R Academy · Lesson

Matrix Indexing and Subsetting

Access rows, columns, and elements using [row, col] notation.

Lesson 2 of 413 steps

Matrix Indexing and Subsetting is a free R Academy lesson on CoddyKit. This is lesson 2 of 4. Any 3 lessons of this learning path are free to read in full — after that, CoddyKit PRO unlocks every lesson, plus hands-on practice with a built-in code editor and a 24/7 AI tutor. It is part of the R Academy learning path, and your progress syncs across the web and the CoddyKit app. The R Academy course includes 4 lessons in total.

Matrix Index Basics

To access elements in a matrix, use m[row, col] notation. Row and column indices start at 1 in R. The comma is required — omitting it treats the matrix as a flat vector.

m <- matrix(1:12, nrow = 3, ncol = 4)
cat('Full matrix:
')
print(m)

# Element at row 2, column 3
cat('m[2, 3] =', m[2, 3], '
')

# Element at row 1, column 4
cat('m[1, 4] =', m[1, 4], '
')

Extracting a Full Row

Leave the column index blank to extract an entire row. The result is a vector (the matrix dimension is dropped). The comma must still be present.

scores <- matrix(c(88,92,75,81, 79,85,90,73, 95,78,82,88),
                 nrow = 3, byrow = TRUE)
rownames(scores) <- c('Alice', 'Bob', 'Carol')
colnames(scores) <- c('Math', 'Sci', 'Eng', 'Hist')
print(scores)

# Extract Alice's row
alice_scores <- scores[1, ]
cat('Alice:', alice_scores, '
')
cat('Alice mean:', mean(alice_scores), '
')

Extracting a Full Column

Leave the row index blank to extract an entire column. Like row extraction, the result is a vector. The comma separating row and column positions must still be present.

scores <- matrix(c(88,92,75,81, 79,85,90,73, 95,78,82,88),
                 nrow = 3, byrow = TRUE)
rownames(scores) <- c('Alice', 'Bob', 'Carol')
colnames(scores) <- c('Math', 'Sci', 'Eng', 'Hist')

# Extract the Math column
math_scores <- scores[, 1]
cat('Math scores:', math_scores, '
')
cat('Math mean:  ', mean(math_scores), '
')
cat('Math max:   ', max(math_scores), '
')

Subsetting Multiple Rows and Columns

Use a vector of indices to extract multiple rows or columns at once. The result is a submatrix (still a matrix, not a vector), preserving the two-dimensional structure.

m <- matrix(1:16, nrow = 4, ncol = 4)
cat('Original 4x4:
')
print(m)

# Extract rows 1,3 and columns 2,4
sub_m <- m[c(1, 3), c(2, 4)]
cat('Submatrix rows 1,3 cols 2,4:
')
print(sub_m)
cat('Class:', class(sub_m), '
')

Accessing by Row/Column Name

If a matrix has row and column names, you can index by name instead of position. This is more readable and robust to reordering rows or columns.

scores <- matrix(c(88,92,75,81, 79,85,90,73, 95,78,82,88),
                 nrow = 3, byrow = TRUE,
                 dimnames = list(
                   c('Alice', 'Bob', 'Carol'),
                   c('Math', 'Sci', 'Eng', 'Hist')
                 ))

# Access by name
cat('Bob Science:', scores['Bob', 'Sci'], '
')
cat('Carol row:
')
print(scores['Carol', ])
cat('English column:
')
print(scores[, 'Eng'])

Logical Indexing on Matrices

You can use a logical vector (or logical matrix) to select elements. When a logical vector is used in one dimension, it selects rows/columns where the value is TRUE.

scores <- matrix(c(88,92,75,81, 79,85,90,73, 95,78,82,88),
                 nrow = 3, byrow = TRUE)
rownames(scores) <- c('Alice', 'Bob', 'Carol')

# Select students with mean score >= 85
row_means <- rowMeans(scores)
high_achievers <- scores[row_means >= 85, ]
cat('High achievers (mean >= 85):
')
print(high_achievers)

Negative Indexing — Excluding Rows/Cols

Prefix an index with - to exclude that row or column. This is a clean way to drop specific rows or columns without listing all the others you want to keep.

m <- matrix(1:12, nrow = 3, ncol = 4, byrow = TRUE)
cat('Original:
')
print(m)

