R Academy · Lesson

rep() for Repeating Values

Repeat values and vectors with rep(), times, and each arguments.

Lesson 3 of 413 steps

rep() for Repeating Values is a free R Academy lesson on CoddyKit. This is lesson 3 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.

Introduction to rep()

rep() stands for replicate. It repeats values or vectors a specified number of times. It is one of the most useful tools for creating structured test data, balanced datasets, and filling vectors with default values.

# Repeat a single value
zeros <- rep(0, times = 5)
cat('Five zeros:', zeros, '
')

# Repeat a string
status <- rep('active', times = 4)
cat('Status:', status, '
')

rep(x, times) — Repeat Whole Vector

When x is a vector and times is a single integer, rep() repeats the entire vector that many times. The output length is length(x) * times.

# Repeat a vector 3 times
weekend <- c('Saturday', 'Sunday')
four_weekends <- rep(weekend, times = 4)
cat('4 weekends:', four_weekends, '
')

# Repeat numeric vector
pattern <- c(1, 2, 3)
cat('Pattern x3:', rep(pattern, times = 3), '
')

rep(x, each) — Repeat Each Element

The each argument repeats each individual element before moving to the next one. This is the key difference from times: each repeats elements in place.

groups <- c('A', 'B', 'C')

# times: repeats whole vector
cat('times=3:', rep(groups, times = 3), '
')
# A B C A B C A B C

# each: repeats each element before next
cat('each=3 :', rep(groups, each = 3), '
')
# A A A B B B C C C

rep(x, times = c(...)) — Variable Repeats

When times is a vector of the same length as x, each element of x is repeated the corresponding number of times. This gives fine-grained control over repetition.

# Different number of repeats per element
fruits <- c('apple', 'banana', 'cherry')
counts <- c(3, 1, 2)

fruit_basket <- rep(fruits, times = counts)
cat('Fruit basket:', fruit_basket, '
')
# apple apple apple banana cherry cherry

# Useful for creating category labels
cat('Length:', length(fruit_basket), '
')

rep_len() — Repeat to Fixed Length

rep_len(x, length.out) repeats vector x until the result reaches exactly length.out elements. The repetition is cyclic — it wraps around the vector as needed.

# Cycle through seasons to fill 12 months
seasons <- c('Spring', 'Summer', 'Autumn', 'Winter')
year_seasons <- rep_len(seasons, length.out = 12)
cat('12 months by season:', year_seasons, '
')

# Cycle through 1, 2, 3 for 10 items
rotation <- rep_len(1:3, length.out = 10)
cat('Rotation:', rotation, '
')

Combining times and each

You can use times and each together to create more complex repetition patterns. R applies each first (expanding each element), then repeats the resulting vector times times.

# each=2 then times=3
vals <- c(1, 2)
result <- rep(vals, each = 2, times = 3)
cat('each=2, times=3:', result, '
')
# 1 1 2 2  repeated 3 times: 1 1 2 2 1 1 2 2 1 1 2 2

cat('Length:', length(result), '
')

Creating Balanced Group Labels

A classic use of rep() is creating balanced group labels for experiments — where you want an equal number of observations in each group.

# 5 subjects per treatment group
treatments <- c('Control', 'LowDose', 'HighDose')
n_per_group <- 5

group_labels <- rep(treatments, each = n_per_group)
cat('Group labels:', group_labels, '
')
cat('Total subjects:', length(group_labels), '
')

# Count per group
for (g in treatments) {
  cat(g, ':', sum(group_labels == g), '
')
}

Repeating Logical Values

rep() works with any data type, including logical values. This is useful for creating masks, flags, or alternating TRUE/FALSE patterns.

# Alternating TRUE/FALSE pattern
alternate <- rep(c(TRUE, FALSE), times = 5)
cat('Alternating mask:', alternate, '
')

# Apply mask to select every other element
data_vals <- 10:19
cat('Every other value:', data_vals[alternate], '
')

# Create a block pattern: 3 TRUE then 3 FALSE
block <- rep(c(TRUE, FALSE), each = 3)
cat('Block pattern:', block, '
')

rep() for Matrix Filling

You can use rep() to quickly fill matrices with patterned data. Since matrix() accepts a vector as input, combining it with rep() makes pattern matrices easy.

# Fill a 3x4 matrix with a repeating pattern
pattern <- rep(c(1, 0), times = 6)
checker <- matrix(pattern, nrow = 3, ncol = 4)
cat('Checkerboard-like matrix:
')
print(checker)

# Fill diagonal-like with rep
fill <- rep(1:4, each = 3)
cat('Repeated fill:', fill, '
')

Expanding a Summary Table

A practical use of rep() with variable times: converting a frequency table back into a raw data vector. If you know how many times each value occurred, rep() reconstructs the observations.

# Summary: grade frequencies
grades <- c('A', 'B', 'C', 'D')
frequencies <- c(5, 12, 8, 3)

# Reconstruct raw grade vector
raw_grades <- rep(grades, times = frequencies)
cat('Raw grades (', length(raw_grades), 'students):', raw_grades, '
')

# Verify frequency table
cat('Freq table:
')
print(table(raw_grades))

rep() Summary

Here is a quick reference for rep():

rep(x, times = n) — repeat entire x n times
rep(x, each = n) — repeat each element n times
rep(x, times = c(...)) — variable repeats per element
rep_len(x, n) — cycle x to reach exactly n elements
Works with any type: numeric, character, logical, factor

x <- c(10, 20, 30)
cat('times=2  :', rep(x, times = 2), '
')
cat('each=2   :', rep(x, each = 2), '
')
cat('var times:', rep(x, times = c(1, 2, 3)), '
')
cat('rep_len  :', rep_len(x, 7), '
')

Quick Check

What does rep(c(1, 2), each = 3) produce?

Answer it in the interactive lesson

Recap: rep() for Repeating Values

Excellent! Key takeaways from this lesson:

rep(x, times) repeats the whole vector; rep(x, each) repeats each element individually
Passing a vector to times gives variable repetition counts per element
rep_len(x, n) cycles x until exactly n elements are produced
Common uses: balanced group labels, masks, frequency table expansion, pattern matrices
Works identically with numeric, character, and logical data types

# Create a balanced experiment design
treatments <- c('Placebo', 'Drug_A', 'Drug_B')
subjects_per <- 4

design <- rep(treatments, each = subjects_per)
cat('Experiment design:
')
cat(design, '
')
cat('Total N:', length(design), '
')

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