shiny/SDG_app2.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
# ---
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)
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:maxYear)
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 #####
Losses <- getLossData_LossDomain(areaVar,itemVar,yearVar,elementVar,selectedYear,'5126')
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)
#fbsTree <- ReadDatatable("fbs_tree")
gfli_basket <- ReadDatatable("gfli_basket")
CountryGroup <- ReadDatatable("a2017regionalgroupings_sdg_feb2017")
FAOCrops <- ReadDatatable("fcl2cpc_ver_2_1")
intPrice <- ReadDatatable("int_dollar_prices_all") #int_$_prices_2005
names(Losses) <- tolower(names(Losses))
names(production) <- tolower(names(production))
names(imports) <- tolower(names(imports))
names(nutrient_table) <- tolower(names(nutrient_table))
production$geographicaream49 <- as.character(production$geographicaream49)
Losses$geographicaream49 <- as.character(Losses$geographicaream49)
nutrient_table$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$m49_region
opt <- as.data.table(cbind( c("m49_code","iso2code","isocode","m49_region","sdgregion_code","sdg_regions","m49_level1_code",
"m49_level1_region","m49_level2_code","m49_level2_region","mdgregions_code","mdgregions_region","ldcs_code","ldcs_region",
"lldcssids_code","lldcssids_region","fao_region","fao_operational_agg", "worldbank_income2018_agg", "sofa_agg"),
c("m49_code","ISO2","ISO3","Country","sdgregion_code","SDG Regions","m49_level1_code",
"Geographic Regions(m49) Level1","m49_level2_region_code","Geographic Regions(m49) Level2","mdgregions_code","MDG Regions","ldcs_code","Least Developed Countries (LDC)",
"lldcssids_code","Land Locked Developing Countries (LLDC)","FAO Operational Region","FAO Operational Coverage", "World Bank Income Groups", "SOFA Aggregations")))
names(opt) <- c("code", "Aggregates")
opt2 <- c("m49_code","sdgregion_code","m49_level1_code",
"m49_level2_code","mdgregions_code","ldcs_code",
"lldcssids_code","fao_operational_agg", "worldbank_income2018_agg", "sofa_agg","gfli_basket")
FAOCrops$measureditemcpc <- FAOCrops$cpc
FAOCrops[, "crop" := FAOCrops$description]
#FAOCrops[, "measureditemcpc" := addHeadingsCPC(FAOCrops$cpc)]
intPrice <- merge(intPrice, FAOCrops[,c("measureditemcpc","crop"),with=F], by= c("measureditemcpc"))
gfli_basket[foodgroupname %in% c(2905,2911), gfli_basket :='Cereals & Pulses',]
names(Losses)[names(Losses) =="value"] <- "value_measuredelement_5126"
names(Losses)[names(Losses) =="measureditemsuafbs"] <- "measureditemcpc"
#intPrice <- merge(intPrice, FAOCrops[,c("measureditemcpc","crop"),with=F], by= c("measureditemcpc"))
# ProdQtySWS <- subset(prod_imports,
# select = c(keys_lower,"prod_imports")) %>% filter(timepointyears >= BaseYear[1] & timepointyears <= BaseYear[2])
ProdQtySWS <- subset(production,
select = c(keys_lower,"value")) %>% filter(timepointyears >= BaseYear[1] & timepointyears <= BaseYear[2])
Base_Prod <- ProdQtySWS[,qty_avey1y2 := mean(value),by = c("geographicaream49",'measureditemcpc')]
Base_Prod <- Base_Prod[timepointyears == as.numeric(BaseYear[2])-1,]
Base_Prod <- Base_Prod[,c("geographicaream49",'measureditemcpc','qty_avey1y2'),with=F]
## Remove working system packagesto upload to shinyapps ###########
if(shinyapps){
rm( swsContext.computationParams,swsContext.datasets,swsContext.executionId,swsContext.token,swsContext.userEmail,
swsContext.userId,swsContext.username,swsContext.baseRestUrl,settings,USER)
detach("package:faoswLosss", unload=TRUE)
detach("package:faosws", unload=TRUE)
detach("package:faoswsUtil", unload=TRUE)
detach("package:faoswsModules", unload=TRUE)
###
load(file.path(paste(getwd(),"shiny", "Input_Data_flp", "Input_data_FLP.RData", sep='/')))
settings <- yaml.load_file(file.path(paste(getwd(),"shiny", "Shiny.yml", sep='/')))
rsconnect::setAccountInfo(name = settings$shinyio$name, token= settings$shinyio$token, secret=settings$shinyio$secret)
rsconnect::deployApp(file.path(paste(getwd(),"shiny/Input_Data_flp", sep='/')))
## Remove working system packagesto upload to shinyapps ###########
}
# # #---- Data In ------------------------------------------
#GlobalfoodLoss <- GFLI_SDG_fun(selectedYear,BaseYear,keys_lower,"WORLD",weights,basketn,FLIData)
ui <- fluidPage(theme = shinytheme("lumen"),
sidebarLayout(
sidebarPanel(
#Input()
sliderInput(
inputId = "Year",
label = "Year Range",
value = c(2005,maxYear),step =1,sep = "", min = as.integer(min(selectedYear)), max = as.integer(max(selectedYear))
),
selectInput(
inputId = "AggLev",
