sandbox/analysis_prod_trade_cereals_sugar_pulses.R
In SWS-Methodology/faoswsStock: Package to perform the imputation of stock changes for the FAO food balance sheets
# rewriting
## load the library
suppressMessages({
library(faosws)
library(data.table)
library(ggplot2)
library(faoswsUtil)
library(faoswsFlag)
})
## set up for the test environment and parameters
#initialYear = 1961 # Change documentation if this changes!
R_SWS_SHARE_PATH <- Sys.getenv("R_SWS_SHARE_PATH")
# This return FALSE if on the Statistical Working System
if(CheckDebug()){
message("Not on server, so setting up environment...")
library(faoswsModules)
SETTINGS <- ReadSettings("modules/impute_stocks/sws.yml")
# If you're not on the system, your settings will overwrite any others
R_SWS_SHARE_PATH <- SETTINGS[["share"]]
# Define where your certificates are stored
SetClientFiles(SETTINGS[["certdir"]])
# Get session information from SWS. Token must be obtained from web interface
GetTestEnvironment(baseUrl = SETTINGS[["server"]],
token = SETTINGS[["token"]])
}
## Using the new data on SWS instead of oldSuaData
stockCode <- "5071"
prodCode <- "5510"
codes <- c(stockCode, prodCode)
item <- ReadDatatable("fbs_stocks_comm_codes")[fbs_key == "measuredItemCPC",
fbs_code]
stockProdkey = DatasetKey(
domain = "agriculture",
dataset = "aproduction",
dimensions = list(
Dimension(
name = "geographicAreaM49",
keys = GetCodeList("agriculture", "aproduction", "geographicAreaM49")[, code]
),
Dimension(name = "measuredElement", keys = codes),
Dimension(name = "timePointYears", keys = as.character(2009:2013)),
Dimension(
name = "measuredItemCPC",
keys = GetCodeList("agriculture", "aproduction", "measuredItemCPC")[code %in% item, code]
)
)
)
stockProdData <- GetData(stockProdkey, flags = T)
stockData <- stockProdData[measuredElement == 5071]
# stockData[, c("measuredElement", "flagObservationStatus", "flagMethod") := NULL]
setnames(stockData, "Value", "deltaStocks")
# production data
prodData <- stockProdData[measuredElement == 5510]
prodData[, c("measuredElement", "flagObservationStatus", "flagMethod") := NULL]
setnames(prodData, "Value", "production")
# Trade
tradeCode <- c("5610", "5910")
totalTradeKey = DatasetKey(
domain = "trade",
dataset = "total_trade_cpc_m49",
dimensions = list(
Dimension(
name = "geographicAreaM49",
keys = GetCodeList("trade", "total_trade_cpc_m49", "geographicAreaM49")[, code]
),
Dimension(name = "measuredElementTrade", keys = tradeCode),
Dimension(name = "timePointYears", keys = as.character(2009:2013)),
Dimension(
name = "measuredItemCPC",
keys = GetCodeList("trade", "total_trade_cpc_m49", "measuredItemCPC")[code %in% item, code]
)
)
)
totalTradeData <- GetData(totalTradeKey, flags = F)
totalTradeData <- dcast.data.table(totalTradeData, geographicAreaM49 + measuredItemCPC +
timePointYears ~ measuredElementTrade, value.var = "Value")
setnames(totalTradeData, "5610", "imports")
setnames(totalTradeData, "5910", "exports")
totalTradeData[is.na(imports), imports := 0]
totalTradeData[is.na(exports), exports := 0]
totalTradeData[, netTrade := (imports - exports)]
library(countrycode)
## Country groups
dlpath <- file.path("C:", "Users", "caetano", "Documents", "Github", "faoswsFeed",
"data-raw", "IR_factor", "class.csv")
