Categorizing 26 Variables into Two Groups in R for Multiple Linear Regression

Introduction

As a data analyst, working with large datasets can be challenging, especially when dealing with categorical variables. In this article, we will explore how to categorize 26 variables into two groups in R for multiple linear regression.

Understanding the Problem

The question posed by the original poster involves categorizing sector names into two groups: environmentally sensitive and non-environmentally sensitive sectors. The goal is to use these categories as predictor variables in a multiple linear regression model. To achieve this, we need to create new columns that contain these labels.

Using Vectors for Categorization

The original poster has already created vectors for the two groups:

env_sensitive_sectors <- c("Airlines", "Energy", "GroundandMaritimeTransportation","Healthcare", 
                           "Industrials", "Manufacturing", "Mining",  "Materials", 
                           "TechnologyandTelecommunication")

nonenv_sensitive_sectors <- c("Agriculture", "Consumergoods", "ConsumerGoods",  
                               "ConsumerServices", "CosmeticIndustry", "Education", 
                               "Fashion", "FinancialServices", "InternationalOrganization", 
                               "LawFirms", "LuxuryGoods", "Media","Municipality", 
                               "Non-GovernmentalOrganization", "ProfessionalServicesFirms", 
                               "PublicSector", "Publicsector")

However, creating these vectors separately might not be the most efficient approach. We can use a single vector that contains all sector names and then use R’s built-in dplyr package to create the desired categories.

Using dplyr for Categorization

The solution provided by the original poster uses the mutate() function from the dplyr package:

data <- data %>%
  mutate(sector = case_when(
    value %in% env_sensitive_sectors ~ "environmentally sensitive",
    value %in% nonenv_sensitive_sectors ~ "non-environmentally sensitive",
    TRUE ~ "Not in any vector"
  ))

This code creates a new column called sector that contains the labels for each sector. The case_when() function is used to specify multiple conditions and corresponding actions.

How it Works

Let’s break down what’s happening inside the case_when() function:

value %in% env_sensitive_sectors: This condition checks if the value in the sector column is present in the env_sensitive_sectors vector.
value %in% nonenv_sensitive_sectors: This condition checks if the value in the sector column is present in the nonenv_sensitive_sectors vector.
TRUE ~ "Not in any vector": If none of the above conditions are met, the value is labeled as “Not in any vector”.

Using `str()` to Verify the Categorization

To ensure that the categorization has been successful, we can use the str() function to verify the structure of the dataset:

head(data %>% str())

This will show us the first few rows of the data with the new sector column included.

Creating a Factor Variable for Multiple Linear Regression

Now that we have successfully categorized our sectors, we can create a factor variable that can be used in multiple linear regression. To do this, we need to convert the sector column into a factor:

data$sector <- as.factor(data$sector)

This will allow us to use the sector column as a predictor variable in our multiple linear regression model.

Conclusion

In this article, we have explored how to categorize 26 variables into two groups in R for multiple linear regression. We used the dplyr package to create new categories and verified the success of the categorization using the str() function. Finally, we created a factor variable that can be used in multiple linear regression.

Common Pitfalls

Not converting categorical variables into factors before fitting a model.
Using vectors instead of creating categories within the dataset.
Failing to verify the success of categorization using data visualization or summary statistics.

Real-World Applications

Categorizing categorical variables is an essential step in many machine learning and statistical modeling applications. Some examples include:

Sentiment analysis: Categorizing text as positive, negative, or neutral.
Image classification: Classifying images into different categories (e.g., animals, vehicles, buildings).
Text classification: Classifying text into different categories (e.g., spam vs. non-spam emails).

Recommendations

Always explore and understand the nature of your data before starting a modeling project.
Use visualization techniques to verify the success of data transformations and categorization.
Consider using dplyr package for data manipulation and transformation tasks.

## Table of Contents
1. [Categorizing 26 Variables into Two Groups in R for Multiple Linear Regression](#categorizing-26-variables-in-r-for-multiple-linear-regression)
2. [How it Works](#how-it-works)
3. [Common Pitfalls](#common-pitfalls)
4. [Real-World Applications](#real-world-applications)
5. [Recommendations](#recommendations)

Last modified on 2023-08-30