Data Analysis with R Programming Coursera Quiz Answers

Week 01: Data Analysis with R Programming Coursera Quiz Answers

Data Analysis with R Programming Coursera Quiz Answers

Test your knowledge of programming languages

Q1. Fill in the blank: Programming involves _____ a computer to perform an action or set of actions.

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instructing

Q2. What are Python, JavaScript, SAS, Scala, and Julia?

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Programming languages

Q3. What are the benefits of using a programming language to work with your data? Select all that apply.

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1.Clarify the steps of your analysis
2.Easily reproduce and share your work
3.Save time

Quiz- 2

Q1. Open-source code is only available to people who pay a subscription fee.

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False

Q2. The R programming language can be used for which of the following tasks? Select all that apply.

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1.Data analysis
2.Visualization
3.Statistical analysis

Q3. Which of the following terms best describes the R programming language?

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Data-centric

Test your knowledge of programming with RStudio

Q1. What type of software application is RStudio?

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Integrated development environment

Q2. RStudio includes which of the following panes? Select all that apply.

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1.Environment pane
2.Source editor pane

Q3. If you write code directly in the R console, RStudio will automatically save your code when you close your current session.

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False

Data Analysis with R Programming Weekly Challenge 1 Answers

Q1. A data analyst uses words and symbols to give instructions to a computer. What are the words and symbols known as?

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Programming language

Q2. Many data analysts prefer to use a programming language for which of the following reasons? Select all that apply.

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1.To easily reproduce and share an analysis
2.To clarify the steps of an analysis
3.To save time

Q3. Which of the following are the benefits of open-source code? Select all that apply.

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1.Anyone can fix bugs in the code
2.Anyone can create an add-on package for the code
3.Anyone can use the code for free

Q4. Fill in the blank: The benefits of using _____ for data analysis include the ability to quickly process lots of data and create high-quality visualizations.

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The R programming language

Q5. A data analyst needs to quickly create a series of scatterplots to visualize a very large dataset. What should they use for the analysis?

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R programming language

Q6. RStudio’s integrated development environment lets you perform which of the following actions? Select all that apply.

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1.Install R packages
2.Create data visualizations
3.Import data from spreadsheets

Q7. In which two parts of RStudio can you execute code? Select all that apply.

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1.The source editor pane
2.The R console pane

Q8. Fill in the blank: In RStudio, the _____ is where you can find all the data you currently have loaded, and can easily organize and save it.

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environment pane

Week 02: Data Analysis with R Programming Coursera Quiz Answers

Test your knowledge of programming concepts

Q1. Why do analysts use comments In R programming? Select all that apply.

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To make an R Script more readable

Q2. What should you use to assign a value to a variable in R?

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An operator

Q3. Which of the following examples is the proper syntax for calling a function in R? 1 point

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print()

Q4. Which of the following examples can you use in R for date/time data? Select all that apply. 1 point

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1.2018-12-21 16:35:28 UTC
2.2019-04-16
3.06:11:13 UTC
4.07/24-2018

Test your knowledge of coding in R

Q1. An analyst includes the following calculation in their R programming: midyear_sales <- (quarter_1_sales + quarter_2_sales) - overhead_costs Which variable will the total from this calculation be assigned to?

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1.midyear_sales
2.quarter_1_sales
3.quarter_2_sales
4.overhead_costs

Q2. An analyst is checking the value of the variable x using a logical operator, so they run the following code:

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x > 35 & x < 65[/expand]

Which values of x would return TRUE when the analyst runs the code? Select all that apply.

View 1.50
2.60

Q3. Which of the following functions can analysts use to create conditional statements in their R programming? Select all that apply.

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1.print()
2.else()
3.if()
4.c()

Test your knowledge of R packages

Q1. When using RStudio, what does the installed.packages() function do?

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Presents a list of packages currently installed in an RStudio session

Q2. In data analytics, what is CRAN?

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A commonly used online archive with R packages and other R resources

Q3. What are ggplot2, tidyr, dplyr, and forcats all a part of?

