Summary  
This chapter demonstrates how to remove single or multiple elements from a vector using negative indexing and how to make those removals permanent by reassigning the modified vector.  

General domain of usage  
Data manipulation

Elements can be removed from a vector by placing a minus sign (`-`) before their indices. This creates a new vector without those elements.

## Removing a Single Element
You can exclude one element by its index.

grades <- c(80, 75, 95, 100)
names(grades) <- c('Math', 'Physics', 'English', 'Literature')

# Remove the third grade (English)
grades[-3]

## Removing Multiple Elements
Provide a vector of indices to remove more than one element.

grades <- c(80, 75, 95, 100)
names(grades) <- c('Math', 'Physics', 'English', 'Literature')

# Remove the second and fourth elements (Physics and Literature)
grades[-c(2,4)]

## Applying Changes
The changes shown in the examples are **temporary**. To make them **permanent**, you must reassign the result to the original (or, if needed, different) variable.

grades <- c(80, 75, 95, 100)
names(grades) <- c('Math', 'Physics', 'English', 'Literature')

# Changes are not applied
grades[-3]
grades
# Changes are applied
grades <- grades[-3]
grades

Gain a solid understanding of R, one of the most widely used languages for data analysis. Explore fundamental data types and one-dimensional values, then advance to multi-dimensional structures, including matrices, data frames, and lists. Build the skills needed to apply R effectively in statistical analysis and data-driven decision-making.

Learn the essentials of R syntax, such as arithmetic, variables, printing functions, and comments. Strengthen your understanding of computational logic through exercises.

Explore R's data types and understand their role in data handling. Build skills in working with vectors as essential structures for organizing and analyzing information.

Develop the ability to manage categorical data effectively. Discover how to structure, group, and interpret values by converting raw data into clear categories.

Understand how to represent and work with structured two-dimensional data. Build confidence in transforming, labeling, and applying operations to matrices for analytical tasks.

Master the most widely used data structure in R for tabular information. Learn to organize, transform, and extract insights from datasets to support data-driven decisions.

Learn how lists extend beyond vectors by storing elements of different types and lengths. Develop the ability to create, access, and modify lists for organizing complex data structures.

Deleting Vector Elements

Removing a Single Element

Example

Removing Multiple Elements

Example

Applying Changes

Solution

Awesome!

Deleting Vector Elements

Removing a Single Element

Example

Removing Multiple Elements

Example

Applying Changes

Solution