shiny/Soup2Nuts/server.R
In SWS-Methodology/faoswsLoss: Package to perform the computation of food loss FAO food balance sheets
#
# This is the server logic of a Shiny web application. You can run the
# application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
library(shinythemes)
library(rmarkdown)
library(ggplot2)
library(plyr)
library(dplyr)
library(dtplyr)
library(DT)
library(magrittr)
library(data.table)
library(plotly)
library(rdrop2)
library(xlsx)
#load("~/faoswsLossa/shiny/Soup2Nuts/data_InternalFAO_jan.RData")
# token <- readRDS("token.rds")
# # Then pass the token to each drop_ function
# drop_acc(dtoken = token)
# drop_download("data_InternalFAO_jan.RData" , path = "shiny", overwrite = TRUE)
load("data_InternalFAO_marc26.RData")
shinyServer(function(input, output, session) {
##### Aggregation Options ####
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[gfli_basket %in% input$Agg_options,measureditemcpc]))))
updateSelectInput(session, "Country", choices=ctry_choices, selected ="All")
}
if (!input$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$geographicaream49),"country",with=F])))
updateSelectInput(session, "Country", choices=ctry_choices, selected = "All")
}
}
if (input$aggregation %in% "sdgregion_code"){
if(input$Agg_options == 'All'){
ctry_choices <- c("All",c(unique(CountryGroup[geographicaream49 %in% Base_Prod$geographicaream49,"country",with=F])))
}else{
ctry_choices <- c("All",c(unique(CountryGroup[(CountryGroup[['sdg_regions']] %in% input$Agg_options),"country",with=F])))
}
updateSelectInput(session, "Country", choices=ctry_choices, selected = "All")
}
if (input$aggregation == "m49_code") {
ctry_choices <- c("All",unlist(CountryGroup[["m49_region"]]))
updateSelectInput(session, "Country", choices=ctry_choices, selected ="Italy")
}
# 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$aggregation == "m49_code") {
mod_choices <- c("SDG-Food Loss Percentage", "Model Estimates", "Input Data")
updateSelectInput(session, "Model_Level", choices=mod_choices, selected ="SDG-Food Loss Percentage")
}else{
mod_choices <- c("SDG-Food Loss Percentage")
mod_choices <- c("SDG-Food Loss Percentage", "Input Data")
updateSelectInput(session, "Model_Level", choices=mod_choices, selected ="SDG-Food Loss Percentage")
}
})
observe({
Comm_grp <- FAOCrops[["cpc"]]
if(!"All" %in% input$Country){
Comm_grp <- Comm_grp[Comm_grp %in% unlist(
production[geographicaream49 %in% unlist(
CountryGroup[country %in% unlist(input$Country),"m49_code", with=F]),'measureditemcpc',with=F])]
}
if(input$BasketItems != "All"){
Comm_grp <- Comm_grp[Comm_grp %in% unlist(
gfli_basket[gfli_basket %in% input$BasketItems, "measureditemcpc", with=F])]
}
if(input$checkbox ==TRUE){
Comm_grp <- Comm_grp[Comm_grp %in% Basket()[
(geographicaream49 %in% unlist(CountryGroup[country %in% unlist(input$Country),"m49_code", with=F]))
,"measureditemcpc",with=F]]
}
itemcpc_choices <- c("All",unique(FAOCrops[cpc %in% unique(Comm_grp), "crop", with=F]))
updateSelectInput(session, "itemcpc", choices=itemcpc_choices, selected ="All")
})
crops <- reactive({
if(input$aggregation == "m49_code"){
Comm_grp <- production[geographicaream49 %in% unlist(CountryGroup[country %in% unlist(input$Country),"m49_code", with=F]),'measureditemcpc',with=F]
if(input$BasketItems != "All"){
Comm_grp <- Comm_grp %>% filter(measureditemcpc %in% unlist(
gfli_basket[gfli_basket%in% input$BasketItems, "measureditemcpc", with=F]))
}
if(input$checkbox ==TRUE){
Comm_grp <- Comm_grp %>% filter(measureditemcpc %in% unlist(Basket()[
(geographicaream49 %in% unlist(CountryGroup[country %in% unlist(input$Country),"m49_code", with=F]))
,"measureditemcpc",with=F]))
}
if(!"All" %in% input$itemcpc){
Comm_grp <- Comm_grp %>% filter(measureditemcpc %in% unlist(
FAOCrops[crop %in% unlist(input$itemcpc[input$itemcpc != "All"]), "measureditemcpc", with=F]
))
}
}
else{
Comm_grp <- FAOCrops[["cpc"]]
}
unique(unlist(Comm_grp))
})
countries <- reactive({
if (input$aggregation == "WORLD") {
ctry_choices <- c(unlist(CountryGroup[["m49_region"]]))
}
if (input$aggregation == "gfli_basket") {
ctry_choices <- c(unlist(na.omit(unique(gfli_basket[gfli_basket %in% input$Agg_options,measureditemcpc]))))
}
if (!input$aggregation %in% c("sdgregion_code", "gfli_basket")) {
if(input$Agg_options == 'All'){
ctry_choices <- c(unique(CountryGroup[geographicaream49 %in% Base_Prod$geographicaream49,"country",with=F]))
}else{
aggregationNameB = sub("code", "region", input$aggregation )
ctry_choices <- c(unique(CountryGroup[(CountryGroup[[aggregationNameB]] %in% input$Agg_options)&(geographicaream49 %in% Base_Prod$geographicaream49),"country",with=F]))
}
}
if (input$aggregation %in% "sdgregion_code"){
if(input$Agg_options == 'All'){
ctry_choices <- c(unique(CountryGroup[geographicaream49 %in% Base_Prod$geographicaream49,"country",with=F]))
}else{
ctry_choices <- c(unique(CountryGroup[(CountryGroup[['sdg_regions']] %in% input$Agg_options),"country",with=F]))
}
}
if (input$aggregation == "m49_code") {
ctry_choices <- input$Country
}
# else{
# aggregationNameB = sub("code", "region", input$aggregation )
# ctry_choices <- c("All",unique(CountryGroup[CountryGroup[[aggregationNameB]] ==input$Agg_options,"country",with=F]))
# updateSelectInput(session, "Country", choices=ctry_choices, selected ="All")
# }
#
ctry_choices
})
#### SDG ###
WeightKeys <- reactive({
if(input$WeightsChoice == "International Dollar Prices (2015)"){
Weights_keys <- c("measureditemcpc")
}
if(input$WeightsChoice == "Calories"){
Weights_keys <- c("geographicaream49","measureditemcpc")
}
