> x <- matrix(1:9,3,3)
> x

     [,1] [,2] [,3]
[1,]    1    4    7
[2,]    2    5    8
[3,]    3    6    9


> crossprod(x)
>t(x)%*%x
     [,1] [,2] [,3]
[1,]   14   32   50
[2,]   32   77  122
[3,]   50  122  194


> tcrossprod(x)
x%*%t(x)
     [,1] [,2] [,3]
[1,]   66   78   90
[2,]   78   93  108
[3,]   90  108  126

> which(letters=="h")

[1] 8


> BOD

  Time demand
1    1    8.3
2    2   10.3
3    3   19.0
4    4   16.0
5    5   15.6
6    7   19.8


> which(BOD$demand == 16)

[1] 4


> x <- matrix(1:9,3,3)
> x

     [,1] [,2] [,3]
[1,]    1    4    7
[2,]    2    5    8
[3,]    3    6    9


> which(x %% 3 == 0, arr.ind=TRUE)

     row col
[1,]   3   1
[2,]   3   2
[3,]   3   3


> which(x %% 3 == 0, arr.ind=FALSE)

[1] 3 6 9

#R_Experts

vector1 <- c(5,9,3)
vector2 <- c(10,11,12,13,14,15)
column.names <- c("COL1","COL2","COL3")
row.names <- c("ROW1","ROW2","ROW3")
matrix.names <- c("Matrix1","Matrix2")

#R_Experts:#Take these vectors as input to the array.
result <- array(c(vector1,vector2),dim = c(3,3,2),dimnames = list(row.names,column.names,
   matrix.names))
print(result)

#R_Experts

data()

 Sys.Date() 

Sys.time()

return()

return(list())

x<-rnorm(1000)

x

y<-matrix(x,10,100)

y

vv<-rep(NA,100)

vv

for(i in 1:100){

vv[i]<-var(y[ ,i])

i=i+1

}

vv

yy<-matrix(vv,100,1)

yy

s=0

for(i in 1:100){

for(k in i:100){

if (yy[i,1] > yy[k,1]){

s=s+1}

else{s=s

}

k=k+1

}

i=i+1

}

s

> set.seed(123)

> rnorm(4)

[1] -0.56047565 -0.23017749  1.55870831  0.07050839

> rnorm(4)

[1]  0.1292877  1.7150650  0.4609162 -1.2650612

> set.seed(123)

> rnorm(4)

[1] -0.56047565 -0.23017749  1.55870831  0.07050839

> sample(1:6, 10, replace=TRUE)

 [1] 2 2 5 3 5 3 5 6 3 5

> set.seed(1)

> sample(1:6, 10, replace=TRUE)

 [1] 2 3 4 6 2 6 6 4 4 1

> sample(1:6, 10, replace=TRUE)

 [1] 2 2 5 3 5 3 5 6 3 5

> set.seed(1)

> sample(1:6, 10, replace=TRUE)

 [1] 2 3 4 6 2 6 6 4 4 1

> set.seed(123)

> index <- sample(1:nrow(iris), 5)

> index

[1] 44 119 62 133 142

> iris[index, ]

  Sepal.Length Sepal.Width Petal.Length Petal.Width  Species

44      5.0     3.5     1.6     0.6   setosa

119     7.7     2.6     6.9     2.3 virginica

62      5.9     3.0     4.2     1.5 versicolor

133     6.4     2.8     5.6     2.2 virginica

142     6.9     3.1     5.1     2.3 virginica

R> sample(LETTERS, 5)
[1] "K" "N" "R" "Z" "G"
R> sample(LETTERS, 5)
[1] "L" "P" "J" "E" "D"
R> set.seed(42); sample(LETTERS, 5)
[1] "X" "Z" "G" "T" "O"
R> set.seed(42); sample(LETTERS, 5)
[1] "X" "Z" "G" "T" "O"

mytrans <- function(x) {
  if (!is.matrix(x)) {
    warning("argument is not a matrix: returning NA")
    return(NA_real_)
  }
  y <- matrix(1, nrow=ncol(x), ncol=nrow(x))
  for (i in 1:nrow(x)) {
    for (j in 1:ncol(x)) {
      y[j,i] <- x[i,j]
    }
  }
return(y)
}

# try it
z <- matrix(1:10, nrow=5, ncol=2)
z
tz <- mytrans(z)
tz

#install.packages("rngWELL")

#install.packages("randtoolbox")

library(rngWELL)

library(randtoolbox)

halt=halton(10,dim=1)

halt

write.csv(halt,"halt.csv")