Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
4 1 5 6 3 5 8 8 4 1 7 5 4 0 8 0 2 2 14 8 2 2 105 10 2 0 11 14 3 2 18 8 2 1 15 6 3 0 13 5 1 2 12 12 6 2 17 1 1 3 13 9 3 2 13 7 1 1 12 13 2 0 22 3 2 0 21 2 1 0 13 12 1 1 12 13 3 3 10 7 3 0 21 0 2 0 21 3 2 1 11 10 2 0 18 5 2 1 11 13 1 0 14 11 3 0 14 7 3 2 12 10 3 0 14 12 1 1 13 12 2 0 11 15 1 1 15 9 3 1 15 5 3 0 17 3 3 3 13 4 1 2 12 11 3 1 115 10 1 0 14 11 2 1 17 6 3 1 20 9 2 1 21 3 4 2 12 6 2 1 12 12 4 1 16 3 4 3 16 11 4 3 16 11 3 2 17 13 2 1 15 6 2 1 15 9 3 2 21 7 3 0 18 2 3 2 135 10 3 1 11 14 3 0 20 3 1 0 13 0 2 0 10 0 4 1 14 6 5 2 -1 -1 3 1 14 12 3 2 105 8 2 0 15 6 1 0 23 2 2 0 22 3 2 0 16 0 3 0 19 0 3 0 20 0 2 1 9 15 6 0 16 3 2 0 15 5 2 1 21 3 3 1 15 14 3 1 16 3 2 1 21 3 1 1 13 12 3 1 17 3 3 1 17 3 2 1 16 8
Names of X columns:
protein fat carbo sugar
Sample Range:
(leave blank to include all observations)
From:
To:
Color
grey
grey
white
blue
red
black
brown
yellow
Omit all rows with missing values?
no
no
yes
Chart options
Title:
Label y-axis:
Label x-axis:
R Code
if(par2=='yes') { z <- na.omit(as.data.frame(t(y))) } else { z <- as.data.frame(t(y)) } bitmap(file='test1.png') (r<-boxplot(z ,xlab=xlab,ylab=ylab,main=main,notch=TRUE,col=par1)) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Boxplot statistics',6,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',1,TRUE) a<-table.element(a,'lower whisker',1,TRUE) a<-table.element(a,'lower hinge',1,TRUE) a<-table.element(a,'median',1,TRUE) a<-table.element(a,'upper hinge',1,TRUE) a<-table.element(a,'upper whisker',1,TRUE) a<-table.row.end(a) for (i in 1:length(y[,1])) { a<-table.row.start(a) a<-table.element(a,dimnames(t(x))[[2]][i],1,TRUE) for (j in 1:5) { a<-table.element(a,r$stats[j,i]) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Boxplot Notches',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',1,TRUE) a<-table.element(a,'lower bound',1,TRUE) a<-table.element(a,'median',1,TRUE) a<-table.element(a,'upper bound',1,TRUE) a<-table.row.end(a) for (i in 1:length(y[,1])) { a<-table.row.start(a) a<-table.element(a,dimnames(t(x))[[2]][i],1,TRUE) a<-table.element(a,r$conf[1,i]) a<-table.element(a,r$stats[3,i]) a<-table.element(a,r$conf[2,i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable1.tab')
Compute
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
Click here to blog (archive) this computation