label = "Model level",
choices = c("SDG", "Model Estimates", "Input Data"),
selected = NULL,
multiple = FALSE,
selectize=TRUE
),
selectInput(
inputId = "aggregation",
label = "Aggregation",
choices = c("WORLD",opt2),
selected = "All"
),
selectInput(
inputId = "Agg_options",
label = "Aggregation Options",
choices = NULL,
selected = NULL,
multiple = FALSE,
selectize=TRUE
),
selectInput(
inputId = "Country",
label = "Country",
choices = NULL, selected =NULL, multiple=TRUE, selectize=TRUE
),
selectInput(
inputId = "WeightsChoice",
label = "Weights",
choices = c("International Dollar Prices (2015)","Calories"),
selected ="International Dollar Prices (2015)"
),
selectInput(
inputId = "BasketChoice",
label = "Commoditiy Aggregation",
choices = c('Production Value- Top 10 by country (Default SDG)','Production Value- Top 10 by World'),
selected ='Production Value- Top 10 by country'
),
selectInput(
inputId = "BasketItems",
label = "Basket Items",
choices = c("All",na.omit(unique(gfli_basket[,"gfli_basket",with=FALSE]))),
selected = "All"
),
# selectInput(
# inputId = "itemcpc",
# label = "measureditemcpc",
# choices = NULL, selected =NULL, multiple=TRUE, selectize=TRUE
# ),
selectInput("dataset", "Choose a dataset for download:",
choices = c("Index", "Weights", "Basket")),
downloadButton("Data.csv", "Download"),
downloadButton("SDG12_3_Plot.jpeg", "Plots")
),
mainPanel(
tabsetPanel(
tabPanel("Graph",
tags$p(""),
tags$p("Food Loss Index"),
plotOutput("plotgraph", height = "900px")
),
tabPanel("Index",
DT::dataTableOutput("IndexOut")
),
tabPanel("Weights",
DT::dataTableOutput("WeightsOut")
),
tabPanel("Basket",
DT::dataTableOutput("BasketsOut")
),
tabPanel("plotly_test",
plotlyOutput("plot_ly")
)
))
))
# Define server logic to summarize and view selected dataset ----
server <- function(input, output, session) {
set.seed(122)
observe({
if(input$aggregation == "WORLD") {
Agg_choices <- c("All")
updateSelectInput(session, "Agg_options",choices=Agg_choices, selected ="All")
}
if(input$aggregation == "m49_code") {
Agg_choices <- c("All")
updateSelectInput(session, "Agg_options", choices=Agg_choices, selected ="All")
}
if(!input$aggregation %in% c("sdgregion_code", "gfli_basket","m49_code")){
aggregationNameC = sub("code", "region", input$aggregation )
Agg_choices <- c("All",unique(CountryGroup[[aggregationNameC]]))
updateSelectInput(session, "Agg_options", choices=Agg_choices, selected ="All")
}
if(length(grep("sdg+", input$aggregation ))>0){
Agg_choices <- c("All",unique(CountryGroup[["sdg_regions"]]))
updateSelectInput(session, "Agg_options", choices=Agg_choices, selected ="All")
}
if(input$aggregation == "gfli_basket") {
Agg_choices <- c("All",unique(na.omit(gfli_basket[,gfli_basket])))
updateSelectInput(session, "Agg_options", choices=Agg_choices, selected ="All")
}
})
observe({
if (input$aggregation == "WORLD") {
ctry_choices <- c("All")
updateSelectInput(session, "Country", choices=ctry_choices, selected ="All")
}
if (input$aggregation == "gfli_basket") {
ctry_choices <- c("All",unlist(na.omit(unique(gfli_basket[,measureditemcpc]))))
updateSelectInput(session, "Country", choices=ctry_choices, selected ="All")
}
if (!input$aggregation %in% c("sdgregion_code", "gfli_basket")) {
if(input$Agg_options == 'All'){
ctry_choices <- c("All",c(unique(CountryGroup[geographicaream49 %in% Base_Prod$geographicaream49,"country",with=F])))
updateSelectInput(session, "Country", choices=ctry_choices, selected = "All")
}else{
aggregationNameB = sub("code", "region", input$aggregation )
ctry_choices <- c("All",c(unique(CountryGroup[(CountryGroup[[aggregationNameB]] %in% input$Agg_options)&(geographicaream49 %in% Base_Prod$geographicaream49),"country",with=F])))
updateSelectInput(session, "Country", choices=ctry_choices, selected = "All")
}
}else{
aggregationNameB = sub("code", "region", input$aggregation )
ctry_choices <- c("All",unique(CountryGroup[CountryGroup[[aggregationNameB]] ==input$Agg_options,"country",with=F]))
updateSelectInput(session, "Country", choices=ctry_choices, selected ="All")
}
})
# observe({
# # if (input$BasketItems == "All") {
# # itemcpc_choices <- c("All", unique(FAOCrops[,'crop',with=F]))
# # updateSelectInput(session, "itemcpc", choices=itemcpc_choices, selected ="All")
# # }
# # if (input$BasketItems != "All") {
# # Comm_grp <- unlist(gfli_basket[gfli_basket == input$BasketItems,measureditemcpc])
# # itemcpc_choices <- c("All", unique(FAOCrops[measureditemcpc %in% Comm_grp,'crop',with=F]))
# # updateSelectInput(session, "itemcpc", choices=itemcpc_choices, selected ="All")
# # }
# itemcpc_choices <- c("All")
# updateSelectInput(session, "itemcpc", choices=itemcpc_choices, selected ="All")
# })
WeightKeys <- reactive({
