Summary  
This chapter covers how to use the `matrix()` function to construct a two-dimensional data structure from a one-dimensional vector by specifying the number of rows and columns and choosing whether to fill it by rows or columns.

General domain of usage  
Data analysis

The `matrix()` function creates a matrix from a single vector. It allows you to specify how many rows and columns the matrix should have, and whether it should be filled by rows or by columns.

## Function Overview
```r
matrix(data = NA, nrow = 1, ncol = 1, byrow = F)
```

- `data`: the vector used to fill the matrix;
- `nrow`: number of rows;
- `ncol`: number of columns;
- `byrow`: if `TRUE`, the matrix is filled row by row; if `FALSE` - column by column.

The length of the vector must be divisible by either `nrow` or `ncol`. If both are specified, then `nrow * ncol` must equal the vector length.

Note

# Vector of integers
num <- 1:9

# Fill by columns (default)
matrix(num, nrow = 3, ncol = 3)

# Fill by rows
matrix(num, nrow = 3, ncol = 3, byrow = T)

The first matrix is filled column by column (default configuration), while the second one is filled row by row.

The `seq()` function can generate ranges similarly to a colon (`:`):
- `seq(a, b)` generates integers from `a` to `b`;
- `seq(a, b, c)` generates integers from `a` to `b` with step size `c`.

Study More

Gain a solid understanding of R, one of the most widely used languages for data analysis. Explore fundamental data types and one-dimensional values, then advance to multi-dimensional structures, including matrices, data frames, and lists. Build the skills needed to apply R effectively in statistical analysis and data-driven decision-making.

Learn the essentials of R syntax, such as arithmetic, variables, printing functions, and comments. Strengthen your understanding of computational logic through exercises.

Explore R's data types and understand their role in data handling. Build skills in working with vectors as essential structures for organizing and analyzing information.

Develop the ability to manage categorical data effectively. Discover how to structure, group, and interpret values by converting raw data into clear categories.

Understand how to represent and work with structured two-dimensional data. Build confidence in transforming, labeling, and applying operations to matrices for analytical tasks.

Master the most widely used data structure in R for tabular information. Learn to organize, transform, and extract insights from datasets to support data-driven decisions.

Learn how lists extend beyond vectors by storing elements of different types and lengths. Develop the ability to create, access, and modify lists for organizing complex data structures.

matrix() Function

Function Overview

Example

Solution

Awesome!

matrix() Function

Function Overview

Example

Solution