Flipped Assignment 1

name<-c("Frank","Bob","Sally","Susan","Joan","Bill","Richard","Jane","Jill","John")
age<-c(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
)
Factor_A= c(-1,-1,-1,-1,-1, 1, 1, 1, 1, 1)
Factor_B= c( 1,-1, 1,-1, 1,-1, 1,-1, 1,-1)

dat<-data.frame(name,age, BMI,Factor_A,Factor_B)

a. Without manually entering the data, create a new column Factor AB that is the product of Factor A and Factor B

dat$FactorAB<-dat$Factor_A*dat$Factor_B

b)Create a new column Factor C that looks as follows

(-1,-1,1,1,-1,-1,1,1,-1,-1)

dat$FactorC<-c(1,-1,1,1,-1,-1,1,1,-1,-1)

3. Without manually entering the data, create a new column Factor ABC that is the product of Factor A, Factor B, and Factor C

dat$FactorABC<-dat$Factor_A*dat$Factor_B*dat$FactorC

d)Make sure that all Factor columns are recognized as factors in R

dat$Factor_A<-as.factor(dat$Factor_A)
dat$Factor_B<-as.factor(dat$Factor_B)
dat$FactorC<-as.factor(dat$FactorC)
dat$FactorAB<-as.factor(dat$FactorAB)
dat$FactorABC<-as.factor(dat$FactorABC)

dat$smoking<-c("Yes","No","No","Yes","Yes","No","Yes","Yes","No","Yes")
dat$smoking<-as.factor(dat$smoking)

3. Replace the BMI of Richard with a NA

dat$BMI[dat$name=="Richard"]<-NA

4. Without manually entering the data and using the dataframe after entering Richard’s BMI as NA , create a new column in dat with the logarithm of BMI

dat$L_BMI<-log(dat$BMI)

5)Create a new dataframe dat2 selecting only the columns from dat corresponding to logarithm of BMI, Factor A, Factor B, and Factor AB

dat2<-data.frame(dat$L_BMI,dat$Factor_A,dat$Factor_B,dat$FactorAB)

6. Create a new dataframe dat3 selecting only the first 5 rows of dat2

dat3<-dat2[1:5,]
dat3

##   dat.L_BMI dat.Factor_A dat.Factor_B dat.FactorAB
## 1  3.186353           -1            1           -1
## 2  2.906901           -1           -1            1
## 3  2.734368           -1            1           -1
## 4  3.122365           -1           -1            1
## 5  3.374169           -1            1           -1

##       name age  BMI Factor_A Factor_B FactorAB
## 1    Frank  34 24.2       -1        1       -1
## 2      Bob  28 18.3       -1       -1        1
## 3    Sally  19 15.4       -1        1       -1
## 4    Susan  28 22.7       -1       -1        1
## 5     Joan  30 29.2       -1        1       -1
## 6     Bill  47 32.4        1       -1       -1
## 7  Richard  24 21.0        1        1        1
## 8     Jane  34 40.4        1       -1       -1
## 9     Jill  32 24.8        1        1        1
## 10    John  64 34.4        1       -1       -1

##    dat.L_BMI dat.Factor_A dat.Factor_B dat.FactorAB
## 1   3.186353           -1            1           -1
## 2   2.906901           -1           -1            1
## 3   2.734368           -1            1           -1
## 4   3.122365           -1           -1            1
## 5   3.374169           -1            1           -1
## 6   3.478158            1           -1           -1
## 7         NA            1            1            1
## 8   3.698830            1           -1           -1
## 9   3.210844            1            1            1
## 10  3.538057            1           -1           -1

##   dat.L_BMI dat.Factor_A dat.Factor_B dat.FactorAB
## 1  3.186353           -1            1           -1
## 2  2.906901           -1           -1            1
## 3  2.734368           -1            1           -1
## 4  3.122365           -1           -1            1
## 5  3.374169           -1            1           -1