Creating subgroups from categorical data by using lapply in R

I was wondering if you kind folks could answer a question I have. In the sample data I've provided below, in column 1 I have a categorical variable, and in column 2 p-values. ``` x <- c(rep("A",0.1*10000),rep("B",0.2*10000),rep("C",0.65*10000),rep("D",0.05*10000)) categorical_data=as.matrix(sample(x,10000)) p_val=as.matrix(runif(10000,0,1)) combi=as.data.frame(cbind(categorical_data,p_val)) head(combi) V1 V2 1 A 0.484525170875713 2 C 0.48046557046473 3 C 0.228440979029983 4 B 0.216991128632799 5 C 0.521497668232769 6 D 0.358560319757089 ``` I want to now take one of the categorical variables, let's say "C", and create another variable if it is C (print 1 in column 3, or 0 if it isn't). ``` combi$NEWVAR[combi$V1=="C"] <-1 combi$NEWVAR[combi$V1!="C" <-0 V1 V2 NEWVAR 1 A 0.484525170875713 0 2 C 0.48046557046473 1 3 C 0.228440979029983 1 4 B 0.216991128632799 0 5 C 0.521497668232769 1 6 D 0.358560319757089 0 ``` I'd like to do this for each of the variables in V1, and then loop over using lapply: ``` variables=unique(combi$V1) loopeddata=lapply(variables,function(x){ combi$NEWVAR[combi$V1==x] <-1 combi$NEWVAR[combi$V1!=x]<-0 } ) ``` My output however looks like this: ``` [[1]] [1] 0 [[2]] [1] 0 [[3]] [1] 0 [[4]] [1] 0 ``` My desired output would be like the table in the second block of code, but when looping over the third column would be A=1, while B,C,D=0. Then B=1, A,C,D=0 etc. If anyone could help me out that would be very much appreciated.