if(input$WeightsChoice == "International Dollar Prices (2015)"){
Weights_keys <- c("measureditemcpc")
}
if(input$WeightsChoice == "Calories"){
Weights_keys <- c("geographicaream49","measureditemcpc")
}
Weights_keys
})
Weights <- reactive({
if(input$WeightsChoice == "International Dollar Prices (2015)"){
pvail <- unique(intPrice$measureditemcpc)
intPriceSelected <-
intPrice %>%
select(measureditemcpc,timepointyears ,crop, value) %>%
filter(timepointyears == as.numeric(BaseYear[2])-1)
intPriceSelected$itemname <- tolower(intPriceSelected$crop)
weights <- intPriceSelected[, c("itemname","measureditemcpc","value"),with=F]
weights <- as.data.table(weights)
weights[, weightname := input$WeightsChoice ]
#distinct(intPriceSelected,measuredItemCPC)
}
if(input$WeightsChoice == "Calories"){
Globalkcal1 <- nutrient_table %>% filter(measuredelement == 1001 &timepointyearssp==0)
weights <- Globalkcal1[, c("geographicaream49","measureditemcpc","value"),with=F]
weights$value <-weights$value*1/1000
weights <- weights[!duplicated(weights)]
weights <- as.data.table(weights)
weights[, weightname := input$WeightsChoice ]
}
weights
})
DataForIndex <- reactive({
Base_Prod1 <- join(Base_Prod,Weights(), by= WeightKeys(),type= 'left')
Base_Prod1$p0q0 =0
Base_Prod1[, p0q0 := qty_avey1y2*value,]
Base_Prod1 <-Base_Prod1 %>% filter(!is.na(p0q0))
DataForIndex <- merge(Losses[,c(keys_lower,"value_measuredelement_5126"),with=F] , Base_Prod1, by =c("measureditemcpc","geographicaream49"),all.x=T)
DataForIndex$l0ptqt =0
DataForIndex[,l0ptqt:=value_measuredelement_5126*p0q0,with=T]
DataForIndex <- merge(DataForIndex ,gfli_basket , by = c('measureditemcpc'), all.x = T)
DataForIndex <- merge(DataForIndex,CountryGroup, by = c('geographicaream49'), all.x = T)
DataForIndex <- DataForIndex[!is.na(gfli_basket),]
#DataForIndexD <- duplicated(DataForIndex)
})
Top10Global <- reactive({
Top10Global<- DataForIndex() %>%
filter(timepointyears == as.numeric(BaseYear[2])-1) %>%
group_by(measureditemcpc,gfli_basket) %>%
dplyr:: summarise(All_p0q0 = sum(p0q0, na.rm = TRUE)) %>%
arrange(-All_p0q0)
Top10Global<-as.data.table(Top10Global)
})
basketKeys <- reactive({
if(input$BasketChoice == "Production Value- Top 10 by country (Default SDG)"){
basketKey <- c('geographicaream49', "measureditemcpc")
}
if(input$BasketChoice == "Production Value- Top 10 by World"){
basketKey <- ( "measureditemcpc")
}
# if(input$BasketChoice == 'Top 10 Loss Commodities - by country'){
# basketKeys <- c('geographicaream49', "measureditemcpc")
# }
# if(input$BasketChoice == 'Caloric Value- Top 10 by World' & input$WeightsChoice == "calories"){
# basketKeys <- c('geographicaream49', "measureditemcpc")
# }
#
basketKey
})
Basket <- reactive({
if(input$BasketChoice == 'Production Value- Top 10 by country (Default SDG)'){
Top10perctry <- DataForIndex() %>%
filter(timepointyears == as.numeric(BaseYear[2])-1) %>%
arrange(geographicaream49, -p0q0)
Top10_VP <- DataForIndex() %>%
filter(timepointyears == as.numeric(BaseYear[2])-1) %>%
group_by(geographicaream49) %>%
dplyr:: summarise(All_p0q0 = sum(p0q0, na.rm = TRUE))
basket <- Top10perctry[ ,head(.SD, 2), by= c('geographicaream49','gfli_basket')]
basket <- basket %>% filter(!is.na(gfli_basket))
basket <- as.data.table(merge(basket,Top10_VP, by =c("geographicaream49"), all.x=TRUE))
basket[,Percent_prod := p0q0/All_p0q0]
}
if(input$BasketChoice == 'Production Value- Top 10 by World'){
Top10Global<- DataForIndex() %>%
filter(timepointyears == as.numeric(BaseYear[2])-1) %>%
group_by(measureditemcpc,gfli_basket) %>%
dplyr:: summarise(All_p0q0 = sum(p0q0, na.rm = TRUE)) %>%
arrange(-All_p0q0)
Top10_VP <- DataForIndex() %>%
filter(timepointyears == as.numeric(BaseYear[2])-1) %>%
group_by(geographicaream49) %>%
dplyr:: summarise(All_p0q0 = sum(p0q0, na.rm = TRUE))
ItemsBasket <- Top10Global[ ,head(.SD, 2), by= c('gfli_basket')]
basket <- DataForIndex() %>% filter((timepointyears == as.numeric(BaseYear[2])-1) & (measureditemcpc %in% unlist(ItemsBasket[!is.na(gfli_basket),measureditemcpc])))
basket <- as.data.table(merge(basket,Top10_VP, by =c("geographicaream49"), all.x=TRUE))
basket[,Percent_prod := p0q0/All_p0q0]
}
if(input$BasketChoice == 'Top 10 Loss Commodities - by country'){
Top10perctry <- DataForIndex() %>%
filter(timepointyears == as.numeric(BaseYear[2])-1) %>%
arrange(geographicaream49, -value_measuredelement_5126)
