Recap of Essential R Programming Concepts

Are you excited to continue your journey in data science with R? In this section, we'll revisit some of the foundational R programming concepts you've already encountered, ensuring you're well-prepared for more advanced topics. Our focus will be on data structures, basic operations, control structures, and custom functions in R.

We'll cover the following key concepts in this lesson:

1. Data Structures: Refresh your understanding of vectors, matrices, lists, and data frames.
2. Basic Operations: Revisit arithmetic operations, calculating mean and sum, and type conversions.
3. Control Structures: Recap how to use conditionals (if-else), loops (for, while), and custom functions in R.

Let's start by refreshing our knowledge of different data structures in R: vectors, matrices, lists, and data frames.

Vectors are one-dimensional arrays that can hold numeric, character, or logical data types.

nums <- c(1, 2, 3, 4, 5)
print(nums)

In this example, nums is a numeric vector containing the numbers 1 through 5.

Matrices are two-dimensional arrays that hold elements of the same data type.

matrix_example <- matrix(1:9, nrow = 3, byrow = TRUE)
print(matrix_example)

Here, matrix_example is a 3x3 matrix filled by rows with numbers from 1 to 9.

Lists in R can hold elements of different types, including other lists.

list_example <- list(nums, matrix_example, c("a", "b", "c"))
print(list_example)

In this example, list_example contains a vector, a matrix, and a character vector.

Data frames are two-dimensional tables where each column can hold different types of data.

df <- data.frame(ID = 1:3, Name = c("Alice", "Bob", "Charlie"), Score = c(9.5, 8.7, 9.8))
print(df)

Here, df is a data frame with three columns: ID, Name, and Score.