countryGroup <- fread(dlpath)
countryGroup[, .N, GroupName]
countryGroup[grepl("income", GroupName), .N, GroupName]
countryIncomeGroup <- countryGroup[GroupCode %in% c("HIC", "LIC", "UMC", "LMC"), ]
# countryRegionGroup <- countryGroup[GroupCode %in% c("NAC", "LCN", "AFR", "EAS", "EUU"),]
# library(countrycode)
countryIncomeGroup[, geographicAreaM49 := as.character(countrycode(CountryCode, "wb", "iso3n"))]
# Sudan has the wrong name (it should be former Sudan)
countryIncomeGroup[geographicAreaM49 == "736", CountryName := "Sudan (former)"]
# China should be 1248
countryIncomeGroup[geographicAreaM49 == "156", geographicAreaM49 := "1248"]
#Exclude Channel Islands and Kosovo (not separately recognised by the UN)
countryIncomeGroup <- countryIncomeGroup[!is.na(geographicAreaM49)]
setnames(countryIncomeGroup, "GroupName", "incomeGroup")
keys <- c("geographicAreaM49", "measuredItemCPC", "timePointYears")
data <- merge(stockData, prodData, by = keys, all = T)
data <- merge(data, totalTradeData, by = keys, all = T)
data <- merge(data, countryIncomeGroup[, c("geographicAreaM49", "incomeGroup"), with = F],
by = "geographicAreaM49", all.x=T)
data[substr(measuredItemCPC, 1, 3) == "011", itemGroup := "cereals"]
data[substr(measuredItemCPC, 1, 3) == "017", itemGroup := "pulses"]
data[substr(measuredItemCPC, 1, 3) == "235", itemGroup := "refined_sugar"]
data <- data[timePointYears %in% c("2011", "2012")]
data[is.na(production), production := 0]
data[is.na(imports), imports := 0]
data[is.na(exports), exports := 0]
data[is.na(netTrade), netTrade := 0]
data[, totalSupply := production + netTrade]
data
setkey(data, geographicAreaM49, measuredItemCPC, timePointYears)
## Compute delta production
data[, deltaTotalSupply := c(0, diff(totalSupply)),
by = list(geographicAreaM49, measuredItemCPC)]
# Let's apply the coefficients fitted by AMIS data for cereals and pulses.
# For refined_sugar we will use coefficients fitted from the F.O Lichts data.
# coefficients for USA and cereals
data[itemGroup == "cereals" & geographicAreaM49 == 840,
deltaStocksEstimated := -1.32490 + 0.28250 * deltaTotalSupply + 0.41081]
# coefficients for USA amd PULSES
data[itemGroup == "pulses" & geographicAreaM49 == 840,
deltaStocksEstimated := -1.32490 + 0.28250 * deltaTotalSupply + 0.41081 * 1 + 0.32012]
data[itemGroup == "refined_sugar" & geographicAreaM49 == 840,
deltaStocksEstimated := -2321.6041785 + 0.8164414 * deltaTotalSupply]
# coefficients for EU and cereals
data[itemGroup == "cereals" & geographicAreaM49 %in% c(40, 56, 100, 196, 203, 208, 233, 246, 250, 276, 300, 348, 372, 380, 428,
440, 442, 470, 528, 616, 620, 642, 703, 705, 724, 752, 826),
deltaStocksEstimated := -1.32490 + 0.28250 * deltaTotalSupply]
data[itemGroup == "pulses" & geographicAreaM49 %in% c(40, 56, 100, 196, 203, 208, 233, 246, 250, 276, 300, 348, 372, 380, 428,
440, 442, 470, 528, 616, 620, 642, 703, 705, 724, 752, 826),
deltaStocksEstimated := -1.32490 + 0.28250 * deltaTotalSupply + 0.32012]
data[itemGroup == "refined_sugar" & geographicAreaM49 %in% c(40, 56, 100, 196, 203,
208, 233, 246, 250, 276,
300, 348, 372, 380, 428,
440, 442, 470, 528, 616,
620, 642, 703, 705, 724,
752, 826),
deltaStocksEstimated := -2321.6041785 + 0.8164414 * deltaTotalSupply]
