The Class of an Object In R Language

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Introduction to Class of an Object in R

In R language, all objects have a class, which can be reported using the class() function. For simple vectors, this is just the mode, such as numeric, character, list, or logical. The other possible modes are array, matrix, factor, and data frame.

A special attribute known as the class of the object is used to allow for an object-oriented style of programming in R language. For example, an object having class as “data.frame” will be printed in a certain way, the plot() function will display it graphically in a certain way, and other generic functions such as summary() will react to it as an argument in a way sensitive to its class.

How to Determine the Class of an Object in R

The class() function is used to determine the class of an object. For example,

class(mtcars)
x <- c(1, 2, 3)
class(x)

y <- c("a", "b", "c")
class(y)

z <- c(TRUE, FALSE)
class(z)
Class of an Object in R Language

Common Object Classes in R

Here are some of the most common object classes in R:

  1. Integer: Represents integer values.
  2. Numeric: Represents numerical data.
  3. Character: Represents text strings.
  4. Factor: Represents categorical data.
  5. Logical: Represents logical values (TRUE or FALSE).
  6. Date: Represents dates.
  7. List: Represents a collection of objects of different types.
  8. Matrix: Represents a two-dimensional array of numbers.
  9. Data Frame: Represents a tabular data structure with rows and columns.
  10. POSIXct: Represents date and time.

It is important to note that one can define one’s classes using S3 or S4 object-oriented systems. This allows the user to define specific methods and behavior for different objects.

Why Classes Matter in R Language

The class of an object determines how R behaves when a user applies functions to it. In simple words, a class defines the object’s type and determines the operations that can be performed on it. For instance:

  • Arithmetic operations: These are typically performed on numeric objects.
  • String manipulation: These are performed on character objects.
  • Statistical analysis: These are often performed on numeric or factor objects.

The importance of classes can be described as:

  • Method Dispatch: The class of an object in R language determines which function to call when you apply a generic function to it. For example, the summary() function behaves differently for numeric vectors, data frames, and linear models.
  • Object-Oriented Programming: R supports object-oriented programming, and classes are fundamental to this paradigm. One can create custom classes to represent complex data structures and define methods to operate on these objects.
  • Data Manipulation: Understanding the class of an object helps one to choose the appropriate functions for data manipulation. For instance, one might use different functions for subsetting, sorting, and summarizing numeric vectors, character vectors, and data frames.

Remove the Class of an Object in R

To remove temporarily the effect of a class from an object, one can use the unclass() function. For example, if mtcars has the class “data.frame” then typing the just mtcars on the command prompt will print it in data frame form, which is rather like a matrix.

mtcars

Whereas, typing unclass(mtcars) will print/display it as an ordinary list.

unclass(mtcars)

## Output
$mpg
 [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2 10.4 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4 15.8 19.7 15.0 21.4

$cyl
 [1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4

$disp
 [1] 160.0 160.0 108.0 258.0 360.0 225.0 360.0 146.7 140.8 167.6 167.6 275.8 275.8 275.8 472.0 460.0 440.0  78.7  75.7  71.1 120.1 318.0 304.0 350.0 400.0  79.0 120.3  95.1 351.0 145.0 301.0 121.0

$hp
 [1] 110 110  93 110 175 105 245  62  95 123 123 180 180 180 205 215 230  66  52  65  97 150 150 245 175  66  91 113 264 175 335 109

$drat
 [1] 3.90 3.90 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 3.92 3.07 3.07 3.07 2.93 3.00 3.23 4.08 4.93 4.22 3.70 2.76 3.15 3.73 3.08 4.08 4.43 3.77 4.22 3.62 3.54 4.11

$wt
 [1] 2.620 2.875 2.320 3.215 3.440 3.460 3.570 3.190 3.150 3.440 3.440 4.070 3.730 3.780 5.250 5.424 5.345 2.200 1.615 1.835 2.465 3.520 3.435 3.840 3.845 1.935 2.140 1.513 3.170 2.770 3.570 2.780

$qsec
 [1] 16.46 17.02 18.61 19.44 17.02 20.22 15.84 20.00 22.90 18.30 18.90 17.40 17.60 18.00 17.98 17.82 17.42 19.47 18.52 19.90 20.01 16.87 17.30 15.41 17.05 18.90 16.70 16.90 14.50 15.50 14.60 18.60

$vs
 [1] 0 0 1 1 0 1 0 1 1 1 1 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 0 1 0 0 0 1

$am
 [1] 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 1 1 1 1 1 1 1

$gear
 [1] 4 4 4 3 3 3 3 4 4 4 4 3 3 3 3 3 3 4 4 4 3 3 3 3 3 4 5 5 5 5 5 4

$carb
 [1] 4 4 1 1 2 1 4 2 2 4 4 3 3 3 4 4 4 1 2 1 1 2 2 4 2 1 2 2 4 6 8 2

attr(,"row.names")
 [1] "Mazda RX4"       "Mazda RX4 Wag"    "Datsun 710"       "Hornet 4 Drive"   "Hornet Sportabout"
 [6] "Valiant"         "Duster 360"       "Merc 240D"        "Merc 230"         "Merc 280"
[11] "Merc 280C"       "Merc 450SE"       "Merc 450SL"       "Merc 450SLC"      "Cadillac Fleetwood" 
[16] "Lincoln Continental" "Chrysler Imperial"   "Fiat 128"      "Honda Civic"     "Toyota Corolla" 
[21] "Toyota Corona"    "Dodge Challenger"  "AMC Javelin"     "Camaro Z28"      "Pontiac Firebird"   
[26] "Fiat X1-9"       "Porsche 914-2"     "Lotus Europa"     "Ford Pantera L"  "Ferrari Dino"       
[31] "Maserati Bora"   "Volvo 142E"

Changing the Class of an Object in R

While it’s generally not recommended to manually change an object’s class, there are functions like as.numeric(), as.character(), as.factor(), etc., that can coerce objects into different classes. However, be cautious, as inappropriate coercion can lead to unexpected results.

