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# R Introduction: Part I

R Introduction: Part I

## Intervals

To categorize numerical 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.

The `cut()` function in R allows you to divide numerical data into categorical factors. Here's how you can use it:

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

• `x` is the numerical 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].

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., small < medium < tall).

• 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
• 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` - `T` (to order the factor values)
• `right` - `F` (to include the left boundary of an interval, not right)
• Output the contents of `grades_f`.

Everything was clear?

Section 3. Chapter 5