coding_assignment_1

This homework is due by 11:55 pm on Sunday, Oct 16, 2021. To complete this assignment, follow these steps:

Download the coding_assignment_1.Rmd file from LMS.
Open coding_assignment_1.Rmd in RStudio.
Replace the “Your Name Here” text in the author: field with your own name.
Supply your solutions to the homework by editing coding_assignment_1.Rmd.
When you have completed the homework and have checked that your code both runs in the Console and knits correctly when you click Knit HTML, rename the R Markdown file to coding_assignment_1_YourNameHere.Rmd, and submit BOTH your rmarkdown AND html file on LMS (YourNameHere should be changed to your own name.)

Homework tips:

Recall the following useful RStudio hotkeys.

Keystroke	Description
`<tab>`	Autocompletes commands and filenames, and lists arguments for functions.
`<up>`	Cycles through previous commands in the console prompt
`<ctrl-up>`	Lists history of previous commands matching an unfinished one
`<ctrl-enter>`	Runs current line from source window to Console. Good for trying things out ideas from a source file.
`<ESC>`	Aborts an unfinished command and get out of the + prompt

Note: Shown above are the Windows/Linux keys. For Mac OS X, the <ctrl> key should be substituted with the <command> (⌘) key.

Instead of sending code line-by-line with <ctrl-enter>, you can send entire code chunks, and even run all of the code chunks in your .Rmd file. Look under the menu of the Source panel.
Run your code in the Console and Knit HTML frequently to check for errors.
You may find it easier to solve a problem by interacting only with the Console at first.

Problem 1: Simple Boolean operations

Tip: Note that each of the code blocks in this Problem contain the expression eval = FALSE. This tells R Markdown to display the code contained in the block, but not to evaluate it. To check that your answer makes sense, be sure to try it out in the console with various choices of values for the variable x.

# Install dplyr package
install.packages("dplyr")   
  
# Load dplyr package    
library("dplyr")

(a) Checking equality.

Given a variable x, write a Boolean expression that evaluates to TRUE if the variable x is equal to -20 (the numeric value).

x <- c(1, 2, 3, 94842, -20, NA)
x == -20   ## checking if x is equal to -20

(b) Checking inequality.

Given a variable x, write a Boolean expression that evaluates to TRUE if the variable x is not NA (i.e., is not missing).

x <- c(1, 2, 3, 94842, -20, NA)

x != "NA"   ## checking if x is not equal to NA

(c) Checking if a number is in a given range.

Given a (possibly negative) number x, write a Boolean expression that returns TRUE if and only if x is smaller than -10 or bigger than 40.

x <- c(1, 2, 3, 94842, -20, NA)

x < -10 |
    x > 40  ## checking if x is less than -10 and greater than 40

(d) A more complicated example.

Given an integer number x, write a Boolean expression that returns TRUE if and only if x is an odd number between -8 and 12 or 100 and 150.

x <- c(1, 2, 3, 94842, -20, NA)

x %% 2==1 & between(x, -8, 20) | between(x, 100, 150)

Tip: Recall the modulus operator we saw in lecture 7: %%. For integers x and y, x %% y is the remainder of x divided by y.

Problem 2: Vector Boolean operations

(a) R has two kinds of Boolean operators implemented, single (`&`, `|`) and double (`&&`, `||`).

One of these operators takes advantage of something called lazy evaluation while the other does not. They also don’t behave the same way when applied to vectors.

Read the help file (help("||")) and construct some examples to help figure out how the two behave.

