Purpose of this document

This document describes the process for updating Ecdat::USGDPpresidents.

Set working directory

First decide the directory in which we want to work and copy this vignette (*.Rmd file) into that directory. (RStudio does not allow setwd inside code chunks to work as one might naively expect. Therefore, it’s best NOT to try to change the working directory but instead to copy this vignette into the desired working directory.)

Are there new data?

Start by checking the span of years in USGDPpresidents:

library(Ecdat)
## Loading required package: Ecfun
## 
## Attaching package: 'Ecfun'
## The following object is masked from 'package:base':
## 
##     sign
## 
## Attaching package: 'Ecdat'
## The following object is masked from 'package:datasets':
## 
##     Orange
(rngYrs <- range(USGDPpresidents$Year))
## [1] 1610 2021

Next download “GDP - US” and “CPI - US” from Measuring Worth. On 2022-02-16 this produced two csv files, which I downloaded and copied into a directory in which we wish to work.

getwd()
## [1] "/private/var/folders/2n/zqk768wj3818l8x2wttbc5kw0000gn/T/Rtmpv2IV55/Rbuild3fa46c9e7873/Ecfun/vignettes"
(csv2 <- dir(pattern='\\.csv$'))
## character(0)
(CPIcsvs <- grep('^USCPI', csv2, value=TRUE))
## character(0)
(CPIcsv <- tail(CPIcsvs, 1))
## character(0)
(GDPcsvs <- grep('^USGDP', csv2, value=TRUE))
## character(0)
(GDPcsv <- tail(GDPcsvs, 1))
## character(0)
if((length(CPIcsv)==1) & (length(GDPcsv)==1)){
  Update0 <- TRUE
} else Update0 <- FALSE

We must verify by visual inspection that CPIcsv and GDPcsv are both of length 1 and are the files we want.

Read them:

Update <- FALSE
if(Update0){
  str(USCPI <- read.csv(CPIcsv, skip=2))
  str(USGDP. <- read.csv(GDPcsv, skip=1))
  library(Ecfun)
  USGDP <- asNumericDF(USGDP.)
  print(rngCPIyrs <- range(USCPI$Year) )
  print(rngGDPyrs <- range(USGDP$Year) )
  endYr <- max(rngCPIyrs, rngGDPyrs)
  if(endYr>rngYrs[2]) print(Update <- TRUE)
}

Update

If Update, create a local copy of USGDPpresidents with the additional rows required to hold the new data:

if(Update){
  rowsNeeded <- (endYr - rngYrs[2])
  Nold <- nrow(USGDPpresidents)
  iRep <- c(1:Nold, rep(Nold, rowsNeeded))
  USGDPp2 <- USGDPpresidents[iRep,]
}

Fix the Year and insert NAs for all other columns for the new rows:

if(Update){
  iNew <- (Nold+(1:rowsNeeded))
  USGDPp2$Year[iNew] <- ((rngYrs[2]+1):endYr)
  rownames(USGDPp2) <- USGDPp2$Year
#
  USGDPp2[iNew, -1] <- NA
}

Now replace CPI by the new numbers:

if(Update){
  selCPI <- (USGDPp2$Year %in% USCPI$Year)
  if(any(!is.na(USGDPp2[!selCPI, 2]))){
    stop('ERROR:  There are CPI numbers ', 
         'in the current USGDPpresidents ', 
         'that are not in the new.  ', 
         'Manual review required.')
  }
  USGDPp2$CPI[selCPI] <- USCPI[,2]
}

Does USGDPpresidents.Rd needs to be updated to reflect the proper reference years for the CPI?

if(Update){
  readLines(CPIcsv, n=4)
}

If this says “Average 1982-84 = 100”, it should be good. Otherwise that (and this) should be updated.

Now let’s update GDPdeflator:

if(Update){
  selGDP <- (USGDPp2$Year %in% USGDP$Year)
#
  if(any(!is.na(USGDPp2[!selGDP, 'GDPdeflator']))){
    stop('ERROR:  There are GDPdeflator numbers ', 
         'in the current USGDPpresidents ', 
         'that are not in the new.  ', 
         'Manual review required.')
  }
  selDefl <- grep('Deflator', names(USGDP))
  USGDPp2$GDPdeflator[selGDP] <- USGDP[,selDefl]
  print(names(USGDP)[selDefl])
}

Compare the index year of “GDP.Deflator” with that in USGDPpresidents.Rd: If they are different, fix USGDPpresidents.Rd.

