FACTORS

Factors are the data objects which are used to categorize the data and store it as levels. They can store both strings and integers. They are useful in the columns which have a limited number of unique values. Like “Male,”Female" and True, False etc. They are useful in data analysis for statistical modeling.

Factors are created using the factor () function by taking a vector as input.

Let us consider the following example

data <- c("East","West","East","North","North","East","West",
          "West","West","East","North")
factor_data=factor(data)
factor_data

 [1] East  West  East  North North East  West  West  West 
[10] East  North
Levels: East North West

By default, the factor function assign the levels alphabetically. We can change this using levels=() command.

data <- c("East","West","East","North","North","East","West",
          "West","West","East","North")
factor_data=factor(data, levels = c("North","East","West"))
factor_data

 [1] East  West  East  North North East  West  West  West 
[10] East  North
Levels: North East West

We can also convert a numeric vector into factor:

num_data=c(0,0,1,0,1,1,1,0,1,0,1,1,0,1,0,1,1)
factor_data=factor(num_data, labels = c("Male","Female"))
factor_data

 [1] Male   Male   Female Male   Female Female Female Male  
 [9] Female Male   Female Female Male   Female Male   Female
[17] Female
Levels: Male Female

Generating Factor Levels

We can generate factor levels by using the gl() function. It takes two integers as input which indicates how many levels and how many times each level.

gl(n, k, labels)

Following is the description of the parameters used −

• n is a integer giving the number of levels.

• k is a integer giving the number of replications.

• labels is a vector of labels for the resulting factor levels.

factor_data=gl(2,8, labels = c("Male","Female"))
factor_data

 [1] Male   Male   Male   Male   Male   Male   Male   Male  
 [9] Female Female Female Female Female Female Female Female
Levels: Male Female

Another example of Factor is:

# Generating 100 random numbers between 1 and 100
data = sample(1:100,100)
# Generating an empty vector of size 100.
result=mat.or.vec(100,1)
for (i in 1:100) {
  if(data[i]<30){
    result[i]=0
    }
  else if(data[i]>=30 && data[i]<45){
    result[i]=1
  }
  else if(data[i]>=45 && data[i]<60){
    result[i]=2
  }
  else {
    result[i]=3
    }
}

f_result=factor(result, labels = c("Fail", "Third Div", "Second Div", "First Div"))
str(f_result)

 Factor w/ 4 levels "Fail","Third Div",..: 2 3 4 4 4 2 2 1 1 3 ...

summary(f_result)

      Fail  Third Div Second Div  First Div 
        29         15         15         41 

plot(f_result)