Sys.getenv() 環境

 1.　全体

>  Sys.getenv()

__CF_USER_TEXT_ENCODING             0x1F5:0x1:0xE
__CFBundleIdentifier                org.rstudio.RStudio
CLICOLOR_FORCE                      1
COMMAND_MODE                        unix2003
DISPLAY                             /private/tmp/com.apple.launchd.zbStA3g2rP/org.xquartz:0
DYLD_FALLBACK_LIBRARY_PATH          /Library/Frameworks/R.framework/Resources/lib:/Users/honomoto/lib:/usr/local/lib:/usr/lib:::/lib:/Library/Java/JavaVirtualMachines/jdk1.8.0_241.jdk/Contents/Home/jre/lib/server
EDITOR                              vi
GIT_ASKPASS                         rpostback-askpass
HOME                                /Users/honomoto
LANG                                en
LANGUAGE                            en
LC_CTYPE                            en
LC_TYPE                             en
LN_S                                ln -s
LOGNAME                             honomoto
MAKE                                make
MPLENGINE                           tkAgg
PAGER                               /usr/bin/less
PATH                                /usr/bin:/bin:/usr/sbin:/sbin:/usr/local/bin:/Library/TeX/texbin:/opt/X11/bin
R_BROWSER                           /usr/bin/open
R_BZIPCMD                           /usr/bin/bzip2
R_DOC_DIR                           /Library/Frameworks/R.framework/Resources/doc
R_GZIPCMD                           /usr/bin/gzip
R_HOME                              /Library/Frameworks/R.framework/Resources
R_INCLUDE_DIR                       /Library/Frameworks/R.framework/Resources/include
R_LIBS_SITE                         
R_LIBS_USER                         ~/Library/R/4.0/library
R_PAPERSIZE                         a4
R_PDFVIEWER                         /usr/bin/open
R_PLATFORM                          x86_64-apple-darwin17.0
R_PRINTCMD                          lpr
R_QPDF                              /Library/Frameworks/R.framework/Resources/bin/qpdf
R_RD4PDF                            times,inconsolata,hyper
R_SESSION_TMPDIR                    /var/folders/yw/p0wwlz5r8xj4zf001s7n3j780000gn/T//RtmppzWaAC
R_SHARE_DIR                         /Library/Frameworks/R.framework/Resources/share
R_STRIP_SHARED_LIB                  strip -x
R_STRIP_STATIC_LIB                  strip -S
R_SYSTEM_ABI                        macos,gcc,gxx,gfortran,gfortran
R_TEXI2DVICMD                       /usr/local/bin/texi2dvi
R_UNZIPCMD                          /usr/bin/unzip
R_ZIPCMD                            /usr/bin/zip
RMARKDOWN_MATHJAX_PATH              /Applications/RStudio.app/Contents/Resources/resources/mathjax-26
RS_PPM_FD_READ                      9
RS_PPM_FD_WRITE                     10
RS_RPOSTBACK_PATH                   /Applications/RStudio.app/Contents/MacOS/rpostback
RS_SHARED_SECRET                    19408203731238894728102252184
RSTUDIO                             1
RSTUDIO_CONSOLE_COLOR               256
RSTUDIO_CONSOLE_WIDTH               123
RSTUDIO_PANDOC                      /Applications/RStudio.app/Contents/MacOS/pandoc
RSTUDIO_SESSION_PORT                25883
RSTUDIO_USER_IDENTITY               honomoto
RSTUDIO_WINUTILS                    bin/winutils
SED                                 /usr/bin/sed
SHELL                               /bin/bash
SSH_ASKPASS                         rpostback-askpass
SSH_AUTH_SOCK                       /private/tmp/com.apple.launchd.RiShkX8Usr/Listeners
TAR                                 /usr/bin/tar
TERM                                xterm-256color
TMPDIR                              /var/folders/yw/p0wwlz5r8xj4zf001s7n3j780000gn/T/
TZDIR                               macOS
USER                                honomoto
XPC_FLAGS                           0x0
XPC_SERVICE_NAME                    application.org.rstudio.RStudio.262356.262643