Understanding object classes is fundamental to effective R programming. By recognizing the class of an object, you can choose the appropriate functions and operations to work with it. By understanding the class of an object, you can effectively work with R’s diverse data structures and leverage its powerful data analysis capabilities.

FAQs about Class of an Object

  1. What is the concept of class in R language?
  2. How can one check the class of an object?
  3. For different data types (modes) what are the common classes used in R?
  4. How can one change the class of an object?
  5. Give examples to determine the class of different objects.

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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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Important Python MCQ Online Test 5

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This post is about the Python MCQ Online Test with Answers. It consists of 20 multiple-choice questions about Data and Data Structures in Python. Let’s start with the Python MCQ Online Test with Answers.

Online Multiple Choice questions about Python Programming with answers

1. Which among the following are mutable objects in Python?

  1. List
  2. Integer
  3. String
  4. Tuple
 
 
 
 

2. Which pandas function does a data professional use to convert categorical variables into dummy variables?

 
 
 
 

3. Which of the following is not a core data type in Python programming?

 
 
 
 

4. How do you create a 25 x 25 identify matrix in numpy?

 
 
 
 

5. A data professional is working with a list of named cities that contains data on global cities. What Python code can they use to add the string ‘Multan’ as the second element in the list?

 
 
 
 

6. What will the following Python code do?
set1={"a", 3, "b", 3}
set1.remove(3)

 
 
 
 

7. How is the data for each row in a CSV file stored once it is read?

 
 
 
 

8. A ————— NumPy array can be created from a list of lists, where each internal list is the same length.

 
 
 
 

9. We have a JSON dataset stored in the file_path directory. Which method is used to import JSON data into a pandas data frame?

 
 
 
 

10. What does the “iloc” method of a pandas data frame do?

 
 
 
 

11. How to find a multi-dimensional numpy array called $x$ in Python?

 
 
 
 

12. What data type is the object below?
L = [1, 23, 'hello', 1]

 
 
 
 

13. What will be the result of the following Python code?
set1 = {1, 2, 3}
set1.add(4)
set1.add(4)
print(set1)

 
 
 
 

14. A 5 x 5 numpy multidimensional array called $x$ is created. How to add a scalar $b$ to the $x$ matrix?

 
 
 
 

15. In pandas, what is the difference between the iloc[] and loc[] methods?

 
 
 
 

16. Which of the following is True regarding lists in Python?

 
 
 
 

17. A data professional is working with a list of named cities that contains data on global cities. The string ‘Houston’ is the third element in the list. What Python code can they use to remove the string ‘Houston’ from the list?

 
 
 
 

18. Which of the functions below can we use to acquire the value at a certain row?

 
 
 
 

19. A 5 x 5 numpy multidimensional array x is created. How do you access elements in the first row?

 
 
 
 

20. In Python, which of the following characters can a data professional use to instantiate a dictionary?

 
 
 
 

Python MCQ Online Test with Answers

Python MCQ Online Test with Answers

  • We have a JSON dataset stored in the file_path directory. Which method is used to import JSON data into a pandas data frame?
  • How do you create a 25 x 25 identify matrix in numpy?
  • A 5 x 5 numpy multidimensional array x is created. How do you access elements in the first row?
  • A 5 x 5 numpy multidimensional array called $x$ is created. How to add a scalar $b$ to the $x$ matrix?
  • How to find a multi-dimensional numpy array called $x$ in Python?
  • How is the data for each row in a CSV file stored once it is read?
  • A ————— NumPy array can be created from a list of lists, where each internal list is the same length.
  • In pandas, what is the difference between the iloc[] and loc[] methods?
  • A data professional is working with a list of named cities that contains data on global cities. The string ‘Houston’ is the third element in the list. What Python code can they use to remove the string ‘Houston’ from the list?
  • What does the “iloc” method of a pandas data frame do?
  • A data professional is working with a list of named cities that contains data on global cities. What Python code can they use to add the string ‘Multan’ as the second element in the list?
  • Which pandas function does a data professional use to convert categorical variables into dummy variables?
  • Which of the functions below can we use to acquire the value at a certain row?
  • In Python, which of the following characters can a data professional use to instantiate a dictionary?
  • What will be the result of the following Python code? set1 = {1, 2, 3} set1.add(4) set1.add(4) print(set1)
  • What will the following Python code do? set1={“a”, 3, “b”, 3} set1.remove(3)
  • Which of the following is True regarding lists in Python?
  • Which among the following are mutable objects in Python?
  1. List
  2. Integer
  3. String
  4. Tuple
  • Which of the following is not a core data type in Python programming?
  • What data type is the object below? L = [1, 23, ‘hello’, 1]
Python MCQ Online Test with Answers

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