Changing Numeric Variable to Categorical in R | R Tutorial 5.4 | MarinStatsLectures

MarinStatsLectures-R Programming & Statistics
25 May 201505:22
EducationalLearning
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TLDRIn this video, Mike Marin explains how to convert a numeric variable into a categorical variable in R using the 'cut' command. He discusses the reasons for doing this, such as for cross-tabulations or when the linearity assumption in regression models is invalid. The video uses the LungCap data and demonstrates creating a categorical height variable with specific break points and labels. Marin also covers the importance of defining labels and the option to let R determine the cut points. This tutorial is practical for anyone looking to manipulate and analyze data in R.

Takeaways
  • 🎯 Mike Marin explains the process of converting a numeric variable into a categorical variable in R.
  • 📊 Reasons for converting a numeric variable include making cross-tabulations or addressing non-linearity in regression models.
  • 📁 The tutorial uses the LungCap dataset, with height as the numeric variable to be converted.
  • ✂️ The 'cut' command in R is used to create categorical variables from numeric ones.
  • 📏 Categories for height are set with specific breakpoints: 0, 50, 55, 60, 65, 70, and 100.
  • 🔍 By default, intervals are left-open (right-closed), meaning border observations fall into the lower interval.
  • 🏷️ Labels for the categories are specified for clarity, such as 'A' for less than 50 and 'B' for 50 to 55.
  • 👀 Example observations show the conversion, like height 62.1 being categorized as 'D' (60 to 65).
  • 🔄 The 'right' argument can change intervals to be left-closed (right-open) if needed.
  • ⚙️ Specifying labels is important to avoid default interval names, ensuring clearer categorical names.
  • 🔢 R can automatically determine interval breakpoints if the number of desired categories is provided, though manual specification is recommended for control.
Q & A

  • What is the main topic of the video by Mike Marin?

    -The main topic of the video is how to convert a numeric variable into a categorical variable in R.

  • Why might someone want to convert a numeric variable to a categorical variable in R?

    -One might want to convert a numeric variable to a categorical variable for reasons such as making cross-tabulations, fitting a regression model when the linearity assumption is not valid, or for other statistical analyses that require categorical data.

  • Which dataset does Mike Marin use to demonstrate the conversion process?

    -Mike Marin uses the LungCap dataset to demonstrate the conversion of a numeric variable to a categorical variable.

  • What numeric variable is being converted into a categorical variable in the video?

    -The numeric variable 'height' is being converted into a categorical variable in the video.

  • What R command is used to perform the conversion of a numeric variable to a categorical variable?

    -The 'cut' command is used in R to perform the conversion of a numeric variable to a categorical variable.

  • What are the default interval types used by the 'cut' command in R?

    -By default, the 'cut' command in R uses left-open or right-closed intervals.

  • How can you change the intervals in the 'cut' command to be left-closed or right-opened?

    -You can change the intervals to be left-closed or right-opened by using the 'right' argument within the 'cut' command and setting it to false for right-opened intervals.

  • Why is it important to specify labels for the categories when using the 'cut' command?

    -It is important to specify labels to avoid using default interval labels that R might choose, which can be less informative or not as meaningful for the analysis.

  • What happens if you do not specify the labels argument in the 'cut' command?

    -If you do not specify the labels argument, R will use the intervals themselves as the labels, which might not be the desired outcome for the analysis.

  • Can R determine the cut points for the intervals automatically?

    -Yes, instead of specifying the breakpoints manually, you can tell R the number of categories or levels you want, and R will determine the cut points for the intervals itself.

  • What is the general recommendation regarding setting interval breakpoints in R?

    -The general recommendation is to set the interval breakpoints yourself to have more control over the categorization process and to ensure it aligns with the analysis requirements.

Outlines
00:00
📊 Converting Numeric Variables to Categorical in R

Mike Marin introduces the topic of converting numeric variables to categorical variables in R. He explains the reasons for doing this, such as making cross-tabulations or fitting regression models where linearity assumptions are invalid. The video uses the LungCap dataset and focuses on converting the numeric variable 'Height' into a categorical variable using the 'cut' command in R. Categories are created with specified breakpoints and labeled accordingly. The importance of defining labels and the use of the 'right' argument to control interval closure is discussed. Examples of categorized heights and the significance of label customization are provided.

