Download composite leading indicator full data and load them into the R environment CLI

# go to OECD, and download 'Full Indicator data(.csv)".
# change the filename to CLI3.csv upon download.
# go to the directory where the downloaded csv exists.
# run the script in git repo. for example as the below.

$~/R/R2/index/cli_download.sed

# there will be

$ls -l *.csv
-rw-r--r--@ 1 honomoto  staff  1283086 11 14 09:50 CLI3.csv
-rw-r--r--  1 honomoto  staff     7105 11 14 09:50 chn.csv
-rw-r--r--  1 honomoto  staff    12848 11 14 09:50 ea19.csv
-rw-r--r--  1 honomoto  staff    13923 11 14 09:50 oecd.csv
-rw-r--r--  1 honomoto  staff    15465 11 14 09:50 usa.csv

# run cli_download.


cli_xts <- merge(as.xts(read.zoo(read.csv("~/Downloads/oecd.csv"))),
as.xts(read.zoo(read.csv("~/Downloads/usa.csv"))),
as.xts(read.zoo(read.csv("~/Downloads/chn.csv"))),
as.xts(read.zoo(read.csv("~/Downloads/ea19.csv"))),
suffixes = c("oecd","usa","china","ea19"))

# draw graphs

par(mfrow=c(4,1))
plot(diff(cli_xts$oecd)["2011::"],type='p',pwd=1,pch='+')
plot(diff(cli_xts$usa)["2011::"],type='p',pwd=1,pch='+')
plot(diff(cli_xts$ea19)["2011::"],type='p',pwd=1,pch='+')
plot(diff(cli_xts$china)["2011::"],type='p',pwd=1,pch='+')

par(mfrow=c(1,1))