Top10_VP <- DataForIndex() %>%
filter(timepointyears == as.numeric(BaseYear[2])-1) %>%
group_by(geographicaream49) %>%
dplyr:: summarise(All_p0q0 = sum(p0q0, na.rm = TRUE))
basket <- Top10perctry[ ,head(.SD, 2), by= c('geographicaream49','gfli_basket')]
basket[geographicaream49 == 100,]
basket <- basket %>% filter(!is.na(gfli_basket))
basket <- as.data.table(merge(basket,Top10_VP, by =c("geographicaream49"), all.x=TRUE))
basket[,Percent_prod := p0q0/All_p0q0]
}
if(input$BasketChoice == 'Caloric Value- Top 10 by World' & input$WeightsChoice == "calories"){
Top10perctry <- DataForIndex() %>%
filter(timepointyears == as.numeric(BaseYear[2])-1) %>%
arrange(-p0q0)
basket <- Top10perctry[ ,head(.SD, 2), by= c('geographicaream49','gfli_basket')]
basket <- basket %>% filter(!is.na(gfli_basket))
basket <- as.data.table(merge(basket,Top10_VP, by =c("geographicaream49"), all.x=TRUE))
basket[,Percent_prod := p0q0/All_p0q0]
}
basket[,basketname := input$BasketChoice ]
basket <-basket[,unique(c("basketname", "country", basketKeys(), "gfli_basket", "value","weightname","p0q0",names(CountryGroup)[seq(2,length(names(CountryGroup)), by=2)][-1])),with=FALSE]
basket
})
FLIndex <- reactive({
FLIndex <- GFLI_SDG_fun(BaseYear,keys_lower,input$aggregation,Basket(),basketKeys(),DataForIndex())
FLIndex[,c("Sum_p0qt","Sum_p0q0"):=NULL]
})
dataR <- reactive({
if((input$aggregation == "m49_code")) {
FLIndex()%>% filter((timepointyears %in% seq(input$Year[1],input$Year[2], by=1)) &
(region_code %in% unlist(CountryGroup[country %in% unlist(input$Country),"m49_code", with=F]))%>%
arrange(-timepointyears)
)
}
else if((input$aggregation != "WORLD") & (input$Agg_options != "All")){
FLIndex()[(timepointyears %in% seq(input$Year[1],input$Year[2], by=1)) &
(unlist(FLIndex()[, "region_name", with=F]) %in% unlist(input$Agg_options))
,]%>%
arrange(-timepointyears)
}
else{
FLIndex()[timepointyears %in% seq(input$Year[1],input$Year[2], by=1),]%>%
arrange(-timepointyears)
}
})
lab <- reactive({
paste("Source: FAO",
paste("Date: ",as.character(Sys.time()),sep=""),
paste("Country Aggregation: ",input$aggregation,sep=""),
paste("Commodity Aggregation: ",input$BasketItems,sep="")
,sep="\n")
})
plotInput = function() {
if(input$aggregation != "m49_code"){
ggplot(dataR(), aes(x = timepointyears, y = FLP)) +
facet_wrap(~ unlist(unique(dataR()[,"region_name",with=F])))+
geom_line()+
xlab('timePointYears') + ylab('FLI') +
scale_y_continuous(labels = scales::percent)+
labs( caption=lab())+
theme(axis.text.x = element_text(angle = 45, vjust = .5)) +
theme(axis.text=element_text(size=12, face="bold"),
axis.title=element_text(size=12,face="bold"))
}else{
ggplot(dataR(), aes(x = timepointyears, y = FLP)) +
facet_wrap(~ unlist(unique(input$Country)))+
geom_line()+
xlab('timePointYears') + ylab('FLI') +
labs( caption=lab())+
theme(axis.text.x = element_text(angle = 45, vjust = .5)) +
theme(axis.text=element_text(size=12, face="bold"),
axis.title=element_text(size=12,face="bold"))
}
}
WeightAgg = function() {
Wt = merge(Weights(),gfli_basket[,c("measureditemcpc" , "gfli_basket"),with=F], by = ("measureditemcpc"), all.x =T, all.y=F)
if(input$BasketItems != 'All'){
Wt %>% filter(gfli_basket %in% input$BasketItems)
}else{Wt}
}
BasketAgg = function() {
if((input$Country != 'All')){
Basket()%>% filter((geographicaream49 %in% as.numeric(unlist(CountryGroup[Country %in% input$Country,"geographicaream49",with=F]))))
}
else if(input$BasketItems != 'All'){
Basket()%>% filter(gfli_basket %in% input$BasketItems)
}
else if((input$Country != 'All') & (input$BasketItems != 'All')){
Basket()%>% filter((gfli_basket %in% input$BasketItems) & (geographicaream49 %in% as.numeric(unlist(CountryGroup[Country %in% input$Country,"geographicaream49",with=F]))))
}
else{Basket()}
}
IndexAgg = function(){
dataR()
}
### Output Dsiplays
output$plotgraph <- renderPlot({
plotInput()
})
output$plot_ly <- renderPlotly({
#aggregationNameB = sub("code", "region", input$aggregation )
p <- plot_ly(dataR(), x = ~timepointyears, y = ~FLP, type = 'scatter', mode = 'lines',color = ~input$Agg_options ) %>%
add_lines()
p
})
output$WeightsOut = DT::renderDataTable({
WeightAgg()
})
output$BasketsOut = DT::renderDataTable({
BasketAgg()
})
output$IndexOut = DT::renderDataTable({
IndexAgg()
})
### R Check##
obsB <- observe({
print(input$aggregation)
print(input$Agg_options)
print( Weights())
# print( DataForIndex())
# print( Basket())
})
}
# Create Shiny app ----
shinyApp(ui = ui, server = server)
SWS-Methodology/faoswsLoss documentation built on Dec. 31, 2019, 12:02 p.m.
