Hi,
Try:
x <-
c(rep("A",0.1*10000),rep("B",0.2*10000),rep("C",0.65*10000),rep("D",0.05*10000))
set.seed(24)
categorical_data <- sample(x,10000)
set.seed(49)
p_val <- runif(10000,0,1)
combi <- data.frame(V1=categorical_data,V2=p_val)
variables <- unique(combi$V1)
?res <- lapply(levels(variables),function(x){
combi$NEWVAR<-(combi$V1==x)*1; combi})
A.K.
I was wondering if you kind folks could answer a question I have. In the sample
data I've provided below, in column 1 I have a categorical
variable labeled A,B,C and D, and in column 2 simulated p-values.
x <-
c(rep("A",0.1*10000),rep("B",0.2*10000),rep("C",0.65*10000),rep("D",0.05*10000))
categorical_data=as.matrix(sample(x,10000))
p_val=as.matrix(runif(10000,0,1))
combi=as.data.frame(cbind(categorical_data,p_val))
This is simulated data, but my example comes out as
head(combi)
? V1 ? ? ? ? ? ? ? ?V2
1 ?A 0.484525170875713
2 ?C ?0.48046557046473
3 ?C 0.228440979029983
4 ?B 0.216991128632799
5 ?C 0.521497668232769
6 ?D 0.358560319757089
I want to now take one of the categorical variables, let's say
"C", and create another variable (coded as 1 if it's C or 0 if it
isn't).
combi$NEWVAR[combi$V1=="C"] <-1
combi$NEWVAR[combi$V1!="C" <-0
? V1 ? ? ? ? ? ? ? ?V2 NEWVAR
1 ?A 0.484525170875713 0
2 ?C ?0.48046557046473 1
3 ?C 0.228440979029983 1
4 ?B 0.216991128632799 0
5 ?C 0.521497668232769 1
6 ?D 0.358560319757089 0
I'd like to do this for each of the variables in V1, creating a new table
each time, by looping over using lapply:
variables=unique(combi$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, for each table created.
Any help would me very much appreciated