##'
##' **Author: Carlo Del Bello**
##'
##' **Description:**
##'
##' This module is designed to identify outliers in DES at sua balanced level
##'
##'
##' **Inputs:**
##'
##' * sua balanced
## load the libraries
library(faosws)
library(data.table)
library(faoswsUtil)
library(sendmailR)
library(dplyr)
library(faoswsFlag)
## set up for the test environment and parameters
R_SWS_SHARE_PATH = Sys.getenv("R_SWS_SHARE_PATH")
if(CheckDebug()){
message("Not on server, so setting up environment...")
library(faoswsModules)
SETT <- ReadSettings("modules/outlierDetection/sws.yml")
R_SWS_SHARE_PATH <- SETT[["share"]]
## Get SWS Parameters
SetClientFiles(dir = SETT[["certdir"]])
GetTestEnvironment(
baseUrl = SETT[["server"]],
token = SETT[["token"]]
)
}
startYear = 2013
endYear = 2017 # TODO: parameterise
geoM49 = swsContext.computationParams$geom49
stopifnot(startYear <= endYear)
yearVals = startYear:endYear
##' Get data configuration and session
sessionKey = swsContext.datasets[[1]]
sessionCountries =
getQueryKey("geographicAreaM49", sessionKey)
geoKeys = GetCodeList(domain = "agriculture", dataset = "aproduction",
dimension = "geographicAreaM49")[type == "country", code]
top48FBSCountries = c(4,24,50,68,104,120,140,144,148,1248,170,178,218,320,
324,332,356,360,368,384,404,116,408,450,454,484,508,
524,562,566,586,604,608,716,646,686,762,834,764,800,
854,704,231,887,894,760,862,860)
# top48FBSCountries<-as.character(top48FBSCountries)
#
# selectedCountries = setdiff(geoKeys,top48FBSCountries) #229
#
# ##Select the countries based on the user input parameter
selectedGEOCode =
switch(geoM49,
"session" = sessionCountries,
"all" = geoKeys)
#########################################
##### Pull from SUA unbalanced data #####
#########################################
message("Pulling SUA Unbalanced Data")
#take geo keys
geoDim = Dimension(name = "geographicAreaM49", keys = selectedGEOCode)
#Define element dimension. These elements are needed to calculate net supply (production + net trade)
eleKeys = GetCodeList(domain = "suafbs", dataset = "sua_balanced", "measuredElementSuaFbs")
eleKeys <-eleKeys[, code]
eleDim <- Dimension(name = "measuredElementSuaFbs", keys = eleKeys)
#Define item dimension
itemKeys = GetCodeList(domain = "suafbs", dataset = "sua_balanced", "measuredItemFbsSua")
itemKeys = itemKeys[, code]
itemDim <- Dimension(name = "measuredItemFbsSua", keys = itemKeys)
# Define time dimension
timeDim <- Dimension(name = "timePointYears", keys = as.character(yearVals))
#Define the key to pull SUA data
key = DatasetKey(domain = "suafbs", dataset = "sua_balanced", dimensions = list(
geographicAreaM49 = geoDim,
measuredElementSuaFbs = eleDim,
measuredItemFbsSua = itemDim,
timePointYears = timeDim
))
sua_balanced_data = GetData(key)
sua_balanced_data <- subset(sua_balanced_data, measuredElementSuaFbs %in% c("664"))
sua_balanced_data <- sua_balanced_data[,perc.change:= Value/shift(Value, type="lead")-1, by=c("geographicAreaM49","measuredItemFbsSua","measuredElementSuaFbs")]
sua_balanced_data <- subset(sua_balanced_data, (shift(Value, type="lead")>5 | Value>5) & abs(perc.change)>0.1 & timePointYears>2013)
sua_balanced_data<-nameData("suafbs","sua_balanced",sua_balanced_data)
#sua_balanced_data$measuredItemFbsSua <- paste0("'",sua_balanced_data$measuredItemFbsSua,sep = "")
bodySuaBALOutliers= paste("The Email contains a list of items where the caloric intakes increases more than 10% in abslolute value.",
"Consider it as a list of items where to start the validation.",
sep='\n')
sendMailAttachment(sua_balanced_data,"SuaBALOutliers",bodySuaBALOutliers)
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