05:00
🔧 Tips for Setting Interval Breakpoints in R

Mike Marin continues by explaining how R can automatically determine interval breakpoints if the number of categories is specified, rather than manually setting breakpoints. He recommends manually setting interval breakpoints to maintain control over the categorization. The video concludes with a reminder to check out other instructional videos.

Mindmap
Keywords
💡Numeric Variable
A numeric variable is a type of data that consists of numerical values. In the context of the video, the numeric variable 'height' is being discussed, which represents a measurable quantity. The video's theme revolves around converting such numeric data into a categorical format for various analytical purposes, such as regression modeling where linearity assumptions may not hold.
💡Categorical Variable
A categorical variable is a type of data that consists of categories or groups. The video explains how to transform a numeric variable into a categorical one, specifically using the 'height' variable from the LungCap dataset. The conversion allows for more nuanced analysis, such as cross-tabulation or when the linearity assumption for regression models is not valid.
💡R
R is a programming language and environment commonly used for statistical computing and graphics. The video script is focused on demonstrating how to perform data manipulation tasks in R, specifically converting a numeric variable to a categorical variable using the 'cut' command.
💡Cut Command
The 'cut' command in R is used to divide a numeric variable into several intervals or categories. In the video, Mike Marin uses the 'cut' command to create a new categorical variable 'CatHeight' from the numeric 'Height' variable, with specific intervals and labels.
💡LungCap Data
The LungCap data is a dataset that is referenced in the video for the purpose of demonstrating the conversion process. It is used as an example to show how one might work with real-world data to convert a numeric variable into a categorical one using R.
💡Categories
Categories are the groups or intervals into which a numeric variable is divided when converting it to a categorical variable. The video script outlines the creation of specific categories for 'CatHeight', such as 'A' for heights less than 50, 'B' for 50 to 55, and so on.
💡Break Points
Break points are the numerical values that define the boundaries of each category in a categorical variable. The script mentions specific break points like 0, 50, 55, 60, 65, 70, and 100, which are used to segment the 'Height' variable into distinct categories.
💡Left-Open or Right-Closed Intervals
This term refers to how the intervals for categories are defined in relation to their boundaries. In the video, it is explained that by default, the intervals created by the 'cut' command are left-open or right-closed, meaning that border observations are included in the category to their left.
💡Labels
Labels are the names given to the categories in a categorical variable. The video emphasizes the importance of defining custom labels, such as 'A', 'B', 'C', etc., for the categories of 'CatHeight', rather than using the default interval labels provided by R.
💡Right Argument
The 'right' argument in the 'cut' command is used to specify whether the intervals should be right-closed or right-opened. Setting this argument to false, as shown in the video, results in right-opened intervals, which affects how observations are categorized.
💡Levels
Levels refer to the number of distinct categories in a categorical variable. The video script mentions the option to let R determine the number of levels or categories by specifying an integer instead of explicit break points, although it is generally recommended to set break points manually for more control.
Highlights

Introduction to converting a numeric variable into a categorical variable in R.

Reasons for conversion include cross-tabulations and regression model fitting when linearity is not valid.

Use of the 'cut' command to convert numeric variables.

Importing and attaching the LungCap data for demonstration.

Conversion of the 'height' variable into a categorical variable 'CatHeight'.

Specifying category ranges with breakpoints for the 'cut' command.

Explanation of left-open or right-closed intervals in 'cut' command.

Demonstration of how observations are binned based on intervals.

Assigning labels to categories for clarity and understanding.

Example of how to view the first 10 observed heights and their categorical counterparts.

Adjusting intervals to be left-closed or right-opened using the 'right' argument.

Importance of specifying labels to avoid default interval labels.

Demonstration of the effect of not specifying labels in the 'cut' command.

Alternative method of specifying the number of categories instead of breakpoints.

Recommendation to set interval breakpoints manually for better control.

Conclusion and invitation to watch other instructional videos.

Transcripts

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Related Tags
Data ConversionR ProgrammingCategorical DataNumeric VariablesRegression ModelLinearity AssumptionCross-TabulationsLungCap DataCut CommandData AnalysisR Tutorial