Weights_keys
})
basketKeys <- reactive({
if(input$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
})
#---end of Aggregation Options ---#
###### SDG data #####
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 == "Quantities"){
pvail <- unique(intPrice$measureditemcpc)
intPriceSelected <-
intPrice %>%
select(measureditemcpc,timepointyears ,crop, value) %>%
filter(timepointyears == as.numeric(BaseYear[2])-1)
intPriceSelected$itemname <- tolower(intPriceSelected$crop)
intPriceSelected$value <- 1
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$l0ptqt =0
DataForIndex[,l0ptqt:=value_measuredelement_5126*p0q0,with=T]
DataForIndex <- merge(DataForIndex ,gfli_basket , by = c('measureditemcpc'), all.x = T)
DataForIndex <- merge(DataForIndex,CountryGroup, by = c('geographicaream49'), all.x = T)
DataForIndex <- DataForIndex[!is.na(gfli_basket),]
#DataForIndexD <- duplicated(DataForIndex)
})
Top10Global <- reactive({
Top10Global<- DataForIndex() %>%
filter(timepointyears == as.numeric(BaseYear[2])-1) %>%
group_by(measureditemcpc,gfli_basket) %>%
dplyr:: summarise(All_p0q0 = sum(p0q0, na.rm = TRUE)) %>%
arrange(-All_p0q0)
Top10Global<-as.data.table(Top10Global)
})
Basket <- reactive({
if(input$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]
names(FLIndex)[names(FLIndex) == "Sum_p0qt"] <- "Sum_value_p0qt"
FLIndex
})
#--- End SDG data ----#
##################Data setting############################
### ---- SDG Data Set ----- #####
dataR <- reactive({
if((input$aggregation == "m49_code") ) {
if("All" %in% input$Country ){
FLIndex()[timepointyears %in% seq(input$Year[1],input$Year[2], by=1),]%>%
arrange(-timepointyears)
}else{
FLIndex()%>% filter((timepointyears %in% seq(input$Year[1],input$Year[2], by=1)) &
(region_code %in% unlist(CountryGroup[country %in% unlist(countries()),"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$Year[2], by=1),]%>%
arrange(-timepointyears)
}
})
##### ---- Model Estimate Dataset ---- ####
dataR_loss <- reactive({
if((input$aggregation == "m49_code") ) {
if("All" %in% input$Country ){
Losses_Out%>% filter((timepointyears %in% seq(input$Year[1],input$Year[2], by=1))
)%>% arrange(geographicaream49,-timepointyears)
}
if((!"All" %in% input$Country)){
Losses_Out%>% filter((timepointyears %in% seq(input$Year[1],input$Year[2], by=1)) &
(geographicaream49 %in% unlist(CountryGroup[country %in% unlist(countries()),"m49_code", with=F])) &
(measureditemcpc %in% crops())
)%>%
arrange(-timepointyears)
}
}
})
dataR_maggs <- reactive({
if("All" %in% input$Country ){
M_aggregates%>% filter((timepointyears %in% seq(input$Year[1],input$Year[2], by=1))
)%>% arrange(geographicaream49,-timepointyears)
}
if((!"All" %in% input$Country)){
M_aggregates%>% filter((timepointyears %in% seq(input$Year[1],input$Year[2], by=1)) &
(geographicaream49 %in% unlist(CountryGroup[country %in% unlist(input$Country),"m49_code", with=F])) &
(measureditemcpc %in% crops())
)%>%
arrange(-timepointyears)
}
})
##### ---- Input Dataset ---- ####
dataR_Input <- reactive({
#if((input$aggregation == "m49_code") ) {
IO <- InputData_Out%>% filter((timepointyears %in% seq(input$Year[1],input$Year[2], by=1)) )%>%
arrange(geographicaream49,-timepointyears)
if(!"WORLD" %in% input$aggregation){
IO <- IO %>% filter((geographicaream49 %in% unlist(CountryGroup[country %in% unlist(countries()),"m49_code", with=F]) ) &
(measureditemcpc %in% crops()))
}
if((!"All" %in% input$Country)){
IO <- IO %>% filter((geographicaream49 %in% unlist(CountryGroup[country %in% unlist(input$Country),"m49_code", with=F])) &
(measureditemcpc %in% crops())
)%>%
arrange(-timepointyears)
}
if((!"All" %in% input$BasketItems)){
IO <- IO %>% filter( (gfli_basket %in% input$BasketItems[!input$BasketItems %in% "All"]))
}
if((!"All" %in% input$Stage)){
IO <- IO %>% filter( (fsc_location1 %in% input$Stage[!input$Stage %in% "All"]))
}
if((!"All" %in% input$DataCollect)){
IO <- IO %>% filter( (tag_datacollection %in% input$DataCollect[!input$DataCollect %in% "All"]))
}
IO
#}
})
dataR_Markov <- reactive({
#if((input$aggregation == "m49_code") ) {
if("All" %in% input$Country ){
IO_M <- M_aggregates%>% filter((timepointyears %in% seq(input$Year[1],input$Year[2], by=1))
)%>% arrange(geographicaream49,-timepointyears)
}
if((!"All" %in% input$Country)){
IO_M <- M_aggregates%>% filter((timepointyears %in% seq(input$Year[1],input$Year[2], by=1)) &
(geographicaream49 %in% unlist(CountryGroup[country %in% unlist(input$Country),"m49_code", with=F])) &
(measureditemcpc %in% crops())
)%>%
arrange(-timepointyears)
}
if((!"All" %in% input$Stage)){
IO_M <- IO_M %>% filter( (fsc_location1 %in% input$Stage[!input$Stage %in% "All"]))
}
if((!"All" %in% input$DataCollect)){
IO_M <- IO_M %>% filter( (tag_datacollection %in% input$DataCollect[!input$DataCollect %in% "All"]))
}
IO_M
#}
})
##### ---- Plots ---- ####
flags <-list(
list(target = "I;e", value = list(marker =list(color = 'orange'))),
list(target = "T;-", value = list(marker =list(color = 'blue'))),
list(target = "E;f", value = list(marker =list(color = 'blue'))),
list(target = "M;-", value = list(marker =list(color = 'red'))),
list(target = ";-", value = list(marker =list(color = 'blue'))),
list(target = ";q", value = list(marker =list(color = 'blue'))),
list(target = "T;p", value = list(marker =list(color = 'blue'))),
list(target = ";p", value = list(marker =list(color = 'blue')))
)
colorlist = c("Crimson","LightSeaGreen","SteelBlue",
"MediumOrchid","OrangeRed","DarkGreen" ,
"Tan","RosyBrown","SandyBrown",
"Goldenrod","DarkGoldenrod",
"Peru","Chocolate","SaddleBrown",