# other countries
data[itemGroup == "cereals" & !(geographicAreaM49 %in% c(840, 40, 56, 100, 196, 203, 208, 233, 246, 250, 276, 300, 348, 372, 380, 428,
440, 442, 470, 528, 616, 620, 642, 703, 705, 724, 752, 826)),
deltaStocksEstimated := -1.32490 + 0.28250 * deltaTotalSupply + 1.00563]
data[itemGroup == "pulses" & !(geographicAreaM49 %in% c(840, 40, 56, 100, 196, 203, 208, 233, 246, 250, 276, 300, 348, 372, 380, 428,
440, 442, 470, 528, 616, 620, 642, 703, 705, 724, 752, 826)),
deltaStocksEstimated := -1.32490 + 0.28250 * deltaTotalSupply + 1.00563 + 0.32012]
data[itemGroup == "refined_sugar" & geographicAreaM49 %in% !(geographicAreaM49 %in%
c(840, 40, 56, 100,
196, 203, 208, 233,
246, 250, 276, 300,
348, 372, 380, 428,
440, 442, 470, 528,
616, 620, 642, 703,
705, 724, 752, 826)) &
incomeGroup %in% c("Lower middle income", "Low income"),
deltaStocksEstimated := -2321.6041785 + 0.8164414 * deltaTotalSupply + 2167.8907130]
data[itemGroup == "refined_sugar" & geographicAreaM49 %in% !(geographicAreaM49 %in%
c(840, 40, 56, 100,
196, 203, 208, 233,
246, 250, 276, 300,
348, 372, 380, 428,
440, 442, 470, 528,
616, 620, 642, 703,
705, 724, 752, 826)) &
incomeGroup %in% c("Upper middle income", "High income"),
deltaStocksEstimated := -2321.6041785 + 0.8164414 * deltaTotalSupply]
tab <- data[measuredItemCPC %in% c("0111", "0112", "0113", "0114", "0141", "23520"),
c("geographicAreaM49", "timePointYears", "measuredItemCPC", "deltaStocksEstimated"), with=F]
tab <- dcast.data.table(tab, geographicAreaM49 + timePointYears ~ measuredItemCPC, value.var = "deltaStocksEstimated")
setnames(tab, "0111", "wheat")
setnames(tab, "0112", "maize")
setnames(tab, "0113", "rice")
setnames(tab, "0114", "sorghum")
setnames(tab, "23520", "refined_sugar")
tab <- tab[timePointYears == 2012]
tab[geographicAreaM49 == 840]
data[itemGroup == "refined_sugar"]
tabItemIncomeGroup <- data[, list(production = sum(production, na.rm = T),
netTrade = sum(netTrade, na.rm = T),
deltaStocks = sum(deltaStocks, na.rm = T),
nCountry = length(unique(geographicAreaM49))),
by = list(timePointYears, itemGroup, incomeGroup)]
tabItemIncomeGroup <- tabItemIncomeGroup[!is.na(incomeGroup)]
tabItemIncomeGroup[itemGroup == "cereals"]
tabItemIncomeGroup[itemGroup == "pulses"]
tabItemIncomeGroup[itemGroup == "refined_sugar"]
tabItemIncomeGroup$incomeGroup <- factor(tabItemIncomeGroup$incomeGroup,
levels = c("High income",
"Upper middle income",
"Lower middle income",
"Low income"))
plotcolors <- colorRampPalette(c("gray70", "blue", "green", "black"))
ggplot(data=tabItemIncomeGroup,
aes(x=as.factor(timePointYears), y=netTrade, group=incomeGroup, col = incomeGroup)) +
geom_line(aes(), stat = "identity", position=position_dodge(), size=1) +
facet_wrap(~ itemGroup
, scales = "free"
) +
scale_color_manual( values = plotcolors(length(unique(tabItemIncomeGroup$incomeGroup)))) +
theme(strip.text.x = element_text(size = 10, colour = "black", angle = 0)) +
theme(legend.title=element_blank(), strip.text = element_text(size=7)) +
geom_hline(yintercept=0, col = "red") +
theme(legend.title=element_blank(), strip.text = element_text(size=7)) +
xlab('Year') + ylab('Imports - Exports') +
#scale_x_continuous(lim=c(2005, 2012), breaks=seq(2005, 2012, 1)) +
theme(
