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Learn Grouping Numeric Data | Factors
R Introduction

bookGrouping Numeric Data

Continuous numeric data can be transformed into categories using the cut() function. This is helpful when you want to analyze ranges rather than individual values.

Function Overview

The cut() function divides numbers into intervals and returns a factor:

cut(x, breaks, labels = NULL, right = TRUE, ordered_result = FALSE)
  • x: numeric vector to categorize;
  • breaks: number of intervals or specific cut points;
  • labels: names for categories;
  • right: whether intervals are closed on the right;
  • ordered_result: whether the categories should be ordered.

Example

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heights <- c(170, 165, 195, 172, 189, 156, 178, 198, 157, 182, 171, 184, 163, 176, 169, 153) # Split heights into 3 groups heights_f <- cut(heights, breaks = c(0, 160, 190, 250), labels = c('short', 'medium', 'tall'), ordered_result = TRUE) heights_f
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As a result:

  • The data is divided into three intervals: (0,160], (160,190], and (190,250];
  • They are labeled as 'short', 'medium', and 'tall';
  • The categories follow a natural order.
Task

Swipe to start coding

You have a vector of numerical grades. Here's how to categorize them as factor levels:

  • [0, 60) - 'F';
  • [60, 75) - 'D';
  • [75, 85) - 'C';
  • [85, 95) - 'B';
  • [95, 100) - 'A'.

Your task is to:

  1. Create a variable called grades_f that categorizes the grades using the cut() function. Use the following parameters:
    • breaks - c(0, 60, 75, 85, 95, 100);
    • labels - c('F', 'D', 'C', 'B', 'A');
    • ordered_result - TRUE (to order the factor values);
    • right - FALSE (to include the left boundary of an interval, not the right).
  2. Output the contents of grades_f.

Solution

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SectionΒ 3. ChapterΒ 5
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bookGrouping Numeric Data

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Continuous numeric data can be transformed into categories using the cut() function. This is helpful when you want to analyze ranges rather than individual values.

Function Overview

The cut() function divides numbers into intervals and returns a factor:

cut(x, breaks, labels = NULL, right = TRUE, ordered_result = FALSE)
  • x: numeric vector to categorize;
  • breaks: number of intervals or specific cut points;
  • labels: names for categories;
  • right: whether intervals are closed on the right;
  • ordered_result: whether the categories should be ordered.

Example

12345678910
heights <- c(170, 165, 195, 172, 189, 156, 178, 198, 157, 182, 171, 184, 163, 176, 169, 153) # Split heights into 3 groups heights_f <- cut(heights, breaks = c(0, 160, 190, 250), labels = c('short', 'medium', 'tall'), ordered_result = TRUE) heights_f
copy

As a result:

  • The data is divided into three intervals: (0,160], (160,190], and (190,250];
  • They are labeled as 'short', 'medium', and 'tall';
  • The categories follow a natural order.
Task

Swipe to start coding

You have a vector of numerical grades. Here's how to categorize them as factor levels:

  • [0, 60) - 'F';
  • [60, 75) - 'D';
  • [75, 85) - 'C';
  • [85, 95) - 'B';
  • [95, 100) - 'A'.

Your task is to:

  1. Create a variable called grades_f that categorizes the grades using the cut() function. Use the following parameters:
    • breaks - c(0, 60, 75, 85, 95, 100);
    • labels - c('F', 'D', 'C', 'B', 'A');
    • ordered_result - TRUE (to order the factor values);
    • right - FALSE (to include the left boundary of an interval, not the right).
  2. Output the contents of grades_f.

Solution

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SectionΒ 3. ChapterΒ 5
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