2. 日本語

LANG                                ja_JP.UTF-8

LANGUAGE                            jp 

LC_CTYPE                            ja_JP.UTF-8

3. Locale

> Sys.getlocale()

[1] "ja_JP.UTF-8/ja_JP.UTF-8/ja_JP.UTF-8/C/ja_JP.UTF-8/ja_JP.UTF-8"

>   Sys.setlocale("LC_TIME","C")

[1] "C"

>     Sys.getlocale("LC_TIME", "ja_JP.UTF-8/ja_JP.UTF-8/ja_JP.UTF-8/C/ja_JP.UTF-8/ja_JP.UTF-8")

>     system("locale")

LANG="en"

LC_COLLATE="C"

LC_CTYPE="C"

LC_MESSAGES="C"

LC_MONETARY="C"

LC_NUMERIC="C"

LC_TIME="C"

LC_ALL=

> system("locale")

LANG="ja_JP.UTF-8"

LC_COLLATE="ja_JP.UTF-8"

LC_CTYPE="ja_JP.UTF-8"

LC_MESSAGES="ja_JP.UTF-8"

LC_MONETARY="ja_JP.UTF-8"

LC_NUMERIC="ja_JP.UTF-8"

LC_TIME="ja_JP.UTF-8"

LC_ALL=

4.　日本語環境サンプル

system("locale")
LANG="ja_JP.UTF-8"
LC_COLLATE="ja_JP.UTF-8"
LC_CTYPE="ja_JP.UTF-8"
LC_MESSAGES="ja_JP.UTF-8"
LC_MONETARY="ja_JP.UTF-8"
LC_NUMERIC="ja_JP.UTF-8"
LC_TIME="ja_JP.UTF-8"
LC_ALL=

Personal income forecast 個人所得予測 個人所得 personalincome

 2021MAR09

Sys.setlocale("LC_TIME","C")

data.frame(t=(PI  %>% last(.,8) %>%  index() %>% format(., "%b %Y")), d=(PI  %>% last(.,8) %>%  as.vector()))

Sys.setlocale("LC_TIME", "ja_JP.UTF-8") 

1 Jun 2020 20032.7

2 Jul 2020 20173.9

3 Aug 2020 19624.3

4 Sep 2020 19762.2

5 Oct 2020 19627.8

6 Nov 2020 19386.2

7 Dec 2020 19499.2

8 Jan 2021 21453.9

2020JUL31

1 Nov 2020 19435.0

2 Dec 2020 19562.2

3 Jan 2021 21504.5

4 Feb 2021 19955.1

5 Mar 2021 24142.4

6 Apr 2021 20853.2

7 May 2021 20388.2

8 Jun 2021 20414.3

2020SEP15

1 Dec 2020 19562.2

2 Jan 2021 21504.5

3 Feb 2021 19955.1

4 Mar 2021 24142.4

5 Apr 2021 20848.2

6 May 2021 20404.6

7 Jun 2021 20441.8

8 Jul 2021 20667.7

then,

Jun 2020     20032.7

Jul 2020     20173.9

Aug 2020     19624.3

Sep 2020     19762.2

Oct 2020     19628.7

Nov 2020     19386.3

Dec 2020     19491.3

Jan 2021     21462.2

Feb 2021     19945.6

---------------------

Mar 2021     20120 (2021 MAR 21242.06)
Apr 2021     19995
May 2021     20066
Jun 2021     20185
Jul 2021     20254
Aug 2021     20316
Sep 2021     20369

2021MAR31

Feb 2021 19945.6

-------------------------

Mar 2021 20840 (2021 MAR 21242.06)

Apr 2021 20150

May 2021 20066

Jun 2021 20185

Jul 2021 20254

Aug 2021 20316

Sep 2021 20369

Oct 2021 20423

2021JUL31

May 2021 20804.2 Jun 2021 20659 Jul 2021 20733 Aug 2021 20919 Sep 2021 21229 Oct 2021 21419 Nov 2021 21106 Dec 2021 21261 Jan 2022 21404