# Drop row 2
cat('Without row 2:
')
print(m[-2, ])

# Drop columns 1 and 3
cat('Without cols 1 and 3:
')
print(m[, c(-1, -3)])

Single Element with drop = FALSE

By default, extracting a single row or column drops the dimension (returns a vector). Use drop = FALSE to keep the result as a matrix (preserving row/column structure).

m <- matrix(1:9, nrow = 3, ncol = 3)

# Default: drops to vector
row1_vector <- m[1, ]
cat('m[1,] class:', class(row1_vector), '
')  # numeric (vector)

# drop = FALSE: keeps as 1-row matrix
row1_matrix <- m[1, , drop = FALSE]
cat('m[1,,drop=FALSE] class:', class(row1_matrix), '
')
cat('Dimensions:', dim(row1_matrix), '
')

Replacing Values by Index

You can modify specific elements by combining indexing with assignment. This works for single elements, whole rows, whole columns, or submatrices.

m <- matrix(0, nrow = 4, ncol = 4)
cat('Zero matrix:
')
print(m)

# Set diagonal to 5
for (i in 1:4) m[i, i] <- 5
cat('After setting diagonal:
')
print(m)

# Replace entire row 2
m[2, ] <- c(1, 2, 3, 4)
cat('After replacing row 2:
')
print(m)

Linear (Flat) Indexing

A matrix is stored as a flat vector internally (column by column). You can access elements with a single integer index in this flat order. This is sometimes useful for bulk operations.

m <- matrix(c(10,20,30,40,50,60), nrow = 2, ncol = 3)
cat('Matrix:
')
print(m)

# Flat indices: column by column
cat('m[1] =', m[1], '(row 1, col 1)
')
cat('m[3] =', m[3], '(row 1, col 2)
')
cat('m[5] =', m[5], '(row 1, col 3)
')

# Replace flat index 4
m[4] <- 99
print(m)

Indexing Summary

Quick reference for matrix indexing in R:

m[r, c] — single element at row r, col c
m[r, ] — entire row r
m[, c] — entire column c
m[c(r1,r2), c(c1,c2)] — submatrix
m['name', 'name'] — access by dimname
m[-r, ] — exclude row r
m[logical_vec, ] — logical row selection
m[r, , drop=FALSE] — keep as matrix

m <- matrix(c(5,10,15,20,25,30), nrow = 2, ncol = 3)
cat('m[1,2] =', m[1,2], '
')
cat('m[2,]  =', m[2,], '
')
cat('m[,3]  =', m[,3], '
')
cat('m[-1,] :
'); print(m[-1, ])

Quick Check

What does m[-2, ] return for a matrix m with 3 rows?

Answer it in the interactive lesson

Recap: Matrix Indexing

Great work! Key takeaways from this lesson:

m[r, c] uses row then column — both positions separated by comma
Leave one side blank (m[r, ] or m[, c]) to extract entire rows/columns
Negative indices exclude rows or columns
Logical vectors can filter rows or columns
Named dimensions allow indexing by name for readability
drop = FALSE prevents dimension loss when extracting single rows/columns

# Practical: find best student per subject
scores <- matrix(c(88,92,75,81, 79,85,90,73, 95,78,82,88),
                 nrow = 3, byrow = TRUE)
rownames(scores) <- c('Alice', 'Bob', 'Carol')
colnames(scores) <- c('Math', 'Sci', 'Eng', 'Hist')

for (subj in colnames(scores)) {
  best <- rownames(scores)[which.max(scores[, subj])]
  cat('Best in', subj, ':', best, '
')
}

Free to start

Learn R with an AI tutor — free

Write and run real code in your browser, get instant help from a 24/7 AI tutor, and pick up where you left off on the web or in the app.

Courses: 43
Lessons: 159

Start learning free View the full course

Frequently Asked Questions

Is the “Matrix Indexing and Subsetting” lesson free?

Yes — any 3 lessons of the R Academy learning path, including “Matrix Indexing and Subsetting”, are free to read in full here on the web. After that, CoddyKit PRO unlocks every lesson, plus interactive practice with a built-in code editor and a 24/7 AI tutor. The R Academy course includes 4 lessons in total.

What will I learn in “Matrix Indexing and Subsetting”?

Access rows, columns, and elements using [row, col] notation. You practise R Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start R Academy?

No prior experience is required. R Academy on CoddyKit is structured for beginners through advanced learners, so you can start here or from the beginning and move at your own pace. This is lesson 2 of 4.

How long does the “Matrix Indexing and Subsetting” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this R Academy lesson?

Yes. Every R Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

← Back to R Academy