Create new dummy variable columns from categorical variable

Question

I have a several data sets with 75,000 observations and a type variable that can take on a value 0-4. I want to add five new dummy variables to each data set for all types. The best way I could come up with to do this is as follows:

# For the 'binom' data set create dummy variables for all types in all data sets binom.dummy.list<-list() for(i in 0:4){     binom.dummy.list[[i+1]]<-sapply(binom$type,function(t) ifelse(t==i,1,0)) }  # Add and merge data binom.dummy.df<-as.data.frame(do.call("cbind",binom.dummy.list)) binom.dummy.df<-transform(binom.dummy.df,id=1:nrow(binom)) binom<-merge(binom,binom.dummy.df,by="id") 

While this works, it is incredibly slow (the merge function has even crashed a few times). Is there a more efficient way to do this? Perhaps this functionality is part of a package that I am not familiar with?

Answer 1

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.