axis.text.x = element_text(size = 8, face = "bold", angle = 45, vjust = .5),
legend.position = "top"
)
# F.0 Lichts
dlpath <- file.path("C:", "Users", "caetano", "Documents", "Github", "faoswsStock", "sandbox", "fo_licht", "sugar_prod_trade_stocks_fo_lichts2016_06_08.csv")
sugarData <- fread(dlpath)
sugarData[, supply := production + imports - exports + opening_stocks]
sugarData[, utilization := consumption + ending_stocks]
sugarData[, delta_stocks := ending_stocks - opening_stocks]
sugarData[, stocks_percent := ending_stocks/supply]
sugarData[, delta_total_supply := diff(c(NA, supply)),
by=list(country_group)]
sugarData[, netTrade := imports - exports]
ggplot(data=sugarData,
aes(x=as.factor(timePointYears), y=netTrade, group=country_group, col = country_group)) +
geom_line(aes(), stat = "identity", position=position_dodge(), size=1) +
# facet_wrap(~ itemGroup
# , scales = "free"
# ) +
#scale_color_manual( values = plotcolors(length(unique(sugarData$country_group)))) +
# theme(strip.text.x = element_text(size = 10, colour = "black", angle = 0)) +
theme(legend.title=element_blank(), strip.text = element_text(size=7)) +
geom_hline(yintercept=0, col = "black") +
theme(legend.title=element_blank(), strip.text = element_text(size=7)) +
xlab('Year') + ylab('Imports - Exports') +
#scale_x_continuous(lim=c(2005, 2012), breaks=seq(2005, 2012, 1)) +
theme(
axis.text.x = element_text(size = 8, face = "bold", angle = 45, vjust = .5),
legend.position = "top"
)
SWS-Methodology/faoswsStock documentation built on May 23, 2021, 7:35 a.m.
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# rewriting
## load the library
suppressMessages({
library(faosws)
library(data.table)
library(ggplot2)
library(faoswsUtil)
library(faoswsFlag)
})
## set up for the test environment and parameters
#initialYear = 1961 # Change documentation if this changes!
R_SWS_SHARE_PATH <- Sys.getenv("R_SWS_SHARE_PATH")
# This return FALSE if on the Statistical Working System
if(CheckDebug()){
message("Not on server, so setting up environment...")
library(faoswsModules)
SETTINGS <- ReadSettings("modules/impute_stocks/sws.yml")
# If you're not on the system, your settings will overwrite any others
R_SWS_SHARE_PATH <- SETTINGS[["share"]]
# Define where your certificates are stored
SetClientFiles(SETTINGS[["certdir"]])
# Get session information from SWS. Token must be obtained from web interface
GetTestEnvironment(baseUrl = SETTINGS[["server"]],
token = SETTINGS[["token"]])
}
## Using the new data on SWS instead of oldSuaData
stockCode <- "5071"
prodCode <- "5510"
codes <- c(stockCode, prodCode)
item <- ReadDatatable("fbs_stocks_comm_codes")[fbs_key == "measuredItemCPC",
fbs_code]
stockProdkey = DatasetKey(
domain = "agriculture",
dataset = "aproduction",
dimensions = list(
Dimension(
name = "geographicAreaM49",
keys = GetCodeList("agriculture", "aproduction", "geographicAreaM49")[, code]
),
Dimension(name = "measuredElement", keys = codes),
Dimension(name = "timePointYears", keys = as.character(2009:2013)),
Dimension(
name = "measuredItemCPC",
keys = GetCodeList("agriculture", "aproduction", "measuredItemCPC")[code %in% item, code]
)
)
)
stockProdData <- GetData(stockProdkey, flags = T)
stockData <- stockProdData[measuredElement == 5071]