To help you get started, try out the following two examples in your console:

# Example:  The variable y.prob2a is never defined.  
# (Do not define it!)
# What happens when you run this code?
x.prob2a <- 5
(x.prob2a < 10) | (y.prob2a > 2)
(x.prob2a < 10) || (y.prob2a > 2)

x.prob1a <- -3

(x.prob1a<5) | (y.prob1a>-10)
(x.prob1a<5) || (y.prob1a>-10)

# Define vectors
x.prob2a.vec <- c(TRUE, FALSE, FALSE)
y.prob2a.vec <- c(TRUE, TRUE, FALSE)

# Apply various Boolean operations to see what happens
x.prob2a.vec & y.prob2a.vec
x.prob2a.vec && y.prob2a.vec
x.prob2a.vec | y.prob2a.vec
x.prob2a.vec || y.prob2a.vec

# Self made examples
##Define vectors
x.prob1a.vec <- c(TRUE, FALSE, FALSE)
y.prob1a.vec <- c(FALSE, FALSE, TRUE)
 
## Apply various Boolean operations to see what happens
x.prob1a.vec & y.prob1a.vec
x.prob1a.vec && y.prob1a.vec
x.prob1a.vec | y.prob1a.vec
x.prob1a.vec || y.prob1a.vec

Can you explain what’s happening? Write up a brief explanation below.

Basically, the & operation gives us FALSE if any of the two entries in both vectors is FALSE, it gives us TRUE only if the result is exactly same in both vectors and | gives us TRUE if any of the entries are TRUE and gives us FALSE only if both are FALSE. On the other hand && and || just returns us the first element of the vector given us by the & and |

(b) Using `all()`

Two people were asked to give their preferences between two options: [Facebook, Twitter], [Safari, Chrome], [Mac, PC], [Summer, Winter]. Their results are given below.

alice.prefs <- c("Twitter", "Safari", "Mac", "Summer")
bob.prefs <- c("Facebook", "Chrome", "PC", "Summer")

Use the all() function to determine if the two people have identical preferences. (Your code should ouput a single Boolean value, either TRUE or FALSE)

##Your code here 
all(alice.prefs == bob.prefs) ## checking if both people have exact same preferences

## [1] FALSE

(c) Using `any()`

Use the any() function to determine if the two people have any preferences in common. (Your code should output a single Boolean value, either TRUE or FALSE)

##Your code here 
any (alice.prefs == bob.prefs)

## [1] TRUE

(d) Missing values.

Let age be the vector defined below.

age <- c(18, NA, 25, 71, NA, 45, NA, NA, 18)

Write a Boolean expression that checks whether each entry of age is missing (recall missing values are denoted by NA). Your expression should return a Boolean vector having the same length as age.

##Your code here 

###checking if any entry of age is missing and denoting it by NA
age == "NA"

## [1] FALSE    NA FALSE FALSE    NA FALSE    NA    NA FALSE

Problem 3: Referencing vector elements

(a) `which()` practice

Write code that returns the indexes of age that are missing.

##Your code here 
which(is.na(age))

## [1] 2 5 7 8

(b) Getting non-missing values

Write code that uses negative indexes and your solution from (a) to return only the values of age that are not missing. (i.e., your code should result in a vector with elements: 18, 25, 71, 45, 18)

##Your code here 
age[-c(2,5,7,8)]

## [1] 18 25 71 45 18

(c) A more direct way of getting non-missing values

Add the negation operator ! just before is.na() function to write an expression that returns only the values of age that are not missing.

##Your code here 

age[which(!is.na(age))]

## [1] 18 25 71 45 18

(d) More `which()` practice

For the next three problems we’ll use the preloaded cars data set.

speed <- cars$speed
dist <- cars$dist
dist

##  [1]   2  10   4  22  16  10  18  26  34  17  28  14  20  24  28  26  34  34  46
## [20]  26  36  60  80  20  26  54  32  40  32  40  50  42  56  76  84  36  46  68
## [39]  32  48  52  56  64  66  54  70  92  93 120  85

Write code to figure out which cars had a stopping distance of 30 feet or more.

 which(dist >= 30)        ## the cars that had a stopping distance pf 30 or more feet

##  [1]  9 17 18 19 21 22 23 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43
## [26] 44 45 46 47 48 49 50

(e) `which.min`, `which.max` practice

Use the which.min() function to figure out which car had the shortest stopping distance. (Your code should return the car’s index.)

which.min(dist)

## [1] 1

(f) More practice

Use the which.max() function to figure out the speed of the car that had the longest stopping distance. (Your code should return the car’s speed.)

dist[which.max(dist)]

## [1] 120

Problem 4: Data frame basics

(a) Importing data.

