Table of Contents
Factors are a fundamental R data type for representing categorical variables. Whether you have experience with factors or find them confusing, this guide aims to build your intuition and skills for working with categoricals and numerics in a unified framework.
As an experienced data scientist and R expert, I‘ll share my insider knowledge to help you become proficient with factors. We‘ll explore key concepts along with plenty of illustrative examples and visualizations. My goal is for you to finish this guide with an advanced yet accessible mastery of factors in R!
What Makes Factors Special
In statistics, we categorize variables as either categorical or continuous:
- Categorical – Finite distinct groups or categories
- Example: Gender, blood type
- Continuous – Infinite numeric measurements
- Example: Age, income
R stores categorical values in factor objects. Under the hood, factors get mapped to integer codes while retaining original category labels.
This makes factors very special – they allow us to build statistical models combining the power of both categorical and continuous inputs!
Factors become even more useful when you leverage attributes like:
- Level ordering
- Reference level settings
- Contrast encodings
We‘ll explore how to utilize these later after covering factor basics.
Creating Factors in R
The factor() function converts vector objects into factor categorical variables.
genders <- c("Male", "Female", "Female", "Male")
factor_genders <- factor(genders)By default, R defines factor levels based on the unique values. We can customize many options:
blood_types <- c("O", "AB", "A", "AB", "B")
factor_blood <- factor(blood_types,
levels = c("O", "A", "B", "AB"),
labels = c("Type O", "Type A", "Type B", "Type AB"))Here we set specific level orders and labels – useful for changing level references or disambiguating coded outputs.
Working with Factor Variables
Many functions help us explore factors:
summary(factor_blood)
table(factor_genders)
barplot(factor_blood)
We observe the frequency distribution across levels. Visualizations like bar plots make patterns even more apparent.
Modifying factor attributes changes how they get analyzed or modeled:
factor_blood_ordered <- factor(blood_types,
levels = c("O", "A", "B", "AB"),
ordered = TRUE)Here we indicate an ordinal sequence for types. This allows fitting models that exploit the ordering.
Statistical Modeling with Factors
We often want to model relationships between variables. Factors can be incorporated as both predictors and outcomes:
fit <- lm(income ~ factor_education + age, data = survey)This regresses income on an ordinal education factor and a continuous age variable.
To avoid pitfalls, heed this factor modeling advice:
- Check for unused levels
- Set appropriate orderings
- Try different default contrasts
- Watch for extrapolation across groups
Thankfully, R makes factor-based modeling very intuitive – we just reference them like any other variable!
Encoding Categories for Machine Learning
While statistical models play nicely with factors, machine learning algorithms require numeric data. This means translating categories into numbers via encoding schemes.
Popular types include:
- Dummy coding: 1/0 indicators for category membership
- One-hot encoding: Like dummy but with k-1 columns for k levels
- Effect coding: Compares against a defined reference
- Helmert contrast: Each level versus subsequent level means
R provides shortcuts to automate encoding when training models. We can also manually create encoded matrices using model.matrix():
dummy_codes <- model.matrix(~ factor_blood - 1) 
The coded matrix gets generated with blood types represented numerically!
Choosing encodings depends on your goals and constraints – experiment to see what works best!
Advanced Factor Applications
So far we‘ve covered factor fundamentals. Now let‘s discuss some more advanced applications.
Interactive Factors in Models
We can include interaction effects between factors and other variables:
fit <- lm(sales ~ gender*store_type + age, data = sales_data)This allows sales differences between gender and store types to vary along with age. Very powerful for gaining nuanced insights!
Be cautious with higher-order interaction effects to avoid overfitting. Carefully check interpreted significance.
Alternate Contrast Schemes
When comparing factor level coefficients, R uses "treatment contrasts" by default. However other schemes may better match experimental constraints.
We can set specialized contrasts like Helmert coding with a single function argument:
fit <- lm(y ~ x + factor_z, data = d, contrasts = list(factor_z = contr.helmert))Now model estimates follow the Helmert contrast mindset!
Converting Categories to Scores
For machine learning with pure numerics, we can convert categories to simple scores based on ordered averages:
scores <- c(Type1 = 1, Type2 = 2, Type3 = 3)
data$blood_type <- scores[data$blood_type]This encodes an underlying score for each blood type. Be careful not to imply false precision though!
Key Takeaways
We‘ve covered a lot of ground when it comes to understanding and applying factors in R! Let‘s recap the key lessons:
- Factors represent categorical data using integer coding
- Set factor attributes like levels, orderings and labels
- Explore and visualize factors to understand behaviors
- Incorporate factors as predictors or outcomes in models
- Encode categories appropriately based on algorithms
- Take advantage of interactions, contrasts and other extensions
I hope this guide has enhanced your skills and intuition for working with factors in R. They open up many expanding modeling possibilities at the intersection of statistics and machine learning!
With the knowledge you‘ve gained here, you‘re now equipped to conduct powerful analyses leveraging both categorical and continuous data!