Assignment 1

My first assignment has three parts.

(a)

Tutorial

The tutorial outlines the process of building a blog with Quarto Pub from the ground up. It entails initiating a new project and directory within the RStudio IDE, conducting blog tests, and ultimately deploying it to the web through Quarto CLI. The author furnishes comprehensive, step-by-step guidance on publishing the blog to Quarto Pub. The tutorial wraps up with a prompt to explore additional resources at quarto-dot-org for a more in-depth understanding of utilizing Quarto as a computational notebook.

(b)

3 Difference with Python and Posit

  1. Python and Posit (currently R) has different sytanx(the structure of statements in a computer language).

    for loop example in Python:

    for i in range(10):
  print(i)

    for loop example in R:

    for (i in 0:9) {
  print(i)
}

  2. Python is an object-oriented programming language but R is a functional programming language.

    Object-Oriented Programming Language Python

    Functional Programming Language R

  3. Python is a general-purpose programming language while R is a statistical programming language.

    Article link is here.

(c)

library(dslabs)
data(na_example)

print("na_example data set:")
[1] "na_example data set:"
na_example
   [1]  2  1  3  2  1  3  1  4  3  2  2 NA  2  2  1  4 NA  1  1  2  1  2  2  1
  [25]  2  5 NA  2  2  3  1  2  4  1  1  1  4  5  2  3  4  1  2  4  1  1  2  1
  [49]  5 NA NA NA  1  1  5  1  3  1 NA  4  4  7  3  2 NA NA  1 NA  4  1  2  2
  [73]  3  2  1  2  2  4  3  4  2  3  1  3  2  1  1  1  3  1 NA  3  1  2  2  1
  [97]  2  2  1  1  4  1  1  2  3  3  2  2  3  3  3  4  1  1  1  2 NA  4  3  4
 [121]  3  1  2  1 NA NA NA NA  1  5  1  2  1  3  5  3  2  2 NA NA NA NA  3  5
 [145]  3  1  1  4  2  4  3  3 NA  2  3  2  6 NA  1  1  2  2  1  3  1  1  5 NA
 [169] NA  2  4 NA  2  5  1  4  3  3 NA  4  3  1  4  1  1  3  1  1 NA NA  3  5
 [193]  2  2  2  3  1  2  2  3  2  1 NA  2 NA  1 NA NA  2  1  1 NA  3 NA  1  2
 [217]  2  1  3  2  2  1  1  2  3  1  1  1  4  3  4  2  2  1  4  1 NA  5  1  4
 [241] NA  3 NA NA  1  1  5  2  3  3  2  4 NA  3  2  5 NA  2  3  4  6  2  2  2
 [265] NA  2 NA  2 NA  3  3  2  2  4  3  1  4  2 NA  2  4 NA  6  2  3  1 NA  2
 [289]  2 NA  1  1  3  2  3  3  1 NA  1  4  2  1  1  3  2  1  2  3  1 NA  2  3
 [313]  3  2  1  2  3  5  5  1  2  3  3  1 NA NA  1  2  4 NA  2  1  1  1  3  2
 [337]  1  1  3  4 NA  1  2  1  1  3  3 NA  1  1  3  5  3  2  3  4  1  4  3  1
 [361] NA  2  1  2  2  1  2  2  6  1  2  4  5 NA  3  4  2  1  1  4  2  1  1  1
 [385]  1  2  1  4  4  1  3 NA  3  3 NA  2 NA  1  2  1  1  4  2  1  4  4 NA  1
 [409]  2 NA  3  2  2  2  1  4  3  6  1  2  3  1  3  2  2  2  1  1  3  2  1  1
 [433]  1  3  2  2 NA  4  4  4  1  1 NA  4  3 NA  1  3  1  3  2  4  2  2  2  3
 [457]  2  1  4  3 NA  1  4  3  1  3  2 NA  3 NA  1  3  1  4  1  1  1  2  4  3
 [481]  1  2  2  2  3  2  3  1  1 NA  3  2  1  1  2 NA  2  2  2  3  3  1  1  2
 [505] NA  1  2  1  1  3  3  1  3  1  1  1  1  1  2  5  1  1  2  2  1  1 NA  1
 [529]  4  1  2  4  1  3  2 NA  1  1 NA  2  1  1  4  2  3  3  1  5  3  1  1  2
 [553] NA  1  1  3  1  3  2  4 NA  2  3  2  1  2  1  1  1  2  2  3  1  5  2 NA
 [577]  2 NA  3  2  2  2  1  5  3  2  3  1 NA  3  1  2  2  2  1  2  2  4 NA  6
 [601]  1  2 NA  1  1  2  2  3 NA  3  2  3  3  4  2 NA  2 NA  4 NA  1  1  2  2
 [625]  3  1  1  1  3 NA  2  5 NA  7  1 NA  4  3  3  1 NA  1  1  1  1  3  2  4
 [649]  2  2  3 NA NA  1  4  3  2  2  2  3  2  4  2  2  4 NA NA NA  6  3  3  1
 [673]  4  4  2  1 NA  1  6 NA  3  3  2  1  1  6 NA  1  5  1 NA  2  6  2 NA  4
 [697]  1  3  1  2 NA  1  1  3  1  2  4  2  1  3  2  4  3  2  2  1  1  5  6  4
 [721]  2  2  2  2  4 NA  1  2  2  2  2  4  5 NA NA NA  4  3  3  3  2  4  2  4
 [745] NA NA NA NA  2  1 NA  2  4  3  2 NA  2  3  1  3  4 NA  1  2  1  2 NA  3
 [769]  1  2  1  2  1  2  1  2  2  2  2  1  1  3  3  1  3  4  3 NA NA  4  2  3
 [793]  2  1  3  2  4  2  2  3  1  2  4  3  3  4 NA  1  4  2  1  1  1  3  1  5
 [817]  2  2  4  2 NA  1  3  1  2 NA  1  2  1  2  1 NA  1  3  2  3  2 NA  2  1
 [841]  4  2 NA NA NA  2  4  2 NA NA  3  1 NA  5  5  2  2  2 NA  2  1  3  1  3
 [865]  2  4  2  4 NA  4  1  2  3  2  3  3  2  3  2  2  2  1  3  2  4  2 NA  3
 [889]  3  2  2 NA NA  3  2  1  2  4  1  1  1  1  4  3  2 NA  3  2 NA  1 NA  3
 [913]  2  1  1  1  2 NA  2  2  3  3  2 NA NA  4  5  2  2  2  1  2  3  1  3  3
 [937]  4  3 NA  1  1  1 NA  4  3  5  1  1  2 NA  2  2  2  2  5  2  2  3  1  2
 [961]  3 NA  1  2 NA NA  2 NA  3  1  1  2  5  3  5  1  1  4 NA  2  1  3  1  1
 [985]  2  4  3  3  3 NA  1  1  2  2  1  1  2  2 NA  2
sprintf("Number of NAs in na_example data set. %d:", sum(is.na(na_example)))
[1] "Number of NAs in na_example data set. 145:"
print("Without NAs")
[1] "Without NAs"
data <- na_example
data[is.na(data)] <-0
data
   [1] 2 1 3 2 1 3 1 4 3 2 2 0 2 2 1 4 0 1 1 2 1 2 2 1 2 5 0 2 2 3 1 2 4 1 1 1 4
  [38] 5 2 3 4 1 2 4 1 1 2 1 5 0 0 0 1 1 5 1 3 1 0 4 4 7 3 2 0 0 1 0 4 1 2 2 3 2
  [75] 1 2 2 4 3 4 2 3 1 3 2 1 1 1 3 1 0 3 1 2 2 1 2 2 1 1 4 1 1 2 3 3 2 2 3 3 3
 [112] 4 1 1 1 2 0 4 3 4 3 1 2 1 0 0 0 0 1 5 1 2 1 3 5 3 2 2 0 0 0 0 3 5 3 1 1 4
 [149] 2 4 3 3 0 2 3 2 6 0 1 1 2 2 1 3 1 1 5 0 0 2 4 0 2 5 1 4 3 3 0 4 3 1 4 1 1
 [186] 3 1 1 0 0 3 5 2 2 2 3 1 2 2 3 2 1 0 2 0 1 0 0 2 1 1 0 3 0 1 2 2 1 3 2 2 1
 [223] 1 2 3 1 1 1 4 3 4 2 2 1 4 1 0 5 1 4 0 3 0 0 1 1 5 2 3 3 2 4 0 3 2 5 0 2 3
 [260] 4 6 2 2 2 0 2 0 2 0 3 3 2 2 4 3 1 4 2 0 2 4 0 6 2 3 1 0 2 2 0 1 1 3 2 3 3
 [297] 1 0 1 4 2 1 1 3 2 1 2 3 1 0 2 3 3 2 1 2 3 5 5 1 2 3 3 1 0 0 1 2 4 0 2 1 1
 [334] 1 3 2 1 1 3 4 0 1 2 1 1 3 3 0 1 1 3 5 3 2 3 4 1 4 3 1 0 2 1 2 2 1 2 2 6 1
 [371] 2 4 5 0 3 4 2 1 1 4 2 1 1 1 1 2 1 4 4 1 3 0 3 3 0 2 0 1 2 1 1 4 2 1 4 4 0
 [408] 1 2 0 3 2 2 2 1 4 3 6 1 2 3 1 3 2 2 2 1 1 3 2 1 1 1 3 2 2 0 4 4 4 1 1 0 4
 [445] 3 0 1 3 1 3 2 4 2 2 2 3 2 1 4 3 0 1 4 3 1 3 2 0 3 0 1 3 1 4 1 1 1 2 4 3 1
 [482] 2 2 2 3 2 3 1 1 0 3 2 1 1 2 0 2 2 2 3 3 1 1 2 0 1 2 1 1 3 3 1 3 1 1 1 1 1
 [519] 2 5 1 1 2 2 1 1 0 1 4 1 2 4 1 3 2 0 1 1 0 2 1 1 4 2 3 3 1 5 3 1 1 2 0 1 1
 [556] 3 1 3 2 4 0 2 3 2 1 2 1 1 1 2 2 3 1 5 2 0 2 0 3 2 2 2 1 5 3 2 3 1 0 3 1 2
 [593] 2 2 1 2 2 4 0 6 1 2 0 1 1 2 2 3 0 3 2 3 3 4 2 0 2 0 4 0 1 1 2 2 3 1 1 1 3
 [630] 0 2 5 0 7 1 0 4 3 3 1 0 1 1 1 1 3 2 4 2 2 3 0 0 1 4 3 2 2 2 3 2 4 2 2 4 0
 [667] 0 0 6 3 3 1 4 4 2 1 0 1 6 0 3 3 2 1 1 6 0 1 5 1 0 2 6 2 0 4 1 3 1 2 0 1 1
 [704] 3 1 2 4 2 1 3 2 4 3 2 2 1 1 5 6 4 2 2 2 2 4 0 1 2 2 2 2 4 5 0 0 0 4 3 3 3
 [741] 2 4 2 4 0 0 0 0 2 1 0 2 4 3 2 0 2 3 1 3 4 0 1 2 1 2 0 3 1 2 1 2 1 2 1 2 2
 [778] 2 2 1 1 3 3 1 3 4 3 0 0 4 2 3 2 1 3 2 4 2 2 3 1 2 4 3 3 4 0 1 4 2 1 1 1 3
 [815] 1 5 2 2 4 2 0 1 3 1 2 0 1 2 1 2 1 0 1 3 2 3 2 0 2 1 4 2 0 0 0 2 4 2 0 0 3
 [852] 1 0 5 5 2 2 2 0 2 1 3 1 3 2 4 2 4 0 4 1 2 3 2 3 3 2 3 2 2 2 1 3 2 4 2 0 3
 [889] 3 2 2 0 0 3 2 1 2 4 1 1 1 1 4 3 2 0 3 2 0 1 0 3 2 1 1 1 2 0 2 2 3 3 2 0 0
 [926] 4 5 2 2 2 1 2 3 1 3 3 4 3 0 1 1 1 0 4 3 5 1 1 2 0 2 2 2 2 5 2 2 3 1 2 3 0
 [963] 1 2 0 0 2 0 3 1 1 2 5 3 5 1 1 4 0 2 1 3 1 1 2 4 3 3 3 0 1 1 2 2 1 1 2 2 0
[1000] 2
sprintf("Number of NAs in new dataset. %d:", sum(is.na(data)))
[1] "Number of NAs in new dataset. 0:"
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