R Tutorial

# Indexing and Subsetting Data in R

Indexing and subsetting data in R involves selecting specific elements, rows, or columns from vectors, matrices, data frames, or other data structures. Indexing in R starts from 1, and there are various methods to access and extract the desired data.

## Vector Indexing

my_vector <- c(10, 20, 30, 40, 50) element_3 <- my_vector[3] # Accessing the 3rd element (30) subset_vector <- my_vector[c(2, 4)] # Accessing elements at indices 2 and 4 (20, 40)

## Matrix Indexing

my_matrix <- matrix(1:9, ncol = 3) element_2_3 <- my_matrix[2, 3] # Accessing element at row 2, column 3 (8) row_1 <- my_matrix[1, ] # Accessing all elements in row 1 (1, 4, 7) column_2 <- my_matrix[, 2] # Accessing all elements in column 2 (2, 5, 8)

## Data Frame Indexing

my_df <- data.frame(name = c("William", "Harry", "Charlie"), age = c(25, 30, 28)) name_bob <- my_df[2, "name"] # Accessing Harry's name (second row, "name" column) age_all <- my_df\$age # Accessing the "age" column (25, 30, 28) subset_df <- my_df[my_df\$age > 25, ] # Subsetting rows with age greater than 25

## Logical Indexing

my_vector <- c(10, 20, 30, 40, 50) logical_index <- c(TRUE, FALSE, TRUE, FALSE, TRUE) subset_logical <- my_vector[logical_index] # Subsetting using a logical vector

## Subsetting

Subsetting is the process of extracting a subset of a data structure. To subset a data structure, you use the [] operator. The first element of the square brackets is the logical expression that determines which elements of the data structure you want to keep. The second element is the index of the row you want to access, and the third element is the index of the column you want to access.

For example, the following code subsets the vector vector_a to keep only the even numbers:

vector_a <- c(1, 2, 3, 4, 5) even_numbers <- vector_a[vector_a %% 2 == 0]

The even_numbers variable now contains the vector (2, 4).

You can also use logical operators to combine multiple conditions in a subsetting expression. For example, the following code subsets the vector vector_a to keep only the even numbers that are greater than 2:

vector_a <- c(1, 2, 3, 4, 5) even_numbers_greater_than_2 <- vector_a[vector_a %% 2 == 0 & vector_a > 2]

The even_numbers_greater_than_2 variable now contains the vector (4).

Points to remember:
1. You can use indexing and subsetting to access elements of any data structure in R, such as vectors, matrices, and data frames.
2. You can use negative indices to index from the end of a data structure.
3. You can use logical operators to combine multiple conditions in a subsetting expression.
4. It is a good practice to use descriptive variable names when indexing and subsetting data.

### Conclusion

Indexing and subsetting data in R involve selecting specific elements, rows, or columns from vectors, matrices, or data frames. By using numerical indices, column/row names, or logical conditions, you can precisely extract the desired data for further analysis and manipulation. This process is essential for navigating and utilizing data effectively within R programming.