# Generate two variables with precise pre-specified correlation [duplicate]

## UPDATE: Solution

Thanks to Greg Snow for pointing out the empirical = TRUE command in mvrnorm (multivariate random normal stuff)! Here's the explicit code:

samples = 200
r = 0.83

library('MASS')
data = mvrnorm(n=samples, mu=c(0, 0), Sigma=matrix(c(1, r, r, 1), nrow=2), empirical=TRUE)
X = data[, 1]  # standard normal (mu=0, sd=1)
Y = data[, 2]  # standard normal (mu=0, sd=1)

# Assess that it works
cor(X, Y)  # yay, r = 0.83!
cor(X*0.01 + 42, Y*3 - 1)  # Linear transformations of X and Y won't change r.


## Original question

I want to generate two variables with (pseudo-) random numbers with an exact pearson's r. How do I do that? Python and/or R solutions would be nice!

I am able to generate random data that approximates a pre-specified r in python in the following way. I'm not searching for approximations but for data that with an exact pre-specified r, i.e. with r=0.83000 in the example below:

samples = 200
r = 0.83

# Generate pearson correlated data with approximately cor(X, Y) = r
import numpy as np
data = np.random.multivariate_normal([0, 0], [[1, r], [r, 1]], size=samples)
X, Y = data[:,0], data[:,1]

# That's it! Now let's take a look at the actual correlation:
import scipy.stats as stats
print 'r=', stats.pearsonr(X, Y)[0]


The motivation for knowing r is that I'm testing out (bayesian) statistical models that can infer r from data and they are a lot easier to evaluate when r is well specified.

For R you can use the mvrnorm function in the MASS package and set empirical=TRUE.