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 .

What You'll Learn

We'll cover the following key concepts in this lesson: Data Structures : Refresh your understanding of vectors, matrices, lists, and data frames. Basic Operations : Revisit arithmetic operations, calculating mean and sum, and type conversions. 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. R nums <- c(1, 2, 3, 4, 5) print(nums) 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. R matrix_example <- matrix(1:9, nrow = 3, byrow = TRUE) print(matrix_example) 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. R list_example <- list(nums, matrix_example, c("a", "b", "c")) print(list_example) 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. R df <- data.frame(ID = 1:3, Name = c("Alice", "Bob", "Charlie"), Score = c(9.5, 8.7, 9.8)) print(df) 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 .

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

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

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

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

sum_example <- sum(nums)
print(sum_example)

sum_example <- sum(nums)
print(sum_example)

mean_example <- mean(nums)
print(mean_example)

mean_example <- mean(nums)
print(mean_example)

converted_nums <- as.character(nums)
print(converted_nums)

converted_nums <- as.character(nums)
print(converted_nums)

x <- 5
if (x > 0) {
  cat("x is positive\n")
} else {
  cat("x is non-positive\n")
}

x <- 5
if (x > 0) {
  cat("x is positive\n")
} else {
  cat("x is non-positive\n")
}

for (i in nums) {
  cat(sprintf("Value: %d\n", i))
}

for (i in nums) {
  cat(sprintf("Value: %d\n", i))
}

i <- 1
while (i <= 3) {
  cat(sprintf("Iteration: %d\n", i))
  i <- i + 1
}

i <- 1
while (i <= 3) {
  cat(sprintf("Iteration: %d\n", i))
  i <- i + 1
}

custom_function <- function(a, b) {
  return(a + b)
}
result <- custom_function(3, 4)
cat(sprintf("Custom Function Result: %d\n", result))

custom_function <- function(a, b) {
  return(a + b)
}
result <- custom_function(3, 4)
cat(sprintf("Custom Function Result: %d\n", result))

sum_example <- sum(nums)
print(sum_example)

mean_example <- mean(nums)
print(mean_example)

converted_nums <- as.character(nums)
print(converted_nums)

x <- 5
if (x > 0) {
  cat("x is positive\n")
} else {
  cat("x is non-positive\n")
}

for (i in nums) {
  cat(sprintf("Value: %d\n", i))
}

i <- 1
while (i <= 3) {
  cat(sprintf("Iteration: %d\n", i))
  i <- i + 1
}

custom_function <- function(a, b) {
  return(a + b)
}
result <- custom_function(3, 4)
cat(sprintf("Custom Function Result: %d\n", result))