# stockData[, c("measuredElement", "flagObservationStatus", "flagMethod") := NULL]
setnames(stockData, "Value", "deltaStocks")
# production data
prodData <- stockProdData[measuredElement == 5510]
prodData[, c("measuredElement", "flagObservationStatus", "flagMethod") := NULL]
setnames(prodData, "Value", "production")
# Trade
tradeCode <- c("5610", "5910")
totalTradeKey = DatasetKey(
domain = "trade",
dataset = "total_trade_cpc_m49",
dimensions = list(
Dimension(
name = "geographicAreaM49",
keys = GetCodeList("trade", "total_trade_cpc_m49", "geographicAreaM49")[, code]
),
Dimension(name = "measuredElementTrade", keys = tradeCode),
Dimension(name = "timePointYears", keys = as.character(2009:2013)),
Dimension(
name = "measuredItemCPC",
keys = GetCodeList("trade", "total_trade_cpc_m49", "measuredItemCPC")[code %in% item, code]
)
)
)
totalTradeData <- GetData(totalTradeKey, flags = F)
totalTradeData <- dcast.data.table(totalTradeData, geographicAreaM49 + measuredItemCPC +
timePointYears ~ measuredElementTrade, value.var = "Value")
setnames(totalTradeData, "5610", "imports")
setnames(totalTradeData, "5910", "exports")
totalTradeData[is.na(imports), imports := 0]
totalTradeData[is.na(exports), exports := 0]
totalTradeData[, netTrade := (imports - exports)]
library(countrycode)
## Country groups
dlpath <- file.path("C:", "Users", "caetano", "Documents", "Github", "faoswsFeed",
"data-raw", "IR_factor", "class.csv")
countryGroup <- fread(dlpath)
countryGroup[, .N, GroupName]
countryGroup[grepl("income", GroupName), .N, GroupName]
countryIncomeGroup <- countryGroup[GroupCode %in% c("HIC", "LIC", "UMC", "LMC"), ]
# countryRegionGroup <- countryGroup[GroupCode %in% c("NAC", "LCN", "AFR", "EAS", "EUU"),]
# library(countrycode)
countryIncomeGroup[, geographicAreaM49 := as.character(countrycode(CountryCode, "wb", "iso3n"))]
# Sudan has the wrong name (it should be former Sudan)
countryIncomeGroup[geographicAreaM49 == "736", CountryName := "Sudan (former)"]
# China should be 1248
countryIncomeGroup[geographicAreaM49 == "156", geographicAreaM49 := "1248"]
#Exclude Channel Islands and Kosovo (not separately recognised by the UN)
countryIncomeGroup <- countryIncomeGroup[!is.na(geographicAreaM49)]
setnames(countryIncomeGroup, "GroupName", "incomeGroup")
keys <- c("geographicAreaM49", "measuredItemCPC", "timePointYears")
data <- merge(stockData, prodData, by = keys, all = T)
data <- merge(data, totalTradeData, by = keys, all = T)
data <- merge(data, countryIncomeGroup[, c("geographicAreaM49", "incomeGroup"), with = F],
by = "geographicAreaM49", all.x=T)
data[substr(measuredItemCPC, 1, 3) == "011", itemGroup := "cereals"]
data[substr(measuredItemCPC, 1, 3) == "017", itemGroup := "pulses"]
data[substr(measuredItemCPC, 1, 3) == "235", itemGroup := "refined_sugar"]
data <- data[timePointYears %in% c("2011", "2012")]
data[is.na(production), production := 0]
data[is.na(imports), imports := 0]
data[is.na(exports), exports := 0]
data[is.na(netTrade), netTrade := 0]
data[, totalSupply := production + netTrade]
data
setkey(data, geographicAreaM49, measuredItemCPC, timePointYears)
## Compute delta production
data[, deltaTotalSupply := c(0, diff(totalSupply)),
by = list(geographicAreaM49, measuredItemCPC)]
