Working with Text in R

Text in detail

R is widely used for statistical computing and data analysis, making it a preferred choice for statisticians and data miners. It includes support for machine learning algorithms, regression models, time series analysis, and various statistical inference techniques. R and its libraries provide numerous tools for handling statistical and graphical operations, such as linear and non-linear modeling, hypothesis testing, classification, clustering, and more.

Working with Strings in R

In R, any text enclosed in double quotes (" ") is treated as a string. Internally, R stores all strings in double quotes, even if they are initially defined with single quotes.

String Basics in R

# Creating a string variable
text <- "Hello, R Programming!"
print(text)

Rules for Working with Strings in R

• Strings must start and end with the same type of quote (either both double or both single quotes).
• Double quotes can be used inside a string enclosed by single quotes.
• Single quotes can be used inside a string enclosed by double quotes.

String Manipulation in R

1. Combining Strings using paste(): The paste() function joins multiple strings into a single string with an optional separator.

Syntax

paste(..., sep = " ", collapse = NULL)

• ... → Multiple string inputs.
• sep → Defines a separator between strings (default is a space).
• collapse → Removes spaces between combined strings (does not affect spaces within words).

Example

str1 <- "Welcome"
str2 <- "to R programming!"
result <- paste(str1, str2, sep = " ")
print(result)

Output:

[1] "Welcome to R programming!"

2. Formatting Strings and Numbers using format()

The format() function is used to format numbers and text with specific styles.

Syntax:

format(x, digits, nsmall, scientific, width, justify)

• x → Input value.
• digits → Number of total displayed digits.
• nsmall → Minimum decimal places.
• scientific → Uses scientific notation (TRUE/FALSE).
• width → Pads output with spaces to a specific width.
• justify → Aligns text to "left", "right", or "center".

Example:

# Formatting numbers
num <- format(123.456789, digits = 5)
print(num)

# Using scientific notation
num_scientific <- format(5400, scientific = TRUE)
print(num_scientific)

# Justifying text
text_justified <- format("Data", width = 10, justify = "right")
print(text_justified)

Output:

[1] "123.46"
[1] "5.400000e+03"
[1] "      Data"

3. Counting Characters using nchar()

The nchar() function counts the total number of characters (including spaces) in a string.

Example

text_length <- nchar("Data Science")
print(text_length)

Output:

[1] 12

4. Changing Case using toupper() and tolower()

These functions convert text to uppercase or lowercase.

Example

upper_case <- toupper("analytics")
lower_case <- tolower("DATA MINING")
print(upper_case)
print(lower_case)

Output:

[1] "ANALYTICS"
[1] "data mining"

5. Extracting Substrings using substring()

The substring() function extracts specific parts of a string.

Syntax

substring(x, first, last)

• x → Input string.
• first → Start position.
• last → End position.

Example:

sub_text <- substring("Visualization", 1, 5)
print(sub_text)

Output:

[1] "Visual"

Text Processing in R using Tidyverse

Tidyverse is a powerful collection of packages for data science, including the stringr package, which provides advanced string manipulation tools.

1. Detecting a String using str_detect()

library(tidyverse)
text <- "Welcome to Data Science!"
result <- str_detect(text, "Data")
print(result)

Output:

[1] TRUE

2. Finding String Positions using str_locate()

position <- str_locate(text, "Data")
print(position)

Output:

start end
[1,]     12  15

3. Extracting a Substring using str_extract()

extract_text <- str_extract(text, "Science")
print(extract_text)

Output:

[1] "Science"

4. Replacing Text using str_replace()

modified_text <- str_replace(text, "Data", "Machine Learning")
print(modified_text)

Output:

[1] "Welcome to Machine Learning Science!"

Regular Expressions (Regex) in R

Regular expressions allow pattern-based text searching and manipulation.

1. Selecting Characters using str_extract_all()

string <- "WelcomeToDataScience!"
match_pattern <- str_extract_all(string, "D..a")
print(match_pattern)

Output:

[1] "Data"

2. Finding Words using \\D

match_pattern2 <- str_extract_all(string, "T\\D\\Dcome")
print(match_pattern2)

Output:

[1] "ToCome"

Finding Pattern Matches using grep()

The grep() function searches for patterns within character vectors and returns their positions.

Syntax:

grep(pattern, string, ignore.case = FALSE)

• pattern → Regex pattern.
• string → Character vector.
• ignore.case → Case-insensitive search (TRUE/FALSE).

Example

text_list <- c("Python", "R", "Data Science", "Machine Learning")
match_position <- grep("Data", text_list)
print(match_position)

Output:

[1] 3