🔬 Tidyverse Series – Post 6: Handling Categorical Data with forcats Link to heading

🛠 Why forcats? Link to heading

Categorical variables (factors) are essential in data analysis, but handling them in base R can be frustrating. {forcats} simplifies working with factors by providing intuitive, readable functions for:

✔️ Reordering categories for better visualization
✔️ Lumping small groups together
✔️ Handling missing values efficiently
✔️ Creating consistent factor levels across datasets

Working with factors is crucial when dealing with biological classifications, categorical survey responses, or any grouped data. Let’s explore the key functions in {forcats} and how they enhance data manipulation.

📚 Key forcats Functions Link to heading

forcats provides multiple functions for handling categorical data efficiently. Below are the most commonly used ones, along with practical applications.

Let’s examine these functions in detail with real-world examples.

🔄 Reordering Factor Levels with fct_reorder() Link to heading

By default, R orders factor levels alphabetically, which isn’t always meaningful. fct_reorder() allows us to sort factors based on a numerical variable.

🔹 Example: Ordering Genes by Mean Expression Link to heading

Imagine we have gene expression data, and we want to order genes by expression level in a plot.

Dataset Before Reordering Link to heading

Reorder Genes by Expression Level Link to heading

library(dplyr)
library(forcats)

df <- df %>%
  mutate(Gene = fct_reorder(Gene, Expression))

✅ Now, ggplot2 will automatically order genes by their expression levels instead of alphabetically.

Before vs. After Reordering in a ggplot2 Visualization Link to heading

library(ggplot2)

ggplot(df, aes(x = Gene, y = Expression)) +
  geom_bar(stat = "identity")

Without reordering, bars would be plotted alphabetically. With fct_reorder() applied, genes are sorted by expression level!

🔄 Reversing Factor Levels with fct_rev() Link to heading

Reversing the order of a factor is useful when plotting ordered categories.

🔹 Example: Reversing Treatment Groups Link to heading

df <- df %>%
  mutate(Treatment = fct_rev(Treatment))

This ensures that “Control” appears at the top rather than the bottom in plots.

🔄 Lumping Small Categories with fct_lump() Link to heading

Often, datasets contain many categories with very few observations. fct_lump() allows us to group infrequent categories into an “Other” category, simplifying analysis.

🔹 Example: Merging Small Sample Groups Link to heading

df <- df %>%
  mutate(SampleType = fct_lump(SampleType, n = 3))

✅ This retains the three most common categories and groups the rest under “Other”.

Before & After fct_lump() Application Link to heading

➡️ After fct_lump(n = 3): | SampleType | Count | |———–|——-| | Control | 150 | | Treated | 120 | | Disease | 80 | | Other | 15 |

🔄 Renaming Factor Levels with fct_recode() Link to heading

Sometimes, category names are too long or not informative. fct_recode() allows us to rename them efficiently.

🔹 Example: Simplifying Condition Labels Link to heading

df <- df %>%
  mutate(Condition = fct_recode(Condition,
                                "Ctrl" = "Control",
                                "Trt" = "Treatment"))

✅ Now, "Control" becomes "Ctrl" and "Treatment" becomes "Trt".

🔄 Making Missing Values Explicit with fct_explicit_na() Link to heading

By default, R treats missing factor levels as NA. Sometimes, it’s useful to explicitly label them.

🔹 Example: Labeling Missing Values as “Unknown” Link to heading

df <- df %>%
  mutate(Condition = fct_explicit_na(Condition, na_level = "Unknown"))

✅ Now, NA values are replaced with "Unknown".

📈 Complete Example: Using forcats in a Workflow Link to heading

Scenario: We have clinical trial data with patient conditions and treatment responses. We need to: Link to heading

✔️ Reorder conditions by severity
✔️ Lump minor conditions together
✔️ Rename conditions for clarity

Full Workflow Link to heading

library(dplyr)
library(forcats)

clinical_data <- clinical_data %>%
  mutate(Condition = fct_reorder(Condition, SeverityScore),
         Condition = fct_lump(Condition, n = 4),
         Condition = fct_recode(Condition,
                                "Mild" = "Low",
                                "Severe" = "Critical"))

✅ Now, the dataset is clean, structured, and ready for visualization!

📈 Key Takeaways Link to heading

✅ {forcats} makes working with categorical data intuitive and flexible
✅ fct_reorder() improves visualizations by ordering factors logically
✅ fct_lump() is useful for handling rare categories
✅ fct_recode() renames factors efficiently
✅ Handling missing values with fct_explicit_na() improves data clarity

📌 Next up: Importing & Handling Data Efficiently with readr! Stay tuned! 🚀

👇 How do you manage categorical data in your analyses? Let’s discuss!

#Tidyverse #forcats #RStats #DataScience #Bioinformatics #OpenScience #ComputationalBiology