Strings in R Language

In R language, any value within a pair of single or double quotes is treated as a string or character. Strings in R language are internally stored within double quotes, even if the user created the sting with a single quote. In other words, the strings in R language are sequences of characters that are enclosed within either single or double quotation marks. They are fundamental data structures used to represent textual data.

Rules Applied in Constructing Strings

Some rules are applied when Strings are constructed.

• The quotes at the beginning and end of a string should be both single quotes or both double quotes. Single or double quotes cannot be mixed in a single-string construction.
• Double quotes can be inserted into a string starting and ending with a single quote.
• A single quote can be inserted into a string starting and ending with double quotes.
• Double quotes cannot be inserted into a string starting and ending with double quotes.
• A single quote cannot be inserted into a string starting and ending with a single quote.

Examples of Valid Strings in R Language

The following are a few examples that clarify the rules about creating/ constructing a string in R Language.

a <- 'Single quote string in R Language'
print(a)

b <- "Double quote String in R Language"

c <- "Single quote ' within the double quote string"
print(c)
d<- 'Double quotes " within the single quote string'
print(d)

Examples of invalid Strings in R Language

The following are a few invalid strings in R

s1 <- 'Mixed quotes"
print(s)

s2 <- 'Single quote ' inside single quote'
print(s)

s3 <- "Double quote " inside double quotes"
print(s3)

String Manipulation in R Language

The Strings in R Language can be manipulated.

Concatenating Strings using paste() Function

In R language, strings can be combined using the paste() function. The paste() function takes any number of arguments (strings) to be combined together. For example,

a <- "Hello"
b <- "How"
c <- "are you?"
paste(a, b, c)

## Output
[1] "Hello How are you?"

Formatting Numbers and Strings using format() Function

The numbers and strings can be formatted easily using format() function. For example,

# Total number of digits printed and last digit rounded off
format(12.123456789, digits = 9)

# Display numbers in scientific notation
format(c (4, 13.123456), scientific = TRUE)

# Minimum number of digits to the right of the decimal point
format(123.47, nsmall = 5)

# Everything a string
format(6)

# Numbers with blank in the beginning
format(12.7, width = 6)

# Left Justify Strings
format("Hello", width = 8, justify = "l")

# Justify Strings with Centers
format ("Hello", width = 8, justify = "c")

Counting Numbers of Characters in Strings

The nchar() function can be used to count the number of characters in a string. For example,

nchar("This is a string")

Changing the case toupper() and tolower() Functions

The and tolower functions are used to change the case of the characters of a string. For example,

toupper("rfaqs.com")
tolower("RFAQS.COM")
tolower("Rfaqs.com")

Extracting parts of a String using substring() Function

The substring() function can be used to extract a part of a string. For example,

# Extract characters from 5th to 8th position
substring("Strings in R Language", 5, 8)

Importance of Strings in R Language

1. Handling Textual Data:
   • Data Cleaning: Strings are used to clean and preprocess textual data, for example, removing extra spaces, punctuation, or standardizing formats.
   • Web Scraping: Extracting data from websites often involves parsing HTML and XML, which are primarily composed of strings.
   • Text Mining: Extracting meaningful insights from textual data, such as sentiment analysis, text classification, and topic modeling. All these heavily rely on string manipulation techniques.
2. Data Categorization and Labeling:
   • Label Encoding: Assigning numerical codes to categorical variables often involves converting string labels into numerical representations.
   • Categorical Variables: Strings can be used to represent categorical variables, which are essential for statistical analysis and machine learning models.
3. File Paths and Input/ Output Operations:
   • Data Import and Export: Reading data from CSV, Excel, or text files and exporting results to various formats involves string-based operations.
   • File Reading and Writing: Specifying file paths and file names in R often requires strings.
4. Visualization and Reporting:
   • Plot Labels and Titles: Creating informative visualizations requires using strings to label axes, add titles, and provide descriptive text.
   • Report Generation: Generating reports in formats like HTML, PDF, or Word involves formatting text, creating tables, and incorporating graphical elements, all of which rely on string manipulation.
5. Programming and Scripting:
   • Comments and Documentation: Adding comments to code to explain its functionality is crucial for readability and maintainability.
   • Function and Variable Names: Strings are used to define meaningful names for functions and variables.

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