name<-c("frank","bob","sally","susan","joan","bill","richard","jane","jill","john")
str(name)
## chr [1:10] "frank" "bob" "sally" "susan" "joan" "bill" "richard" "jane" ...
age<-c(34,28,19,28,30,47,24,34,32,64)
str(age)
## num [1:10] 34 28 19 28 30 47 24 34 32 64
BMI<-c(24.2,18.3,15.4,22.7,29.2,32.4,21.0,40.4,24.8,34.4)
str(BMI)
## num [1:10] 24.2 18.3 15.4 22.7 29.2 32.4 21 40.4 24.8 34.4
FactorA<-c(-1,-1,-1,-1,-1,1,1,1,1,1)
str(FactorA)
## num [1:10] -1 -1 -1 -1 -1 1 1 1 1 1
FactorB<-c(1,-1,1,-1,1,-1,1,-1,1,-1)
str(FactorB)
## num [1:10] 1 -1 1 -1 1 -1 1 -1 1 -1
Dat<-data.frame(name,age,BMI,FactorA,FactorB)
Dat
## name age BMI FactorA FactorB
## 1 frank 34 24.2 -1 1
## 2 bob 28 18.3 -1 -1
## 3 sally 19 15.4 -1 1
## 4 susan 28 22.7 -1 -1
## 5 joan 30 29.2 -1 1
## 6 bill 47 32.4 1 -1
## 7 richard 24 21.0 1 1
## 8 jane 34 40.4 1 -1
## 9 jill 32 24.8 1 1
## 10 john 64 34.4 1 -1
Dat
## name age BMI FactorA FactorB
## 1 frank 34 24.2 -1 1
## 2 bob 28 18.3 -1 -1
## 3 sally 19 15.4 -1 1
## 4 susan 28 22.7 -1 -1
## 5 joan 30 29.2 -1 1
## 6 bill 47 32.4 1 -1
## 7 richard 24 21.0 1 1
## 8 jane 34 40.4 1 -1
## 9 jill 32 24.8 1 1
## 10 john 64 34.4 1 -1
Dat$FactorAB<-FactorA*FactorB
Dat$FactorC<-c(-1,-1,1,1,-1,-1,1,1,-1,-1)
Dat
## name age BMI FactorA FactorB FactorAB FactorC
## 1 frank 34 24.2 -1 1 -1 -1
## 2 bob 28 18.3 -1 -1 1 -1
## 3 sally 19 15.4 -1 1 -1 1
## 4 susan 28 22.7 -1 -1 1 1
## 5 joan 30 29.2 -1 1 -1 -1
## 6 bill 47 32.4 1 -1 -1 -1
## 7 richard 24 21.0 1 1 1 1
## 8 jane 34 40.4 1 -1 -1 1
## 9 jill 32 24.8 1 1 1 -1
## 10 john 64 34.4 1 -1 -1 -1
Dat$FactorABC<-FactorA*FactorB*Dat$FactorC
Dat
## name age BMI FactorA FactorB FactorAB FactorC FactorABC
## 1 frank 34 24.2 -1 1 -1 -1 1
## 2 bob 28 18.3 -1 -1 1 -1 -1
## 3 sally 19 15.4 -1 1 -1 1 -1
## 4 susan 28 22.7 -1 -1 1 1 1
## 5 joan 30 29.2 -1 1 -1 -1 1
## 6 bill 47 32.4 1 -1 -1 -1 1
## 7 richard 24 21.0 1 1 1 1 1
## 8 jane 34 40.4 1 -1 -1 1 -1
## 9 jill 32 24.8 1 1 1 -1 -1
## 10 john 64 34.4 1 -1 -1 -1 1
Dat$FactorA<-as.factor(Dat$FactorA)
Dat
## name age BMI FactorA FactorB FactorAB FactorC FactorABC
## 1 frank 34 24.2 -1 1 -1 -1 1
## 2 bob 28 18.3 -1 -1 1 -1 -1
## 3 sally 19 15.4 -1 1 -1 1 -1
## 4 susan 28 22.7 -1 -1 1 1 1
## 5 joan 30 29.2 -1 1 -1 -1 1
## 6 bill 47 32.4 1 -1 -1 -1 1
## 7 richard 24 21.0 1 1 1 1 1
## 8 jane 34 40.4 1 -1 -1 1 -1
## 9 jill 32 24.8 1 1 1 -1 -1
## 10 john 64 34.4 1 -1 -1 -1 1
Dat$FactorB<-as.factor(Dat$FactorB)
Dat
## name age BMI FactorA FactorB FactorAB FactorC FactorABC
## 1 frank 34 24.2 -1 1 -1 -1 1
## 2 bob 28 18.3 -1 -1 1 -1 -1
## 3 sally 19 15.4 -1 1 -1 1 -1
## 4 susan 28 22.7 -1 -1 1 1 1
## 5 joan 30 29.2 -1 1 -1 -1 1
## 6 bill 47 32.4 1 -1 -1 -1 1
## 7 richard 24 21.0 1 1 1 1 1
## 8 jane 34 40.4 1 -1 -1 1 -1
## 9 jill 32 24.8 1 1 1 -1 -1
## 10 john 64 34.4 1 -1 -1 -1 1
Dat$FactorC<-as.factor(Dat$FactorC)
Dat