"Sienna","Brown","Maroon")
l1 <- unique(InputData_Out$fsc_location1)
l2 <- unique(InputData_Out$tag_datacollection)
tags <- list()
for( nn in 1:length(l2)){
tags[[nn]] <- list(target = l2[nn], value = list(marker =list(color =colorlist[nn])))
}
plotly_Input = function(){
#### Plots the SDG at different aggregations ###
trace_names = unique(dataR()[["region_name"]])
trace1 = dataR()[region_name %in% trace_names[1],]
p <-plot_ly(y=trace1$FLP, x= trace1$timepointyears , type="scatter", mode="markers+lines",name = trace_names[1] )
if(length(trace_names)>1){
for (nm in 2:length(trace_names)){
trace2 = dataR()[region_name %in% trace_names[nm],]
p <-p%>% add_trace(y=trace2$FLP, x= trace2$timepointyears , type="scatter", mode="markers+lines",name = trace_names[nm] )
}}
p
}
plotly_Input2 = function(){
#### Plots the loss estimates for different aggregations
trace_namesa = unique(dataR_loss()[["geographicaream49"]])
trace_namesb = unique(dataR_loss()[["measureditemcpc"]]) #crops()
trace1 = dataR_loss() %>% filter((geographicaream49 %in% trace_namesa[1]) & (measureditemcpc %in% trace_namesb[1]))
crop_name = unlist(FAOCrops[cpc == trace_namesb[1],"crop",with=F])
ctry_name = unlist(CountryGroup[geographicaream49 == trace_namesa[1], "country",with=F ])
hovertext = unlist(dataR_loss()[geographicaream49 %in% trace_namesa[1] & measureditemcpc %in% trace_namesb[1],"flagcombo",with=F])
p <-plot_ly(y=trace1$value_measuredelement_5126, x= trace1$timepointyears ,text=hovertext, showlegend = TRUE, type="scatter", mode="lines", name = paste(ctry_name,crop_name,sep=" - "))
if(input$checkboxflags){
p <- p %>% add_trace(y=trace1$value_measuredelement_5126, x= trace1$timepointyears,text=hovertext, showlegend =FALSE, type="scatter", mode="markers", text = trace1$flagcombo,
transforms =list(
list(
type = 'groupby',
groups = trace1$flagcombo,
styles = flags
)),
name = paste(ctry_name,crop_name,sep=" - ") )
}
##### Challenges here for crop commodity combos less than 1 <>
# if((length(trace_namesa)>1) ){
# ## Multiple countries ###
# for (nm in 1:length(trace_namesa)){
# for (nmt in 2:length(trace_namesb)){
# trace2 = dataR_loss() %>% filter((geographicaream49 %in% trace_namesa[nm]) & (measureditemcpc %in% trace_namesb[nmt]))
# crop_name = unlist(FAOCrops[cpc %in% trace_namesb[nmt],"crop",with=F])
# ctry_name = unlist(CountryGroup[geographicaream49 %in% trace_namesa[nm], "country",with=F ])
# hovertext = unlist(dataR_loss()[geographicaream49 %in% trace_namesa[nm] & measureditemcpc %in% trace_namesb[nmt],"flagcombo",with=F])
# if(length(trace2)>1){
# p <-p%>% add_trace(y=trace2$value_measuredelement_5126, x= trace2$timepointyears , showlegend = TRUE, type="scatter", mode="lines",text=hovertext,name = paste(ctry_name,crop_name,sep=" - "))
# }else{
# p <-p
# }
# if(input$checkboxflags){
# p <- p %>% add_trace(y=trace2$value_measuredelement_5126, x= trace2$timepointyears,text=hovertext, showlegend = FALSE, type="scatter", mode="markers", text = trace2$flagcombo,
# transforms =list(
# list(
# type = 'groupby',
# groups = trace2$flagcombo,
# styles = flags
# )),
# name = paste(ctry_name,crop_name,sep=" - ") )
# }
# }}
# }else{
# for (nmt in 2:length(trace_namesb)){
# trace2 = dataR_loss()[geographicaream49 %in% trace_namesa[1] & measureditemcpc %in% trace_namesb[nmt],]
# crop_name = unlist(FAOCrops[cpc == trace_namesb[nmt],"crop",with=F])
# ctry_name = unlist(CountryGroup[geographicaream49 == trace_namesa[1], "country",with=F ])
# hovertext = unlist(dataR_loss()[geographicaream49 %in% trace_namesa[1] & measureditemcpc %in% trace_namesb[nmt],"flagcombo",with=F])
#
#
# p <-p%>% add_trace(y=trace2$value_measuredelement_5126, x= trace2$timepointyears , showlegend = TRUE , type="scatter", mode="markers+lines",text=hovertext,name = paste(ctry_name,crop_name,sep=" - "))
# if(input$checkboxflags){
# p <- p %>% add_trace(y=trace2$value_measuredelement_5126, x= trace2$timepointyears, type="scatter", showlegend = FALSE, mode="markers",
# transforms =list(
# list(
# type = 'groupby',
# groups = trace2$flagcombo,
# styles = flags
# )),
# name = paste(ctry_name,crop_name,sep=" - ") )
# }
#
# }
# }
# # if(input$checkbox_input){
# if(nrow(InputData_Out)>0){
# trace3 <- dataR_Input()
# p <- p %>% add_trace(y= trace3$loss_per_clean/100, x= trace3$timepointyears, type="scatter", showlegend = FALSE, mode="markers",
# text = ~paste('Crop: ', trace3$crop,
# '</br> Location: ', trace3$fsc_location1,
# '</br> Method of Data Collection: ', trace3$method_datacollection,
# '</br> Data Tag: ', trace3$tag_datacollection,
# '</br> Reference: ', trace3$ reference
# ),
# transforms =list(
# list(
# type = 'groupby',
# groups = trace3$tag_datacollection,
# styles = tags
# )),
# name = paste(ctry_name,crop_name,sep=" - ") )
#
# }
# }
#
p
}
plotly_Input3 = function(){
#### Plots the loss estimates for different aggregations
if(nrow(InputData_Out)>0){
trace3 <- dataR_Input()
p <-plot_ly(trace3 )
p <- p %>% add_trace(y= trace3$loss_per_clean, x= trace3$timepointyears, type="scatter", showlegend = TRUE, mode="markers",
text = ~paste('Crop: ', trace3$crop,
'</br> Country: ', trace3$country,
'</br> Location: ', trace3$fsc_location1,
'</br> Method of Data Collection: ', trace3$method_datacollection,
'</br> Data Tag: ', trace3$tag_datacollection,
'</br> Reference: ', trace3$ reference
),
transforms =list(
list(
type = 'groupby',
groups = trace3$tag_datacollection,
styles = tags
)),
legendgroup = trace3$tag_datacollection)
}
if(input$checkboxflags){
trace4 <- dataR_loss()
p <- p %>% add_trace(y= trace4$value_measuredelement_5126, x= trace4$timepointyears, type="scatter", showlegend = T, mode="markers",
