Great! So far, you know how to create a vector, assign names to values, and extract elements from it. The next thing we will consider is modifying: adding and deleting existing items from a vector.

There are at least two ways to add a new value to an existing vector. Assume we have a vector named `vec` and want to add `new_value` to it. The name we want to assign to this value is `'new_name'`. Both ways are displayed on the schema below.

<img src = 'https://codefinity-content-media.s3.eu-west-1.amazonaws.com/22214341-aba2-400e-ba6b-6acc65d61550/s.2+ch.9+schema.png'>

For example, let me show you how both ways will work. Let's use the same grades example and add a new grade `60` for the `'Philosophy'` subject. The first way is using vectors:

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

# Add new value using vectors
grades <- c(grades, 60) # Add new value
names(grades)[length(grades)] <- 'Philosophy' # Add name
grades # Output the vector

The output of the script above is:
```
      Math    Physics    English Literature Philosophy
      80         75         95        100         60
```
Now let's perform the second method - assigning names with values simultaneously.

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

# Add new value 
grades['Philosophy'] <- 60 # Add value with name
grades # Output the vector

The output of the script above is the same:
```
      Math    Physics    English Literature Philosophy
      80         75         95        100         60
```
So, if your vector has no names, then the first method is better. But if the vector has names - then it's better to use the second method.
Using the second method, you can not only add new values but also reassign them. For example, if you write `grades['Math'] <- 100` - then the original value of `80` will be rewritten with `100`. Also, you can refer to a specific index. The same trick can be done by writing `grades[1] <- 100`.

In the first section, you will consider the very basics of R - outputting and performing basic operations. You will finish the section with the variables theory.

In the second section, you will learn the vector class in R, which represents single values too. You will consider the main data types for single values and how to work with multiple values of a kind at once.

Factors (or categorical variables) are widely used in real life. These are blood types, currencies, nationalities, brands, etc. Unlike numerical variables, factors are limited to a finite number of values.