.emacs

Don't forget good old days.

(global-set-key "\C-h" 'delete-backward-char)
(setenv "PATH" "/usr/bin:/bin:/usr/sbin:/sbin:/usr/local/bin")
(setq exec-path (parse-colon-path (getenv "PATH")))
; (add-to-list 'load-path "~/.emacs.d")                                       
(global-visual-line-mode)
(autoload 'wikipedia-mode "wikipedia-mode.el"
   "Major mode for editing documents in Wikipedia markup." t)
(require 'epg)
(setq epa-armor t)

; 緯度経度は小数点一桁までで指定@東京駅
(setq calendar-latitude 35.7)  ;
(setq calendar-longitude 139.8)  ;
(setq calendar-location-name "Tokyo,Japan")

H1B Visa Holder Salary

オラクル　=  Oracle America Inc

グーグル = Google Inc

で各社のH1Bビザを持ってる(??)人の給料が比較る。やはりグーグルのほうが高い。

http://h1bdata.info/index.php?em=Oracle+America+Inc&job=&city=&year=All+Years

Retrieve time data from POSIXct and filter data

bp.xts["2018-06"][strptime(format(index(bp.xts["2018-06"]),"%H:%M:%S"),"%H:%M:%S") > strptime("09:00:00","%H:%M:%S") & strptime(format(index(bp.xts["2018-06"]),"%H:%M:%S"),"%H:%M:%S") < strptime("12:00:00","%H:%M:%S")]
                    High Low Pulse
2018-06-02 10:20:00  136  91    70
2018-06-17 09:58:00  133  89    64


bp.xts[strptime(format(index(bp.xts),"%H:%M:%S"),"%H:%M:%S") > strptime("09:00:00","%H:%M:%S") & strptime(format(index(bp.xts),"%H:%M:%S"),"%H:%M:%S") < strptime("12:00:00","%H:%M:%S")]

                    High Low Pulse
2018-01-02 10:15:00  158 107    NA
2018-01-03 09:30:00  153 110    NA
2018-01-06 10:00:00  143 108    75
<skip....>
2018-05-26 09:33:00  126  85    73
2018-06-02 10:20:00  136  91    70
2018-06-17 09:58:00  133  89    64

> mean(na.omit(bp.xts[strptime(format(index(bp.xts),"%H:%M:%S"),"%H:%M:%S") > strptime("07:00:00","%H:%M:%S") & strptime(format(index(bp.xts),"%H:%M:%S"),"%H:%M:%S") < strptime("08:00:00","%H:%M:%S")])[,1])
[1] 126.0638
> mean(na.omit(bp.xts[strptime(format(index(bp.xts),"%H:%M:%S"),"%H:%M:%S") > strptime("08:00:00","%H:%M:%S") & strptime(format(index(bp.xts),"%H:%M:%S"),"%H:%M:%S") < strptime("09:00:00","%H:%M:%S")])[,1])
[1] 126.5357
> mean(na.omit(bp.xts[strptime(format(index(bp.xts),"%H:%M:%S"),"%H:%M:%S") > strptime("09:00:00","%H:%M:%S") & strptime(format(index(bp.xts),"%H:%M:%S"),"%H:%M:%S") < strptime("10:00:00","%H:%M:%S")])[,1])
[1] 132.1429
> mean(na.omit(bp.xts[strptime(format(index(bp.xts),"%H:%M:%S"),"%H:%M:%S") > strptime("10:00:00","%H:%M:%S") & strptime(format(index(bp.xts),"%H:%M:%S"),"%H:%M:%S") < strptime("11:00:00","%H:%M:%S")])[,1])
[1] 133.125

> time_base_mean <- function(x,y){return(mean(na.omit(bp.xts[strptime(format(index(bp.xts),"%H:%M:%S"),"%H:%M:%S") > strptime(x,"%H:%M:%S") & strptime(format(index(bp.xts),"%H:%M:%S"),"%H:%M:%S") < strptime(y,"%H:%M:%S")])[,1]))}
> time_base_mean("08:00:00","09:00:00")
[1] 126.5357
> mapply(time_base_mean, c("08:00:00","09:00:00","10:00:00"),c("09:00:00","10:00:00","11:00:00"))
08:00:00 09:00:00 10:00:00
126.5357 132.1429 133.1250


#
# use anonymous function in line
#
mapply(function(x,y){return(mean(na.omit(bp.xts[strptime(format(index(bp.xts),"%H:%M:%S"),"%H:%M:%S") > strptime(x,"%H:%M:%S") & strptime(format(index(bp.xts),"%H:%M:%S"),"%H:%M:%S") < strptime(y,"%H:%M:%S")])
[,1]))},c("05:00:00","06:00:00","07:00:00","08:00:00","09:00:00","10:00:00","11:00:00","23:00:00","00:00:00"),c("05:00:00","06:59:00","07:59:00","08:59:00","09:59:00","10:59:00","11:59:00","23:59:00","00:59:00"))

05:00:00 06:00:00 07:00:00 08:00:00 09:00:00 10:00:00 11:00:00 23:00:00 00:00:00
     NaN 128.0000 125.0566 125.1176 129.8182 133.1250 132.8333 117.7000 117.4062

Upload EPS data

download eps raw data from here.

select Additional Info = index earnings

Choose necessary data and cut&paste to the new file. This is important as number only support to convert the entire original file into the separated CSV.
Save it as ".csv".

expected as below.