Use the read.csv() function to import the survey data provided to you on LMS into a variable called survey.

survey <- read.csv(file="E:/work/semester 3/Telling stories with data/assignments/survey_data.csv" )

(b) `$` notation

Use the $ operator to select the Rexperience column from the survey data

survey $ Rexperience

##  [1] "Never used"           "Never used"           "Basic competence"    
##  [4] "Basic competence"     "Never used"           "Never used"          
##  [7] "Basic competence"     "Basic competence"     "Basic competence"    
## [10] "Never used"           "Basic competence"     "Installed on machine"
## [13] "Basic competence"     "Never used"           "Basic competence"    
## [16] "Installed on machine" "Basic competence"     "Installed on machine"
## [19] "Installed on machine" "Never used"           "Basic competence"    
## [22] "Installed on machine" "Basic competence"     "Basic competence"    
## [25] "Basic competence"     "R Wizard"             "Basic competence"    
## [28] "Installed on machine" "Installed on machine" "Never used"          
## [31] "Never used"

(c) [,] notation

Repeat part (b) using [,] notation. i.e., Use [,] notation to select the TVhours column from the survey data by name (i.e., obtain this column by using the name “Rexperience” instead of using the column number)

survey[,"TVhours"]

##  [1]  2 15 16  0  2  5  0  4  0  0 14  6 10 15  4  4 10  7 33  8  8  3  0  0  0
## [26]  0  6  1  0  0  4

(d) [[]] notation

Repeat part (c) with [[]] notation.

survey[["TVhours"]]

##  [1]  2 15 16  0  2  5  0  4  0  0 14  6 10 15  4  4 10  7 33  8  8  3  0  0  0
## [26]  0  6  1  0  0  4

(e) [] notation

Repeat part (d), but this time using single blackets ([ ]) notation.

(Observe that this returns a new single-column data frame, not just a vector.)

survey["TVhours"]

##    TVhours
## 1        2
## 2       15
## 3       16
## 4        0
## 5        2
## 6        5
## 7        0
## 8        4
## 9        0
## 10       0
## 11      14
## 12       6
## 13      10
## 14      15
## 15       4
## 16       4
## 17      10
## 18       7
## 19      33
## 20       8
## 21       8
## 22       3
## 23       0
## 24       0
## 25       0
## 26       0
## 27       6
## 28       1
## 29       0
## 30       0
## 31       4

(f) Data Types

What are datatypes for the Rexperience and TVhours columns in the data provided? Write code to confirm the datatypes.

#First method
library(tidyverse)
survey_specific <- select(survey, TVhours, Rexperience)
sapply(survey_specific, class)

##alternate method
class(survey[["TVhours"]])
class(survey[["Rexperience"]])

Rexperience is a character and TVhours is integer

(g) `subset()` practice

Use the subset() function to select all the survey data on Program, OperatingSystem, and TVhours for respondents whose Rexperience is “Basic competence” or who watched 8 or more hours of TV last week.

## selecting columns for which the conditions of TVhours and Rexperience meet
survey_specific_1 <- subset(survey, Rexperience == "Basic competence" | TVhours >= 8 ) 

## sel;ecting the specific columns for which the conditions meet
library(tidyverse)
survey_specific_2 <- select(survey_specific_1, Program, OperatingSystem, TVhours)
subset(survey_specific_2)

Problem 5: Inline code practice.

One of the greatest benefits of r markdown is its ability to process inline code. Copy the text inside the code chunk below to a space outside the code chunk just below this paragraph and see what happens when you knit the file.

Students of this class on average watch `r round(mean(survey_data$TVhours), 2)` hours of TV.