text = ~paste('Crop: ', trace4$crop,
'</br> Country: ', trace4$country,
'</br> Flag: ', trace4$flagcombo
),
transforms =list(
list(
type = 'groupby',
groups = trace4$flagcombo,
styles = flags
)))
}
p
}
#---- end Graphs ------- ##
lab <- reactive({
paste("Source: FAO",
paste("Date: ",as.character(Sys.time()),sep=""),
paste("Aggregation: ",input$aggregation,sep=""),
paste("Aggregation Option: ",input$Agg_options,sep=""),
paste("Country : ",input$Country,sep=""),
paste("Commodity Aggregation: ",input$BasketItems,sep="")
,sep="\n")
})
MetaDataOut <- reactive({
meta <- as.data.table(rbind("Source: FAO",
paste("Date: ",as.character(Sys.time()),sep=""),
paste("Weight: ",input$WeightsChoice,sep=""),
paste("Aggregation: ",input$aggregation,sep=""),
paste("Aggregation Option: ",input$Agg_options,sep=""),
paste("Country : ",input$Country,sep=""),
paste("Commodity Aggregation: ",input$BasketItems,sep="")
))
meta
})
Bmarg = -.8
output$plot_ly <- renderPlotly({
if(input$Model_Level == "SDG-Food Loss Percentage"){
plotly_Input()%>%layout(
xaxis = list(
title = "Years",
titlefont = f,
range = c(seq(input$Year[1],input$Year[2], by=1)),
dtick = 1
),
yaxis = list(
title = paste0(c(rep(" ", 20),
"Percentage",
rep(" ", 20),
rep("\n ", 3)),
collapse = ""),
tickformat = ".2%",
titlefont = f,
range = c(input$min_ax, input$max_ax)
),
margin = list(l =80, r = 50, t = 20, b = 100),
annotations = list(text = lab(),
font = list(size = 12),
showarrow = FALSE,
align='right',
xref = 'paper', x = 0.98,
yref = 'paper', y = Bmarg),
autosize = TRUE,
legend = list(orientation = 'l')
)
}
else if(input$Model_Level == "Model Estimates"){
plotly_Input2()%>%layout(
xaxis = list(
title = "Years",
titlefont = f,
range = c(seq(input$Year[1],input$Year[2], by=1)),
dtick = 1
),
yaxis = list(
title = paste0(c(rep(" ", 20),
"Percentage",
rep(" ", 20),
rep("\n ", 3)),
collapse = ""),
tickformat = ".2%",
titlefont = f
),
margin = list(l =80, r = 50, t = 20, b = 80),
annotations = list(text = lab(),
font = list(size = 12),
showarrow = FALSE,
align='right',
xref = 'paper', x = 0.98,
yref = 'paper', y = Bmarg),
autosize = TRUE,
legend = list(orientation = 'l')
)
}
else if(input$Model_Level == "Input Data"){
plotly_Input3()%>%layout(
xaxis = list(
title = "Years",
titlefont = f,
range = c(seq(input$Year[1],input$Year[2], by=1)),
dtick = 1
),
yaxis = list(
title = paste0(c(rep(" ", 20),
"Percentage",
rep(" ", 20),
rep("\n ", 3)),
collapse = ""),
#tickformat = ".2%",
titlefont = f
),
margin = list(l =80, r = 50, t = 20, b = 200),
annotations = list(text = lab(),
font = list(size = 12),
showarrow = FALSE,
align='right',
xref = 'paper', x = 0.6,
yref = 'paper', y = Bmarg),
autosize = TRUE,
legend = list(legend = list(x = 100, y = 0.5)),
height = 800
)
}
else{
plot_ly(y=0, x=0)
}
})
##########Other Tabs#################
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()
}
DataAgg = function(){
countT <- dataR_maggs() %>%
group_by_(.dots = list("measureditemcpc")) %>%
dplyr:: summarise(Number_Obs = n())
countT <- as.data.table(countT)
countT
}
DataINOutput <- function()({
a <- dataR_Input()
# setnames(a, old = c("geographicaream49","isocode","country","region","measureditemcpc","crop","timepointyears","loss_per_clean","percentage_loss_of_quantity",
# "loss_quantity","loss_qualitiative","loss_monetary",
# "activity","fsc_location","periodofstorage","treatment","causeofloss","samplesize",
# "units","method_datacollection","tag_datacollection","reference","url"),
# new = c("geographicaream49","isocode","Country","Region","measureditemcpc","Crop","Year","loss_per_clean","Range of Quantity Loss (%)",
# "Loss (quantity, tons)","Loss (Qualitative, tons)","Loss (Monetary, LCU)",
# "Activity","Stage","period of storage","treatment","Causes of loss","Sample Size",
# "Sampling Units","Method of Data Collection","Data Collection Tag","Reference","Url"))
#
# a[ ,c("geographicaream49","Country","Region","measureditemcpc","Crop","Year","Stage","Average Quantity Loss (%)",
# "Loss (quantity, tons)","Loss (Qualitative, tons)","Loss (Monetary, LCU)","Causes of loss","Activity",
# "period of storage","treatment","Sampling Units","Method of Data Collection","Data Collection Tag","Reference","Url"),with=FALSE]
#
a[, c("geographicaream49","country","region","measureditemcpc","crop","timepointyears","loss_per_clean","percentage_loss_of_quantity",
"loss_quantity","loss_qualitiative","loss_monetary",
"activity","fsc_location","periodofstorage","treatment","causeofloss","samplesize",
"units","method_datacollection","tag_datacollection","reference","url"),with=F]
})
output$DataTab = DT::renderDataTable({
DataINOutput()
})
output$WeightsOut = DT::renderDataTable({
WeightAgg()
})
output$BasketsOut = DT::renderDataTable({
BasketAgg()
})
output$IndexOut = DT::renderDataTable({
IndexAgg()
})
output$Maggregrates = DT::renderDataTable({
DataAgg()
})
#################################
output$FLP.xlsx <- downloadHandler(
filename = function(){
paste("FLP", ".xlsx", sep = "")
},
content = function(file) {
write.xlsx2(FLIndex(), file, sheetName = input$aggregation,
col.names = TRUE, row.names = FALSE, append = TRUE)
write.xlsx2(MetaDataOut(), file, sheetName = "Meta Data",
col.names = F, row.names = FALSE, append = TRUE)
}
)
################################
obsB <- observe({
print(input$aggregation)
# print(input$Agg_options)
print(FLIndex())
print( unique(dataR_loss()[["measureditemcpc"]]))
print( crops() )
})
})
SWS-Methodology/faoswsLoss documentation built on Dec. 31, 2019, 12:02 p.m.