2021SEP15

Aug 2021 20740 ±34 Sep 2021 21229 ±39 Oct 2021 21419 ±42 Nov 2021 21106 ±44 Dec 2021 21261 ±46 Jan 2022 21404 ±47 Feb 2022 21469 ±48 Mar 2022 21553 ±49

EPS 2021FEB26

 

eps_year_xts["2020::"]

             [,1]

2020-01-01 116.33
2020-04-01  99.23
2020-07-01  98.22
2020-10-01  96.45
2021-01-01 119.73
2021-04-01 139.51
2021-07-01 146.75
2021-10-01 154.78

$    tac eps.txt | awk '{gsub("\\$","",$NF);print "eps_year_xts[\"2019::\"]["NR"] <- "$NF}'

eps_year_xts["2019::"][1] <- 134.39

eps_year_xts["2019::"][2] <- 135.27

eps_year_xts["2019::"][3] <- 132.90

eps_year_xts["2019::"][4] <- 139.47

eps_year_xts["2019::"][5] <- 116.33

eps_year_xts["2019::"][6] <- 99.23

eps_year_xts["2019::"][7] <- 98.22

eps_year_xts["2019::"][8] <- 91.15

eps_year_xts["2019::"][9] <- 114.36

eps_year_xts["2019::"][10] <- 133.35

eps_year_xts["2019::"][11] <- 141.08

eps_year_xts["2019::"][12] <- 155.56

ゼロサプレス zero supress bash awk split 文字列分割

 bash

$ seq -f %03g 1 10

001
002
003
004
005
006
007
008
009
010

ループ  loop

for i in `seq -f %03g 1 90`; do wget https://blog-imgs-77-origin.fc2.com/u/r/u/urutoraerogazou/matsushima-kaede-166-$i.jpg ; done

for i in `seq 414 453`; do wget  https://blog-imgs-76-origin.fc2.com/d/e/n/densetsuav/1543tachibanarikopin-$i.jpg ; done

二重ループ　double loop サンプル

for j in `seq 1 34`
    do for i in `seq 1 12`
      # do wget -r https://javtube.com/javpic/ameri-ichinose/$j/ameri-ichinose-$i.jpg
      do wget -r https://jjgirls.com/japanese/rin-aikawa/$j/rin-aikawa-$i.jpg
  done
done

R

 for(i in seq(1,47,1)){ colnames(mdf)[i] <-  (paste(sprintf("%02d",i),colnames(mdf)[i],sep=""))}

sprintf("%.2f",100.0111)

[1] "100.01"

sprintf("%.3f",100.0111)

[1] "100.011"

sprintf("%.5f",100.0111)

[1] "100.01110"

sprintf("%.6f",100.0111)

[1] "100.011100"

awk

awk '{$3=sprintf("%02d", $3);

find . -print | awk -F\. '{print $2}' | grep k | awk -F\/ '{print $3"-"$2}' | awk '{split($0,a,"-"); print "cp \.\/"a[4]"\/"a[1]"-"a[2]"-"a[3]".jpg  ~\/tmpimage\/kaede\/jjgirls.com\/"a[1]a[2]a[4]"-"a[3]".jpg"}' > kaedecp.txt

split($0,a,"-")で行全体を-で区切って分割する。分割した各要素はa[i]でアクセスできる。

append new entries to xts object.

 

last(tmp.predict,3)

        SP5.Open SP5.High  SP5.Low SP5.Close   SP5.Volume   spline      eps
10 2020 3385.870 3549.850 3233.940  3269.960  89737600000 3475.842 3450.187
11 2020 3296.200 3645.990 3279.740  3621.630 100977880000 3465.431 3396.378
12 2020 3645.870 3760.200 3633.400  3756.070  96056410000 3420.055 3238.727

create the entry for "2021-01-01". first to create single row matrix and convert to the xts.

as.xts(matrix(c(as.vector(apply.monthly(SP5["2021-01"],mean)),3395,3180),nrow=1),as.Date("2021-01-01"))[,-5]

