#VECTOR CODE TO GENERATE NUMBERS IN R PLUS#

Otherwise in most simulations we don’t actually want or need to set the seed. You’ll also see me set the seed when I’m making a function for a simulation and I want to make sure it works correctly. It is also is useful when making an example dataset to demonstrate a coding issue, like if you were asking a code question on Stack Overflow. Making sure someone else will be able to exactly reproduce your results when running the same code can be desirable in teaching. To get reproducible random numbers we need to set the seed via set.seed(). Second, if we run this code again we’ll get different numbers. Setting the random seed for reproducible random numbers Since I used the default values for mean and sd, it’s not clear exactly what distribution I drew the numbers from. What I might refer to as lazy coding on my part can look pretty mysterious to someone reading my code (or to my future self reading my code). However, the code itself isn’t particularly clear. There are a couple things about this code and the output to discuss.įirst, the code did get me 5 numbers, which is what I wanted.

Not all r functions have defaults to the parameter arguments like this. The mean and sd arguments show what the default values of the parameters are (note that sd is the standard deviation, not the variance). The n argument is the number of observations we want to generate. From the Usage section of the documentation: I use rnorm() a lot, sometimes with good reason and other times when I need some numbers and I really don’t care too much about what they are. Rnorm() to generate random numbers from the normal distribution This is easier to see with practice, so let’s get started. We define how many random numbers we want to generate in the first argument ( n) and then define the parameters for the distribution we want to draw from. The r functions for a chosen distribution all work basically the same way. I recently needed to generate data from the Tweedie distribution to test a modeling tool, which I could do via package tweedie. There are many other distributions available as part of the stats package (e.g., binomial, F, log normal, beta, exponential, Gamma) and, as you can imagine, even more available in add-on packages.

#VECTOR CODE TO GENERATE NUMBERS IN R PLUS#

We’ll look at those today, plus the Poisson ( rpois()) distribution for generating discrete counts. The basic distributions that I use the most for generating random numbers are the normal ( rnorm()) and uniform ( runif()) distributions. These functions always start with r (for “random”). This can be done via the functions for generating random deviates.

Using replicate() to repeatedly make a datasetĪn easy way to generate numeric data is to pull random numbers from some distribution.

Repeatedly simulate data with replicate().

Multiple quantitative variables with groups.

Simulate data with a difference among groups.

Simulate data with no differences among two groups.

Creating datasets with quantiative and categorical variables.

Repeat each element a different number of times.

Set the output vector length with the length.out argument.

Repeat a whole vector with the times argument.

Repeat each element of a vector with each.

Example of using the simulated numbers from rpois().

Example of using the simulated numbers from runif().

runif() pulls from the uniform distribution.

Example of using the simulated numbers from rnorm().

rnorm() to generate random numbers from the normal distribution.

Vector code to generate numbers in r