How to Check if a Column Contains a String in R

Introduction

Whether you’re doing some data cleaning or exploring your dataset, checking if a column contains a specific string can be a crucial task. Today, I’ll show you how to do this using both str_detect() from the stringr package and base R methods. We’ll also tackle finding partial strings and counting occurrences. Let’s dive right in!

Using str_detect from stringr

First, we’ll use the str_detect function. The stringr package is part of the tidyverse collection, which brings a set of user-friendly functions to text manipulation. We’ll start by ensuring it’s installed and loaded:

install.packages("stringr")

Now, let’s create a sample dataset:

library(stringr)
# Sample data
data <- data.frame(
  name = c("Alice", "Bob", "Carol", "Dave", "Eve"),
  description = c("Software developer", "Data analyst", "UX designer", "Project manager", "Data scientist")
)
data

   name        description
1 Alice Software developer
2   Bob       Data analyst
3 Carol        UX designer
4  Dave    Project manager
5   Eve     Data scientist

Examples

Using stringr

Check for Full String

Suppose we want to check if any of the description column contains “Data analyst”:

# Detect if 'description' contains 'Data analyst'
data$has_data_analyst <- str_detect(data$description, "Data analyst")
print(data)

   name        description has_data_analyst
1 Alice Software developer            FALSE
2   Bob       Data analyst             TRUE
3 Carol        UX designer            FALSE
4  Dave    Project manager            FALSE
5   Eve     Data scientist            FALSE

In the output, the has_data_analyst column will be TRUE for “Bob” and FALSE for others.

Check for Partial String

Let’s expand our search to any string containing “Data”:

# Detect if 'description' contains any word with 'Data'
data$has_data <- str_detect(data$description, "Data")
print(data)

   name        description has_data_analyst has_data
1 Alice Software developer            FALSE    FALSE
2   Bob       Data analyst             TRUE     TRUE
3 Carol        UX designer            FALSE    FALSE
4  Dave    Project manager            FALSE    FALSE
5   Eve     Data scientist            FALSE     TRUE

This will show TRUE for “Bob” and “Eve,” where both “Data analyst” and “Data scientist” are detected.

Count Occurrences

If you need to count how many times “Data” appears, use str_count:

# Count occurrences of 'Data'
data$data_count <- str_count(data$description, "Data")
print(data)

   name        description has_data_analyst has_data data_count
1 Alice Software developer            FALSE    FALSE          0
2   Bob       Data analyst             TRUE     TRUE          1
3 Carol        UX designer            FALSE    FALSE          0
4  Dave    Project manager            FALSE    FALSE          0
5   Eve     Data scientist            FALSE     TRUE          1

This will add a column data_count with the exact count of occurrences per row.

Using Base R

For those who prefer base R, the grepl and gregexpr functions can help.

Check for Full or Partial String

grepl is ideal for checking if a string is present:

# Using grepl for full/partial string detection
data$has_data_grepl <- grepl("Data", data$description)
print(data)

   name        description has_data_analyst has_data data_count has_data_grepl
1 Alice Software developer            FALSE    FALSE          0          FALSE
2   Bob       Data analyst             TRUE     TRUE          1           TRUE
3 Carol        UX designer            FALSE    FALSE          0          FALSE
4  Dave    Project manager            FALSE    FALSE          0          FALSE
5   Eve     Data scientist            FALSE     TRUE          1           TRUE

This will yield the same output as str_detect.

Count Occurrences

For counting occurrences, gregexpr is helpful:

# Count occurrences using gregexpr
matches <- gregexpr("Data", data$description)
data$data_count_base <- sapply(
  matches, 
  function(x) ifelse(x[1] == -1, 0, length(x))
  )
print(data)

   name        description has_data_analyst has_data data_count has_data_grepl
1 Alice Software developer            FALSE    FALSE          0          FALSE
2   Bob       Data analyst             TRUE     TRUE          1           TRUE
3 Carol        UX designer            FALSE    FALSE          0          FALSE
4  Dave    Project manager            FALSE    FALSE          0          FALSE
5   Eve     Data scientist            FALSE     TRUE          1           TRUE
  data_count_base
1               0
2               1
3               0
4               0
5               1

This will add a new data_count_base column containing the count of “Data” in each row.

Give It a Try!

The best way to master string detection in R is to experiment with different patterns and datasets. Whether you use str_detect, grepl, or any other approach, you’ll find plenty of ways to customize the search. Try it out with your own datasets, and soon you’ll be searching like a pro!