# Let's apply the coefficients fitted by AMIS data for cereals and pulses.
# For refined_sugar we will use coefficients fitted from the F.O Lichts data.
# coefficients for USA and cereals
data[itemGroup == "cereals" & geographicAreaM49 == 840,
deltaStocksEstimated := -1.32490 + 0.28250 * deltaTotalSupply + 0.41081]
# coefficients for USA amd PULSES
data[itemGroup == "pulses" & geographicAreaM49 == 840,
deltaStocksEstimated := -1.32490 + 0.28250 * deltaTotalSupply + 0.41081 * 1 + 0.32012]
data[itemGroup == "refined_sugar" & geographicAreaM49 == 840,
deltaStocksEstimated := -2321.6041785 + 0.8164414 * deltaTotalSupply]
# coefficients for EU and cereals
data[itemGroup == "cereals" & geographicAreaM49 %in% c(40, 56, 100, 196, 203, 208, 233, 246, 250, 276, 300, 348, 372, 380, 428,
440, 442, 470, 528, 616, 620, 642, 703, 705, 724, 752, 826),
deltaStocksEstimated := -1.32490 + 0.28250 * deltaTotalSupply]
data[itemGroup == "pulses" & geographicAreaM49 %in% c(40, 56, 100, 196, 203, 208, 233, 246, 250, 276, 300, 348, 372, 380, 428,
440, 442, 470, 528, 616, 620, 642, 703, 705, 724, 752, 826),
deltaStocksEstimated := -1.32490 + 0.28250 * deltaTotalSupply + 0.32012]
data[itemGroup == "refined_sugar" & geographicAreaM49 %in% c(40, 56, 100, 196, 203,
208, 233, 246, 250, 276,
300, 348, 372, 380, 428,
440, 442, 470, 528, 616,
620, 642, 703, 705, 724,
752, 826),
deltaStocksEstimated := -2321.6041785 + 0.8164414 * deltaTotalSupply]
# other countries
data[itemGroup == "cereals" & !(geographicAreaM49 %in% c(840, 40, 56, 100, 196, 203, 208, 233, 246, 250, 276, 300, 348, 372, 380, 428,
440, 442, 470, 528, 616, 620, 642, 703, 705, 724, 752, 826)),
deltaStocksEstimated := -1.32490 + 0.28250 * deltaTotalSupply + 1.00563]
data[itemGroup == "pulses" & !(geographicAreaM49 %in% c(840, 40, 56, 100, 196, 203, 208, 233, 246, 250, 276, 300, 348, 372, 380, 428,
440, 442, 470, 528, 616, 620, 642, 703, 705, 724, 752, 826)),
deltaStocksEstimated := -1.32490 + 0.28250 * deltaTotalSupply + 1.00563 + 0.32012]
data[itemGroup == "refined_sugar" & geographicAreaM49 %in% !(geographicAreaM49 %in%
c(840, 40, 56, 100,
196, 203, 208, 233,
246, 250, 276, 300,
348, 372, 380, 428,
440, 442, 470, 528,
616, 620, 642, 703,
705, 724, 752, 826)) &
incomeGroup %in% c("Lower middle income", "Low income"),
deltaStocksEstimated := -2321.6041785 + 0.8164414 * deltaTotalSupply + 2167.8907130]
data[itemGroup == "refined_sugar" & geographicAreaM49 %in% !(geographicAreaM49 %in%
c(840, 40, 56, 100,
196, 203, 208, 233,
246, 250, 276, 300,
348, 372, 380, 428,
440, 442, 470, 528,
616, 620, 642, 703,
705, 724, 752, 826)) &
incomeGroup %in% c("Upper middle income", "High income"),
deltaStocksEstimated := -2321.6041785 + 0.8164414 * deltaTotalSupply]
tab <- data[measuredItemCPC %in% c("0111", "0112", "0113", "0114", "0141", "23520"),
c("geographicAreaM49", "timePointYears", "measuredItemCPC", "deltaStocksEstimated"), with=F]
tab <- dcast.data.table(tab, geographicAreaM49 + timePointYears ~ measuredItemCPC, value.var = "deltaStocksEstimated")
setnames(tab, "0111", "wheat")
setnames(tab, "0112", "maize")
