CLI - composite leading indicator - OECD

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1. preparation

# download csv from oecd(https://data.oecd.org/leadind/composite-leading-indicator-cli.htm) to "~/Downloads"
# use file name "CLI3.csv".
# this file contains multiple regions data. you have to specify its name.
# extract USA only entries
# execute commands below at "~/Download".
#
sed -n '/USA/p' CLI3.csv |awk -F, '{print $6"-01,"$7}'  |sed 's/\"//g' |awk 'BEGIN{print "DATE,DATA"}{print $0}' > usa.csv
# extract OECD entries and exclude OECDE
sed -n '/OECD[^E]/p' CLI3.csv |awk -F, '{print $6"-01,"$7}'  |sed 's/\"//g' |awk 'BEGIN{print "DATE,DATA"}{print $0}' > oecd.csv

2.drawing graph

# read data from csv.
#
cli_xts <- merge(as.xts(read.zoo(read.csv("~/Downloads/oecd.csv"))),as.xts(read.zoo(read.csv("~/Downloads/usa.c
sv"))),suffixes = c("oecd","usa"
))
#
#  set start date and end date
#
start_date <- as.Date("2014-07-01")
end_date <- as.Date("2018-11-01")
#
#
cli_xts$oecd[paste(start_date,end_date,sep="::")]
period_base <- paste(start_date,end_date,sep="::")
diff_mon <- 6
period_compare <- paste(as.Date(as.yearmon(mondate(as.Date(start_date))-diff_mon )),as.Date(as.yearmon(mondate(as.Date(end_date))-diff_mon )),sep="::")
paste("2018-01",end_date,sep="::")
paste("2017-07",as.Date(as.yearmon(mondate(as.Date(end_date))-diff_mon )),sep="::")

plot.default((cli_xts$oecd[period_base]   / as.vector(cli_xts$oecd[period_compare])-1)*100,cli_xts$oecd[period_base])
tmp <- par('usr')
plot.default((cli_xts$oecd[period_base] / as.vector(cli_xts$oecd[period_compare])-1)*100,cli_xts$oecd[period_base] ,xlim=c( tmp[1],tmp[2]), ylim=c(tmp[3], tmp[4]),type='b')
par(new=T)
#
#  code for CY2019 and after.
#
#
if(as.Date("2018-12-31") < end_date){
  # add line to data beyond "2019-01-01" when time has come.
  plot.default((cli_xts$oecd[paste("2019-01",end_date,sep="::")] / as.vector(cli_xts$oecd[paste("2018-07",as.Date(as.yearmon(mondate(as.Date(end_date))-diff_mon )),sep="::")])-1)*100,cli_xts$oecd[paste("2019-01",end_date,sep="::")] ,xlim=c( tmp[1],tmp[2]), ylim=c(tmp[3], tmp[4]),col=2,lwd=2)

  par(new=T)
  plot.default((cli_xts$oecd["2018-01::2018-12"] / as.vector(cli_xts$oecd["2017-07::2018-06"])-1)*100,cli_xts$oecd["2018-01::2018-12"], xlim=c( tmp[1],tmp[2]), ylim=c(tmp[3], tmp[4]),col=6)

} else{
  plot.default((cli_xts$oecd[paste("2018-01",end_date,sep="::")] / as.vector(cli_xts$oecd[paste("2017-07",as.Date(as.yearmon(mondate(as.Date(end_date))-diff_mon )),sep="::")])-1)*100,cli_xts$oecd[paste("2018-01",end_date,sep="::")] ,xlim=c( tmp[1],tmp[2]), ylim=c(tmp[3], tmp[4]),col=2,lwd=2)
}

par(new=T)
plot.default((cli_xts$oecd["2017-01::2017-12"] / as.vector(cli_xts$oecd["2016-07::2017-06"])-1)*100,cli_xts$oecd["2017-01::2017-12"], xlim=c( tmp[1],tmp[2]), ylim=c(tmp[3], tmp[4]),col=3)
par(new=T)
plot.default((cli_xts$oecd["2016-01::2016-12"] / as.vector(cli_xts$oecd["2015-07::2016-06"])-1)*100,cli_xts$oecd["2016-01::2016-12"], xlim=c( tmp[1],tmp[2]), ylim=c(tmp[3], tmp[4]),col=4)
par(new=T)
plot.default((cli_xts$oecd["2015-01::2015-12"] / as.vector(cli_xts$oecd["2014-07::2015-06"])-1)*100,cli_xts$oecd["2015-01::2015-12"], xlim=c( tmp[1],tmp[2]), ylim=c(tmp[3], tmp[4]),col=5
)
abline(h=100)
abline(v=0)
legend("topleft", legend = "Light Blue: 2015\nBlue: 2016\nLime: 2017\nRed: 2018",bty='n')