Dataframe Indexing

Understanding how to index and subset data frames is crucial for effective data manipulation. Let's explore various methods of indexing in data frames.

Here is a dataframe we will work with as example:

df <- data.frame(
  name = c("Alice", "Bob", "Charlie", "David", "Eve"),
  age = c(25, 30, 35, 28, 22),
  city = c("New York", "London", "Paris", "Tokyo", "Sydney"),
  salary = c(50000, 60000, 75000, 55000, 45000)
)

Indexing By Position

• # Single element
  df[2, 3]  # Returns the element in the 2nd row and 3rd column (character: "London")

• # Entire row
  df[3, ]  # Returns the 3rd row as a data frame with 1 row and all columns

• # Entire column
  df[, 2]  # Returns the 2nd column as a vector
  df[[2]]  # Also returns the 2nd column as a vector

• # Multiple rows or columns:
  df[1:3, ]  # Returns the first 3 rows
  df[, c(1, 3)]  # Returns the 1st and 3rd columns

Indexing by Name

• # Single column
  df$age  # Returns the "age" column as a vector
  df[["age"]]  # Also returns the "age" column as a vector

• # Multiple columns
  df[c("name", "city")]  # Returns a data frame with "name" and "city" columns

Logical Indexing

• df[df$age > 30, ]  # Returns rows where age is greater than 30

Combining Methods

• df[df$salary > 50000, "name"]  # Returns names of people with salary > 50000

Adding and Modifying Data

• # Adding a new column
  df$department <- c("HR", "IT", "Finance", "Marketing", "Sales")

• # Modifying existing data
  df[df$name == "Alice", "salary"] <- 52000