setnames(tab, "0113", "rice")
setnames(tab, "0114", "sorghum")
setnames(tab, "23520", "refined_sugar")
tab <- tab[timePointYears == 2012]
tab[geographicAreaM49 == 840]
data[itemGroup == "refined_sugar"]
tabItemIncomeGroup <- data[, list(production = sum(production, na.rm = T),
netTrade = sum(netTrade, na.rm = T),
deltaStocks = sum(deltaStocks, na.rm = T),
nCountry = length(unique(geographicAreaM49))),
by = list(timePointYears, itemGroup, incomeGroup)]
tabItemIncomeGroup <- tabItemIncomeGroup[!is.na(incomeGroup)]
tabItemIncomeGroup[itemGroup == "cereals"]
tabItemIncomeGroup[itemGroup == "pulses"]
tabItemIncomeGroup[itemGroup == "refined_sugar"]
tabItemIncomeGroup$incomeGroup <- factor(tabItemIncomeGroup$incomeGroup,
levels = c("High income",
"Upper middle income",
"Lower middle income",
"Low income"))
plotcolors <- colorRampPalette(c("gray70", "blue", "green", "black"))
ggplot(data=tabItemIncomeGroup,
aes(x=as.factor(timePointYears), y=netTrade, group=incomeGroup, col = incomeGroup)) +
geom_line(aes(), stat = "identity", position=position_dodge(), size=1) +
facet_wrap(~ itemGroup
, scales = "free"
) +
scale_color_manual( values = plotcolors(length(unique(tabItemIncomeGroup$incomeGroup)))) +
theme(strip.text.x = element_text(size = 10, colour = "black", angle = 0)) +
theme(legend.title=element_blank(), strip.text = element_text(size=7)) +
geom_hline(yintercept=0, col = "red") +
theme(legend.title=element_blank(), strip.text = element_text(size=7)) +
xlab('Year') + ylab('Imports - Exports') +
#scale_x_continuous(lim=c(2005, 2012), breaks=seq(2005, 2012, 1)) +
theme(
axis.text.x = element_text(size = 8, face = "bold", angle = 45, vjust = .5),
legend.position = "top"
)
# F.0 Lichts
dlpath <- file.path("C:", "Users", "caetano", "Documents", "Github", "faoswsStock", "sandbox", "fo_licht", "sugar_prod_trade_stocks_fo_lichts2016_06_08.csv")
sugarData <- fread(dlpath)
sugarData[, supply := production + imports - exports + opening_stocks]
sugarData[, utilization := consumption + ending_stocks]
sugarData[, delta_stocks := ending_stocks - opening_stocks]
sugarData[, stocks_percent := ending_stocks/supply]
sugarData[, delta_total_supply := diff(c(NA, supply)),
by=list(country_group)]
sugarData[, netTrade := imports - exports]
ggplot(data=sugarData,
aes(x=as.factor(timePointYears), y=netTrade, group=country_group, col = country_group)) +
geom_line(aes(), stat = "identity", position=position_dodge(), size=1) +
# facet_wrap(~ itemGroup
# , scales = "free"
# ) +
#scale_color_manual( values = plotcolors(length(unique(sugarData$country_group)))) +
# theme(strip.text.x = element_text(size = 10, colour = "black", angle = 0)) +
theme(legend.title=element_blank(), strip.text = element_text(size=7)) +
geom_hline(yintercept=0, col = "black") +
theme(legend.title=element_blank(), strip.text = element_text(size=7)) +
xlab('Year') + ylab('Imports - Exports') +
#scale_x_continuous(lim=c(2005, 2012), breaks=seq(2005, 2012, 1)) +
theme(
axis.text.x = element_text(size = 8, face = "bold", angle = 45, vjust = .5),
legend.position = "top"
)
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