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#' Part of the FAO Loss Module
#'
#' @author Alicia English
#'
#'
#'
# ---
# runtime: shiny
# output: html_document
# ---
library(faoswsLoss)
library(shiny)
library(shinythemes)
library(rmarkdown)
library(ggplot2)
library(plyr)
library(dplyr)
library(dtplyr)
library(DT)
library(magrittr)
library(data.table)
library(plotly)
suppressMessages({
library(faosws)
library(faoswsUtil)
library(faoswsFlag)
library(lme4)
library(data.table)
library(magrittr)
library(reshape2)
library(plyr)
library(dplyr)
})
############# Computation Parameters #####################################
savesws <- TRUE
LocalRun <- TRUE # For if you are running the model on a local environment and loading data tables from local fiiles
if(CheckDebug()){
maxYear <- format(Sys.Date(), "%Y")
selectedYear <- as.character(1991:maxYear)
ReportingYear<- as.character(c(2015))
aggregation <- "geographicaream49"
weights <- "intl_prices"
basketn <- "top2perhead_byCtry"
ComparisonYear <- (c(2005,2016))
gfli_Reporting <- TRUE
gfli_compare <- TRUE
}
#####################
BaseYear = as.character(c(2014,2016)) ## This is not an option to choose after the movement to the SDG base yr
areaVar = "geographicAreaM49"
yearVar = "timePointYears"
itemVar = "measuredItemCPC"
elementVar = "measuredElement"
keys =c(areaVar,yearVar,itemVar)
keys_lower =tolower(keys)
##### Load Data ######
## These two tables are constantly needing to be merged - country groups and food groups
if(CheckDebug()){
message("Not on server, so setting up environment...")
USER <- if_else(.Platform$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("(?<=/).+$", swsContext.username, perl = TRUE)
)
options(error = function(){
dump.frames()
filename <- file.path(Sys.getenv("R_SWS_SHARE_PATH"), USER, "PPR")
dir.create(filename, showWarnings = FALSE, recursive = TRUE)
save(last.dump, file = file.path(filename, "last.dump.RData"))
})
}
#### Data In #####
Losses <- getLossData_LossDomain(areaVar,itemVar,yearVar,elementVar,selectedYear,'5126')
production <- getProductionData(areaVar,itemVar,yearVar,elementVar,selectedYear) # Value_measuredElement_5510
imports <- getImportData(areaVar,itemVar,yearVar, selectedYear)
nutrient_table <- getNutritionData(areaVar,itemVar,yearVar,elementVar,selectedYear, protected = FALSE)
#fbsTree <- ReadDatatable("fbs_tree")
gfli_basket <- ReadDatatable("gfli_basket")
CountryGroup <- ReadDatatable("a2017regionalgroupings_sdg_feb2017")
FAOCrops <- ReadDatatable("fcl2cpc_ver_2_1")
intPrice <- ReadDatatable("int_dollar_prices_all") #int_$_prices_2005
names(Losses) <- tolower(names(Losses))
names(production) <- tolower(names(production))
names(imports) <- tolower(names(imports))
names(nutrient_table) <- tolower(names(nutrient_table))
production$geographicaream49 <- as.character(production$geographicaream49)
Losses$geographicaream49 <- as.character(Losses$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
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", "sofa_agg","gfli_basket")
FAOCrops$measureditemcpc <- FAOCrops$cpc
FAOCrops[, "crop" := FAOCrops$description]
#FAOCrops[, "measureditemcpc" := addHeadingsCPC(FAOCrops$cpc)]
intPrice <- merge(intPrice, FAOCrops[,c("measureditemcpc","crop"),with=F], by= c("measureditemcpc"))
gfli_basket[foodgroupname %in% c(2905,2911), gfli_basket :='Cereals & Pulses',]
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])
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]
## 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)
detach("package:faoswLosss", unload=TRUE)
detach("package:faosws", unload=TRUE)
detach("package:faoswsUtil", unload=TRUE)
detach("package:faoswsModules", unload=TRUE)
###
load(file.path(paste(getwd(),"shiny", "Input_Data_flp", "Input_data_FLP.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/Input_Data_flp", sep='/')))
## Remove working system packagesto upload to shinyapps ###########
}
# # #---- Data In ------------------------------------------
#GlobalfoodLoss <- GFLI_SDG_fun(selectedYear,BaseYear,keys_lower,"WORLD",weights,basketn,FLIData)
ui <- fluidPage(theme = shinytheme("lumen"),
sidebarLayout(
sidebarPanel(
#Input()
sliderInput(
inputId = "Year",
label = "Year Range",
value = c(2005,maxYear),step =1,sep = "", min = as.integer(min(selectedYear)), max = as.integer(max(selectedYear))
),
selectInput(
inputId = "AggLev",
label = "Model level",
choices = c("SDG", "Model Estimates", "Input Data"),
selected = NULL,
multiple = FALSE,
selectize=TRUE
