Data Structure

Strings in R Language

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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)
Strings in R Language

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)
Invalid Strings in R Language

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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Data Frames in R Language (2024)

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Data frames in R are one of the most essential data structures. A data frame in R is a list with the class “data.frame“. The data frame structure is used to store tabular data. Data frames in R Language are essentially lists of vectors of equal length, where each vector represents a column and each element of the vector corresponds to a row.

Data frames in R are the workhorse of data analysis, providing a flexible and efficient way to store, manipulate, and analyze data.

Restrictions on Data Frames in R

The following are restrictions on data frames in R:

  1. The components (Columns or features) must be vectors (numeric, character, or logical), numeric matrices, factors, lists, or other data frames.
  2. Lists, Matrices, and data frames provide as many variables to the new data frame as they have columns, elements, or variables.
  3. Numeric vectors, logical vectors, and factors are included as is, by default, character vectors are coerced to be factors, whose levels are the unique values appearing in the vector.
  4. Vecture structures appearing as variables of the data frame must all have the same length, and matrix structures must all have the same row size.

A data frame may for many purposes be regarded as a matrix with columns possibly of differing modes and attributes. It may be displayed in matrix form, and its rows and columns are extracted using matrix indexing conventions.

Key Characteristics of Data Frame

  • Column-Based Operations: R language provides powerful functions and operators for performing operations on entire columns or subsets of columns, making data analysis and manipulation efficient.
  • Heterogeneous Data: Data frames can store data of different data types within the same structure, making them versatile for handling various kinds of data.
  • Named Columns: Each column in a data frame has a unique name, which is used to reference and access specific data within the frame.
  • Row-Based Indexing: Data frames are indexed based on their rows, allowing you to easily extract or manipulate data based on row numbers.

Making/ Creating Data Frames in R

Objects satisfying the restrictions placed on the columns (components) of a data frame may be used to form one using the function data.frame(). For example:

BMI <- data.frame(
  age = c(20, 40, 33, 45),
  weight = c(65, 70, 53, 69),
  height = c(62, 65, 55, 58)
)
Creating Data frames in R manually

Note that a list whose components conform to the restrictions of a data frame may coerced into a data frame using the function as.data.frame().

Other Way of Creating a Data Frame

One can also use read.table(), read.csv(), read_excel(), and read_csv() functions to read an entire data frame from an external file.

Accessing and Manipulating Data

  • Accessing Data: Use column names or row indices to extract specific values or subsets of data.
  • Creating New Columns: Calculate new columns based on existing ones using arithmetic operations, logical expressions, or functions.
  • Grouping and Summarizing: Group data by specific columns and calculate summary statistics (e.g., mean, median, sum).
  • Sorting Data: Arrange rows in ascending or descending order based on column values.
  • Filtering Data: Select rows based on conditions using logical expressions and indexing.
# Create a data frame manually
data <- data.frame(
  Name = c("Ali", "Usman", "Hamza"),
  Age  = c(25, 30, 35),
  City = c("Multan", "Lahore", "Faisalabad")
)

# Accessing data
print(data$Age)      # Displays the "Age" column
print(data[2, ])  # Displays the second row

# Creating a new column
data$Age_Category <- ifelse(data$Age < 30, "Young", "Old")

# Filtering data
young_people <- data[data$Age < 30, ]

# Sort data
sorted_data <- data[order(data$Age), ]
data frame after manipulation

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Logical Vectors in R: A Quick Guide

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The logical vectors in R Language are the vectors whose elements are TRUE, FALSE, or NA (Not Available). R language allows the easy manipulation of logical (or relational) quantities. The TRUE and FALSE values are often used to represent the conditions or Boolean expressions.

In R, the reserved words TRUE and FALSE are often abbreviated as T and F, respectively. However, the T and F are not reserved words and hence can be overwritten by the user. Therefore, instead of T and F; it is better to use TRUE and FALSE.

Logical vectors in R can be created by:

  • Direct assignment of TRUE and FALSE values to the elements of a vector
  • By using conditions (use of logical or comparison operators) on elements of the vectors. (Operators in R Language)
  • Using ifelse statement

Creating Logical Vectors in R Using Direct Assignment

v1 <- c(TRUE, FALSE, TRUE)
print(v1)
## Output
[1]  TRUE FALSE  TRUE

Creating Logical Vectors using Comparison Operators

x <- 5
y <- 10
v2 <- x > y
print(v2)
## Output
FALSE
Logical Vectors in R using Comparison Operators
data <- c(1, 2, 3, 4, 5)
v3 <- data < 3
print(v3)
## Output
[1]  TRUE  TRUE FALSE FALSE FALSE
Logical Vectors in R

Creating Logical Vectors using ifelse Statement

The ifelse statement can also be used to create/generate logical vectors in R Language. For example,

data <- c(3, 4, 6, 8, 4, 4, 6, 10, -5)
v4 <- ifelse(data > 5, TRUE, FALSE)
print(v4)

## Output
[1] FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE

From the above examples, the logical vectors are usually generated by conditions. The length of the logical vector will be the same as that of the vectors to which the condition is applied. Depending on the condition, the corresponding elements result in FALSE if the element of the vectors does not meet the condition specified and TRUE where it is.

Logical Operators

The following is the list of logical operators

Logical OperatorShort Description
<Less than
>Greater than
<=Less than or Equal to
>=Greater than or Equal to
==Exactly Equal to
!=Not Equal to

In addition to logical operators, the relational/logical operators are:

OperatorShort Description
& (and)It takes two logical values and returns TRUE only if both values are TRUE themselves
| (or)It takes two logical values and returns TRUE if just one value is TRUE.
! (not)It negates the logical value it’s used on

Use of Logical Operators

Filtering Data

The logical vectors in R language are commonly used for filtering the data. For example,

data <- data.frame(x = c(1, 2, 3, 4, 5), y = c("a", "b", "c", "d", "e"))
filtered_data <- data[data$x > 3, ]
Logical Vectors in R: Filtering Data

Ordinary Arithmetic

Logical vectors may be used in ordinary arithmetic, in which case they are coerced into numeric vectors, FALSE becoming 0 and TRUE becoming. For example,

x = c(TRUE, FALSE, FALSE, TRUE)
y = c(5, 10, 6, 15)
x+y

## Output
[1]  6 10  6 16

sum(x)
## Output
[1] 2

Logical vectors in R language are a fundamental tool for working with conditions and Boolean expressions. Understanding how to create, manipulate, and use logical vectors is essential for effective data analysis and programming in R.

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