R Package Documentation
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#
# This is the server logic of a Shiny web application. You can run the
# application by clicking 'Run App' above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
library(shiny)
library(shinythemes)
library(rmarkdown)
library(ggplot2)
library(plyr)
library(dplyr)
library(dtplyr)
library(DT)
library(magrittr)
library(data.table)
library(plotly)
library(rdrop2)
library(xlsx)
#load("~/faoswsLossa/shiny/Soup2Nuts/data_InternalFAO_jan.RData")
# token <- readRDS("token.rds")
# # Then pass the token to each drop_ function
# drop_acc(dtoken = token)
# drop_download("data_InternalFAO_jan.RData" , path = "shiny", overwrite = TRUE)
load("data_InternalFAO_marc26.RData")
shinyServer(function(input, output, session) {
##### Aggregation Options ####
observe({
if(input$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$aggregation == "gfli_basket") {
ctry_choices <- c("All",unlist(na.omit(unique(gfli_basket[gfli_basket %in% input$Agg_options,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")
}
}
if (input$aggregation %in% "sdgregion_code"){
if(input$Agg_options == 'All'){
ctry_choices <- c("All",c(unique(CountryGroup[geographicaream49 %in% Base_Prod$geographicaream49,"country",with=F])))
}else{
ctry_choices <- c("All",c(unique(CountryGroup[(CountryGroup[['sdg_regions']] %in% input$Agg_options),"country",with=F])))
}
updateSelectInput(session, "Country", choices=ctry_choices, selected = "All")
}
if (input$aggregation == "m49_code") {
ctry_choices <- c("All",unlist(CountryGroup[["m49_region"]]))
updateSelectInput(session, "Country", choices=ctry_choices, selected ="Italy")
}
# else{
# aggregationNameB = sub("code", "region", input$aggregation )
# ctry_choices <- c("All",unique(CountryGroup[CountryGroup[[aggregationNameB]] ==input$Agg_options,"country",with=F]))
# updateSelectInput(session, "Country", choices=ctry_choices, selected ="All")
# }
#
})
observe({
if (input$aggregation == "m49_code") {
mod_choices <- c("SDG-Food Loss Percentage", "Model Estimates", "Input Data")
updateSelectInput(session, "Model_Level", choices=mod_choices, selected ="SDG-Food Loss Percentage")
}else{
mod_choices <- c("SDG-Food Loss Percentage")
mod_choices <- c("SDG-Food Loss Percentage", "Input Data")
updateSelectInput(session, "Model_Level", choices=mod_choices, selected ="SDG-Food Loss Percentage")
}
})
observe({
Comm_grp <- FAOCrops[["cpc"]]
if(!"All" %in% input$Country){
Comm_grp <- Comm_grp[Comm_grp %in% unlist(
production[geographicaream49 %in% unlist(
CountryGroup[country %in% unlist(input$Country),"m49_code", with=F]),'measureditemcpc',with=F])]
}
if(input$BasketItems != "All"){
Comm_grp <- Comm_grp[Comm_grp %in% unlist(
gfli_basket[gfli_basket %in% input$BasketItems, "measureditemcpc", with=F])]
}
if(input$checkbox ==TRUE){
Comm_grp <- Comm_grp[Comm_grp %in% Basket()[
(geographicaream49 %in% unlist(CountryGroup[country %in% unlist(input$Country),"m49_code", with=F]))
,"measureditemcpc",with=F]]
}
itemcpc_choices <- c("All",unique(FAOCrops[cpc %in% unique(Comm_grp), "crop", with=F]))
updateSelectInput(session, "itemcpc", choices=itemcpc_choices, selected ="All")
})
crops <- reactive({
if(input$aggregation == "m49_code"){
Comm_grp <- production[geographicaream49 %in% unlist(CountryGroup[country %in% unlist(input$Country),"m49_code", with=F]),'measureditemcpc',with=F]
if(input$BasketItems != "All"){
Comm_grp <- Comm_grp %>% filter(measureditemcpc %in% unlist(
gfli_basket[gfli_basket%in% input$BasketItems, "measureditemcpc", with=F]))
}
if(input$checkbox ==TRUE){
Comm_grp <- Comm_grp %>% filter(measureditemcpc %in% unlist(Basket()[
(geographicaream49 %in% unlist(CountryGroup[country %in% unlist(input$Country),"m49_code", with=F]))
,"measureditemcpc",with=F]))
}
if(!"All" %in% input$itemcpc){
Comm_grp <- Comm_grp %>% filter(measureditemcpc %in% unlist(
FAOCrops[crop %in% unlist(input$itemcpc[input$itemcpc != "All"]), "measureditemcpc", with=F]
))
}
}
else{
Comm_grp <- FAOCrops[["cpc"]]
}
unique(unlist(Comm_grp))
})
countries <- reactive({
if (input$aggregation == "WORLD") {
ctry_choices <- c(unlist(CountryGroup[["m49_region"]]))
}
if (input$aggregation == "gfli_basket") {
ctry_choices <- c(unlist(na.omit(unique(gfli_basket[gfli_basket %in% input$Agg_options,measureditemcpc]))))
}
if (!input$aggregation %in% c("sdgregion_code", "gfli_basket")) {
if(input$Agg_options == 'All'){
ctry_choices <- c(unique(CountryGroup[geographicaream49 %in% Base_Prod$geographicaream49,"country",with=F]))
}else{
aggregationNameB = sub("code", "region", input$aggregation )
ctry_choices <- c(unique(CountryGroup[(CountryGroup[[aggregationNameB]] %in% input$Agg_options)&(geographicaream49 %in% Base_Prod$geographicaream49),"country",with=F]))
}
}
if (input$aggregation %in% "sdgregion_code"){
if(input$Agg_options == 'All'){
ctry_choices <- c(unique(CountryGroup[geographicaream49 %in% Base_Prod$geographicaream49,"country",with=F]))
}else{
ctry_choices <- c(unique(CountryGroup[(CountryGroup[['sdg_regions']] %in% input$Agg_options),"country",with=F]))
}
}
if (input$aggregation == "m49_code") {
ctry_choices <- input$Country
}
# 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")
# }
#
ctry_choices
})
#### SDG ###
WeightKeys <- reactive({
if(input$WeightsChoice == "International Dollar Prices (2015)"){
Weights_keys <- c("measureditemcpc")
}
if(input$WeightsChoice == "Calories"){
Weights_keys <- c("geographicaream49","measureditemcpc")
}