## name age BMI FactorA FactorB FactorAB FactorC FactorABC
## 1 frank 34 24.2 -1 1 -1 -1 1
## 2 bob 28 18.3 -1 -1 1 -1 -1
## 3 sally 19 15.4 -1 1 -1 1 -1
## 4 susan 28 22.7 -1 -1 1 1 1
## 5 joan 30 29.2 -1 1 -1 -1 1
## 6 bill 47 32.4 1 -1 -1 -1 1
## 7 richard 24 21.0 1 1 1 1 1
## 8 jane 34 40.4 1 -1 -1 1 -1
## 9 jill 32 24.8 1 1 1 -1 -1
## 10 john 64 34.4 1 -1 -1 -1 1
Dat$FactorABC<-as.factor(Dat$FactorABC)
Dat
## name age BMI FactorA FactorB FactorAB FactorC FactorABC
## 1 frank 34 24.2 -1 1 -1 -1 1
## 2 bob 28 18.3 -1 -1 1 -1 -1
## 3 sally 19 15.4 -1 1 -1 1 -1
## 4 susan 28 22.7 -1 -1 1 1 1
## 5 joan 30 29.2 -1 1 -1 -1 1
## 6 bill 47 32.4 1 -1 -1 -1 1
## 7 richard 24 21.0 1 1 1 1 1
## 8 jane 34 40.4 1 -1 -1 1 -1
## 9 jill 32 24.8 1 1 1 -1 -1
## 10 john 64 34.4 1 -1 -1 -1 1
Dat$smoking<-c("yes","no","no","yes","yes","no","yes","yes","no","yes")
Dat
## name age BMI FactorA FactorB FactorAB FactorC FactorABC smoking
## 1 frank 34 24.2 -1 1 -1 -1 1 yes
## 2 bob 28 18.3 -1 -1 1 -1 -1 no
## 3 sally 19 15.4 -1 1 -1 1 -1 no
## 4 susan 28 22.7 -1 -1 1 1 1 yes
## 5 joan 30 29.2 -1 1 -1 -1 1 yes
## 6 bill 47 32.4 1 -1 -1 -1 1 no
## 7 richard 24 21.0 1 1 1 1 1 yes
## 8 jane 34 40.4 1 -1 -1 1 -1 yes
## 9 jill 32 24.8 1 1 1 -1 -1 no
## 10 john 64 34.4 1 -1 -1 -1 1 yes
Dat$smoking<-as.factor(Dat$smoking)
Dat
## name age BMI FactorA FactorB FactorAB FactorC FactorABC smoking
## 1 frank 34 24.2 -1 1 -1 -1 1 yes
## 2 bob 28 18.3 -1 -1 1 -1 -1 no
## 3 sally 19 15.4 -1 1 -1 1 -1 no
## 4 susan 28 22.7 -1 -1 1 1 1 yes
## 5 joan 30 29.2 -1 1 -1 -1 1 yes
## 6 bill 47 32.4 1 -1 -1 -1 1 no
## 7 richard 24 21.0 1 1 1 1 1 yes
## 8 jane 34 40.4 1 -1 -1 1 -1 yes
## 9 jill 32 24.8 1 1 1 -1 -1 no
## 10 john 64 34.4 1 -1 -1 -1 1 yes
Dat[7,3]<-c(NA)
Dat
## name age BMI FactorA FactorB FactorAB FactorC FactorABC smoking
## 1 frank 34 24.2 -1 1 -1 -1 1 yes
## 2 bob 28 18.3 -1 -1 1 -1 -1 no
## 3 sally 19 15.4 -1 1 -1 1 -1 no
## 4 susan 28 22.7 -1 -1 1 1 1 yes
## 5 joan 30 29.2 -1 1 -1 -1 1 yes
## 6 bill 47 32.4 1 -1 -1 -1 1 no
## 7 richard 24 NA 1 1 1 1 1 yes
## 8 jane 34 40.4 1 -1 -1 1 -1 yes
## 9 jill 32 24.8 1 1 1 -1 -1 no
## 10 john 64 34.4 1 -1 -1 -1 1 yes
Dat$logBMI<-log(Dat[,3])
Dat
## name age BMI FactorA FactorB FactorAB FactorC FactorABC smoking logBMI
## 1 frank 34 24.2 -1 1 -1 -1 1 yes 3.186353
## 2 bob 28 18.3 -1 -1 1 -1 -1 no 2.906901
## 3 sally 19 15.4 -1 1 -1 1 -1 no 2.734368
## 4 susan 28 22.7 -1 -1 1 1 1 yes 3.122365
## 5 joan 30 29.2 -1 1 -1 -1 1 yes 3.374169
## 6 bill 47 32.4 1 -1 -1 -1 1 no 3.478158
## 7 richard 24 NA 1 1 1 1 1 yes NA
## 8 jane 34 40.4 1 -1 -1 1 -1 yes 3.698830
## 9 jill 32 24.8 1 1 1 -1 -1 no 3.210844
## 10 john 64 34.4 1 -1 -1 -1 1 yes 3.538057
Dat2<-Dat[,c(4,5,6,9)]
Dat2
## FactorA FactorB FactorAB smoking
## 1 -1 1 -1 yes
## 2 -1 -1 1 no
## 3 -1 1 -1 no
## 4 -1 -1 1 yes
## 5 -1 1 -1 yes
## 6 1 -1 -1 no
## 7 1 1 1 yes
## 8 1 -1 -1 yes
## 9 1 1 1 no
## 10 1 -1 -1 yes
Dat3<-Dat[c(1,2,3,4,5),]
Dat3
## name age BMI FactorA FactorB FactorAB FactorC FactorABC smoking logBMI
## 1 frank 34 24.2 -1 1 -1 -1 1 yes 3.186353
## 2 bob 28 18.3 -1 -1 1 -1 -1 no 2.906901
## 3 sally 19 15.4 -1 1 -1 1 -1 no 2.734368
## 4 susan 28 22.7 -1 -1 1 1 1 yes 3.122365
## 5 joan 30 29.2 -1 1 -1 -1 1 yes 3.374169