a 'scale' matrix can be any matrix that is positive definite. Wishart distribution is often used in Bayesian hierarchical model to capture characteristics of a inverse covariance matrix.
Back to your problem, if you read the help page of
rWishart carefully, it says:
If X1,...,Xm, Xi in R^p is a sample of m independent multivariate Gaussians with mean (vector) 0, and covariance matrix Σ, the distribution of M = X'X is W_p(Σ, m).
However in your toy example, you chose to sample $X_i$'s with different means and different variance, and the degree of freedom $p$ is predetermined by the size of your sample $X_i$, not randomly chosen.
A better example can be constructed as such:
data = cbind(rnorm(100,0,5),rnorm(100,0,2),rnorm(100,0,3))
Sigma = cov(data) % this is a 3 by 3 matrix
eigen(Sigma) %check positiv definite
X = mvtnorm(100,rep(0,3),Sigma)
df = dim(X)
%generate random wishart sample with df and Sigma
% compute X'X
You'll find that these random samples are roughly in the range of X'X, a more rigorous check can be done by looping said X'X a few times(~100,000) and take the empirical mean. In theory this should agree with the first moment of Wishart distribution m*Σ by law of large numbers.
You can certainly generate non-central Wishart distribution, a good reference on this topic (or in fact any matrix variate distribution) is to look at Matrix Variate Distributions by Gupta and Nagar.
Hope this helps :)