                    [,1]          [,2]            [,3]             [,4]       [,5]                 [,6]    [,7]
2021-01-01 3797.387 3818.136 3768.962 3793.748 5555199474 3395 3180

run append.

append(tmp.predict,as.xts(matrix(c(as.vector(apply.monthly(SP5["2021-01"],mean)),3395,3180),nrow=1),as.Date("2021-01-01"))[,-5]) %>% last(.,6)

        SP5.Open SP5.High  SP5.Low SP5.Close   SP5.Volume   spline      eps
 8 2020 3288.260 3514.770 3284.530  3500.310  84402300000 3270.029 3340.749
 9 2020 3507.440 3588.110 3209.450  3363.000  92084120000 3416.319 3423.604
10 2020 3385.870 3549.850 3233.940  3269.960  89737600000 3475.842 3450.187
11 2020 3296.200 3645.990 3279.740  3621.630 100977880000 3465.431 3396.378
12 2020 3645.870 3760.200 3633.400  3756.070  96056410000 3420.055 3238.727
 1 2021 3797.387 3818.136 3768.962  3793.748   5555199474 3395.000 3180.000

EPS 2021FEB05

 

>  eps_year_xts["2020::"]

             [,1]

2020-01-01 116.33
2020-04-01  99.23
2020-07-01  96.47
2020-10-01  92.18
2021-01-01 106.60
2021-04-01 125.68
2021-07-01 132.68
2021-10-01 140.24

$ tac eps.txt | awk '{gsub("\\$","",$NF);print "eps_year_xts[\"2019::\"]["NR"] <- "$NF}'

eps_year_xts["2019::"][1] <- 134.39

eps_year_xts["2019::"][2] <- 135.27

eps_year_xts["2019::"][3] <- 132.90

eps_year_xts["2019::"][4] <- 139.47

eps_year_xts["2019::"][5] <- 116.33

eps_year_xts["2019::"][6] <- 99.23

eps_year_xts["2019::"][7] <- 98.22

eps_year_xts["2019::"][8] <- 96.45

eps_year_xts["2019::"][9] <- 119.73

eps_year_xts["2019::"][10] <- 139.51

eps_year_xts["2019::"][11] <- 146.75

eps_year_xts["2019::"][12] <- 154.78

R 4.0 ggplot

scale_fill_hue(name='regions',labels= as.character(unique(w[,5])) )

ラベルの使用順序はscale で明示すること。これがないとalphabeticalにソートされてしまう。

上記だけをやると列名とラベルが不整合を起こすので、列名にも数字を先頭に付加してソート順序を制御しなくてはならない。

  for(i in seq(1,47,1)){ colnames(mdf)[i] <-  (paste(sprintf("%02d",i),colnames(mdf)[i],sep=""))}

reshape2はもう使えない。したがってmelt() も使えない。

melt(data=mdf, id.vars="t", measure.vars=as.character(unique(w[,5])))　は

  df.melt <- mdf  %>% tidyr::gather(variable,value,as.character(colnames(mdf)[-48]))

になる。

以下をインストールしておくこと。

• tidyr                                Tidy Messy Data
• dplyr                                A Grammar of Data Manipulation

unloadするときは。

detach("package:dplyr", unload=TRUE)
detach("package:tidyr", unload=TRUE)

EPS 2020NOV14

 > eps_year_xts["2020::"]
             [,1]
2020-01-01 116.33
2020-04-01  99.23
2020-07-01  93.03
2020-10-01  87.16
2021-01-01 101.75
2021-04-01 119.53
2021-07-01 128.19
2021-10-01 135.75

$ tac eps.txt | awk '{gsub("\\$","",$NF);print "eps_year_xts[\"2019::\"]["NR"] <- "$NF}'

eps_year_xts["2019::"][1] <- 134.39

eps_year_xts["2019::"][2] <- 135.27

eps_year_xts["2019::"][3] <- 132.90

eps_year_xts["2019::"][4] <- 139.47

eps_year_xts["2019::"][5] <- 116.33

eps_year_xts["2019::"][6] <- 99.23

eps_year_xts["2019::"][7] <- 96.47

eps_year_xts["2019::"][8] <- 92.18

eps_year_xts["2019::"][9] <- 106.60

eps_year_xts["2019::"][10] <- 125.68

eps_year_xts["2019::"][11] <- 132.68

eps_year_xts["2019::"][12] <- 140.24

EPS 2020OCT25

 

