save.image("C:\\Documents and Settings\\EmirB\\Desktop\\LDesktop\\Columbia\\Computational Statistics Course\\Lecture\\.RData")
x = c(1:10)
x
x <- c(1:10)
x
x <- c(1:10); x
mean(X)
ls()
mean(x)
sequence(1,10,.5)
sequence(1:10,.5)
seq(1:10,.5)
?seq
?sequence
?seq
seq(,10,by=.5)
seq(1,10,by=.5)
seq(1,10,by=.5)
seq(from=1,to=10,by=.5)
seq(from=1,to=10,by=.05)
X=seq(from=1,to=10,by=.05)
ls()
x
X
rm(X)
ls()
history(100)
getwd()
help(options)
options(digits=3) 
help(mean)
?mean
x <- c(0:10, 50)
xm <- mean(x)
c(xm, mean(x, trim = 0.10))
x
xm
apropos("mean")
?kmeans
RSiteSearch("foo") 
RSiteSearch("Robust") 
data()
data(AirPassengers)
names(AirPassengers)
summary(AirPassengers)
AirPassengers[1:5,]
AirPassengers
local({pkg <- select.list(sort(.packages(all.available = TRUE)))
if(nchar(pkg)) library(pkg, character.only=TRUE)})
?Desing
?Design
utils:::menuInstallPkgs()
library()
mean(x)
x
mean=mean(x)
mean
conflicts()
lds()
ls()
rm(mean)
mean
conflicts()
xmean(mean(x)
xmean=mean(x)
data(mtcars)
newdata = mtcars[order(mpg),] 
summary(mtcars)
newdata = mtcars[order(mtcars$mpg),] 
mtcars[1:5,]
newdata[1:5,]
newdata <- mtcars[order(mtcars$mpg, -mtcars$cyl),] 
newdata[1:5,]
newdata[1:10,]
newdata = mtcars[order(mtcars$mpg),] # sort by mpg and cylnewdata <- mtcars[order(mtcars$mpg, mtcars$cyl),]
newdata[1:10,]
newdata[1:10,]
newdata = mtcars[order(mtcars$mpg),] 
newdata[1:10,]
data(mtcars)
newdata[1:10,]
newdata = mtcars[order(mtcars$mpg),] 
newdata[1:10,]
summary(mtcars)
newdata <- mtcars[order(mtcars$mpg, mtcars$cyl),]
newdata
attach(mtcars)aggdata <-aggregate(mtcars, by=list(cyl,vs),   FUN=mean, na.rm=TRUE)print(aggdata)
attach(mtcars)aggdata <-aggregate(mtcars, by=list(cyl,vs),   FUN=mean, na.rm=TRUE)print(aggdata)
attach(mtcars)
aggdata <-aggregate(mtcars, by=list(cyl,vs),  FUN=mean, na.rm=TRUE)
print(aggdata)
summary(mtcars)
aggregate(mtcars, by=list(cyl),  FUN=mean, na.rm=TRUE)
aggregate(mtcars, by=list(vs),  FUN=mean, na.rm=TRUE)
utils:::menuInstallPkgs()
library(Rcmdr)
growthcurve<- sqlQuery(channel = 1, select * from [GrowthCurve$])
growthcurve<- sqlQuery(channel = 1, select * from [GRWOTHCURVE$])
growthcurve<- sqlQuery(channel = 1, select * from [GROWTHCURVE$])
?sqlQuery
ls()
eff
eff[1:5,]
eff[1:5,1:5]
eff[1:5,1:10]
summary
summary(eff)
names(eff)
summary(eff[,c(8,11,12,13)])
eff$csex=as.factor(eff$nsex)
eff$CSEX=as.factor(eff$NSEX)
eff[1:5,c(5,19)]
summary(eff[,c(5,19)])
summary(eff[,c("NSEX","CSEX")])
numSummary(eff[,c("AGE", "SLEEP", "SLEEP0", "SLPCHG")], 
  groups=eff$STATUS, statistics=c("mean", "sd", "quantiles"), quantiles=c(0,.025,
  .25,.5,.75,.95,1))
names(eff)
levels(eff$STATUS)
levels(eff$CWEEK)
levels(eff$NWEEK)
sub= eff$NWEEK == 99
sub
eff[sub,]
eff[sub,]
unique(eff$NWEEEK)
unique(eff$NWEEK)
summary(eff$NWEEK)
sub= eff$WEEK == 99
sub
eff1=eff[sub,]
dim(eff)
dim(eff1)
unique(eff$WEEK)
unique(eff1$WEEK)
save.image("C:\\Documents and Settings\\EmirB\\Desktop\\LDesktop\\Columbia\\Computational Statistics Course\\Lecture\\.RData")
eff1
summary(eff1)
eff1$TRT=as.factor(eff1$RXGP)
dim(eff1)
names(eff1)
eff2=eff1[,-c("RXGP","NSEX")]
eff2=eff1[,-c(4,5)]
dim(eff2)
names(eff2)
save.image("C:\\Documents and Settings\\EmirB\\Desktop\\LDesktop\\Columbia\\Computational Statistics Course\\Lecture\\.RData")