),
selectInput(
inputId = "aggregation",
label = "Aggregation",
choices = c("WORLD",opt2),
selected = "All"
),
selectInput(
inputId = "Agg_options",
label = "Aggregation Options",
choices = NULL,
selected = NULL,
multiple = FALSE,
selectize=TRUE
),
selectInput(
inputId = "Country",
label = "Country",
choices = NULL, selected =NULL, multiple=TRUE, selectize=TRUE
),
selectInput(
inputId = "WeightsChoice",
label = "Weights",
choices = c("International Dollar Prices (2015)","Calories"),
selected ="International Dollar Prices (2015)"
),
selectInput(
inputId = "BasketChoice",
label = "Commoditiy Aggregation",
choices = c('Production Value- Top 10 by country (Default SDG)','Production Value- Top 10 by World'),
selected ='Production Value- Top 10 by country'
),
selectInput(
inputId = "BasketItems",
label = "Basket Items",
choices = c("All",na.omit(unique(gfli_basket[,"gfli_basket",with=FALSE]))),
selected = "All"
),
# selectInput(
# inputId = "itemcpc",
# label = "measureditemcpc",
# choices = NULL, selected =NULL, multiple=TRUE, selectize=TRUE
# ),
selectInput("dataset", "Choose a dataset for download:",
choices = c("Index", "Weights", "Basket")),
downloadButton("Data.csv", "Download"),
downloadButton("SDG12_3_Plot.jpeg", "Plots")
),
mainPanel(
tabsetPanel(
tabPanel("Graph",
tags$p(""),
tags$p("Food Loss Index"),
plotOutput("plotgraph", height = "900px")
),
tabPanel("Index",
DT::dataTableOutput("IndexOut")
),
tabPanel("Weights",
DT::dataTableOutput("WeightsOut")
),
tabPanel("Basket",
DT::dataTableOutput("BasketsOut")
),
tabPanel("plotly_test",
plotlyOutput("plot_ly")
)
))
))
# Define server logic to summarize and view selected dataset ----
server <- function(input, output, session) {
set.seed(122)
observe({
if(input$aggregation == "WORLD") {
Agg_choices <- c("All")
updateSelectInput(session, "Agg_options",choices=Agg_choices, selected ="All")
}
if(input$aggregation == "m49_code") {
Agg_choices <- c("All")
updateSelectInput(session, "Agg_options", choices=Agg_choices, selected ="All")
}
if(!input$aggregation %in% c("sdgregion_code", "gfli_basket","m49_code")){
aggregationNameC = sub("code", "region", input$aggregation )
Agg_choices <- c("All",unique(CountryGroup[[aggregationNameC]]))
updateSelectInput(session, "Agg_options", choices=Agg_choices, selected ="All")
}
if(length(grep("sdg+", input$aggregation ))>0){
Agg_choices <- c("All",unique(CountryGroup[["sdg_regions"]]))
updateSelectInput(session, "Agg_options", choices=Agg_choices, selected ="All")
}
if(input$aggregation == "gfli_basket") {
Agg_choices <- c("All",unique(na.omit(gfli_basket[,gfli_basket])))
updateSelectInput(session, "Agg_options", choices=Agg_choices, selected ="All")
}
})
observe({
if (input$aggregation == "WORLD") {
ctry_choices <- c("All")
updateSelectInput(session, "Country", choices=ctry_choices, selected ="All")
}
if (input$aggregation == "gfli_basket") {
ctry_choices <- c("All",unlist(na.omit(unique(gfli_basket[,measureditemcpc]))))
updateSelectInput(session, "Country", choices=ctry_choices, selected ="All")
}
if (!input$aggregation %in% c("sdgregion_code", "gfli_basket")) {
if(input$Agg_options == 'All'){
ctry_choices <- c("All",c(unique(CountryGroup[geographicaream49 %in% Base_Prod$geographicaream49,"country",with=F])))
updateSelectInput(session, "Country", choices=ctry_choices, selected = "All")
}else{
aggregationNameB = sub("code", "region", input$aggregation )
ctry_choices <- c("All",c(unique(CountryGroup[(CountryGroup[[aggregationNameB]] %in% input$Agg_options)&(geographicaream49 %in% Base_Prod$geographicaream49),"country",with=F])))
updateSelectInput(session, "Country", choices=ctry_choices, selected = "All")
}
}else{
aggregationNameB = sub("code", "region", input$aggregation )
ctry_choices <- c("All",unique(CountryGroup[CountryGroup[[aggregationNameB]] ==input$Agg_options,"country",with=F]))
updateSelectInput(session, "Country", choices=ctry_choices, selected ="All")
}
})
# observe({
# # if (input$BasketItems == "All") {
# # itemcpc_choices <- c("All", unique(FAOCrops[,'crop',with=F]))
# # updateSelectInput(session, "itemcpc", choices=itemcpc_choices, selected ="All")
# # }
# # if (input$BasketItems != "All") {
# # Comm_grp <- unlist(gfli_basket[gfli_basket == input$BasketItems,measureditemcpc])
# # itemcpc_choices <- c("All", unique(FAOCrops[measureditemcpc %in% Comm_grp,'crop',with=F]))
# # updateSelectInput(session, "itemcpc", choices=itemcpc_choices, selected ="All")
# # }
# itemcpc_choices <- c("All")
# updateSelectInput(session, "itemcpc", choices=itemcpc_choices, selected ="All")
# })
WeightKeys <- reactive({
