MGMT 17300: Data Mining Lab

Data Types and Handling Data Types in R

Professor: Davi Moreira

August 01, 2024

Overview

  • Lesson 04 and Lesson 05 Exercises Review
  • Data Types
  • Viewing Data Types in R
  • Redefining Data Types in R
  • Data Types and Their Impact

Lesson 04 Exercises Review

Lesson 05 Exercises Review

Data Types

Introduction to Data Types

Data can take various forms, and identifying the correct data type is essential for analysis. In this course, we focus on:

  • Numerical data (e.g., age, temperature)
  • Logical data (e.g., TRUE, FALSE)
  • Categorical data (e.g., gender, color)
  • Ordinal data (e.g., academic performance levels)
  • Text data (e.g., essays, speeches)
  • Date and time data (e.g., 1/1/2023)

Importance of Data Types in R

Data types are critical for:

  • Correct interpretation during analysis
  • Efficient memory usage and performance
  • Avoiding erroneous calculations (e.g., treating categorical data as numeric)

Excel Example

Viewing Data Types in R

Viewing Data Types in R

To inspect the data types in an R dataframe:

  • class(): View the type of a single variable
  • str(): Get a concise summary of the dataframe, including the data type of each variable
# View the type of a single variable
class(mtcars$gear)

# View the structure and data types of the entire dataframe
str(mtcars)

Redefining Data Types in R

Redefining Data Types in R

Sometimes, R may misinterpret the data type. You can redefine the data type using functions like as.numeric(), as.character(), and as.factor().

# Redefine the vs and am variables in mtcars as categorical
mtcars$vs <- as.factor(mtcars$vs)
mtcars$am <- as.factor(mtcars$am)

# Confirm the changes
str(mtcars)

Example: mtcars Dataset

The mtcars dataset includes variables that could be redefined as categorical. For instance:

  • vs and am are numeric by default but represent categorical data.

By redefining them as factors, we avoid incorrect numeric operations.

# Redefining as factors
mtcars$vs <- as.factor(mtcars$vs)
mtcars$am <- as.factor(mtcars$am)

Data Types and Their Impact

Choosing the correct data type impacts:

  • Performance: Using integers vs floats

  • Memory Usage: Numerical types consume more memory

  • Analysis: Properly defined categorical variables ensure meaningful grouping and sorting.

Summary

Summary

  • R supports various data types: numerical, categorical, text, date/time, etc.

  • Use class() and str() to inspect data types in a dataframe.

  • Correctly define or redefine data types using as.numeric(), as.character(), as.factor(), and other functions for accurate analysis.

Thank you!

Data Mining Lab