>   eps_year_xts["2020::"]
             [,1]
2020-01-01 116.33
2020-04-01  99.16
2020-07-01  93.44
2020-10-01  89.68
2021-01-01 110.65
2021-04-01 129.66
2021-07-01 139.84
2021-10-01 148.29

$   tac eps.txt | awk '{gsub("\\$","",$NF);print "eps_year_xts[\"2019::\"]["NR"] <- "$NF}'

eps_year_xts["2019::"][1] <- 134.39

eps_year_xts["2019::"][2] <- 135.27

eps_year_xts["2019::"][3] <- 132.90

eps_year_xts["2019::"][4] <- 139.47

eps_year_xts["2019::"][5] <- 116.33

eps_year_xts["2019::"][6] <- 99.23

eps_year_xts["2019::"][7] <- 93.03

eps_year_xts["2019::"][8] <- 87.16

eps_year_xts["2019::"][9] <- 101.75

eps_year_xts["2019::"][10] <- 119.53

eps_year_xts["2019::"][11] <- 128.19

eps_year_xts["2019::"][12] <- 135.75

for reference

$    cat eps.txt 

12/31/2021 $44.69 $37.23 21.02 25.44 $164.30 $135.75

9/30/2021 $42.32 $36.45 22.20 26.94 $155.59 $128.19

6/30/2021 $40.10 $35.60 23.54 28.89 $146.68 $119.53

3/31/2021 $37.19 $26.47 25.89 33.94 $133.37 $101.75

12/31/2020 $35.98 $29.66 29.85 39.62 $115.68 $87.16

9/30/2020 (25.0%) 3363.00 $33.41 $27.79 29.05 37.12 $118.88 $93.03

6/30/2020 3100.29 $26.79 $17.83 24.75 31.24 $125.28 $99.23

3/31/2020 2584.59 $19.50 $11.88 18.64 22.22 $138.63 $116.33

12/31/2019 3230.78 $39.18 $35.53 20.56 23.16 $157.12 $139.47

9/30/2019  2976.74 $39.81 $33.99 19.46 22.40 $152.97 $132.90

6/30/2019 2941.76 $40.14 $34.93 19.04 21.75 $154.54 $135.27

3/31/2019 2834.40 $37.99 $35.02 18.52 21.09 $153.05 $134.39

awk tr sed mondate CLI download

1. download cli from this url 

1.1 change downloaded file name to CLI3.csv

1.2 ~/R/R2/index/cli_download.sed  run at the terminal.

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

# 

sed -n '/CHN/p' CLI3.csv |awk -F, '{print $6"-01,"$7}'  |sed 's/\"//g' |awk 'BEGIN{print "DATE,DATA"}{print $0}' > chn.csv

# 

sed -n '/EA19/p' CLI3.csv |awk -F, '{print $6"-01,"$7}'  |sed 's/\"//g' |awk 'BEGIN{print "DATE,DATA"}{print $0}' > ea19.csv

#

sed -n '/JPN/p' CLI3.csv |awk -F, '{print $6"-01,"$7}'  |sed 's/\"//g' |awk 'BEGIN{print "DATE,DATA"}{print $0}' > jpn.csv

1.3 run cli_load.r in R.

