knitr::opts_chunk$set(echo = TRUE)
This document describes the process for updating Ecdat::USGDPpresidents
.
First decide the directory in which we want to work and copy this vignette (*.Rmd
file) into that directory. (RStudio
does not allow setwd
inside code chunks to work as one might naively expect. Therefore, it's best NOT to try to change the working directory but instead to copy this vignette into the desired working directory.)
Start by checking the span of years in USGDPpresidents
:
library(Ecdat) (rngYrs <- range(USGDPpresidents$Year))
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() (csv2 <- dir(pattern='\\.csv$')) (CPIcsvs <- grep('^USCPI', csv2, value=TRUE)) (CPIcsv <- tail(CPIcsvs, 1)) (GDPcsvs <- grep('^USGDP', csv2, value=TRUE)) (GDPcsv <- tail(GDPcsvs, 1)) if((length(CPIcsv)==1) & (length(GDPcsv)==1)){ Update0 <- TRUE } else Update0 <- FALSE
We must verify by visual inspection that CPIcsv
and GDPcsv
are both of length 1 and are the files we want.
Read them:
Update <- FALSE if(Update0){ str(USCPI <- read.csv(CPIcsv, skip=2)) str(USGDP. <- read.csv(GDPcsv, skip=1)) library(Ecfun) USGDP <- asNumericDF(USGDP.) print(rngCPIyrs <- range(USCPI$Year) ) print(rngGDPyrs <- range(USGDP$Year) ) endYr <- max(rngCPIyrs, rngGDPyrs) if(endYr>rngYrs[2]) print(Update <- TRUE) }
If Update, create a local copy of USGDPpresidents
with the additional rows required to hold the new data:
if(Update){ rowsNeeded <- (endYr - rngYrs[2]) Nold <- nrow(USGDPpresidents) iRep <- c(1:Nold, rep(Nold, rowsNeeded)) USGDPp2 <- USGDPpresidents[iRep,] }
Fix the Year and insert NAs for all other columns for the new rows:
if(Update){ iNew <- (Nold+(1:rowsNeeded)) USGDPp2$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 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$fedOutlays_pGDP[selGDP] <- fedOp }
if(Update){ USGDPpresidents <- USGDPp2 sel <- !is.na(USGDPpresidents$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') }
if(Update){ save(USGDPpresidents, file='USGDPpresidents.rda') getwd() }
Now copy this file from the current working directory
to ~Ecdat\data
, overwriting the previous version.
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