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

 
 
 
 

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

 
 
 
 

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

 
 
 
 

4. 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?

 
 
 
 

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

 
 
 
 

6. 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?

 
 
 
 

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

 
 
 
 

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

 
 
 
 

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

 
 
 
 

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

 
 
 
 

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

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

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

 
 
 
 

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

 
 
 
 

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

 
 
 
 

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

 
 
 
 

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

 
 
 
 

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

 
 
 
 

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

 
 
 
 

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

 
 
 
 

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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Generating Regular Sequences in R

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R language has a number of facilities for generating commonly used sequences of numbers. There are a number of functions for generating regular sequences in R to perform data analysis tasks:

  • Colon Operator (:)
  • seq() Function
  • rep() Function

Generating Regular Sequences in R Language

Usually, the functions related to generating regular sequences in R are used to create index vectors, vectors of evenly spaced numbers, repeating the patterns, and creating sequences for plotting.

Colon Operator (:)

The colon operator generates a sequence of integers, for example, 1:30 is the vector c(1, 2, …, 29, 30). The colon operator has a high priority within an expression, for example, 2*1:15 is the vector c(2, 4, …, 28, 30).

Let set $n=10$ and then compare the sequences $1:n-1$ and $1:(n-1)$:

n = 10
1:n-1
1:(n-1)
Generating Regular Sequences in R Language

The 30:1 may be used to generate a sequence backward.

30:1
## Output
 [1] 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10  9  8  7  6
[26]  5  4  3  2  1

The seq() Function

The seq() functions offer more flexibility and control over generating sequences. The seq() functions have five arguments, some of which may be specified in any call. The first two arguments of the function specify the beginning and end of the sequence.

Like other R functions, the arguments to seq() can also given in named form, in which case the order in which they appear is irrelevant. The first two arguments of seq() functions may be named from=value and to=value. Therefore seq(1, 30), seq(from = 1, to = 30) and seq(to = 30, from = 1) are all the same as 1:30. The other two arguments may be named by = value and length = value, which specify a step size and a length for the sequence, respectively. By default the by argument is set to 1, that is, by = 1. The examples of seq() functions are

seq(1, 20)
## Output
[1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20

seq(from = 1, to = 20)
## Output 
[1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20

seq(from = 1, to = 20, by = 1)
## OUtput
[1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20

seq(-5, 5, by = 0.2)
##
 [1] -5.0 -4.8 -4.6 -4.4 -4.2 -4.0 -3.8 -3.6 -3.4 -3.2 -3.0 -2.8 -2.6 -2.4 -2.2
[16] -2.0 -1.8 -1.6 -1.4 -1.2 -1.0 -0.8 -0.6 -0.4 -0.2  0.0  0.2  0.4  0.6  0.8
[31]  1.0  1.2  1.4  1.6  1.8  2.0  2.2  2.4  2.6  2.8  3.0  3.2  3.4  3.6  3.8
[46]  4.0  4.2  4.4  4.6  4.8  5.0

seq(length = 51, from = -5, by = 0.2)

Note that if only the first two arguments are given the result is the same as the colon operator. For example, seq(2, 10) results in the same output as 2:10.

The length.out argument may be used to generate a sequence of evenly spaced numbers, for example,

# generate a sequence of evenly spaced numbers between 0 and 1
seq(from = 0, to = 1, length.out = 11) 

## Output
[1] 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

The fifth argument may be named along = vector, which is normally used as the only argument to create the sequence 1,2, …., length(vector) or the empty sequence if the vector is empty. For example

x = rnorm(10)
seq(along = x)

## Output
[1]  1  2  3  4  5  6  7  8  9 10

The rep() Function

The rep function is used for replicating or repeating an object in various complicated ways. The simplest form of the rep() function is

rep(1:5, times = 5)

## Output
[1] 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5

The rep(1:5, times = 5) will put five copies of 1:5 end-to-end. The other useful version of rep() function is

rep(1:5, each = 5)

## Output
[1] 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 4 4 4 4 4 5 5 5 5 5

The rep(1:5, each = 5) repeats each element of 1:5 five times before moving on to the next number.

Frequently Asked Questions About R, generating regular sequences in R

R Language Quiz

General Knowledge Quizzes

Statistics and Data Analysis

Frequently Asked Questions about Generating Sequences

  • Describe R functions that are used to generate regular sequences.
  • What is the use of seq() function in R?
  • Give some examples of colon operators in R?
  • Describe rep() function in R with examples.
  • What is the length.out argument in seq() function?
  • Write about important arguments of seq() function in R language.
  • How one can generate a sequence backward, give an example.