2.when cli is downloaded form this url 

2.1. do as below in terminal change FILE_NAME accordingly.

grep "OECD - Total" <FILE_NAME>.csv  | grep LOLITOAA |gawk -F, 'BEGIN{print "c("}{print $(NF-2)","}END{print ")  " }' | tr -d "\n" | sed 's/,)/)/'

output is like 

c(100.1448,100.0088,99.88226,99.7745,99.68903,99.62392,99.56622,99.5041,99.44289,99.39536,99.37106,99.38042,99.41794,99.46317,99.49193,99.47968,99.41767,97.69222,93.17708,94.81512,97.03869,98.08919,98.53291,98.79954) 

> length(c(100.1448,100.0088,99.88226,99.7745,99.68903,99.62392,99.56622,99.5041,99.44289,99.39536,99.37106,99.38042,99.41794,99.46317,99.49193,99.47968,99.41767,97.69222,93.17708,94.81512,97.03869,98.08919,98.53291,98.79954) )

[1] 24

# start and end dates are fixed. caution!

seq( as.Date(mondate(Sys.Date())-24)-day(mondate(Sys.Date()))+1,as.Date(mondate(Sys.Date())-1),by='months' )

>seq( as.Date(mondate(Sys.Date())-24)-day(mondate(Sys.Date()))+1,as.Date(mondate(Sys.Date())-1),by='months' )

 [1] "2018-10-01" "2018-11-01" "2018-12-01" "2019-01-01" "2019-02-01" "2019-03-01" "2019-04-01" "2019-05-01" "2019-06-01"

[10] "2019-07-01" "2019-08-01" "2019-09-01" "2019-10-01" "2019-11-01" "2019-12-01" "2020-01-01" "2020-02-01" "2020-03-01"

[19] "2020-04-01" "2020-05-01" "2020-06-01" "2020-07-01" "2020-08-01" "2020-09-01"

 

2.2. do as below in R

as.xts(c(100.1448,100.0088,99.88226,99.7745,99.68903,99.62392,99.56622,99.5041,99.44289,99.39536,99.37106,99.38042,99.41794,99.46317,99.49193,99.47968,99.41767,97.69222,93.17708,94.81512,97.03869,98.08919,98.53291,98.79954), seq( as.Date(mondate(Sys.Date())-24)-day(mondate(Sys.Date()))+1,as.Date(mondate(Sys.Date())-1),by='months' ))

2.3. alternatively do as below 

grep "OECD - Total" <FILE_NAME>.csv  | grep LOLITOAA |gawk -F, 'BEGIN{print "as.xts(c("}{print $(NF-2)","}END{print "), seq( as.Date(mondate(Sys.Date())-24)-day(mondate(Sys.Date()))+1,as.Date(mondate(Sys.Date())-1),by=\"months\" ))  " }' | tr -d "\n" | sed 's/,)/)/'

Enable GingerCannot connect to Ginger Check your internet connection
or reload the browserDisable in this text fieldEditEdit in GingerEdit in Ginger×Enable GingerCannot connect to Ginger Check your internet connection
or reload the browserDisable in this text fieldEditEdit in GingerEdit in Ginger×Enable GingerCannot connect to Ginger Check your internet connection
or reload the browserDisable in this text fieldEditEdit in GingerEdit in Ginger×Enable GingerCannot connect to Ginger Check your internet connection
or reload the browserDisable in this text fieldEditEdit in GingerEdit in Ginger×

mac addr DHCP LAN

 

• 58:E2:8F:22:5F:C0       iphone se
• F8:4E:73:02:B7:6C      (iPhone11onomoto) via br0 ?
• 34:A8:EB:81:58:4A       ipad mini
• F8:FF:C2:65:B5:0C       macbook pro 16inch
• 78:4F:43:98:2F:8C       macbook pro 15inch
• 00:1E:C2:A7:D3:EB      macbook kuro
• 18:C2:BF:16:51:90      LS5110 NAS  wired LAN
• 82:BA:85:04:AB:ED      iphone 11 pro
• 74:75:48:1E:CD:4F       kindle  from mac address
• 00:F7:6F:CF:4E:E6 airmac express
• 58:55:CA:30:E6:4C      apple tv
• 50:c4:dd:e9:70:b0        router

身元不明macアドレス

• 192.168.11.102        74:75:48:1E:CD:4F       31:15:20        kindle 
• 192.168.11.110 72:8C:56:0F:7B:73