:::::::::::::::::::::::::::::::::
Students of this class on average watch 5.71 hours of TV. :::::::::::::::::::::::::::::::::

subset (survey, Program == "HSS" & Rexperience =="Basic competence" )

##     X Program        PriorExp      Rexperience OperatingSystem TVhours
## 3   3     HSS Some experience Basic competence        Mac OS X      16
## 7   7     HSS Some experience Basic competence        Mac OS X       0
## 9   9     HSS Some experience Basic competence         Windows       0
## 15 15     HSS Some experience Basic competence        Mac OS X       4
## 17 17     HSS Some experience Basic competence        Mac OS X      10
## 21 21     HSS Some experience Basic competence         Windows       8
## 23 23     HSS Some experience Basic competence         Windows       0
## 24 24     HSS Some experience Basic competence         Windows       0
## 25 25     HSS Some experience Basic competence         Windows       0
## 27 27     HSS Some experience Basic competence        Mac OS X       6
##            Editor
## 3  Microsoft Word
## 7  Microsoft Word
## 9  Microsoft Word
## 15 Microsoft Word
## 17     R Markdown
## 21 Microsoft Word
## 23 Microsoft Word
## 24          Excel
## 25     R Markdown
## 27     R Markdown

Replace all occurrences of ___in the paragraph below with an inline code chunk supplying the appropriate information.

Of the 31 survey respondents, 10 were NOT from the HSS program. We found that 35.4% of the all students in the class use the Mac OS X operating system. 47.6% of HSS students report having Basic competence in R.

coding_assignment_1_solutions

Laiba Shahid

Assigned Oct 6th, 2021

This homework is due by 11:55 pm on Sunday, Oct 16, 2021. To complete this assignment, follow these steps:

Homework tips:

Problem 1: Simple Boolean operations

(a) Checking equality.

(b) Checking inequality.

(c) Checking if a number is in a given range.

(d) A more complicated example.

Problem 2: Vector Boolean operations

(a) R has two kinds of Boolean operators implemented, single (`&`, `|`) and double (`&&`, `||`).

(b) Using `all()`

(c) Using `any()`

(d) Missing values.

Problem 3: Referencing vector elements

(a) `which()` practice

(b) Getting non-missing values

(c) A more direct way of getting non-missing values

(d) More `which()` practice

(e) `which.min`, `which.max` practice

(f) More practice

Problem 4: Data frame basics

(a) Importing data.

(b) `$` notation

(c) [,] notation

(d) [[]] notation

(e) [] notation

(f) Data Types

(g) `subset()` practice

Problem 5: Inline code practice.

coding_assignment_1_solutions

Laiba Shahid

Assigned Oct 6th, 2021

This homework is due by 11:55 pm on Sunday, Oct 16, 2021. To complete this assignment, follow these steps:

Homework tips:

Problem 1: Simple Boolean operations

(a) Checking equality.

(b) Checking inequality.

(c) Checking if a number is in a given range.

(d) A more complicated example.

Problem 2: Vector Boolean operations

(a) R has two kinds of Boolean operators implemented, single (&, |) and double (&&, ||).

(b) Using all()

(c) Using any()

(d) Missing values.

Problem 3: Referencing vector elements

(a) which() practice

(b) Getting non-missing values

(c) A more direct way of getting non-missing values

(d) More which() practice

(e) which.min, which.max practice

(f) More practice

Problem 4: Data frame basics

(a) Importing data.

(b) $ notation

(c) [,] notation

(d) [[]] notation

(e) [] notation

(f) Data Types

(g) subset() practice

Problem 5: Inline code practice.

(a) R has two kinds of Boolean operators implemented, single (`&`, `|`) and double (`&&`, `||`).

(b) Using `all()`

(c) Using `any()`

(a) `which()` practice

(d) More `which()` practice

(e) `which.min`, `which.max` practice

(b) `$` notation

(g) `subset()` practice