Generate systematic and random data

Empty objects
Systematic numerical sequences
- Ordered sequences
- Repeated sequences
Random data
- Sample from an urn
- Data from random variables with different distributions
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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] 2 5 3 6 6 1 4 6 4 3 6 3 5 4 1 6 2 1 2 6

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

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

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

[1] 4 8

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

[1] 8 7 5 4 1 2 3 6

Data from random variables with different distributions

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

[1] 2.590905 2.158129 1.714000 3.072732 3.068622

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

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

rchisq(4, df=7)

[1] 4.102661 9.427503 9.183538 8.622633

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

[1] 108.30876  99.07132  95.41056  94.29293  89.57940  96.88124

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

[1] -0.3553506  6.6387468  2.2656603 -0.1390429  0.4654493

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

[1] 1.4005108 2.9532047 0.4913562 7.5093655 0.6704753

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

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