Weights_keys
})
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$WeightsChoice == "calories"){
# basketKeys <- c('geographicaream49', "measureditemcpc")
# }
#
basketKey
})
#---end of Aggregation Options ---#
###### SDG data #####
Weights <- reactive({
if(input$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$WeightsChoice == "Quantities"){
pvail <- unique(intPrice$measureditemcpc)
intPriceSelected <-
intPrice %>%
select(measureditemcpc,timepointyears ,crop, value) %>%
filter(timepointyears == as.numeric(BaseYear[2])-1)
intPriceSelected$itemname <- tolower(intPriceSelected$crop)
intPriceSelected$value <- 1
weights <- intPriceSelected[, c("itemname","measureditemcpc","value"),with=F]
weights <- as.data.table(weights)
weights[, weightname := input$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)
})
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$aggregation,Basket(),basketKeys(),DataForIndex())
#FLIndex[,c("Sum_p0qt","Sum_p0q0"):=NULL]
names(FLIndex)[names(FLIndex) == "Sum_p0qt"] <- "Sum_value_p0qt"
FLIndex
})
#--- End SDG data ----#
##################Data setting############################
### ---- SDG Data Set ----- #####
dataR <- reactive({
if((input$aggregation == "m49_code") ) {
if("All" %in% input$Country ){
FLIndex()[timepointyears %in% seq(input$Year[1],input$Year[2], by=1),]%>%
arrange(-timepointyears)
}else{
FLIndex()%>% filter((timepointyears %in% seq(input$Year[1],input$Year[2], by=1)) &
(region_code %in% unlist(CountryGroup[country %in% unlist(countries()),"m49_code", with=F])))%>%
arrange(-timepointyears)
}
}
else if((input$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)
}
})
##### ---- Model Estimate Dataset ---- ####
dataR_loss <- reactive({
if((input$aggregation == "m49_code") ) {
if("All" %in% input$Country ){
Losses_Out%>% filter((timepointyears %in% seq(input$Year[1],input$Year[2], by=1))
)%>% arrange(geographicaream49,-timepointyears)
}
if((!"All" %in% input$Country)){
Losses_Out%>% filter((timepointyears %in% seq(input$Year[1],input$Year[2], by=1)) &
(geographicaream49 %in% unlist(CountryGroup[country %in% unlist(countries()),"m49_code", with=F])) &
(measureditemcpc %in% crops())
)%>%
arrange(-timepointyears)
}
}
})
dataR_maggs <- reactive({
if("All" %in% input$Country ){
M_aggregates%>% filter((timepointyears %in% seq(input$Year[1],input$Year[2], by=1))
)%>% arrange(geographicaream49,-timepointyears)
}
if((!"All" %in% input$Country)){
M_aggregates%>% filter((timepointyears %in% seq(input$Year[1],input$Year[2], by=1)) &
(geographicaream49 %in% unlist(CountryGroup[country %in% unlist(input$Country),"m49_code", with=F])) &
(measureditemcpc %in% crops())
)%>%
arrange(-timepointyears)
}
})
##### ---- Input Dataset ---- ####
dataR_Input <- reactive({
#if((input$aggregation == "m49_code") ) {
IO <- InputData_Out%>% filter((timepointyears %in% seq(input$Year[1],input$Year[2], by=1)) )%>%
arrange(geographicaream49,-timepointyears)
if(!"WORLD" %in% input$aggregation){
IO <- IO %>% filter((geographicaream49 %in% unlist(CountryGroup[country %in% unlist(countries()),"m49_code", with=F]) ) &
(measureditemcpc %in% crops()))
}
if((!"All" %in% input$Country)){
IO <- IO %>% filter((geographicaream49 %in% unlist(CountryGroup[country %in% unlist(input$Country),"m49_code", with=F])) &
(measureditemcpc %in% crops())
)%>%
arrange(-timepointyears)
}
if((!"All" %in% input$BasketItems)){
IO <- IO %>% filter( (gfli_basket %in% input$BasketItems[!input$BasketItems %in% "All"]))
}
if((!"All" %in% input$Stage)){
IO <- IO %>% filter( (fsc_location1 %in% input$Stage[!input$Stage %in% "All"]))
}
if((!"All" %in% input$DataCollect)){
IO <- IO %>% filter( (tag_datacollection %in% input$DataCollect[!input$DataCollect %in% "All"]))
}
IO
#}
})
dataR_Markov <- reactive({
#if((input$aggregation == "m49_code") ) {
if("All" %in% input$Country ){
IO_M <- M_aggregates%>% filter((timepointyears %in% seq(input$Year[1],input$Year[2], by=1))
)%>% arrange(geographicaream49,-timepointyears)
}
if((!"All" %in% input$Country)){
IO_M <- M_aggregates%>% filter((timepointyears %in% seq(input$Year[1],input$Year[2], by=1)) &
(geographicaream49 %in% unlist(CountryGroup[country %in% unlist(input$Country),"m49_code", with=F])) &
(measureditemcpc %in% crops())
)%>%
arrange(-timepointyears)
}
if((!"All" %in% input$Stage)){
IO_M <- IO_M %>% filter( (fsc_location1 %in% input$Stage[!input$Stage %in% "All"]))
}
if((!"All" %in% input$DataCollect)){
IO_M <- IO_M %>% filter( (tag_datacollection %in% input$DataCollect[!input$DataCollect %in% "All"]))
}
IO_M
#}
})
##### ---- Plots ---- ####
flags <-list(
list(target = "I;e", value = list(marker =list(color = 'orange'))),
list(target = "T;-", value = list(marker =list(color = 'blue'))),
list(target = "E;f", value = list(marker =list(color = 'blue'))),
list(target = "M;-", value = list(marker =list(color = 'red'))),
list(target = ";-", value = list(marker =list(color = 'blue'))),
list(target = ";q", value = list(marker =list(color = 'blue'))),
list(target = "T;p", value = list(marker =list(color = 'blue'))),
list(target = ";p", value = list(marker =list(color = 'blue')))
)
colorlist = c("Crimson","LightSeaGreen","SteelBlue",
"MediumOrchid","OrangeRed","DarkGreen" ,
"Tan","RosyBrown","SandyBrown",
"Goldenrod","DarkGoldenrod",
"Peru","Chocolate","SaddleBrown",
"Sienna","Brown","Maroon")
l1 <- unique(InputData_Out$fsc_location1)
l2 <- unique(InputData_Out$tag_datacollection)
tags <- list()
for( nn in 1:length(l2)){
