Learning R - Matrices

Data Camp: Introduction to R

Using my own data and examples

Chapter 4 - Factors

Types of variables - Categorical with limited number of categories (e.g. sex) - Continuous with infinite number of values (e.g. income)

Factor: a data type with limited categories

Steps to create a factor using sex as an example 1. Create a character vector with elements containing the values of the categories using the c function to combine them.

#1. Create sex_vector
sex_vector <- c("Male","Male","Female","Male","Female","Male","Female","Male","Male","Female")
sex_vector

 [1] "Male"   "Male"   "Female" "Male"   "Female" "Male"  
 [7] "Female" "Male"   "Male"   "Female"

2. Convert the vector to a factor and assign the result to a variable

#Convert sex_vector to a factor, giving it a new name
factor_sex <- factor(sex_vector)
factor_sex

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

Types of categorical factors: - Nominal - having no implied order (like sex) - Ordinal - having an implied order (Example: low, medium, high) The factor function needs two arguments: - order=TRUE - levels = c(“1st category”,“2nd category” …)

# Temperature
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

If your vector only contains single letters, the order matters when you map the vector elements to levels. R assumes alphabetical order. In this survey vector: “M”, “F”, “F”, “M”, “M”), R will show the levels as “F” “M”.

To correctly map “F” to “Female” and “M” to “Male”, the levels should be set to c(“Female”, “Male”), in this order.

#Create survey_vector
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

#Assign levels to the factor_survey_vector
levels(factor_survey_vector) <- c("Female","Male")
factor_survey_vector

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

Summary Function - This function gives you a summary of a variable. The summary of the factor is more useful than the summary of the vector.

# Generate summary for survey_vector
summary(survey_vector)

   Length     Class      Mode 
        5 character character 

# Generate summary for factor_survey_vector
summary(factor_survey_vector)

Female   Male 
     2      3 

Compare elements of a factor

Assign each element of the factor to a variable using square brackets: variable_name <- factor_name[# of element]

# Create factor_speed_vector
speed_vector <- c("medium", "slow", "slow", "medium", "fast")
factor_speed_vector <- factor(speed_vector, ordered = TRUE, levels = c("slow", "medium", "fast"))

# Factor value for second data analyst
da2 <- factor_speed_vector[2]

# Factor value for fifth data analyst
da5 <- factor_speed_vector[5]

# Is data analyst 2 faster than data analyst 5?
da2 > da5

[1] FALSE