How to apply a function to each row in R

Applying a function to each row is a common task in R. Here are several ways to do it, from simple to more elegant.

With base R

The apply() function works on matrices and data frames:

df <- data.frame(
  a = c(1, 2, 3, 4),
  b = c(5, 6, 7, 8),
  c = c(9, 10, 11, 12)
)

# Sum each row
row_sums <- apply(df, 1, sum)
# [1] 15 18 21 24

# Custom function
row_means <- apply(df, 1, function(x) mean(x))
# [1]  5  6  7  8

With dplyr and rowwise()

The dplyr package makes this readable:

library(dplyr)

df <- data.frame(
  a = c(1, 2, 3, 4),
  b = c(5, 6, 7, 8),
  c = c(9, 10, 11, 12)
)

df %>%
  rowwise() %>%
  mutate(row_sum = sum(c_across(everything()))) %>%
  ungroup()

With purrr

The purrr package provides functional programming tools:

library(purrr)

df <- data.frame(
  a = c(1, 2, 3, 4),
  b = c(5, 6, 7, 8)
)

# Using pmap
df %>%
  mutate(row_max = pmap_dbl(., ~max(c(...))))

Using base R by()

The by() function splits data and applies functions:

df <- data.frame(
  group = c("A", "A", "B", "B"),
  value = c(10, 20, 30, 40)
)

by(df$value, df$group, sum)

Performance comparison

For large data frames, dplyr::rowwise() is often the clearest, while apply() can be faster for very large datasets. The purrr family gives you type safety.

See Also

• apply() — Apply functions over array margins
• lapply() — Apply a function to list or vector
• dplyr-mutate — Create new columns