if(input$WeightsChoice == "International Dollar Prices (2015)"){
Weights_keys <- c("measureditemcpc")
}
if(input$WeightsChoice == "Calories"){
Weights_keys <- c("geographicaream49","measureditemcpc")
}
Weights_keys
})
Weights <- reactive({
if(input$WeightsChoice == "International Dollar Prices (2015)"){
pvail <- unique(intPrice$measureditemcpc)
intPriceSelected <-
intPrice %>%
select(measureditemcpc,timepointyears ,crop, value) %>%
filter(timepointyears == as.numeric(BaseYear[2])-1)
intPriceSelected$itemname <- tolower(intPriceSelected$crop)
weights <- intPriceSelected[, c("itemname","measureditemcpc","value"),with=F]
weights <- as.data.table(weights)
weights[, weightname := input$WeightsChoice ]
#distinct(intPriceSelected,measuredItemCPC)
}
if(input$WeightsChoice == "Calories"){
Globalkcal1 <- nutrient_table %>% filter(measuredelement == 1001 &timepointyearssp==0)
weights <- Globalkcal1[, c("geographicaream49","measureditemcpc","value"),with=F]
weights$value <-weights$value*1/1000
weights <- weights[!duplicated(weights)]
weights <- as.data.table(weights)
weights[, weightname := input$WeightsChoice ]
}
weights
})
DataForIndex <- reactive({
Base_Prod1 <- join(Base_Prod,Weights(), by= WeightKeys(),type= 'left')
Base_Prod1$p0q0 =0
Base_Prod1[, p0q0 := qty_avey1y2*value,]
Base_Prod1 <-Base_Prod1 %>% filter(!is.na(p0q0))
DataForIndex <- merge(Losses[,c(keys_lower,"value_measuredelement_5126"),with=F] , Base_Prod1, by =c("measureditemcpc","geographicaream49"),all.x=T)
DataForIndex$l0ptqt =0
DataForIndex[,l0ptqt:=value_measuredelement_5126*p0q0,with=T]
DataForIndex <- merge(DataForIndex ,gfli_basket , by = c('measureditemcpc'), all.x = T)
DataForIndex <- merge(DataForIndex,CountryGroup, by = c('geographicaream49'), all.x = T)
DataForIndex <- DataForIndex[!is.na(gfli_basket),]
#DataForIndexD <- duplicated(DataForIndex)
})
Top10Global <- reactive({
Top10Global<- DataForIndex() %>%
filter(timepointyears == as.numeric(BaseYear[2])-1) %>%
group_by(measureditemcpc,gfli_basket) %>%
dplyr:: summarise(All_p0q0 = sum(p0q0, na.rm = TRUE)) %>%
arrange(-All_p0q0)
Top10Global<-as.data.table(Top10Global)
})
basketKeys <- reactive({
if(input$BasketChoice == "Production Value- Top 10 by country (Default SDG)"){
basketKey <- c('geographicaream49', "measureditemcpc")
}
if(input$BasketChoice == "Production Value- Top 10 by World"){
basketKey <- ( "measureditemcpc")
}
# if(input$BasketChoice == 'Top 10 Loss Commodities - by country'){
# basketKeys <- c('geographicaream49', "measureditemcpc")
# }
# if(input$BasketChoice == 'Caloric Value- Top 10 by World' & input$WeightsChoice == "calories"){
# basketKeys <- c('geographicaream49', "measureditemcpc")
# }
#
basketKey
})
Basket <- reactive({
if(input$BasketChoice == 'Production Value- Top 10 by country (Default SDG)'){
Top10perctry <- DataForIndex() %>%
filter(timepointyears == as.numeric(BaseYear[2])-1) %>%
arrange(geographicaream49, -p0q0)
Top10_VP <- DataForIndex() %>%
filter(timepointyears == as.numeric(BaseYear[2])-1) %>%
group_by(geographicaream49) %>%
dplyr:: summarise(All_p0q0 = sum(p0q0, na.rm = TRUE))
basket <- Top10perctry[ ,head(.SD, 2), by= c('geographicaream49','gfli_basket')]
basket <- basket %>% filter(!is.na(gfli_basket))
basket <- as.data.table(merge(basket,Top10_VP, by =c("geographicaream49"), all.x=TRUE))
basket[,Percent_prod := p0q0/All_p0q0]
}
if(input$BasketChoice == 'Production Value- Top 10 by World'){
Top10Global<- DataForIndex() %>%
filter(timepointyears == as.numeric(BaseYear[2])-1) %>%
group_by(measureditemcpc,gfli_basket) %>%
dplyr:: summarise(All_p0q0 = sum(p0q0, na.rm = TRUE)) %>%
arrange(-All_p0q0)
Top10_VP <- DataForIndex() %>%
filter(timepointyears == as.numeric(BaseYear[2])-1) %>%
group_by(geographicaream49) %>%
dplyr:: summarise(All_p0q0 = sum(p0q0, na.rm = TRUE))
ItemsBasket <- Top10Global[ ,head(.SD, 2), by= c('gfli_basket')]
basket <- DataForIndex() %>% filter((timepointyears == as.numeric(BaseYear[2])-1) & (measureditemcpc %in% unlist(ItemsBasket[!is.na(gfli_basket),measureditemcpc])))
basket <- as.data.table(merge(basket,Top10_VP, by =c("geographicaream49"), all.x=TRUE))
basket[,Percent_prod := p0q0/All_p0q0]
}
if(input$BasketChoice == 'Top 10 Loss Commodities - by country'){
Top10perctry <- DataForIndex() %>%
filter(timepointyears == as.numeric(BaseYear[2])-1) %>%
arrange(geographicaream49, -value_measuredelement_5126)
Top10_VP <- DataForIndex() %>%
