read file and normalize data to adjust TZ. for 2019

THIS ENTRY SUPERCEDES 2018-10-06.

# read data as csv format and convert to xts to plot
#
Sys.setenv(TZ=Sys.timezone())
# 
# Update below to check file.exists.
#
if(file.exists("~/~/Downloads/bp2018.csv")){
  bp2018 <- read.csv("~/Downloads/bp2018.csv")
  system("rm \"$HOME/Downloads/bp2018.csv\"")
}else{
  print("!!!FILE DOESN'T EXIST!!!!!")
}
#
# Update ends.
#
# bp2018.xts <- xts(bp2018[,c(-1,-2,-6)],as.POSIXct(paste(bp2018$Date,bp2018$Time,sep=" "),tz=Sys.timezone()),tz=Sys.timezone())
bp2018.xts <- xts(bp2018[,c(3,4,5)],as.POSIXct(paste(bp2018$Date,bp2018$Time,sep=" "),tz=Sys.timezone()),tz=Sys.timezone())
bp2019 <- read.csv("~/Downloads/bp - シート1.csv")
system("rm \"$HOME/Downloads/bp - シート1.csv\"")
bp2019.xts <- xts(bp2019[,c(3,4,5)],as.POSIXct(paste(bp2019$Date,bp2019$Time,sep=" "),tz=Sys.timezone()),tz=Sys.timezone())
bp.xts <- append(bp2018.xts,bp2019.xts)
# weekly average
apply.weekly(bp.xts[bp.xts$High > 95],mean)
#
#
# prepare data according to system timezone. "Asia/Tokyo" in most cases.
#
bp.day <- merge(as.xts(as.vector(bp.xts[,1]),as.Date(index(bp.xts),tz=tzone(bp.xts))),as.vector(bp.xts[,2]))
colnames(bp.day)[1] <- "high"
colnames(bp.day)[2] <- "low"
#
# prepare timezone 2 hours behind "Asia/Tokyo".
#
bp.bangkok <- merge(as.xts(as.vector(bp.xts[,1]),as.Date(index(bp.xts),tz="Asia/Bangkok")),as.vector(bp.xts[,2]))
colnames(bp.bangkok)[1] <- "high"
colnames(bp.bangkok)[2] <- "low"
apply.weekly(bp.bangkok,mean)


# read data as csv format and convert to xts to plot
#
Sys.setenv(TZ=Sys.timezone())
bp2018 <- read.csv("~/Downloads/bp2018.csv")
system("rm \"$HOME/Downloads/bp2018.csv\"")
# bp2018.xts <- xts(bp2018[,c(-1,-2,-6)],as.POSIXct(paste(bp2018$Date,bp2018$Time,sep=" "),tz=Sys.timezone()),tz=Sys.timezone())
bp2018.xts <- as.xts(bp2018[,c(3,4,5)],as.POSIXct(paste(bp2018$Date,bp2018$Time,sep=" "),tz=Sys.timezone()),tz=Sys.timezone())
bp2019 <- read.csv("~/Downloads/bp - シート1.csv")
system("rm \"$HOME/Downloads/bp - シート1.csv\"")
bp2019.xts <- xts(bp2019[,c(3,4,5)],as.POSIXct(paste(bp2019$Date,bp2019$Time,sep=" "),tz=Sys.timezone()),tz=Sys.timezone())
bp.xts <- append(bp2018.xts,bp2019.xts)
# weekly average
apply.weekly(bp.xts[bp.xts$High > 95],mean)
#
#
# prepare data according to system timezone. "Asia/Tokyo" in most cases.
#
bp.day <- merge(as.xts(as.vector(bp.xts[,1]),as.Date(index(bp.xts),tz=tzone(bp.xts))),as.vector(bp.xts[,2]))
colnames(bp.day)[1] <- "high"
colnames(bp.day)[2] <- "low"
#
# prepare timezone 2 hours behind "Asia/Tokyo".
#
bp.bangkok <- merge(as.xts(as.vector(bp.xts[,1]),as.Date(index(bp.xts),tz="Asia/Bangkok")),as.vector(bp.xts[,2]))
colnames(bp.bangkok)[1] <- "high"
colnames(bp.bangkok)[2] <- "low"
apply.weekly(bp.bangkok,mean)