# The data set used
SmokeBan <- read.csv("https://vincentarelbundock.github.io/Rdatasets/csv/AER/SmokeBan.csv", header = TRUE)
# CREATE A NEW DATA FRAME WITH COLUMNS AND ROWS
# Creating and displaying a new data frame called "Smokers50AndUnder".
Smokers50AndUnder <- subset(SmokeBan,smoker=="yes" & age<= 50,select=c(gender,ban,age))
#Displaying the first 20 records
head(Smokers50AndUnder,20)
## gender ban age
## 1 female yes 41
## 2 female yes 44
## 4 female no 29
## 7 female yes 47
## 8 male no 36
## 19 female yes 28
## 20 male yes 24
## 21 male yes 39
## 25 male yes 31
## 26 female no 33
## 29 male no 24
## 34 male no 48
## 40 female no 32
## 42 male no 27
## 43 female yes 30
## 45 female no 18
## 47 female yes 48
## 50 male yes 41
## 56 female yes 46
## 80 male yes 43
# ADD A NEW COLUMN TO THE DATA FRAME.
# Adding the years_smoking column. This column denotes the number of years an employee has been smoking. The values for the new column will be randomly generated up to 25 years.
Smokers50AndUnder$years_smoked<-sample(1:25, size = 2054, replace = T)
#Displaying the first 20 records
head(Smokers50AndUnder,20)
## gender ban age years_smoked
## 1 female yes 41 21
## 2 female yes 44 14
## 4 female no 29 2
## 7 female yes 47 24
## 8 male no 36 9
## 19 female yes 28 2
## 20 male yes 24 21
## 21 male yes 39 14
## 25 male yes 31 16
## 26 female no 33 13
## 29 male no 24 6
## 34 male no 48 19
## 40 female no 32 17
## 42 male no 27 21
## 43 female yes 30 16
## 45 female no 18 10
## 47 female yes 48 22
## 50 male yes 41 24
## 56 female yes 46 15
## 80 male yes 43 20
# CREATING S SUBSET OF THE DATA FRAME.
# for all smokers who are over 40 add ban.
Smokers50AndUnder$ban[Smokers50AndUnder$age > 40]<-"yes"
#Displaying the first 20 records
head(Smokers50AndUnder,20)
## gender ban age years_smoked
## 1 female yes 41 21
## 2 female yes 44 14
## 4 female no 29 2
## 7 female yes 47 24
## 8 male no 36 9
## 19 female yes 28 2
## 20 male yes 24 21
## 21 male yes 39 14
## 25 male yes 31 16
## 26 female no 33 13
## 29 male no 24 6
## 34 male yes 48 19
## 40 female no 32 17
## 42 male no 27 21
## 43 female yes 30 16
## 45 female no 18 10
## 47 female yes 48 22
## 50 male yes 41 24
## 56 female yes 46 15
## 80 male yes 43 20