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Data X:
1 1 41 38 13 12 14 1 1 39 32 16 11 18 1 1 30 35 19 15 11 1 0 31 33 15 6 12 1 1 34 37 14 13 16 1 1 35 29 13 10 18 1 1 39 31 19 12 14 1 1 34 36 15 14 14 1 1 36 35 14 12 15 1 1 37 38 15 9 15 1 0 38 31 16 10 17 1 1 36 34 16 12 19 1 0 38 35 16 12 10 1 1 39 38 16 11 16 1 1 33 37 17 15 18 1 0 32 33 15 12 14 1 0 36 32 15 10 14 1 1 38 38 20 12 17 1 0 39 38 18 11 14 1 1 32 32 16 12 16 1 0 32 33 16 11 18 1 1 31 31 16 12 11 1 1 39 38 19 13 14 1 1 37 39 16 11 12 1 0 39 32 17 12 17 1 1 41 32 17 13 9 1 0 36 35 16 10 16 1 1 33 37 15 14 14 1 1 33 33 16 12 15 1 0 34 33 14 10 11 1 1 31 31 15 12 16 1 0 27 32 12 8 13 1 1 37 31 14 10 17 1 1 34 37 16 12 15 1 0 34 30 14 12 14 1 0 32 33 10 7 16 1 0 29 31 10 9 9 1 0 36 33 14 12 15 1 1 29 31 16 10 17 1 0 35 33 16 10 13 1 0 37 32 16 10 15 1 1 34 33 14 12 16 1 0 38 32 20 15 16 1 0 35 33 14 10 12 1 1 38 28 14 10 15 1 1 37 35 11 12 11 1 1 38 39 14 13 15 1 1 33 34 15 11 15 1 1 36 38 16 11 17 1 0 38 32 14 12 13 1 1 32 38 16 14 16 1 0 32 30 14 10 14 1 0 32 33 12 12 11 1 1 34 38 16 13 12 1 0 32 32 9 5 12 1 1 37 35 14 6 15 1 1 39 34 16 12 16 1 1 29 34 16 12 15 1 0 37 36 15 11 12 1 1 35 34 16 10 12 1 0 30 28 12 7 8 1 0 38 34 16 12 13 1 1 34 35 16 14 11 1 1 31 35 14 11 14 1 1 34 31 16 12 15 1 0 35 37 17 13 10 1 1 36 35 18 14 11 1 0 30 27 18 11 12 1 1 39 40 12 12 15 1 0 35 37 16 12 15 1 0 38 36 10 8 14 1 1 31 38 14 11 16 1 1 34 39 18 14 15 1 0 38 41 18 14 15 1 0 34 27 16 12 13 1 1 39 30 17 9 12 1 1 37 37 16 13 17 1 1 34 31 16 11 13 1 0 28 31 13 12 15 1 0 37 27 16 12 13 1 0 33 36 16 12 15 1 1 35 37 16 12 15 1 0 37 33 15 12 16 1 1 32 34 15 11 15 1 1 33 31 16 10 14 1 0 38 39 14 9 15 1 1 33 34 16 12 14 1 1 29 32 16 12 13 1 1 33 33 15 12 7 1 1 31 36 12 9 17 1 1 36 32 17 15 13 1 1 35 41 16 12 15 1 1 32 28 15 12 14 1 1 29 30 13 12 13 1 1 39 36 16 10 16 1 1 37 35 16 13 12 1 1 35 31 16 9 14 1 0 37 34 16 12 17 1 0 32 36 14 10 15 1 1 38 36 16 14 17 1 0 37 35 16 11 12 1 1 36 37 20 15 16 1 0 32 28 15 11 11 1 1 33 39 16 11 15 1 0 40 32 13 12 9 1 1 38 35 17 12 16 1 0 41 39 16 12 15 1 0 36 35 16 11 10 1 1 43 42 12 7 10 1 1 30 34 16 12 15 1 1 31 33 16 14 11 1 1 32 41 17 11 13 1 1 37 34 12 10 18 1 0 37 32 18 13 16 1 1 33 40 14 13 14 1 1 34 40 14 8 14 1 1 33 35 13 11 14 1 1 38 36 16 12 14 1 0 33 37 13 11 12 1 1 31 27 16 13 14 1 1 38 39 13 12 15 1 1 37 38 16 14 15 1 1 36 31 15 13 15 1 1 31 33 16 15 13 1 0 39 32 15 10 17 1 1 44 39 17 11 17 1 1 33 36 15 9 19 1 1 35 33 12 11 15 1 0 32 33 16 10 13 1 0 28 32 10 11 9 1 1 40 37 16 8 15 1 0 27 30 12 11 15 1 0 37 38 14 12 15 1 1 32 29 15 12 16 1 0 28 22 13 9 11 1 0 34 35 15 11 14 1 1 30 35 11 10 11 1 1 35 34 12 8 15 1 0 31 35 11 9 13 1 1 32 34 16 8 15 1 0 30 37 15 9 16 1 1 30 35 17 15 14 1 0 31 23 16 11 15 1 1 40 31 10 8 16 1 1 32 27 18 13 16 1 0 36 36 13 12 11 1 0 32 31 16 12 12 1 0 35 32 13 9 9 1 1 38 39 10 7 16 1 1 42 37 15 13 13 1 0 34 38 16 9 16 1 1 35 39 16 6 12 1 1 38 34 14 8 9 1 1 33 31 10 8 13 1 1 32 37 13 6 14 1 1 33 36 15 9 19 1 1 34 32 16 11 13 1 1 32 38 12 8 12 0 0 27 26 13 10 10 0 0 31 26 12 8 14 0 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0 36 35 13 8 8 0 1 36 39 15 12 15 0 0 35 33 13 12 11 0 0 38 36 14 10 16 0 1 33 32 15 12 10 0 0 31 32 12 9 15 0 1 32 36 8 6 16 0 0 31 32 14 10 19 0 0 33 34 14 9 12 0 0 34 33 11 9 8 0 0 34 35 12 9 11 0 1 34 30 13 6 14 0 0 33 38 10 10 9 0 0 32 34 16 6 15 0 1 41 33 18 14 13 0 1 34 32 13 10 16 0 0 36 31 11 10 11 0 0 37 30 4 6 12 0 0 36 27 13 12 13 0 1 29 31 16 12 10 0 0 37 30 10 7 11 0 0 27 32 12 8 12 0 0 35 35 12 11 8 0 0 28 28 10 3 12 0 0 35 33 13 6 12 0 0 29 35 12 8 11 0 0 32 35 14 9 13 0 1 36 32 10 9 14 0 1 19 21 12 8 10 0 1 21 20 12 9 12 0 0 31 34 11 7 15 0 0 33 32 10 7 13 0 1 36 34 12 6 13 0 1 33 32 16 9 13 0 0 37 33 12 10 12 0 0 34 33 14 11 12 0 0 35 37 16 12 9 0 1 31 32 14 8 9 0 1 37 34 13 11 15 0 1 35 30 4 3 10 0 1 27 30 15 11 14 0 0 34 38 11 12 15 0 0 40 36 11 7 7 0 0 29 32 14 9 14 0 0 38 34 15 12 8 0 1 34 33 14 8 10 0 0 21 27 13 11 13 0 0 36 32 11 8 13 0 1 38 34 15 10 13 0 0 30 29 11 8 8 0 0 35 35 13 7 12 0 1 30 27 13 8 13 0 1 36 33 16 10 12 0 0 34 38 13 8 10 0 1 35 36 16 12 13 0 0 34 33 16 14 12 0 0 32 39 12 7 9 0 1 33 29 7 6 15 0 0 33 32 16 11 13 0 1 26 34 5 4 13 0 0 35 38 16 9 13 0 0 21 17 4 5 15 0 0 38 35 12 9 15 0 0 35 32 15 11 14 0 1 33 34 14 12 15 0 0 37 36 11 9 11 0 0 38 31 16 12 15 0 1 34 35 15 10 14 0 0 27 29 12 9 13 0 1 16 22 6 6 12 0 0 40 41 16 10 16 0 0 36 36 10 9 16 0 1 42 42 15 13 9 0 1 30 33 14 12 14
Names of X columns:
Pop Gender Connected Separate Learning Software Happiness
Type of Correlation


