R Data Frames

In the world of data science and programming, you will often work with data. A data frame is a way to organize and store this data in R, a popular programming language. Think of a data frame like a table you would see in an Excel spreadsheet, where each row is a record and each column is a feature or variable. In simpler terms, it’s a table that holds your data in a structured way.

If you’ve ever used Excel, you can think of a data frame as a sheet with rows and columns. Each row holds information about a specific thing, and each column represents a different kind of information about that thing. For example, if you’re keeping track of your friends, each row could hold details about one friend, while each column could hold a different piece of information about them, like their name, age, and phone number.

Why Use Data Frames?

Data frames are useful because they make it easy to store, view, and manipulate data in R. They help you:

• Keep data organized.
• Perform calculations and analysis on your data.
• Easily manipulate and filter data based on your needs.

Creating a Data Frame in R

To create a data frame in R, you can use the data.frame() function. Here’s how you can do it:

# Create a data frame
my_data <- data.frame(
  Name = c("John", "Alice", "Bob"),
  Age = c(25, 30, 22),
  City = c("New York", "Los Angeles", "Chicago")
)

In this example, we created a simple data frame called my_data that has 3 rows (one for each person) and 3 columns: Name, Age, and City.

• The c() function is used to create a vector (a collection of values) for each column.
• The data frame is then created by combining these vectors into a table.

Viewing Data in a Data Frame

To see what your data frame looks like, simply type its name and press Enter:

# View the data frame
my_data

This will display the contents of the my_data data frame:

   Name Age         City
1  John  25     New York
2 Alice  30 Los Angeles
3   Bob  22     Chicago

Accessing Specific Data in a Data Frame

You can access individual pieces of data in a data frame in several ways.

1. Access a Column: To view a particular column (for example, the “Age” column), you can use the $ symbol:

# View the "Age" column
my_data$Age

This will show you all the ages in the data frame:

[1] 25 30 22

2. Access a Row: If you want to see a specific row (for example, the second row), you can use square brackets [ ]:

# View the second row
my_data[2, ]

This will display:

  Name Age         City
2 Alice  30 Los Angeles

3. Access a Specific Value: If you want to see a specific value, like Alice’s age, you can use both row and column indexing:

# View Alice's age
my_data[2, "Age"]

This will give you:

[1] 30

Modifying Data in a Data Frame

You can easily change the values in a data frame. For example, let’s say you want to change Bob’s age to 23:

# Change Bob's age to 23
my_data[3, "Age"] <- 23

Now, if you view the data frame again, you’ll see that Bob’s age has been updated:

   Name Age         City
1  John  25     New York
2 Alice  30 Los Angeles
3   Bob  23     Chicago

Adding New Columns

You can add new columns to an existing data frame. Let’s say you want to add a column for “Phone Number”:

# Add a new column for phone numbers
my_data$Phone <- c("555-1234", "555-5678", "555-9876")

After adding the column, the data frame will look like this:

   Name Age         City   Phone
1  John  25     New York  555-1234
2 Alice  30 Los Angeles  555-5678
3   Bob  23     Chicago  555-9876

Adding New Rows

You can also add new rows to your data frame. For example, let’s add a new person named “Eve”:

# Add a new row to the data frame
new_row <- data.frame(Name = "Eve", Age = 28, City = "Miami", Phone = "555-4321")
my_data <- rbind(my_data, new_row)

Now, your data frame will include Eve:

   Name Age         City   Phone
1  John  25     New York  555-1234
2 Alice  30 Los Angeles  555-5678
3   Bob  23     Chicago  555-9876
4   Eve  28       Miami  555-4321

Filtering Data

If you want to filter or subset your data, you can do that too. For example, if you want to see only people who are 30 or older, you can use the subset() function:

# Filter people who are 30 or older
older_than_30 <- subset(my_data, Age >= 30)
older_than_30

This will display:

   Name Age         City   Phone
2 Alice  30 Los Angeles  555-5678