Hi,
Try either:
Ceramic <- read.table("ceramic.dat",header=TRUE)
Ceramic1 <- Ceramic
Ceramic$indx <- ((seq_len(nrow(Ceramic))-1)%/%60)+1
library(plyr)
DF1 <- data.frame(M=as.vector(t(ddply(Ceramic,.(indx), colwise(mean))[,-1])),
S=as.vector(t(ddply(Ceramic,.(indx),colwise(sd))[,-1])),Rep = 60)
colnames(DF)[3] <- colnames(DF1)[3]
identical(DF,DF1)
#[1] TRUE
#or
indx <- ((seq_len(nrow(Ceramic))-1)%/%60)+1
Ceramic2 <- do.call(data.frame, c(aggregate(.~indx,data=Ceramic1,function(x)
c(mean(x),sd(x))), check.names=FALSE))[,-1]
DF2 <- data.frame(M= as.vector(t(Ceramic2[,seq(1,ncol(Ceramic2),by=2)])), S=
as.vector(t(Ceramic2[,seq(2,ncol(Ceramic2),by=2)])),Rep =60)
identical(DF,DF2)
#[1] TRUE
A.K.
please help me to short the code
#To import data onto R
Ceramic<-read.table("D:/ceramic.dat",header=T)
#to obtain mean, standard deviation and number of observations-
LAB1<-Ceramic[1:60,]
LAB2<-Ceramic[61:120,]
LAB3<-Ceramic[121:180,]
LAB4<-Ceramic[181:240,]
LAB5<-Ceramic[241:300,]
LAB6<-Ceramic[301:360,]
LAB7<-Ceramic[361:420,]
LAB8<-Ceramic[421:480,]
M1<-sapply(LAB1,mean)
M2<-sapply(LAB2,mean)
M3<-sapply(LAB3,mean)
M4<-sapply(LAB4,mean)
M5<-sapply(LAB5,mean)
M6<-sapply(LAB6,mean)
M7<-sapply(LAB7,mean)
M8<-sapply(LAB8,mean)
S1<-sapply(LAB1,sd)
S2<-sapply(LAB2,sd)
S3<-sapply(LAB3,sd)
S4<-sapply(LAB4,sd)
S5<-sapply(LAB5,sd)
S6<-sapply(LAB6,sd)
S7<-sapply(LAB7,sd)
S8<-sapply(LAB8,sd)
#tabulating results-
M<-c(M1,M2,M3,M4,M5,M6,M7,M8)
S<-c(S1,S2,S3,S4,S5,S6,S7,S8)
DF<-data.frame(M,S,c(rep(60)))
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