R Character Data Type

The character data type stores text. Any value wrapped in single or double quotes in R becomes a character — also called a string. Names, addresses, category labels, and any other text in your data all live in character variables.

Creating Character Variables

city <- "Delhi"
greeting <- 'Hello, World!'
product_code <- "PRD-20048"
empty_text <- ""     # empty string — still valid

class(city)    # "character"

Single and double quotes work the same way. Use whichever feels natural, but stay consistent within a project. If your text itself contains a single quote, use double quotes around it (and vice versa):

message <- "It's a sunny day"
title <- 'She said "Hello"'

Checking Length

Two different "length" concepts apply to character data:

phrase <- "Learning R"

nchar(phrase)   # 10 — number of characters in the string
length(phrase)  # 1  — number of elements (just one string here)

Concept             Function     Example Result
────────────────────────────────────────────────────
Characters in text  nchar()      nchar("Hello") → 5
Number of items     length()     length(c("a","b")) → 2

Combining Character Values

Use paste() to join text pieces together. By default it adds a space between pieces.

first <- "Ravi"
last  <- "Kumar"
full  <- paste(first, last)
print(full)   # "Ravi Kumar"

Use paste0() to join with no separator:

id <- paste0("EMP", 101)
print(id)   # "EMP101"

Useful Character Functions

Function             What It Does                     Example
───────────────────────────────────────────────────────────────────
toupper(x)           Convert to UPPERCASE             "delhi" → "DELHI"
tolower(x)           Convert to lowercase             "HELLO" → "hello"
nchar(x)             Count characters                 "R" → 1
substr(x, s, e)      Extract part of text             substr("Hello",1,3) → "Hel"
gsub("old","new",x)  Replace all occurrences          gsub("a","@","banana")
trimws(x)            Remove leading/trailing spaces   "  hi  " → "hi"
strsplit(x, ",")     Split text at delimiter          "a,b,c" → list

A Practical Example: Name Formatter

raw_name <- "   anjali sharma   "

# Step 1: Remove extra spaces
clean_name <- trimws(raw_name)

# Step 2: Convert to title format (capitalize first letter of each word)
clean_name <- gsub("(\\w)(\\w*)", "\\U\\1\\L\\2", clean_name, perl = TRUE)

print(clean_name)

Output:

[1] "Anjali Sharma"

Checking and Converting Character

is.character("hello")   # TRUE
is.character(42)        # FALSE

as.character(99)        # "99"  — number to text
as.character(TRUE)      # "TRUE"
as.numeric("3.14")      # 3.14  — text to number (only if valid)
as.numeric("abc")       # NA with warning

Character in Data Frames

Real datasets almost always mix numeric and character columns. For example, a customer table might look like:

Name (character)   Age (numeric)   City (character)
─────────────────────────────────────────────────────
"Meera"            29              "Pune"
"Arjun"            34              "Chennai"
"Zara"             25              "Kolkata"

R handles character columns in data frames with the same functions — toupper(), nchar(), gsub() — applied across entire columns when needed.

Special Characters and Escape Sequences

Sequence    Meaning               Result in output
──────────────────────────────────────────────────
\n          New line              moves to next line
\t          Tab                   horizontal space
\\          Literal backslash     \
\"          Literal quote         "

cat("Line 1\nLine 2\n")
# Line 1
# Line 2

cat("Name:\tPriya\n")
# Name:	Priya

Character data appears in virtually every real dataset — whether it is product names, city labels, or user feedback. Learning to clean, combine, and transform character values is an essential skill in R data analysis.