Basic R revision - Part 2

Factors

Factors are used to store categorical variables, where categorical variables are those whose value can only be one amongst a well-defined, discrete set of values. For example factor_gender is a factor that stores variables that can contain elements: "male" and "female".

To construct a factor variable out of a vector of values, just wrap the vector using factor(). For example:

> gender_vector = c("Male", "Female", "Female", "Male", "Male")
> factor_gender_vector = factor(gender_vector)
> factor_gender_vector
[1] Male   Female Female Male   Male 
Levels: Female Male

Categorical variables are of two types: nominal and ordinal.
factor_gender would be nominal as there is no grading from lower to higher between male and female unless you are a sexist asshole.
factor_bondratings would be ordinal as there is a natural grading, where we know :

AAA > AA > A > BBB > BB > CCC > CC > C > D

In R, the assumption in for the categorical nominal variable to be nominal. If you wish to specify ordinal, use the order and levels keywords:

temperature_vector = c("High","Low","High","Low","Medium")
factor_temperature_vector = factor(temperature_vector, order=TRUE, levels=c("Low","Medium","High"))
> factor_temperature_vector
[1] High   Low    High   Low    Medium
Levels: Low < Medium < High

Renaming the elements of a factor variable

Use the levels() function to do this.

> survey_vector = c("M", "F", "F", "M", "M")
> factor_survey_vector = factor(survey_vector)
> factor_survey_vector
[1] M F F M M
Levels: F M

> levels(factor_survey_vector) = c("Female", "Male")
> factor_survey_vector
[1] Male   Female Female Male   Male 
Levels: Female Male

Note that it is important to follow the correct order while naming. Using
levels(factor_survey_vector) = c("Female", "Male")
would have been incorrect, since I had run the code earlier to see the unnamed output being "Levels: F M"

Using summary()

summary() is a general R function but it's very useful with factors. For example:

> summary(factor_survey_vector)
Female   Male
     2      3

If a factor is nominal, then the comparison operator > becomes invalid. See the following (continuation) code for my favorite proof for the equality of sexes:

> # Battle of the sexes:
> # Male
> factor_survey_vector[1]
[1] Male
Levels: Female Male
> # Female
> factor_survey_vector[2]
[1] Female
Levels: Female Male
> # Male larger than female?
> factor_survey_vector[1] > factor_survey_vector[2]
'>' not meaningful for factors

Comparison operators meaningful for ordinal categorical variables. See:

> speed_vector = c("Fast", "Slow", "Slow", "Fast", "Ultra-fast")
> # Add your code below
> factor_speed_vector = factor(speed_vector, order = TRUE, levels = c("Slow", "Fast", "Ultra-fast"))
> # Print
> factor_speed_vector
[1] Fast       Slow       Slow       Fast       Ultra-fast
Levels: Slow < Fast < Ultra-fast
> # R prints automagically in the right order
> summary(factor_speed_vector)
      Slow       Fast Ultra-fast
         2          2          1

> compare_them = factor_speed_vector[2] > factor_speed_vector[5]
> # Is data analyst 2 faster than data analyst 5?
> compare_them
[1] FALSE

So Analyst 2 is not faster than Analyst 5.