Now let's learn how to add/delete rows. Let's consider two methods. The first is more applicable for adding a single row.

The method is to assign a new row to the last plus one index of an existing data frame (to get the number of rows, use the `nrow()` function). Remember that you can not store data of different types using vectors. So, you need to assign either a data frame or a list with new values. Do not worry if you do not know lists. In simple words, this is like a vector that allows us to store data of different types. Let's represent that. The initial data frame is shown below. 
```
  name age gender     Job

1  Alex  24      M Teacher 
2 Julia  43      F  Doctor 
3  Finn  32      M Manager 
```

# Creating a data frame
name <- c("Alex", "Julia", "Finn")
age <- c(24, 43, 32)
gender <- c("M", "F", "M")
job <- c('Teacher', 'Doctor', 'Manager')
test <- data.frame(name, age, gender, job)
# Adding new row
test[nrow(test) + 1, ] <- list('Angela', 35, 'F', 'Accountant')
test # Output data frame

The output there is
```
    name age gender        job

1   Alex  24      M    Teacher
2  Julia  43      F     Doctor
3   Finn  32      M    Manager
4 Angela  35      F Accountant
```
You could do this by using a new data frame and the `merge` function. This method requires the same column names and setting necessary parameters (`all = T`).

# Data of new person as data frame
new_person <- data.frame('Angela', 35, 'F', 'Accountant')
colnames(new_person) <- colnames(test) # Set column names
merge(test, new_person, all = T) # Merge data frames

The output there will be:
```
    name age gender        job
1   Alex  24      M    Teacher
2  Julia  43      F     Doctor
3   Finn  32      M    Manager
4 Angela  35      F Accountant
```
As you can see, the outputs are identical. To remove rows out of the data frame, use square quotes and put a minus sign to the left of the row index. For example, `test[-1,]` will remove the first row (the same as for matrices)

In the first section, you will learn the two-dimensional structure that is widely used in math. In R, this data type is used not only for math purposes but also has many real-life purposes.

In the second section, you will learn data frames - an 'upgraded' version of the matrix. Unlike matrix, the data frame may contain data of different types. Most real-life tables can be represented as data frames. You will learn how to create/manipulate data frames and extract necessary data.

In the final section, you will learn the lists in R. This data type works as vectors but may contain data of different types.