Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data:
22577.0 22792.0 23932.0 22321.0 21102.0 22824.0 23129.0 23604.0 24746.0 26911.0 27909.0 28922.0 29800.0 30506.0 30771.0 31976.0 33749.0 34371.0 33246.0 35072.0 35762.0 36179.0 37433.0 38298.0 37559.0 37511.0 39364.0 40084.0 42712.0 41938.0 40799.0 38568.0 41134.0 43955.0 43607.0 45082.0 46464.0 46496.0 46774.0 47890.0 45740.0 42660.0 39190.0 39010.0 41150.0 42530.0 44710.0 46620.0 44560.0 46120.0 48060.0 51970.0 57720.0 63490.0 65370.0 64260.0 58700.0 58630.0 59803.0 59266.0 60570.0 63062.0 63846.0 64726.0 63460.0 65220.0 66659.0 66871.0 65672.0 67182.0 68292.0 68318.0 69530.0 70500.0 72044.0 73811.0 76018.0 77818.0 79455.0 81408.0 81815.0
Sample Range:
(leave blank to include all observations)
From:
To:
# blockwidth
Chart options
R Code
par1 <- as.numeric(par1) (n <- length(x)) (np <- floor(n / par1)) arr <- array(NA,dim=c(par1,np+1)) darr <- array(NA,dim=c(par1,np+1)) ari <- array(0,dim=par1) dx <- diff(x) j <- 0 for (i in 1:n) { j = j + 1 ari[j] = ari[j] + 1 arr[j,ari[j]] <- x[i] darr[j,ari[j]] <- dx[i] if (j == par1) j = 0 } ari arr darr arr.mean <- array(NA,dim=par1) arr.median <- array(NA,dim=par1) arr.midrange <- array(NA,dim=par1) for (j in 1:par1) { arr.mean[j] <- mean(arr[j,],na.rm=TRUE) arr.median[j] <- median(arr[j,],na.rm=TRUE) arr.midrange[j] <- (quantile(arr[j,],0.75,na.rm=TRUE) + quantile(arr[j,],0.25,na.rm=TRUE)) / 2 } overall.mean <- mean(x) overall.median <- median(x) overall.midrange <- (quantile(x,0.75) + quantile(x,0.25)) / 2 bitmap(file='plot1.png') plot(arr.mean,type='b',ylab='mean',main='Mean Plot',xlab='Periodic Index') mtext(paste('#blocks = ',np)) abline(overall.mean,0) dev.off() bitmap(file='plot2.png') plot(arr.median,type='b',ylab='median',main='Median Plot',xlab='Periodic Index') mtext(paste('#blocks = ',np)) abline(overall.median,0) dev.off() bitmap(file='plot3.png') plot(arr.midrange,type='b',ylab='midrange',main='Midrange Plot',xlab='Periodic Index') mtext(paste('#blocks = ',np)) abline(overall.midrange,0) dev.off() bitmap(file='plot4.png') z <- data.frame(t(arr)) names(z) <- c(1:par1) (boxplot(z,notch=TRUE,col='grey',xlab='Periodic Index',ylab='Value',main='Notched Box Plots - Periodic Subseries')) dev.off() bitmap(file='plot4b.png') z <- data.frame(t(darr)) names(z) <- c(1:par1) (boxplot(z,notch=TRUE,col='grey',xlab='Periodic Index',ylab='Value',main='Notched Box Plots - Differenced Periodic Subseries')) dev.off() bitmap(file='plot5.png') z <- data.frame(arr) names(z) <- c(1:np) (boxplot(z,notch=TRUE,col='grey',xlab='Block Index',ylab='Value',main='Notched Box Plots - Sequential Blocks')) dev.off() bitmap(file='plot6.png') z <- data.frame(cbind(arr.mean,arr.median,arr.midrange)) names(z) <- list('mean','median','midrange') (boxplot(z,notch=TRUE,col='grey',ylab='Overall Central Tendency',main='Notched Box Plots')) dev.off()
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation