Adv. Data Science Using R | Assignment – 1

Quiz – 1

Q1. How do you create a variable named x with the numeric value 5?
int x=5
All of the above
x<-5
x : 5

Q2. How do you insert COMMENTS in R code?
// This is a comment
/* This is a comment
None

Q3. What is a correct syntax to output “Hello World” in R?
‘Hello World’
“Hello World”
print(“Hello World”)
All of the other answers are correct

Q4. Who is introduced R Programming Language?
Ross Ihaka
Robert Gentleman
Both (A) and (B)
Florian Hahne

Q5. When the First appeared R Programming Language?
August 1992
August 1994
August 1993
August 1995

Q6. Which function is often used to concatenate elements?
join()
merge()
paste()
concat()

Q7. Which statement is used to stop a loop?
stop
exit
break
return

Q8. Which function is used to find the amount of rows and columns in an array?
dim()
nchar()
length()
dim_len()

Q9. How do you start writing a while loop in R?
while x < y:
x < y while
while x < y
while (x < y)

Q10. How do you start writing an if statement in R?
if (x > y)

if x > y:
if x > y then:
None of the above

Q11. Which function is used to add additional columns in a matrix?
add()
cbind()
join()
append_item()

Q12. Which function is used to draw points (markers) in a diagram?
d()
draw()
plot()
canvas()

Q13. How can you assign the same value to multiple variables in one line?
var1, var2, var3 <- “Orange”
var1, var2, var3 = “Orange”
var1, var2, var3 => “Orange”
var1 <- var2 <- var3 <- “Orange

Q14. Which operator is used to add together two values?
The & sign
The + sign
The * sign
None of the above

Q15. The following values: 10.5, 55 and 787, belongs to which data type?
numeric
integer
complex
All of the above


Assignment – 1

Q1. Explain Some of the Similarities and Differences Between R and Python.

R and Python are both popular programming languages for data analysis and statistics. Some similarities between the two include:

  • Both languages have extensive libraries for data manipulation, visualization, and modeling.
  • Both are open-source and have a large user community, making it easy to find help and resources.

Some differences between R and Python include:

  • R was developed specifically for statistics and data analysis, whereas Python is a general-purpose programming language that can be used for a wide range of tasks, including data analysis.
  • R has a more limited set of data types compared to Python, which can make Python more versatile and easier to work with.
  • R has a more expressive syntax, which allows for more concise code, but also makes it more difficult to read and understand. Python, on the other hand, has a more readable and consistent syntax.
  • R has a more extensive set of libraries specifically designed for statistics and data analysis, while Python has a more extensive set of libraries for general purpose, machine learning and deep learning.

Both R and Python have their own strengths and weaknesses, and the best choice of language often depends on the specific task at hand and the user’s personal preferences.


Q2. Write and Explain Some of the Most Common Syntaxes in R?

Here are some common syntaxes in R:

  1. Assigning values to variables: To assign a value to a variable in R, use the assignment operator (=). For example, to assign the value 5 to a variable x, you would use the following syntax: x = 5.
  2. Creating a vector: To create a vector in R, use the c() function. For example, to create a vector containing the numbers 1, 2, and 3, you would use the following syntax: c(1, 2, 3).
  3. Indexing a vector: To access a specific element of a vector in R, use square brackets and the index of the element. For example, to access the second element of a vector x, you would use the following syntax: x[2].
  4. Creating a matrix: To create a matrix in R, use the matrix() function. For example, to create a matrix with 2 rows and 3 columns, containing the numbers 1, 2, 3, 4, 5, and 6, you would use the following syntax: matrix(c(1, 2, 3, 4, 5, 6), nrow = 2, ncol = 3).
  5. Indexing a matrix: To access a specific element of a matrix in R, use square brackets and the row and column indices of the element. For example, to access the element in the second row and third column of a matrix x, you would use the following syntax: x[2, 3].
  6. Performing calculations: To perform calculations in R, use the standard mathematical operators. For example, to add two numbers x and y, you would use the following syntax: x + y.
  7. Creating a function: To create a function in R, use the function() function. For example, to create a function that takes a single argument and squares it, you would use the following syntax:
square <- function(x) {
  x^2
}
  1. if-else statement: To create an if-else statement in R, use the if() and else() functions. For example, to check if a variable x is greater than 5, you would use the following syntax:
if(x > 5) {
  print("x is greater than 5")
} else {
  print("x is not greater than 5")
}
R- language

These are some of the most common syntaxes in R and provide a good starting point for working with R. However, keep in mind that R has a rich set of functions and tools available, and you may find yourself using other syntaxes as you continue to work with the language.


Q3. (a) How Do You Assign a Variable in R?
(b) List Some of the Best Packages For:
i. Data Visualization
ii. Data Mining
iii. Data Imputation

(a) In R, you can assign a value to a variable using the assignment operator (=). The variable name is placed on the left side of the operator and the value to be assigned is placed on the right side. For example, to assign the value 5 to a variable x, you would use the following syntax:

x <- 5
greeting <- "Hello, World!"
y <- x

x = 5 {You can also use the ‘=’ operator for assignment in R, for example}

Keep in mind that when a value is assigned to a variable, it overwrites the previous value of that variable. so be careful when reusing the same variable names in your R script.

(b) Some of the Best Packages are:

i. Data Visualization:

  • Matplotlib
  • Seaborn
  • Plotly
  • Bokeh
  • ggplot
  • Altair

ii. Data Mining:

  • scikit-learn
  • Rasa
  • NLTK
  • Gensim
  • Orange
  • RapidMiner

iii. Data Imputation:

  • pandas
  • MICE (Multiple Imputation by Chained Equations)
  • Amelia
  • FancyImpute
  • sklearn.impute

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