stringr::str_trim()

str_trim(string, side = "both")

Returns: character · Updated March 16, 2026 · Tidyverse

stringr string whitespace tidyverse

The str_trim() function from stringr removes leading and trailing whitespace from strings. It is useful for cleaning user input, normalizing data, and preparing strings for display or further processing. Unlike base R’s trim.ws() which only works on vectors, str_trim is designed to work seamlessly with the tidyverse ecosystem and handles various edge cases gracefully.

Syntax

str_trim(string, side = "both")

Parameters

Parameter	Type	Default	Description
`string`	character	Required	A character vector to trim
`side`	character	”both”	Which sides to trim: “both”, “left”, or “right”

Examples

Basic usage

library(stringr)

# Trim whitespace from both ends
strings <- c("  hello  ", "  world", "test  ")
str_trim(strings)
# [1] "hello" "world" "test"

Trimming only one side

# Trim only leading whitespace
str_trim("  hello", side = "left")
# [1] "hello"

# Trim only trailing whitespace  
str_trim("hello  ", side = "right")
# [1] "hello"

Cleaning data

library(dplyr)

# Example data with whitespace issues
df <- data.frame(
  name = c("  John  ", "Jane", " Bob "),
  score = c(85, 92, 78)
)

df %>%
  mutate(name = str_trim(name))
#   name score
# 1 John    85
# 2 Jane    92
# 3  Bob    78

Combining with other string functions

# Normalize whitespace: trim and collapse multiple spaces
text <- c("  The   quick   brown  fox  ")

text %>%
  str_trim() %>%
  str_squish()
# [1] "The quick brown fox"

With readr and data import

library(readr)

# When importing CSV, whitespace is sometimes preserved
# str_trim cleans up the imported data
raw_data <- c("  123  ", "  456  ", "  789  ")
str_trim(raw_data)
# [1] "123" "456" "789"

Handling different whitespace characters

# str_trim handles various whitespace types
messy <- c("\t  text  \n", "  another  ")
str_trim(messy)
# [1] "text"      "another"

Common Use Cases

Form validation: Clean user-submitted data before validation

Data cleaning: Standardize imported data from spreadsheets or databases

Text preprocessing: Prepare text for NLP or analysis pipelines

Display formatting: Ensure consistent spacing in output tables or reports