Developing Data Products Quiz Answers – Coursera Graded Solution

Welcome to your ultimate guide for Developing Data Products quiz answers! Whether you’re tackling practice quizzes to improve your skills or preparing for graded quizzes to test your knowledge, this guide is here to help.

Covering all course modules, this resource will teach you how to create and deploy interactive data products, including dashboards, R Shiny apps, and data visualizations to effectively communicate insights.

Developing Data Products Quiz Answers for All Modules

Developing Data Products Quiz 01 Answers

Q1. Which of the following are absolutely necessary for creating a functioning shiny app? (Check all that apply)

Correct Answer: A ui.R file containing a call to shinyUI()
A server.R file containing a call to shinyServer()

Explanation: To create a basic shiny app, a ui.R file defines the user interface, and a server.R file defines the server logic. These files must include calls to shinyUI() and shinyServer() respectively.

---

Q2. What is incorrect about the following syntax in ui.R?

RCopyEditlibrary(shiny)
shinyUI(pageWithSidebar(  
  headerPanel("Data science FTW!"),  
  sidebarPanel(    
     h2('Big text')    
     h3('Sidebar')  
  ),  
  mainPanel(      
       h3('Main Panel text')  
  )
))

Correct Answer: Missing a comma in the sidebar panel

Explanation: In the sidebarPanel, there is a missing comma between h2('Big text') and h3('Sidebar'). All arguments in Shiny functions must be separated by commas.

---

Q3. Consider the following in ui.R:

RCopyEditshinyUI(pageWithSidebar(  
   headerPanel("Example plot"),  
   sidebarPanel(    
     sliderInput('mu', 'Guess at the mu', value = 70, min = 60, max = 80, step = 0.05,)  
   ), 
   mainPanel(    
     plotOutput('newHist')  
   )
))

And the following in server.R:

RCopyEditlibrary(UsingR)
data(galton)

shinyServer(  
    function(input, output) {    
       output$myHist <- renderPlot({      
          hist(galton$child, xlab='child height', col='lightblue', main='Histogram')      
          mu <- input$mu      
          lines(c(mu, mu), c(0, 200), col="red", lwd=5)      
          mse <- mean((galton$child - mu)^2)      
       })
    }
)

Why isn’t it doing what we want?

Correct Answer: The server.R output name isn’t the same as the plotOutput command used in ui.R.

Explanation: In ui.R, the plotOutput is named newHist, but in server.R, the output is defined as myHist. The names must match for the app to function correctly.

---

Q4. What are the main differences between creating a Shiny Gadget and creating a regular Shiny App? (Check all that apply)

Correct Answer: Shiny Gadgets are designed to be used by R users in the middle of a data analysis.
Shiny Gadgets are designed to have small user interfaces that fit on one page.

Explanation:
Shiny Gadgets are intended for quick, interactive tasks during a data analysis workflow. They are lightweight and often have small, self-contained UIs.

---

Q5. Consider the following R script:

RCopyEditlibrary(shiny)
library(miniUI)

pickXY <- function() {
  ui <- miniPage(
    gadgetTitleBar("Select Points by Dragging your Mouse"),
    miniContentPanel(
      plotOutput("plot", height = "100%", brush = "brush")
    )
  )
}

Why isn’t it doing what we want?

Correct Answer: No arguments are defined for pickXY()

Explanation: The function pickXY() is incomplete because it does not define arguments for the input data or provide a server-side logic to handle user interaction. Additionally, it lacks a call to runGadget() to launch the gadget.

Developing Data Products Quiz 02 Answers

Q1. What is rmarkdown? (Check all that apply.)

Correct Answer: A simplified format that, when interpreted, incorporates your R analysis into your document.
A format that can be interpreted into markdown (which is a simplified markup language).

Explanation: RMarkdown is a file format for making dynamic, reproducible documents. It combines code, its output, and formatted text into one document that can be exported to formats such as HTML, PDF, or Word.

---

Q2. In rmarkdown presentations, in the options for code chunks, what command prevents the code from being repeated before results in the final interpreted document?

Correct Answer: echo = FALSE

Explanation: The echo = FALSE option in an RMarkdown code chunk prevents the code from being displayed in the final document, but the results will still appear.

---

Q3. In rmarkdown presentations, in the options for code chunks, what prevents the code from being interpreted?

Correct Answer: eval = FALSE

Explanation: The eval = FALSE option in a code chunk tells RMarkdown not to run the code, and thus the code will not produce any output in the final document.

---

Q4. What is leaflet? (Check all that apply.)

Correct Answer: An R package interface to the javascript library of the same name.
A javascript library for creating interactive maps.

Explanation: Leaflet is a JavaScript library for interactive maps, and the leaflet R package provides an R interface to this library, making it easier to create maps directly from R.

---

Q5. The R command:

RCopyEditdf %>% leaflet() %>% addTiles()

is equivalent to what? (Check all that apply.)

Correct Answer: leaflet(df) %>% addTiles()
addTiles(leaflet(df))

Explanation: The %>% operator allows chaining commands together. The command is equivalent to first creating a leaflet map object from df using leaflet(df) and then adding map tiles using addTiles().

---

Q6. If I want to add popup icons to my leaflet map in R, I should use:

Correct Answer: addMarkers

Explanation: The addMarkers function is used in Leaflet to add markers to maps, which can include popups that display additional information when clicked.

Developing Data Products Quiz 03 Answers

Q1. Which of the following items is required for an R package to pass R CMD check without any warnings or errors?

Correct Answer: DESCRIPTION file

Explanation: The DESCRIPTION file is mandatory for an R package to pass the R CMD check as it contains metadata about the package, such as its name, version, dependencies, and authors. Other items like vignettes and example data are optional.

---

Q2. Which of the following is a generic function in a fresh installation of R, with only the default packages loaded? (Select all that apply.)

Correct Answer:
lm
predict
show
mean

Explanation: In a fresh R installation, generic functions include lm (linear model), predict (for predictions), show (for displaying objects), and mean (for calculating means). Functions like dgamma and colSums are not generic; they are specific functions for density and column summation, respectively.

---

Q3. What function is used to obtain the function body for an S4 method function?

Correct Answer: getMethod()

Explanation: The getMethod() function is used to retrieve the method function body for a specific S4 method, given the method name and signature. Other functions like getClass() and getS3method() are for retrieving class definitions and S3 methods, respectively.

---

Q4. Please download the R package DDPQuiz3 from the course website. Examine the createmean function implemented in the R/ sub-directory. What is the appropriate text to place above the createmean function for Roxygen2 to create a complete help file?

Correct Answer:

RCopyEdit#' This function calculates the mean
#'
#' @param x is a numeric vector
#' @return the mean of x
#' @export
#' @examples
#' x <- 1:10
#' createmean(x)

Explanation: This Roxygen2 markup provides a complete help file with all necessary sections:

• Title: Description of the function’s purpose.
• @param: Describes the function arguments.
• @return: Specifies what the function returns.
• @examples: Includes examples to demonstrate usage.
• @export: Ensures the function is available for package users.

Frequently Asked Questions (FAQ)

Conclusion

We hope this guide to Developing Data Products Quiz Answers helps you master the art of building and sharing data products, and succeed in your course. Bookmark this page for quick reference and share it with your peers. Ready to create impactful data visualizations and deploy interactive data products? Let’s get started and ace your quizzes!

Sources: Developing Data Products

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