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R Introduction: Part I
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Course Content

R Introduction: Part I

R Introduction: Part I

1. Basic Syntax and Operations
Why R?Basic ArithmeticUsing the print() Function Comments in RInteger Division and RemaindersExponentiationcat() FunctionVariablesRevenue CalculationAssignment Operators
2. Basic Data Types and Vectors
Data TypesNumbersType ConversionLogical TypesVectorsLabeling VectorsIndexing in VectorsModifying VectorsDeleting Elements from VectorsArithmetic Operations with Vectors
3. Factors
What is a Factor?Indexing in FactorsLevelsCounting ValuesGrouping Numeric DataGrades Summary

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:

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).

Task
test

Swipe to show code editor

  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.

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Section 3. Chapter 5
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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:

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).

Task
test

Swipe to show code editor

  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.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 3. Chapter 5
toggle bottom row

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:

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).

Task
test

Swipe to show code editor

  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.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

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:

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).

Task
test

Swipe to show code editor

  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.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 3. Chapter 5
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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