**Course Title:** Mastering R Programming: Data Analysis, Visualization, and Beyond **Section Title:** Data Types and Structures in R **Topic:** Create and manipulate vectors, matrices, and data frames to solve data-related tasks.(Lab topic) **Objective:** By the end of this lab topic, you will be able to create, manipulate, and work with vectors, matrices, and data frames in R to solve various data-related tasks. **Creating and Manipulating Vectors in R** In R, a vector is a one-dimensional data structure that can store multiple elements of the same data type. Vectors can be created using the `c()` function, which stands for "combine." ```r # Create a numeric vector vector1 <- c(1, 2, 3, 4, 5) print(vector1) # Create a character vector vector2 <- c("apple", "banana", "orange") print(vector2) # Create a logical vector vector3 <- c(TRUE, FALSE, TRUE, FALSE) print(vector3) ``` You can perform various operations on vectors, such as indexing, subsetting, and concatenation. ```r # Indexing and subsetting a vector print(vector1[2]) # prints the second element of vector1 print(vector1[c(1, 3, 5)]) # prints the first, third, and fifth elements of vector1 # Concatenating vectors vector4 <- c(vector1, vector2) print(vector4) ``` **Creating and Manipulating Matrices in R** A matrix is a two-dimensional data structure that can store multiple rows and columns of data. Matrices can be created using the `matrix()` function. ```r # Create a 2x3 matrix matrix1 <- matrix(c(1, 2, 3, 4, 5, 6), nrow = 2, ncol = 3) print(matrix1) # Create a 3x2 matrix matrix2 <- matrix(c(7, 8, 9, 10, 11, 12), nrow = 3, ncol = 2) print(matrix2) ``` You can perform various operations on matrices, such as matrix multiplication, addition, and subsetting. ```r # Matrix multiplication matrix3 <- matrix1 %*% matrix2 print(matrix3) # Matrix addition matrix4 <- matrix1 + matrix2 print(matrix4) # Subsetting a matrix print(matrix1[1, 2]) # prints the element at row 1 and column 2 of matrix1 print(matrix1[c(1, 2), c(1, 2)]) # prints the first two rows and columns of matrix1 ``` **Creating and Manipulating Data Frames in R** A data frame is a two-dimensional data structure that can store multiple rows and columns of data, with each column being a vector or factor. Data frames can be created using the `data.frame()` function. ```r # Create a data frame data1 <- data.frame( Name = c("John", "Jane", "Bob"), Age = c(25, 30, 35), Country = c("USA", "Canada", "UK") ) print(data1) # Create a data frame with multiple types of data data2 <- data.frame( Name = c("Alice", "Bob", "Charlie"), Age = c(20, 25, 30), Employed = c(TRUE, FALSE, TRUE) ) print(data2) ``` You can perform various operations on data frames, such as subsetting, merging, and aggregating. ```r # Subsetting a data frame print(data1[1, 2]) # prints the age of the first person in data1 print(data1[c(1, 3), c(1, 2)]) # prints the first and third rows and first two columns of data1 # Merging data frames data3 <- merge(data1, data2, by = "Name") print(data3) # Aggregating data frames summarize_data1 <- aggregate(data1$Age, list(data1$Country), mean) print(summarize_data1) ``` **Practice Exercises:** 1. Create a vector that contains the names of the planets in our solar system. 2. Create a 3x4 matrix with random numbers between 1 and 100. 3. Create a data frame with the following columns: Name, Age, and Country. Add three rows of data to the data frame. 4. Subset the data frame to include only the rows where the country is USA. 5. Merge the data frame with another data frame that contains additional information about the individuals. **External Resources:** * For more information on working with vectors, matrices, and data frames in R, see the official R documentation at [https://cran.r-project.org/doc/manuals/R-intro.pdf](https://cran.r-project.org/doc/manuals/R-intro.pdf). * For more practice exercises, see [https://swirlstats.com/users/](https://swirlstats.com/users/). **Comments and Feedback:** If you have any questions or need help with any of the concepts or exercises, leave a comment below.