,"/"), '[', 1)
InputData_Out <- merge(InputData_Out,gfli_basket[,c("gfli_basket", "measureditemcpc"),with=F], by= c("measureditemcpc"), all.x = T)
InputData_Out <- merge(InputData_Out,CountryGroup, by= c("geographicaream49","country"), all.x = T)
dataStages <- unique(InputData_Out$fsc_location1)
datatags <- unique(InputData_Out$tag_datacollection)
#Losses <- getLossData_LossDomain(areaVar,itemVar,yearVar,elementVar,selectedYear,'5126')
#load("~/faoswsLossa/shiny/Soup2Nuts/timeSeriesDataToBeImputed_5126_jan3.RData")
#load("~/faoswsLossa/shiny/Soup2Nuts/timeSeriesDataToBeImputed_5126_jan-FFAll.RData") ## all data sources
#oad("~/faoswsLossa/shiny/Soup2Nuts/timeSeriesDataToBeImputed_5126_jan-s2lb.RData") # smoothed data
#load("~/faoswsLossa/shiny/Soup2Nuts/timeSeriesDataToBeImputed_5126_jan_all_markovadj_noProtected.RData")
#load("~/faoswsLossa/shiny/Soup2Nuts/timeSeriesDataToBeImputed_5126_jan_all_markovadj_Protected.RData")
#load("~/faoswsLossa/shiny/Soup2Nuts/timeSeriesDataToBeImputed_5126_jan_all_markov_1.RData")
#load("~/faoswsLossa/shiny/Soup2Nuts/OnlyFarm.RData")
#load("~/faoswsLossa/shiny/Soup2Nuts/Jan18_v2.RData")
timeSeriesDataToBeImputed_5126 <- DataSave[measuredElementSuaFbs == "5126",]
#Losses <- timeSeriesDataToBeImputed_5126 %>% filter(timePointYears <= 2016)
names(Losses) <- tolower(names(Losses))
Losses$geographicaream49 <- as.character(Losses$geographicaream49)
Losses[ ,flagcombo := paste(flagobservationstatus,flagmethod, sep=";")]
names(Losses)[names(Losses) =="value"] <- "value_measuredelement_5126"
names(Losses)[names(Losses) =="measureditemsuafbs"] <- "measureditemcpc"
#intPrice <- merge(intPrice, FAOCrops[,c("measureditemcpc","crop"),with=F], by= c("measureditemcpc"))
# ProdQtySWS <- subset(prod_imports,
# select = c(keys_lower,"prod_imports")) %>% filter(timepointyears >= BaseYear[1] & timepointyears <= BaseYear[2])
Losses_Out <- merge(Losses,unique(FAOCrops[,c("crop", "measureditemcpc"),with=F]), by= c("measureditemcpc"), all.x = T)
Losses_Out <- merge(Losses_Out,gfli_basket[,c("gfli_basket","basket_sofa_wu", "measureditemcpc"),with=F], by= c("measureditemcpc"), all.x = T)
Losses_Out <- merge(Losses_Out,CountryGroup, by= c("geographicaream49"), all.x = T)
Losses_Out <- Losses_Out %>% filter(timepointyears <2016)
Losses_Out <-Losses_Out[!value_measuredelement_5126==1,]
# ## Remove working system packagesto upload to shinyapps ###########
#if(shinyapps){
rm( swsContext.computationParams,swsContext.datasets,swsContext.executionId,swsContext.token,swsContext.userEmail,
swsContext.userId,swsContext.username,swsContext.baseRestUrl,settings,USER)
#rm (token)
detach("package:faoswLosss", unload=TRUE)
detach("package:faosws", unload=TRUE)
detach("package:faoswsUtil", unload=TRUE)
detach("package:faoswsModules", unload=TRUE)
# }
####
save.image("~/faoswsLossa/shiny/Soup2Nuts/data_InternalFAO_marc26.RData")
#drop_upload("~/faoswsLossa/shiny/Soup2Nuts/data_InternalFAO_jan.RData", path ="shiny")
# if(shinyappsA){
# # ###
load(file.path(paste(getwd(),"shiny", "Soup2Nuts", "data_InternalFAO.RData", sep='/')))
settings <- yaml.load_file(file.path(paste(getwd(),"shiny", "Shiny.yml", sep='/')))
rsconnect::setAccountInfo(name = settings$shinyio$name, token= settings$shinyio$token, secret=settings$shinyio$secret)
rsconnect::deployApp(file.path(paste(getwd(),"shiny","Soup2Nuts", sep='/')))
# # ## Remove working system packagesto upload to shinyapps ###########
# # }
#
#
# If external
# remove(Losses_Out)
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