Generate systematic and random data

Empty objects

N <- 5
numeric(N)

[1] 0 0 0 0 0

matrix(numeric(N))

     [,1]
[1,]    0
[2,]    0
[3,]    0
[4,]    0
[5,]    0

character(N)

[1] "" "" "" "" ""

vector(mode="list", length=N)

[[1]]
NULL

[[2]]
NULL

[[3]]
NULL

[[4]]
NULL

[[5]]
NULL

Systematic numerical sequences

Ordered sequences

20:26

[1] 20 21 22 23 24 25 26

26:20

[1] 26 25 24 23 22 21 20

-4:2

[1] -4 -3 -2 -1  0  1  2

-(4:2)

[1] -4 -3 -2

seq(from=2, to=12, by=2)

[1]  2  4  6  8 10 12

seq(from=2, to=11, by=2)

[1]  2  4  6  8 10

seq(from=0, to=-1, length.out=5)

[1]  0.00 -0.25 -0.50 -0.75 -1.00

age <- c(18, 20, 30, 24, 23, 21)
seq(along=age)

[1] 1 2 3 4 5 6

vec <- numeric(0)
length(vec)

[1] 0

1:length(vec)

[1] 1 0

seq(along=vec)

integer(0)

Repeated sequences

rep(1:3, times=5)

 [1] 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3

rep(c("A", "B", "C"), times=c(2, 3, 4))

[1] "A" "A" "B" "B" "B" "C" "C" "C" "C"

rep(age, each=2)

 [1] 18 18 20 20 30 30 24 24 23 23 21 21

Random data

Strictly, the data is pseudorandom. There are several options for the random number generator, see RNGkind(). Use set.seed() to set the state of the RNG. This allows to replicate the following sequence of numbers. Copy .Random.seed into your own object to save the current state of the RNG. Don’t modify .Random.seed.

Sample from an urn

set.seed(123)
sample(1:6, size=20, replace=TRUE)

 [1] 3 6 3 2 2 6 3 5 4 6 6 1 2 3 5 3 3 1 4 1

sample(c("rot", "gruen", "blau"), size=8, replace=TRUE)

[1] "rot"   "rot"   "blau"  "gruen" "blau"  "gruen" "rot"   "gruen"

x <- c(2, 4, 6, 8)
sample(x[(x %% 4) == 0])

[1] 4 8

sample(x[(x %% 8) == 0])

[1] 6 1 3 5 4 8 2 7

Data from random variables with different distributions

runif(5, min=1, max=6)

[1] 2.844227 1.762224 1.694030 2.165170 3.329812

rbinom(20, size=5, prob=0.3)

 [1] 1 3 0 1 2 0 2 1 0 2 3 1 2 0 1 1 2 1 2 2

rchisq(4, df=7)

[1] 9.183538 8.622633 8.072239 5.787448

rnorm(6, mean=100, sd=15)

[1] 104.29823  81.69232 106.51826 112.00265  97.54104 118.64378

rt(5, df=5, ncp=1)

[1]  0.07261917  1.83920988  1.59345974  0.11567864 -0.56678656

rf(5, df1=2, df2=10)

[1] 2.9532047 0.4913562 7.5093655 0.6704753 0.4034911

See ?Distributions for more distribution types. Even more information can be found in CRAN task view Probability Distributions.

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