R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> x <- c(84543,55735,65814,55382,57422,57729,48213,44789,50998,50328,47719,19946,80608,50747,61687,47539,54412,83362,36911,39254,37597,32557,35537,21500,56125,37432,43347,36870,36247,41293,36190,33381,36609,41861,45926,45079,51469,39013,37036,34865,37505,39379,35653,30515,35984,41068,37851,40333,56004,39797,37960,34357,35069,45299,40475,36277,41910,45234,46425,73241)
> par8 = ''
> par7 = '0.95'
> par6 = 'White Noise'
> par5 = '12'
> par4 = '0'
> par3 = '1'
> par2 = '1'
> par1 = '48'
> par8 <- ''
> par7 <- '0.95'
> par6 <- 'White Noise'
> par5 <- '12'
> par4 <- '0'
> par3 <- '1'
> par2 <- '1'
> par1 <- '48'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 (Mon, 30 Nov 2015 06:58:35 +0000)
> #Author: root
> #To cite this work: Wessa P., (2015), (Partial) Autocorrelation Function (v1.0.12) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_autocorrelation.wasp/
> #Source of accompanying publication: 
> #
> if (par1 == 'Default') {
+ par1 = 10*log10(length(x))
+ } else {
+ par1 <- as.numeric(par1)
+ }
> par2 <- as.numeric(par2)
> par3 <- as.numeric(par3)
> par4 <- as.numeric(par4)
> par5 <- as.numeric(par5)
> if (par6 == 'White Noise') par6 <- 'white' else par6 <- 'ma'
> par7 <- as.numeric(par7)
> if (par8 != '') par8 <- as.numeric(par8)
> x <- na.omit(x)
> ox <- x
> if (par8 == '') {
+ if (par2 == 0) {
+ x <- log(x)
+ } else {
+ x <- (x ^ par2 - 1) / par2
+ }
+ } else {
+ x <- log(x,base=par8)
+ }
> if (par3 > 0) x <- diff(x,lag=1,difference=par3)
> if (par4 > 0) x <- diff(x,lag=par5,difference=par4)
> postscript(file="/var/wessaorg/rcomp/tmp/1xdab1477060230.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> op <- par(mfrow=c(2,1))
> plot(ox,type='l',main='Original Time Series',xlab='time',ylab='value')
> if (par8=='') {
+ mytitle <- paste('Working Time Series (lambda=',par2,', d=',par3,', D=',par4,')',sep='')
+ mysub <- paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='')
+ } else {
+ mytitle <- paste('Working Time Series (base=',par8,', d=',par3,', D=',par4,')',sep='')
+ mysub <- paste('(base=',par8,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='')
+ }
> plot(x,type='l', main=mytitle,xlab='time',ylab='value')
> par(op)
> dev.off()
null device 
          1 
> postscript(file="/var/wessaorg/rcomp/tmp/286xy1477060230.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> racf <- acf(x, par1, main='Autocorrelation', xlab='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=mysub)
> dev.off()
null device 
          1 
> postscript(file="/var/wessaorg/rcomp/tmp/3o66r1477060230.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF',sub=mysub)
> dev.off()
null device 
          1 
> (myacf <- c(racf$acf))
 [1]  1.000000000 -0.530129672  0.149785856 -0.039470330 -0.051334349
 [6]  0.294925832 -0.429782592  0.213540878  0.008142802 -0.088490864
[11]  0.158559374 -0.351528099  0.449694787 -0.256722520  0.077806018
[16] -0.001395852 -0.090512117  0.133044389 -0.074208407  0.048854328
[21]  0.022668198 -0.037942323  0.052042503 -0.139337314  0.193359262
[26] -0.117856007  0.018291715 -0.013373563  0.018897019  0.011555893
[31] -0.064869268  0.059863421 -0.008541433  0.003638208 -0.033058674
[36] -0.023130216  0.144109738 -0.083603389 -0.022742968 -0.006499640
[41]  0.007007153 -0.053560866  0.028973190 -0.002490625 -0.010598199
[46]  0.024595976 -0.047882984  0.064344671 -0.012682022
> (mypacf <- c(rpacf$acf))
 [1] -0.530129672 -0.182556958 -0.060931020 -0.109403068  0.312436540
 [6] -0.178978887 -0.160472238  0.079940596 -0.066966630  0.065879986
[11] -0.181882474  0.157893677  0.026568773  0.048685075 -0.004162137
[16] -0.019214699 -0.179105215  0.162783157  0.117693293  0.041144908
[21]  0.102898565 -0.070906925 -0.132203537  0.098143133  0.073841520
[26] -0.058445330 -0.009930636  0.035578085 -0.010544838 -0.069925665
[31]  0.043046781 -0.041808764 -0.045726070  0.018906604  0.054916902
[36]  0.044048943  0.087638544 -0.087117983 -0.119052157 -0.062422466
[41] -0.153524009  0.040366938  0.069379790 -0.038305309 -0.008379990
[46] -0.029031236  0.022106152  0.012316111
> lengthx <- length(x)
> sqrtn <- sqrt(lengthx)
> 
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
> 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Autocorrelation Function',4,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Time lag k',header=TRUE)
> a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','ACF(k)','click here for more information about the Autocorrelation Function'),header=TRUE)
> a<-table.element(a,'T-STAT',header=TRUE)
> a<-table.element(a,'P-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 2:(par1+1)) {
+ a<-table.row.start(a)
+ a<-table.element(a,i-1,header=TRUE)
+ a<-table.element(a,round(myacf[i],6))
+ mytstat <- myacf[i]*sqrtn
+ a<-table.element(a,round(mytstat,4))
+ a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/4o0xx1477060230.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Partial Autocorrelation Function',4,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Time lag k',header=TRUE)
> a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','PACF(k)','click here for more information about the Partial Autocorrelation Function'),header=TRUE)
> a<-table.element(a,'T-STAT',header=TRUE)
> a<-table.element(a,'P-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:par1) {
+ a<-table.row.start(a)
+ a<-table.element(a,i,header=TRUE)
+ a<-table.element(a,round(mypacf[i],6))
+ mytstat <- mypacf[i]*sqrtn
+ a<-table.element(a,round(mytstat,4))
+ a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/5feds1477060230.tab") 
> 
> try(system("convert tmp/1xdab1477060230.ps tmp/1xdab1477060230.png",intern=TRUE))
character(0)
> try(system("convert tmp/286xy1477060230.ps tmp/286xy1477060230.png",intern=TRUE))
character(0)
> try(system("convert tmp/3o66r1477060230.ps tmp/3o66r1477060230.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.226   0.138   1.380