FAOSTATdemo.R

###########################################################################
## Title: Demo of the FAOSTAT package
## Updated: 16/07/2014
## Notes:
###########################################################################

# Install the package -----------------------------------------------------

if(!is.element("FAOSTAT", .packages(all.available = TRUE)))
  install_github(username = "mkao006", repo = "FAOSTATpackage", ref = "master", subdir = "FAOSTAT")
library(FAOSTAT)
help(package = "FAOSTAT")
vignette("FAOSTAT", package = "FAOSTAT")

# FAOsearch function ------------------------------------------------------

## Use the interective function to search the codes.
FAOsearch()
## Use the result of the search to download the data.
test.df = getFAO(query = .LastSearch)

# getFAO, getFAOtoSYB, and CHMT functions ---------------------------------

## A demonstration query
FAOquery.df = data.frame(varName = c("arableLand", "cerealExp", "cerealProd"),
                         domainCode = c("RL", "TP", "QC"),
                         itemCode = c(6621, 1944, 1717),
                         elementCode = c(5110, 5922, 5510),
                         stringsAsFactors = FALSE)
## Download the data from FAOSTAT
FAO.lst = with(FAOquery.df,
               getFAOtoSYB(name = varName, domainCode = domainCode,
                           itemCode = itemCode, elementCode = elementCode,
                           useCHMT = TRUE, outputFormat = "wide"))
FAO.lst$entity[, "arableLand"] = as.numeric(FAO.lst$entity[, "arableLand"])

# FAOcheck function - multiChina ------------------------------------------

## FAOcheck function
FAOchecked.df = FAOcheck(var = FAOquery.df$varName, year = "Year",
                         data = FAO.lst$entity, type = "multiChina",
                         take = "simpleCheck")

# getWDI and getWDItoSYB functions ----------------------------------------

## Download World Bank data and meta-data
WB.lst = getWDItoSYB(indicator = c("SP.POP.TOTL", "NY.GDP.MKTP.CD"),
                     name = c("totalPopulation", "GDPUSD"),
                     getMetaData = TRUE, printMetaData = TRUE)

# fillCountryCode function ------------------------------------------------

## Just a demonstration
Demo = WB.lst$entity[, c("Country", "Year", "totalPopulation")]
demoResult = fillCountryCode(country = "Country", data = Demo,
                             outCode = "ISO2_WB_CODE")
## Countries have not been filled in
unique(demoResult[is.na(demoResult$ISO2_WB_CODE), "Country"])

# mergeSYB and translateCountryCode functions -----------------------------

merged.df = mergeSYB(FAOchecked.df, WB.lst$entity, outCode = "FAOST_CODE")

# translateUnit and scaleUnit functions -----------------------------------

multipliers = data.frame(Variable = c("arableLand", "cerealExp", "cerealProd", 
                                      "totalPopulation", "GDPUSD"),
                         Multipliers = c("thousand", NA, NA, NA, NA),
                         stringsAsFactors = FALSE)
multipliers[, "Multipliers"] = 
  as.numeric(translateUnit(multipliers[, "Multipliers"]))
preConstr.df = scaleUnit(merged.df, multipliers)

# construct new variables -------------------------------------------------

con.df = data.frame(STS_ID = c("arableLandPC", "arableLandShareOfTotal",
                               "totalPopulationGeoGR", "totalPopulationLsGR",
                               "totalPopulationInd", "totalPopulationCh"),
                    STS_ID_CONSTR1 = c(rep("arableLand", 2), 
                                       rep("totalPopulation", 4)),
                    STS_ID_CONSTR2 = c("totalPopulation", NA, NA, NA, NA, NA),
                    STS_ID_WEIGHT = rep("totalPopulation", 6),
                    CONSTRUCTION_TYPE = c("share", "share", "growth", "growth",
                                          "index", "change"),
                    GROWTH_RATE_FREQ = c(NA, NA, 10, 10, NA, 1),
                    GROWTH_TYPE = c(NA, NA, "geo", "ls", NA, NA),
                    BASE_YEAR = c(NA, NA, NA, NA, 2000, NA),
                    AGGREGATION = rep("weighted.mean", 6),
                    THRESHOLD_PROP = rep(60, 6),
                    stringsAsFactors = FALSE)

postConstr.lst = with(con.df,
                      constructSYB(data = preConstr.df,
                                   origVar1 = STS_ID_CONSTR1, 
                                   origVar2 = STS_ID_CONSTR2, 
                                   newVarName = STS_ID, 
                                   constructType = CONSTRUCTION_TYPE, 
                                   grFreq = GROWTH_RATE_FREQ, 
                                   grType = GROWTH_TYPE, 
                                   baseYear = BASE_YEAR))
  
  

# Aggregation -------------------------------------------------------------

## Compute aggregates under the FAO continental region.
relation.df = FAOregionProfile[, c("FAOST_CODE", "UNSD_MACRO_REG")]

Macroregion.df = Aggregation(data = postConstr.lst$data, 
                             relationDF = relation.df,
                             aggVar = c("arableLand", "totalPopulation", 
                                        "arableLandPC"),
                             weightVar = c(NA, NA, "totalPopulation"),
                             aggMethod = c("sum", "sum", "weighted.mean"),
                             applyRules = TRUE,
                             keepUnspecified = TRUE, 
                             unspecifiedCode = "NotClassified",
                             thresholdProp = c(rep(0.65,3)))