Now update population:

if(Update){
  selPop <- grep('Population', names(USGDP))
  USGDPp2$population.K[selGDP] <- USGDP[,selPop]
  print(names(USGDP)[selPop])
}

Now realGDPperCapita. This also has a reference year, so we need to make sure we get them all:

if(Update){
  if(any(!is.na(USGDPp2[!selGDP, 'readGDPperCapita']))){
    stop('ERROR:  There are realGDPperCapita numbers ', 
         'in the current USGDPpresidents ', 
         'that are not in the new.  ', 
         'Manual review required.')
  }
  selGDPperC <- grep('Real.GDP.per.c', names(USGDP))
  USGDPp2$realGDPperCapita[selGDP] <- USGDP[,selGDPperC]
  print(names(USGDP)[selGDPperC])
}

Compare the index year of Real.GDP.per.capita with that in USGDPpresidents.Rd: If they are different, fix USGDPpresidents.Rd.

Next: executive:

NOTE: THIS MAY NEED TO BE CHANGED MANUALLY HERE BEFORE EXECUTING, BECAUSE IT IS NOT IN USGDP… BOTH: ** WHO WAS PRESIDENT SINCE THE PREVIOUS VERSION? ** WAS THAT PERSON NOT IN THE PREVIOUS VERSION?

if(Update){
  exec <- as.character(USGDPp2$executive)
  exec[is.na(exec)] <- c('Trump', 'Trump', 'Biden')
  lvlexec <- c(levels(USGDPp2$executive), 
               'Biden')
  USGDPp2$executive <- ordered(exec, lvlexec)
}

Similarly: war

NOTE: IF THERE HAS BEEN A MAJOR WAR SINCE THE LAST VERSION, THEN THIS TEXT NEEDS TO BE CHANGED, BECAUSE IT ASSUMES THERE HAS NOT BEEN A MAJOR WAR.

if(Update){
  war <- as.character(USGDPp2$war)
  war[is.na(war)] <- ''
  lvlwar <- levels(USGDPp2$war)
  USGDPp2$war <- ordered(war, lvlwar)
}

Next: battleDeaths and battleDeathsPMP:

NOTE: battleDeaths ARE ONLY BATTLE DEATHS IN MAJOR WARS as defined in help(USGDPpresidents).
Otherwise, they are 0.

if(Update){
  USGDPp2$battleDeaths[iNew] <- 0 
#
  USGDPp2$battleDeathsPMP <- with(USGDPp2, 
          1000*battleDeaths/population.K) 
}

Keynes (per help(USGDPpresidents)):

if(Update){
  USGDPp2$Keynes[iNew] <- 0 
}

Unemployment?

Unemployment figures came from different sources for different years. Since 1940 the source has been the Bureau of Labor Statistics (BLS), series LNS14000000 from the Current Population Survey. These data are available as a monthly series from the Current Population Survey of the Bureau of Labor Statistics.
Download the most recent years as an Excel file, compute row averages, and transfer the numbers for the most recent years here.

NOTE: When I did that on 2022-02-22 I found minor discrepancies in earlier years. Pushing this further I found that I could download data back to 1948. The average unemployment per this BLS computation for 1948 and 1947 were 3.75 and 6.05 percent, respectively, vs. 0.038 and 0.059 in the previous version of USGDPpresidents for those years. I therefore decided to read that *.xlsx file and replace all those numbers.

if(Update){
  (xls <- dir(pattern='\\.xlsx$'))
  (BLSxls <- grep('^Series', xls, value=TRUE))
}
library(readxl)
if(Update){
  str(BLS <- read_excel(BLSxls, skip=11))
}

Compute the average unemployment here, so I don’t have to do this separately.

if(Update){
  UNEMP <- as.matrix(BLS[2:13])
  str(unemp <- apply(UNEMP, 1, mean))
}