tags[[nn]] <- list(target = l2[nn], value = list(marker =list(color =colorlist[nn])))
}
plotly_Input = function(){
#### Plots the SDG at different aggregations ###
trace_names = unique(dataR()[["region_name"]])
trace1 = dataR()[region_name %in% trace_names[1],]
p <-plot_ly(y=trace1$FLP, x= trace1$timepointyears , type="scatter", mode="markers+lines",name = trace_names[1] )
if(length(trace_names)>1){
for (nm in 2:length(trace_names)){
trace2 = dataR()[region_name %in% trace_names[nm],]
p <-p%>% add_trace(y=trace2$FLP, x= trace2$timepointyears , type="scatter", mode="markers+lines",name = trace_names[nm] )
}}
p
}
plotly_Input2 = function(){
#### Plots the loss estimates for different aggregations
trace_namesa = unique(dataR_loss()[["geographicaream49"]])
trace_namesb = unique(dataR_loss()[["measureditemcpc"]]) #crops()
trace1 = dataR_loss() %>% filter((geographicaream49 %in% trace_namesa[1]) & (measureditemcpc %in% trace_namesb[1]))
crop_name = unlist(FAOCrops[cpc == trace_namesb[1],"crop",with=F])
ctry_name = unlist(CountryGroup[geographicaream49 == trace_namesa[1], "country",with=F ])
hovertext = unlist(dataR_loss()[geographicaream49 %in% trace_namesa[1] & measureditemcpc %in% trace_namesb[1],"flagcombo",with=F])
p <-plot_ly(y=trace1$value_measuredelement_5126, x= trace1$timepointyears ,text=hovertext, showlegend = TRUE, type="scatter", mode="lines", name = paste(ctry_name,crop_name,sep=" - "))
if(input$checkboxflags){
p <- p %>% add_trace(y=trace1$value_measuredelement_5126, x= trace1$timepointyears,text=hovertext, showlegend =FALSE, type="scatter", mode="markers", text = trace1$flagcombo,
transforms =list(
list(
type = 'groupby',
groups = trace1$flagcombo,
styles = flags
)),
name = paste(ctry_name,crop_name,sep=" - ") )
}
##### Challenges here for crop commodity combos less than 1 <>
# if((length(trace_namesa)>1) ){
# ## Multiple countries ###
# for (nm in 1:length(trace_namesa)){
# for (nmt in 2:length(trace_namesb)){
# trace2 = dataR_loss() %>% filter((geographicaream49 %in% trace_namesa[nm]) & (measureditemcpc %in% trace_namesb[nmt]))
# crop_name = unlist(FAOCrops[cpc %in% trace_namesb[nmt],"crop",with=F])
# ctry_name = unlist(CountryGroup[geographicaream49 %in% trace_namesa[nm], "country",with=F ])
# hovertext = unlist(dataR_loss()[geographicaream49 %in% trace_namesa[nm] & measureditemcpc %in% trace_namesb[nmt],"flagcombo",with=F])
# if(length(trace2)>1){
# p <-p%>% add_trace(y=trace2$value_measuredelement_5126, x= trace2$timepointyears , showlegend = TRUE, type="scatter", mode="lines",text=hovertext,name = paste(ctry_name,crop_name,sep=" - "))
# }else{
# p <-p
# }
# if(input$checkboxflags){
# p <- p %>% add_trace(y=trace2$value_measuredelement_5126, x= trace2$timepointyears,text=hovertext, showlegend = FALSE, type="scatter", mode="markers", text = trace2$flagcombo,
# transforms =list(
# list(
# type = 'groupby',
# groups = trace2$flagcombo,
# styles = flags
# )),
# name = paste(ctry_name,crop_name,sep=" - ") )
# }
# }}
# }else{
# for (nmt in 2:length(trace_namesb)){
# trace2 = dataR_loss()[geographicaream49 %in% trace_namesa[1] & measureditemcpc %in% trace_namesb[nmt],]
# crop_name = unlist(FAOCrops[cpc == trace_namesb[nmt],"crop",with=F])
# ctry_name = unlist(CountryGroup[geographicaream49 == trace_namesa[1], "country",with=F ])
# hovertext = unlist(dataR_loss()[geographicaream49 %in% trace_namesa[1] & measureditemcpc %in% trace_namesb[nmt],"flagcombo",with=F])
#
#
# p <-p%>% add_trace(y=trace2$value_measuredelement_5126, x= trace2$timepointyears , showlegend = TRUE , type="scatter", mode="markers+lines",text=hovertext,name = paste(ctry_name,crop_name,sep=" - "))
# if(input$checkboxflags){
# p <- p %>% add_trace(y=trace2$value_measuredelement_5126, x= trace2$timepointyears, type="scatter", showlegend = FALSE, mode="markers",
# transforms =list(
# list(
# type = 'groupby',
# groups = trace2$flagcombo,
# styles = flags
# )),
# name = paste(ctry_name,crop_name,sep=" - ") )
# }
#
# }
# }
# # if(input$checkbox_input){
# if(nrow(InputData_Out)>0){
# trace3 <- dataR_Input()
# p <- p %>% add_trace(y= trace3$loss_per_clean/100, x= trace3$timepointyears, type="scatter", showlegend = FALSE, mode="markers",
# text = ~paste('Crop: ', trace3$crop,
# '</br> Location: ', trace3$fsc_location1,
# '</br> Method of Data Collection: ', trace3$method_datacollection,
# '</br> Data Tag: ', trace3$tag_datacollection,
# '</br> Reference: ', trace3$ reference
# ),
# transforms =list(
# list(
# type = 'groupby',
# groups = trace3$tag_datacollection,
# styles = tags
# )),
# name = paste(ctry_name,crop_name,sep=" - ") )
#
# }
# }
#
p
}
plotly_Input3 = function(){
#### Plots the loss estimates for different aggregations
if(nrow(InputData_Out)>0){
trace3 <- dataR_Input()
p <-plot_ly(trace3 )
p <- p %>% add_trace(y= trace3$loss_per_clean, x= trace3$timepointyears, type="scatter", showlegend = TRUE, mode="markers",
text = ~paste('Crop: ', trace3$crop,
'</br> Country: ', trace3$country,
'</br> Location: ', trace3$fsc_location1,
'</br> Method of Data Collection: ', trace3$method_datacollection,
'</br> Data Tag: ', trace3$tag_datacollection,
'</br> Reference: ', trace3$ reference
),
transforms =list(
list(
type = 'groupby',
groups = trace3$tag_datacollection,
styles = tags
)),
legendgroup = trace3$tag_datacollection)
}
if(input$checkboxflags){
trace4 <- dataR_loss()
p <- p %>% add_trace(y= trace4$value_measuredelement_5126, x= trace4$timepointyears, type="scatter", showlegend = T, mode="markers",
