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R Introduction: Part I

bookGrouping Numeric Data

To categorize numeric data into groups, you can use the cut() function in R, which assigns each number to a category based on specified intervals. For instance, if you have a continuous variable like height, you can categorize individuals as 'tall', 'medium', or 'short' based on height ranges.

Here's how you can use it:

cut(x, breaks, labels = NULL, right = TRUE, ordered_result = FALSE, ...)

Among the parameters listed, these are crucial for categorizing data:

  • x is the numeric vector to be categorized;
  • breaks can be an integer specifying the number of intervals or a vector of cut points;
  • labels provide names for the categories;
  • right indicates if the intervals should be closed on the right;
  • ordered_result determines if the resulting factors should have an order.

To create three categories, set breaks to 3 or provide a vector with four cut points to form three intervals, for instance (a,b], (b,c], (c,d].


              
1234567

            
# Vector of heights
heights <- c(170, 165, 195, 172, 189, 156, 178, 198,
            157, 182, 171, 184, 163, 176, 169, 153)
# Convert into factor by cutting into intervals
heights_f <- cut(heights, breaks = c(0, 160, 190, 250), 
                labels = c('small', 'medium', 'tall'), ordered_result = T)
heights_f # Output the factor variable
copy

For our example of categorizing height, we choose c(0, 160, 190, 250) for breaks to divide the data into three groups: (0, 160], (160, 190], and (190, 250]. We also set ordered_result to TRUE to define a logical order among categories (e.g., short < medium < tall).

Tarefa

Swipe to start coding

  1. Given 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.

  2. Create a variable grades_f that stores the factor levels with the specified breaks and labels, considering the ordering, and use right = FALSE to include the left boundary of the intervals;

    • 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).

  3. Output the contents of grades_f.

Solução

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bookGrouping Numeric Data

To categorize numeric data into groups, you can use the cut() function in R, which assigns each number to a category based on specified intervals. For instance, if you have a continuous variable like height, you can categorize individuals as 'tall', 'medium', or 'short' based on height ranges.

Here's how you can use it:

cut(x, breaks, labels = NULL, right = TRUE, ordered_result = FALSE, ...)

Among the parameters listed, these are crucial for categorizing data:

  • x is the numeric vector to be categorized;
  • breaks can be an integer specifying the number of intervals or a vector of cut points;
  • labels provide names for the categories;
  • right indicates if the intervals should be closed on the right;
  • ordered_result determines if the resulting factors should have an order.

To create three categories, set breaks to 3 or provide a vector with four cut points to form three intervals, for instance (a,b], (b,c], (c,d].


              
1234567

            
# Vector of heights
heights <- c(170, 165, 195, 172, 189, 156, 178, 198,
            157, 182, 171, 184, 163, 176, 169, 153)
# Convert into factor by cutting into intervals
heights_f <- cut(heights, breaks = c(0, 160, 190, 250), 
                labels = c('small', 'medium', 'tall'), ordered_result = T)
heights_f # Output the factor variable
copy

For our example of categorizing height, we choose c(0, 160, 190, 250) for breaks to divide the data into three groups: (0, 160], (160, 190], and (190, 250]. We also set ordered_result to TRUE to define a logical order among categories (e.g., short < medium < tall).

Tarefa

Swipe to start coding

  1. Given 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.

  2. Create a variable grades_f that stores the factor levels with the specified breaks and labels, considering the ordering, and use right = FALSE to include the left boundary of the intervals;

    • 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).

  3. Output the contents of grades_f.

Solução

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 3. Capítulo 5
single

single

close

Awesome!

Completion rate improved to 3.85

bookGrouping Numeric Data

Deslize para mostrar o menu

To categorize numeric data into groups, you can use the cut() function in R, which assigns each number to a category based on specified intervals. For instance, if you have a continuous variable like height, you can categorize individuals as 'tall', 'medium', or 'short' based on height ranges.

Here's how you can use it:

cut(x, breaks, labels = NULL, right = TRUE, ordered_result = FALSE, ...)

Among the parameters listed, these are crucial for categorizing data:

  • x is the numeric vector to be categorized;
  • breaks can be an integer specifying the number of intervals or a vector of cut points;
  • labels provide names for the categories;
  • right indicates if the intervals should be closed on the right;
  • ordered_result determines if the resulting factors should have an order.

To create three categories, set breaks to 3 or provide a vector with four cut points to form three intervals, for instance (a,b], (b,c], (c,d].


              
1234567

            
# Vector of heights
heights <- c(170, 165, 195, 172, 189, 156, 178, 198,
            157, 182, 171, 184, 163, 176, 169, 153)
# Convert into factor by cutting into intervals
heights_f <- cut(heights, breaks = c(0, 160, 190, 250), 
                labels = c('small', 'medium', 'tall'), ordered_result = T)
heights_f # Output the factor variable
copy

For our example of categorizing height, we choose c(0, 160, 190, 250) for breaks to divide the data into three groups: (0, 160], (160, 190], and (190, 250]. We also set ordered_result to TRUE to define a logical order among categories (e.g., short < medium < tall).

Tarefa

Swipe to start coding

  1. Given 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.

  2. Create a variable grades_f that stores the factor levels with the specified breaks and labels, considering the ordering, and use right = FALSE to include the left boundary of the intervals;

    • 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).

  3. Output the contents of grades_f.

Solução

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

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