Store these unemp numbers

if(Update){
  selU4GDP <- (USGDPp2$Year %in% BLS$Year)
  selBLS <- (BLS$Year %in% USGDPp2$Year)
  USGDPp2[selU4GDP, 'unemployment'] <- 
          unemp[selBLS]
#  USGDPp2$unemployment[iNew] <- c(4.875, 
#                    4.35, 3.89166666666667)
  USGDPp2$unempSource[iNew] <- USGDPp2$unempSource[
    iNew[1]-1]
  tail(USGDPp2)
}

fedReceipts, fedOutlays

We get fedReceipts and fedOutlays from two different sources. Let’s start with the historical data first.

We manually copied the historical data from series Y 335 and 336 in United States Census Bureau (1975) Bicentennial Edition: Historical Statistics of the United States, Colonial Times to 1970, Part 2. Chapter Y. Government into a LibreOffice *.ods file. We need to read that once and add it to USGDPp:

if(Update){
  (odsFile <- dir(pattern='\\.ods'))
  (odsF <- grep('^hstat', odsFile, value=TRUE))
}
if(Update){
  library(readODS)
  str(hstat <- read_ods(odsF, sheet='Receipts', skip=2))
}
if(Update){
  Hstat <- hstat[!is.na(hstat$Year), 1:3]
  oOld <- order(Hstat$Year)
  head(Hst <- Hstat[oOld, ])
}

Add as new variables to USGDPp2:

if(Update){
  USGDPp2$fedReceipts <- NA 
  USGDPp2$fedOutlays <- NA
  selGDP4Hst <- (USGDPp2$Year %in% Hst$Year)
  USGDPp2[selGDP4Hst, c("fedReceipts", "fedOutlays")] <- 
      (Hst[2:3] / 1000)
  USGDPp2[c('Year', 'fedReceipts', 'fedOutlays')]
}

New let’s add the new data.

if(Update){
  (BudgetFiles <- grep('^BUDGET', xls, value=TRUE))
  (BudgetF2_1 <- grep('2-1', BudgetFiles, value=TRUE))
  (BudgetFile <- tail(BudgetF2_1, 1))
}
if(Update){
  str(Budget <- read_excel(BudgetFile, skip=3))
}
if(Update){
  library(Ecfun)
  str(Budg <- asNumericDF(Budget[-(1:2), 1:3]))
}
if(Update){
  selGDP4budg <- (USGDPp2$Year %in% Budg[, 1])
  selBudg <- (Budg[, 1] %in% USGDPp2$Year)
  USGDPp2[selGDP4budg, 
    c('fedReceipts', 'fedOutlays')] <- Budg[selBudg, 2:3]
}

Finally: fedOutlays_pGDP

if(Update){
  sum(i1843 <- (USGDP$Year==1843))
  GDPnom <- (USGDP$Nominal.GDP..million.of.Dollars.
          / (1+i1843))
  plot(USGDP$Year, GDPnom, type='l', log='y')
  abline(v=1843)

  fedOp <- (USGDPp2$fedOutlays[selGDP] / GDPnom)
  plot(USGDP$Year, fedOp, type='l', log='y')

  USGDPp2$fedOutlays_pGDP <- NA
  USGDPp2$fedOutlays_pGDP[selGDP] <- fedOp
}

Plot US federal outlays

if(Update){
  USGDPpresidents <- USGDPp2

  sel <- !is.na(USGDPpresidents$fedOutlays_pGDP)
  plot(100*fedOutlays_pGDP~Year, 
     USGDPpresidents[sel,], type='l', log='y', 
     xlab='', ylab='US federal outlays, % of GDP')
  abline(h=2:3)
  war <- (USGDPpresidents$war !='')
  abline(v=USGDPpresidents$Year[war], 
    lty='dotted', col='light gray')
  abline(v=c(1929, 1933), col='red', lty='dotted')
  text(1931, 22, 'Hoover', srt=90, col='red')
}

Done: Save

if(Update){
  save(USGDPpresidents, file='USGDPpresidents.rda')
  getwd()
}

Now copy this file from the current working directory to ~Ecdat\data, overwriting the previous version.