R provides several data structures for holding and manipulating data. Here’s a brief overview of each one:
1. Vectors:
Vectors are one-dimensional arrays that can hold values of the same data type. You can create a vector in R using the `c()` function. For example:
x <- c(1, 2, 3, 4, 5) # Create a numeric vector
y <- c("red", "green", "blue") # Create a character vector
2. Matrices:
Matrices are two-dimensional arrays that can hold values of the same data type. You can create a matrix in R using the `matrix()` function. For example:
mat <- matrix(c(1, 2, 3, 4, 5, 6), nrow=2, ncol=3) # Create a 2x3 matrix
3. Arrays:
Arrays are multi-dimensional arrays that can hold values of the same data type. You can create an array in R using the `array()` function. For example:
arr <- array(c(1, 2, 3, 4, 5, 6), dim=c(2, 3, 1)) # Create a 2x3x1 array
4. Lists:
Lists are collections of objects that can hold values of different data types. You can create a list in R usingthe `list()` function. For example:
lst <- list(name="Alice", age=25, colors=c("red", "green", "blue")) # Create a list with three elements
5. Data frames:
Data frames are two-dimensional tables that can hold values of different data types. Each column in a data frame can have a different data type. You can create a data frame in R using the `data.frame()` function. For example:
df <- data.frame(name=c("Alice", "Bob", "Charlie"), age=c(25, 30, 35)) # Create a data frame with two columns
These data structures can be used in different ways to manipulate and analyze data in R. For example, you can subset and index vectors, matrices, and arrays using square brackets `[]`, and you can apply functions to data frames using the `apply()` family of functions. It's important to understand the different data structures available in R and how they can be used to perform different types of data analysis tasks.