$nkf -Lu eps.csv
date,eps
12/31/2019,44.08
9/30/2019,42.26
<..skip...>
1988/6/30,6.22
1988/3/31,5.53


as.xts(rev(read.csv("~/eps.csv")[,2]),seq(as.Date(mondate(as.Date("1988-01-01"))),as.Date(mondate(as.Date("1988-01-01"))+ length(read.csv("~/eps.csv")[,2])*3-1),by="quarters"))

Don't forget "rev()" as eps.csv stores data in the inverse order.
Do as.Date() -> mondate() -> as.Date() conversion otherwise unable to use by="quarters"argument.

           [,1]
1988-01-01 5.53
1988-04-01 6.22
1988-07-01 6.38
1988-10-01 5.62
1989-01-01 6.74
1989-04-01 6.48
<...skip...>
            [,1]
2018-07-01 38.18
2018-10-01 40.03
2019-01-01 38.20
2019-04-01 40.22
2019-07-01 42.26
2019-10-01 44.08

calculate annual eps data with filter(). Don't forget as.vector() as as.xts() accept only vector for the 1st arg.

as.xts(as.vector(na.omit(filter(eps,rep(1,4)))),seq(as.Date(mondate(as.Date("2019-10-01"))-124*3),as.Date(mondate(as.Date("2019-10-01"))),by="quarters"))
             [,1]
1988-10-01  23.75
1989-01-01  24.96
1989-04-01  25.22
<...skip...>
2019-04-01 156.63
2019-07-01 160.71
2019-10-01 164.76

get UNDCONTSA data from www.census.gov

Download "the total number housing under construction" from here.

1. choose "New Residential Construction"
2. "1970 from 2018"
3. choose "Housing Units under Construction"
4. "Total Units" and "United States"

Open CSV file and chop unnecessary lines at header and footer. The result must be like below.

Period,Value

Jan-1970,889

Feb-1970,888

Mar-1970,890

<.........>

Mar-2018,1124

Apr-2018,1124

When file name is "~/iCloud/R/UC-1970-2018.csv" and contains data from 1970-04-01 to 2018-04-01.

as.xts(read.csv("~/iCloud/R/UC-1970-2018.csv")[,2],seq(as.Date("1970-01-01"),as.Date("2018-04-01"),by="months"))

The below calculate data length and the date of termination accordingly.

as.xts(read.csv("~/iCloud/R/UC-1970-2018.csv")[,2],as.Date(seq(mondate(as.Date("1970-01-01")),mondate(as.Date("1970-01-01"))-1+length(read.csv("~/iCloud/R/UC-1970-2018.csv")[,2]))))

           [,1]

1970-01-01  889

1970-02-01  888

1970-03-01  890

<snipet>

2018-03-01 1124

2018-04-01 1124

欧米経済の衰退と民主的世紀の終わり ―― 拡大する「権威主義的民主主義」の富とパワー

欧米経済の衰退と民主的世紀の終わり ――　拡大する「権威主義的民主主義」の富とパワー

Prepare Data and set parameters.

kg <- "1992-01-01::2018-03-31"
kikan <- "1992-01-01::2018-03-31"
k2k <- "2000-01-01::2018-03-31"   #GPUC model is based data only from 2000 because of cs index availability
# prepare parameters.
l <- 48  # # of months to predict
r <- 1.04 # pesumed GDP growth rate
i <- seq(2,l/3,1)  # seq of quarters to predict
d <- as.Date(as.yearqtr(seq(Sys.Date(),as.Date("2100-12-31"),by="quarters")[i])) # pick up the first day of each quarters.

getSymbols("GDP",src="FRED")
G <- GDP
m_GDP <- as.xts(as.vector(last(GDP)) * r**(i/4),d)

getSymbols("PAYEMS",src="FRED")
PA <- PAYEMS
m_PA <- (as.xts(forecast(auto.arima(PA),h=l)$mean[1:l],as.Date(as.yearmon(seq(mondate(index(last(PA)))+1,by=1,length.out=l))))[(3-month(index(last(PA))) %% 3) + seq(1,l-3,3)])[d]
PAq <- apply.quarterly(PA[k2k],mean)

getSymbols('SPCS10RSA',src='FRED')
CS <- SPCS10RSA
m_CS <-
(as.xts(forecast(auto.arima(CS),h=l)$mean[1:l],as.Date(as.yearmon(seq(mondate(index(last(CS)))+1,by=1,length.out=l))))[(3-month(index(last(CS))) %% 3) + seq(1,l-3,3)])[d]
CSq <- apply.quarterly(CS[k2k],mean)

getSymbols("UNDCONTSA",src="FRED")
# UC <- UNDCONTSA
# comment out here until UNDCONTSA update is resumed in FRED.
m_UC <- (as.xts(forecast(auto.arima(UC),h=l)$mean[1:l],as.Date(as.yearmon(seq(mondate(index(last(UC)))+1,by=1,length.out=l))))[(3-month(index(last(UC))) %% 3) + seq(1,l-3,3)])[d]
UCq <- apply.quarterly(UC[k2k],mean)