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Title:

R Code
panel.tau <- function(x, y, digits=2, prefix='', cex.cor) { usr <- par('usr'); on.exit(par(usr)) par(usr = c(0, 1, 0, 1)) rr <- cor.test(x, y, method=par1) r <- round(rr$p.value,2) txt <- format(c(r, 0.123456789), digits=digits)[1] txt <- paste(prefix, txt, sep='') if(missing(cex.cor)) cex <- 0.5/strwidth(txt) text(0.5, 0.5, txt, cex = cex) } panel.hist <- function(x, ...) { usr <- par('usr'); on.exit(par(usr)) par(usr = c(usr[1:2], 0, 1.5) ) h <- hist(x, plot = FALSE) breaks <- h$breaks; nB <- length(breaks) y <- h$counts; y <- y/max(y) rect(breaks[-nB], 0, breaks[-1], y, col='grey', ...) } bitmap(file='test1.png') pairs(t(y),diag.panel=panel.hist, upper.panel=panel.smooth, lower.panel=panel.tau, main=main) dev.off() load(file='createtable') n <- length(y[,1]) n a<-table.start() a<-table.row.start(a) a<-table.element(a,paste('Correlations for all pairs of data series (method=',par1,')',sep=''),n+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,' ',header=TRUE) for (i in 1:n) { a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) } a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,dimnames(t(x))[[2]][i],header=TRUE) for (j in 1:n) { r <- cor.test(y[i,],y[j,],method=par1) a<-table.element(a,round(r$estimate,3)) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Correlations for all pairs of data series with p-values',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'pair',1,TRUE) a<-table.element(a,'Pearson r',1,TRUE) a<-table.element(a,'Spearman rho',1,TRUE) a<-table.element(a,'Kendall tau',1,TRUE) a<-table.row.end(a) cor.test(y[1,],y[2,],method=par1) for (i in 1:(n-1)) { for (j in (i+1):n) { a<-table.row.start(a) dum <- paste(dimnames(t(x))[[2]][i],';',dimnames(t(x))[[2]][j],sep='') a<-table.element(a,dum,header=TRUE) rp <- cor.test(y[i,],y[j,],method='pearson') a<-table.element(a,round(rp$estimate,4)) rs <- cor.test(y[i,],y[j,],method='spearman') a<-table.element(a,round(rs$estimate,4)) rk <- cor.test(y[i,],y[j,],method='kendall') a<-table.element(a,round(rk$estimate,4)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-value',header=T) a<-table.element(a,paste('(',round(rp$p.value,4),')',sep='')) a<-table.element(a,paste('(',round(rs$p.value,4),')',sep='')) a<-table.element(a,paste('(',round(rk$p.value,4),')',sep='')) a<-table.row.end(a) } } a<-table.end(a) table.save(a,file='mytable1.tab')

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	1 seconds
R Server	Big Analytics Cloud Computing Center