Enable GingerCannot connect to Ginger Check your internet connection
or reload the browserDisable in this text fieldEditEdit in GingerEdit in Ginger×Enable GingerCannot connect to Ginger Check your internet connection
or reload the browserDisable in this text fieldEditEdit in GingerEdit in Ginger×Enable GingerCannot connect to Ginger Check your internet connection
or reload the browserDisable in this text fieldEditEdit in GingerEdit in Ginger×Enable GingerCannot connect to Ginger Check your internet connection
or reload the browserDisable in this text fieldEditEdit in GingerEdit in Ginger×Enable GingerCannot connect to Ginger Check your internet connection
or reload the browserDisable in this text fieldEditEdit in GingerEdit in Ginger×Enable GingerCannot connect to Ginger Check your internet connection
or reload the browserDisable in this text fieldEditEdit in GingerEdit in Ginger×

bash for and while

 

全てのcsvを0 length　にする。

$  for i in *.csv ; do  cat /dev/null >  $i; done

無限ループ。「while : (コロン）」で無限ループになる。

$  while : ; do echo 'hello'; sleep 10 ; done

Enable GingerCannot connect to Ginger Check your internet connection
or reload the browserDisable in this text fieldEditEdit in GingerEdit in Ginger×

行列の計算 matrix()

 

> 1:15
 [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15

> matrix(1:15,ncol=3)
     [,1] [,2] [,3]
[1,]    1    6   11
[2,]    2    7   12
[3,]    3    8   13
[4,]    4    9   14
[5,]    5   10   15

> diff(matrix(1:15,ncol=3))
     [,1] [,2] [,3]
[1,]    1    1    1
[2,]    1    1    1
[3,]    1    1    1
[4,]    1    1    1

> c(1:7,NA,9:15)

 [1]  1  2  3  4  5  6  7 NA  9 10 11 12 13 14 15

> w <- matrix(c(1:7,NA,9:15), ncol=5)

> w

     [,1] [,2] [,3] [,4] [,5]

[1,]    1    4    7   10   13

[2,]    2    5   NA   11   14

[3,]    3    6    9   12   15

> v <- as.vector(w)

> v[is.na(v)]

[1] NA

> v[is.na(v)] <- 8

> matrix(v,ncol=dim(w)[2])

     [,1] [,2] [,3] [,4] [,5]

[1,]    1    4    7   10   13

[2,]    2    5    8   11   14

[3,]    3    6    9   12   15

> w <- matrix(v,ncol=dim(w)[2])

> w

     [,1] [,2] [,3] [,4] [,5]

[1,]    1    4    7   10   13

[2,]    2    5    8   11   14

[3,]    3    6    9   12   15

Enable GingerCannot connect to Ginger Check your internet connection
or reload the browserDisable in this text fieldEditEdit in GingerEdit in Ginger×

EPS 2020SEP01

 

> eps_year_xts["2020::"]
             [,1]
2020-01-01 116.33
2020-04-01 100.29
2020-07-01  94.83
2020-10-01  92.54
2021-01-01 112.31
2021-04-01 128.51
2021-07-01 138.90
2021-10-01 146.96

$  tac eps.txt | awk '{gsub("\\$","",$NF);print "eps_year_xts[\"2019::\"]["NR"] <- "$NF}'
eps_year_xts["2019::"][1] <- 134.39
eps_year_xts["2019::"][2] <- 135.27
eps_year_xts["2019::"][3] <- 132.90
eps_year_xts["2019::"][4] <- 139.47
eps_year_xts["2019::"][5] <- 116.33
eps_year_xts["2019::"][6] <- 99.16
eps_year_xts["2019::"][7] <- 93.44
eps_year_xts["2019::"][8] <- 89.68
eps_year_xts["2019::"][9] <- 110.65
eps_year_xts["2019::"][10] <- 129.66
eps_year_xts["2019::"][11] <- 139.84
eps_year_xts["2019::"][12] <- 148.29

Enable GingerCannot connect to Ginger Check your internet connection
or reload the browserDisable in this text fieldEditEdit in GingerEdit in Ginger×