R Introduction: Part I

1. Basic Syntax and Operations

In the initial section, you’ll dive into the foundational elements of R, such as generating output and performing elementary operations. This section will conclude with an exploration of the theory behind variables.

Hello, R!

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Integer Division and Remainders

2. Vectors

You’ll become familiar with the vector class in R, which also handles individual values. Here, you’ll delve into the primary data types for singular values and learn how to manage multiple values of the same type simultaneously.

Operations with Vectors

3. Factors

Factors, also known as categorical variables, are prevalent in the real world. Examples include blood types, currencies, nationalities, and brands. Unlike numerical variables, factors have a limited set of possible values.

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).

Task

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

Course Content

R Introduction: Part I

Intervals

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].

Task

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