filter(timepointyears == as.numeric(BaseYear[2])-1) %>%
group_by(geographicaream49) %>%
dplyr:: summarise(All_p0q0 = sum(p0q0, na.rm = TRUE))
basket <- Top10perctry[ ,head(.SD, 2), by= c('geographicaream49','gfli_basket')]
basket[geographicaream49 == 100,]
basket <- basket %>% filter(!is.na(gfli_basket))
basket <- as.data.table(merge(basket,Top10_VP, by =c("geographicaream49"), all.x=TRUE))
basket[,Percent_prod := p0q0/All_p0q0]
}
if(input$BasketChoice == 'Caloric Value- Top 10 by World' & input$WeightsChoice == "calories"){
Top10perctry <- DataForIndex() %>%
filter(timepointyears == as.numeric(BaseYear[2])-1) %>%
arrange(-p0q0)
basket <- Top10perctry[ ,head(.SD, 2), by= c('geographicaream49','gfli_basket')]
basket <- basket %>% filter(!is.na(gfli_basket))
basket <- as.data.table(merge(basket,Top10_VP, by =c("geographicaream49"), all.x=TRUE))
basket[,Percent_prod := p0q0/All_p0q0]
}
basket[,basketname := input$BasketChoice ]
basket <-basket[,unique(c("basketname", "country", basketKeys(), "gfli_basket", "value","weightname","p0q0",names(CountryGroup)[seq(2,length(names(CountryGroup)), by=2)][-1])),with=FALSE]
basket
})
FLIndex <- reactive({
FLIndex <- GFLI_SDG_fun(BaseYear,keys_lower,input$aggregation,Basket(),basketKeys(),DataForIndex())
FLIndex[,c("Sum_p0qt","Sum_p0q0"):=NULL]
})
dataR <- reactive({
if((input$aggregation == "m49_code")) {
FLIndex()%>% filter((timepointyears %in% seq(input$Year[1],input$Year[2], by=1)) &
(region_code %in% unlist(CountryGroup[country %in% unlist(input$Country),"m49_code", with=F]))%>%
arrange(-timepointyears)
)
}
else if((input$aggregation != "WORLD") & (input$Agg_options != "All")){
FLIndex()[(timepointyears %in% seq(input$Year[1],input$Year[2], by=1)) &
(unlist(FLIndex()[, "region_name", with=F]) %in% unlist(input$Agg_options))
,]%>%
arrange(-timepointyears)
}
else{
FLIndex()[timepointyears %in% seq(input$Year[1],input$Year[2], by=1),]%>%
arrange(-timepointyears)
}
})
lab <- reactive({
paste("Source: FAO",
paste("Date: ",as.character(Sys.time()),sep=""),
paste("Country Aggregation: ",input$aggregation,sep=""),
paste("Commodity Aggregation: ",input$BasketItems,sep="")
,sep="\n")
})
plotInput = function() {
if(input$aggregation != "m49_code"){
ggplot(dataR(), aes(x = timepointyears, y = FLP)) +
facet_wrap(~ unlist(unique(dataR()[,"region_name",with=F])))+
geom_line()+
xlab('timePointYears') + ylab('FLI') +
scale_y_continuous(labels = scales::percent)+
labs( caption=lab())+
theme(axis.text.x = element_text(angle = 45, vjust = .5)) +
theme(axis.text=element_text(size=12, face="bold"),
axis.title=element_text(size=12,face="bold"))
}else{
ggplot(dataR(), aes(x = timepointyears, y = FLP)) +
facet_wrap(~ unlist(unique(input$Country)))+
geom_line()+
xlab('timePointYears') + ylab('FLI') +
labs( caption=lab())+
theme(axis.text.x = element_text(angle = 45, vjust = .5)) +
theme(axis.text=element_text(size=12, face="bold"),
axis.title=element_text(size=12,face="bold"))
}
}
WeightAgg = function() {
Wt = merge(Weights(),gfli_basket[,c("measureditemcpc" , "gfli_basket"),with=F], by = ("measureditemcpc"), all.x =T, all.y=F)
if(input$BasketItems != 'All'){
Wt %>% filter(gfli_basket %in% input$BasketItems)
}else{Wt}
}
BasketAgg = function() {
if((input$Country != 'All')){
Basket()%>% filter((geographicaream49 %in% as.numeric(unlist(CountryGroup[Country %in% input$Country,"geographicaream49",with=F]))))
}
else if(input$BasketItems != 'All'){
Basket()%>% filter(gfli_basket %in% input$BasketItems)
}
else if((input$Country != 'All') & (input$BasketItems != 'All')){
Basket()%>% filter((gfli_basket %in% input$BasketItems) & (geographicaream49 %in% as.numeric(unlist(CountryGroup[Country %in% input$Country,"geographicaream49",with=F]))))
}
else{Basket()}
}
IndexAgg = function(){
dataR()
}
### Output Dsiplays
output$plotgraph <- renderPlot({
plotInput()
})
output$plot_ly <- renderPlotly({
#aggregationNameB = sub("code", "region", input$aggregation )
p <- plot_ly(dataR(), x = ~timepointyears, y = ~FLP, type = 'scatter', mode = 'lines',color = ~input$Agg_options ) %>%
add_lines()
p
})
output$WeightsOut = DT::renderDataTable({
WeightAgg()
})
output$BasketsOut = DT::renderDataTable({
BasketAgg()
})
output$IndexOut = DT::renderDataTable({
IndexAgg()
})
### R Check##
obsB <- observe({
print(input$aggregation)
print(input$Agg_options)
print( Weights())
# print( DataForIndex())
# print( Basket())
})
}
# Create Shiny app ----
shinyApp(ui = ui, server = server)
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