R has a “sub-language” to translate formulas into design matrix, and in the spirit of the language you can take advantage of it. It’s fast and concise. Example: you have a cardinal predictor x, a categorical predictor catVar, and a response y.
> binom <- data.frame(y=runif(1e5), x=runif(1e5), catVar=as.factor(sample(0:4,1e5,TRUE)))
> head(binom)
y x catVar
1 0.5051653 0.34888390 2
2 0.4868774 0.85005067 2
3 0.3324482 0.58467798 2
4 0.2966733 0.05510749 3
5 0.5695851 0.96237936 1
6 0.8358417 0.06367418 2
You just do
> A <- model.matrix(y ~ x + catVar,binom)
> head(A)
(Intercept) x catVar1 catVar2 catVar3 catVar4
1 1 0.34888390 0 1 0 0
2 1 0.85005067 0 1 0 0
3 1 0.58467798 0 1 0 0
4 1 0.05510749 0 0 1 0
5 1 0.96237936 1 0 0 0
6 1 0.06367418 0 1 0 0
Done.