Creating Data Frames

Data frames are a core data structure in R. They organize data in a table format with rows and columns. Each column can contain different types of data, such as numbers or characters.

Creating a data frame is simple using the `data.frame()` function. This function lets you combine vectors into a structured table. Each vector becomes a column in the data frame.

Data frames are highly versatile. They support a range of operations, like subsetting, adding, and removing columns. These features make data frames essential for data manipulation and analysis in R.

# Create a data frame
df <- data.frame(
  Name = c("Alice", "Bob", "Charlie"),
  Age = c(25, 30, 35),
  Salary = c(50000, 55000, 60000)
)
df

Output:

     Name Age Salary
    1 Alice  25  50000
    2   Bob  30  55000
    3 Charlie  35  60000

Accessing Data Frame Elements

Access specific rows, columns, or elements in a data frame using square brackets. Use a comma to separate rows and columns.

# Access the first column
df$Name

# Access the second row
df[2, ]

# Access the element in the second row, third column
df[2, 3]

Output for the first column:

[1] "Alice"   "Bob"     "Charlie"

Output for the second row:

    Name Age Salary
    2   Bob  30  55000

Output for the specific element:

[1] 55000

Adding and Removing Columns

To add a column, simply assign values to a new column name. To remove a column, use the subset() function or NULL assignment.

# Add a new column
df$Department <- c("HR", "Finance", "IT")
df

# Remove a column
df$Salary <- NULL
df

Output after adding a column:

     Name Age Salary Department
    1 Alice  25  50000         HR
    2   Bob  30  55000    Finance
    3 Charlie  35  60000         IT

Output after removing a column:

     Name Age Department
    1 Alice  25         HR
    2   Bob  30    Finance
    3 Charlie  35         IT

Manipulating Data Frames

Use functions like subset(), merge(), and order() to manipulate data frames. These functions help filter, combine, and sort data.

# Filter rows where Age is greater than 28
subset(df, Age > 28)

# Order by Age
df_sorted <- df[order(df$Age), ]
df_sorted

Output after filtering:

     Name Age Department
    2   Bob  30    Finance
    3 Charlie  35         IT

Output after sorting:

     Name Age Department
    1 Alice  25         HR
    2   Bob  30    Finance
    3 Charlie  35         IT

Uses of Data Frames in R

1. Data Organization: Data frames help organize data in a tabular format. Each column can have a different data type, making it easy to manage complex datasets.
2. Data Manipulation: They allow for easy data manipulation. You can add, remove, or modify columns and rows with simple commands.
3. Data Analysis: Data frames are used in various analytical operations. Functions like summary() and aggregate() help summarize and analyze the data.
4. Data Cleaning: They facilitate data cleaning tasks. You can filter rows, handle missing values, and correct data errors efficiently.
5. Data Subsetting: Data frames enable subsetting of data. You can select specific rows or columns based on conditions using functions like subset().
6. Data Visualization: They integrate well with visualization packages. Functions in packages like ggplot2 use data frames to create plots and charts.
7. Data Import and Export: Data frames support importing and exporting data. You can read from and write to various file formats such as CSV and Excel.