text = ~paste('Crop: ', trace4$crop,
'</br> Country: ', trace4$country,
'</br> Flag: ', trace4$flagcombo
),
transforms =list(
list(
type = 'groupby',
groups = trace4$flagcombo,
styles = flags
)))
}
p
}
#---- end Graphs ------- ##
lab <- reactive({
paste("Source: FAO",
paste("Date: ",as.character(Sys.time()),sep=""),
paste("Aggregation: ",input$aggregation,sep=""),
paste("Aggregation Option: ",input$Agg_options,sep=""),
paste("Country : ",input$Country,sep=""),
paste("Commodity Aggregation: ",input$BasketItems,sep="")
,sep="\n")
})
MetaDataOut <- reactive({
meta <- as.data.table(rbind("Source: FAO",
paste("Date: ",as.character(Sys.time()),sep=""),
paste("Weight: ",input$WeightsChoice,sep=""),
paste("Aggregation: ",input$aggregation,sep=""),
paste("Aggregation Option: ",input$Agg_options,sep=""),
paste("Country : ",input$Country,sep=""),
paste("Commodity Aggregation: ",input$BasketItems,sep="")
))
meta
})
Bmarg = -.8
output$plot_ly <- renderPlotly({
if(input$Model_Level == "SDG-Food Loss Percentage"){
plotly_Input()%>%layout(
xaxis = list(
title = "Years",
titlefont = f,
range = c(seq(input$Year[1],input$Year[2], by=1)),
dtick = 1
),
yaxis = list(
title = paste0(c(rep(" ", 20),
"Percentage",
rep(" ", 20),
rep("\n ", 3)),
collapse = ""),
tickformat = ".2%",
titlefont = f,
range = c(input$min_ax, input$max_ax)
),
margin = list(l =80, r = 50, t = 20, b = 100),
annotations = list(text = lab(),
font = list(size = 12),
showarrow = FALSE,
align='right',
xref = 'paper', x = 0.98,
yref = 'paper', y = Bmarg),
autosize = TRUE,
legend = list(orientation = 'l')
)
}
else if(input$Model_Level == "Model Estimates"){
plotly_Input2()%>%layout(
xaxis = list(
title = "Years",
titlefont = f,
range = c(seq(input$Year[1],input$Year[2], by=1)),
dtick = 1
),
yaxis = list(
title = paste0(c(rep(" ", 20),
"Percentage",
rep(" ", 20),
rep("\n ", 3)),
collapse = ""),
tickformat = ".2%",
titlefont = f
),
margin = list(l =80, r = 50, t = 20, b = 80),
annotations = list(text = lab(),
font = list(size = 12),
showarrow = FALSE,
align='right',
xref = 'paper', x = 0.98,
yref = 'paper', y = Bmarg),
autosize = TRUE,
legend = list(orientation = 'l')
)
}
else if(input$Model_Level == "Input Data"){
plotly_Input3()%>%layout(
xaxis = list(
title = "Years",
titlefont = f,
range = c(seq(input$Year[1],input$Year[2], by=1)),
dtick = 1
),
yaxis = list(
title = paste0(c(rep(" ", 20),
"Percentage",
rep(" ", 20),
rep("\n ", 3)),
collapse = ""),
#tickformat = ".2%",
titlefont = f
),
margin = list(l =80, r = 50, t = 20, b = 200),
annotations = list(text = lab(),
font = list(size = 12),
showarrow = FALSE,
align='right',
xref = 'paper', x = 0.6,
yref = 'paper', y = Bmarg),
autosize = TRUE,
legend = list(legend = list(x = 100, y = 0.5)),
height = 800
)
}
else{
plot_ly(y=0, x=0)
}
})
##########Other Tabs#################
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()
}
DataAgg = function(){
countT <- dataR_maggs() %>%
group_by_(.dots = list("measureditemcpc")) %>%
dplyr:: summarise(Number_Obs = n())
countT <- as.data.table(countT)
countT
}
DataINOutput <- function()({
a <- dataR_Input()
# setnames(a, old = c("geographicaream49","isocode","country","region","measureditemcpc","crop","timepointyears","loss_per_clean","percentage_loss_of_quantity",
# "loss_quantity","loss_qualitiative","loss_monetary",
# "activity","fsc_location","periodofstorage","treatment","causeofloss","samplesize",
# "units","method_datacollection","tag_datacollection","reference","url"),
# new = c("geographicaream49","isocode","Country","Region","measureditemcpc","Crop","Year","loss_per_clean","Range of Quantity Loss (%)",
# "Loss (quantity, tons)","Loss (Qualitative, tons)","Loss (Monetary, LCU)",
# "Activity","Stage","period of storage","treatment","Causes of loss","Sample Size",
# "Sampling Units","Method of Data Collection","Data Collection Tag","Reference","Url"))
#
# a[ ,c("geographicaream49","Country","Region","measureditemcpc","Crop","Year","Stage","Average Quantity Loss (%)",
# "Loss (quantity, tons)","Loss (Qualitative, tons)","Loss (Monetary, LCU)","Causes of loss","Activity",
# "period of storage","treatment","Sampling Units","Method of Data Collection","Data Collection Tag","Reference","Url"),with=FALSE]
#
a[, c("geographicaream49","country","region","measureditemcpc","crop","timepointyears","loss_per_clean","percentage_loss_of_quantity",
"loss_quantity","loss_qualitiative","loss_monetary",
"activity","fsc_location","periodofstorage","treatment","causeofloss","samplesize",
"units","method_datacollection","tag_datacollection","reference","url"),with=F]
})
output$DataTab = DT::renderDataTable({
DataINOutput()
})
output$WeightsOut = DT::renderDataTable({
WeightAgg()
})
output$BasketsOut = DT::renderDataTable({
BasketAgg()
})
output$IndexOut = DT::renderDataTable({
IndexAgg()
})
output$Maggregrates = DT::renderDataTable({
DataAgg()
})
#################################
output$FLP.xlsx <- downloadHandler(
filename = function(){
paste("FLP", ".xlsx", sep = "")
},
content = function(file) {
write.xlsx2(FLIndex(), file, sheetName = input$aggregation,
col.names = TRUE, row.names = FALSE, append = TRUE)
write.xlsx2(MetaDataOut(), file, sheetName = "Meta Data",
col.names = F, row.names = FALSE, append = TRUE)
}
)
################################
obsB <- observe({
print(input$aggregation)
# print(input$Agg_options)
print(FLIndex())
print( unique(dataR_loss()[["measureditemcpc"]]))
print( crops() )
})
})
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