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A collection of core tidyverse packages

Test your knowledge of the tidyverse

Q1. When working in R, for which part of the data analysis process do analysts use the tidyr package?

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Data cleaning

Q2. Which tidyverse package contains a set of functions, such as select(), that help with data manipulation?

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dplyr

Q3. An analyst is organizing a dataset in RStudio using the following code:

arrange(filter(Storage_1, inventory >= 40), count)

Which of the following examples is a nested function in the code?

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filter

Data Analysis with R Programming Weekly Challenge 2 Answers

Q1. Which of the following is an example of a piece of R code that contains both a function and an argument?

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1.print(“peaches”)
2.weekly_sales <- 7450 3.#filter 4.mass > 1000

Q2. A data analyst is assigning a variable to a value in their company’s sales dataset for 2020. Which variable name uses the correct syntax?

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1._2020sales
2.sales_2020
3.-sales-2020
4.2020_sales

Q3. You want to create a vector with the values 12, 23, 51, in that exact order. After specifying the variable, what R code chunk allows you to create the vector?

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1.v(12, 23, 51)
2.c(12, 23, 51)
3.c(51, 23, 12)
4.v(51, 23, 12)

Q4. An analyst comes across dates listed as strings in a dataset, for example, December 10th, 2020. To convert the strings to a date/time data type, which function should the analyst use?

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mdy()

Q5. A data analyst inputs the following code in RStudio:

sales_1 <- (3500.00 * 12)

Which of the following types of operators does the analyst use in the code? Select all that apply.

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1.Assignment
2.Arithmetic

Q6. A data analyst is deciding on naming conventions for analysis that they are beginning in R. Which of the following rules are widely accepted stylistic conventions that the analyst should use when naming variables? Select all that apply.

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1.Use an underscore to separate words within a variable name
2.Use all lowercase letters in variable names

Q7. Which of the following are included in R packages? Select all that apply.

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1.Tests for checking your code
2.Sample datasets
3.Reusable R functions

Q8. Packages installed in RStudio are called from CRAN. CRAN is an online archive with R packages and other R-related resources.

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True

Q9. When programming in R, what is a pipe used as an alternative?

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Nested function

Week 03: Data Analysis with R Programming Coursera Quiz Answers

Test your knowledge of R data frames

Q1. Which of the following are best practices for creating data frames? Select all that apply.

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1.Columns should be named
2.Each column should contain the same number of data items

Q2. Why are tibbles a useful variation of data frames?

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Tibbles make printing easier

Q3. Tidy data is a way of standardizing the organization of data within R.

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True

Q4. Which R function can be used to make changes to a data frame?

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mutate()

Test your knowledge of cleaning data

Q1. A data analyst is cleaning their data in R. They want to be sure that their column names are unique and consistent to avoid any errors in their analysis. What R function can they use to do this automatically?

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clean_names()

Q2. A data analyst is trying to sort the penguins bill_length_mm data in descending order. They input the following code:

penguins %>%

What code does the analyst add to organize the column bill_length_mm in descending order?

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1.arrange(-bill_length_mm)
2.arrange(=bill_length_mm)
3.arrange(+bill_length_mm)
4.arrange(%>%bill_length_mm)

Q3. A data analyst is working with customer information from their company’s sales data. The first and last names are in separate columns, but they want to create one column with both names instead. Which of the following functions can they use?

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unite()

Test your knowledge of R functions

Q1. Which of the following functions can a data analyst use to get a statistical summary of their dataset? Select all that apply.

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1.cor()
2.sd()
3.mean()

Q2. A data analyst inputs the following command:

quartet %>% group_by(set) %>% summarize(mean(x), sd(x), mean(y), sd(y), cor(x, y)).

Which of the functions in this command can help them determine how strongly related their variables are?

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cor(x,y)

Q3. Fill in the blank: The bias function compares the actual outcome of the data with the _____ outcome to determine whether or not the model is biased.

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predicted

Data Analysis with R Programming Weekly Challenge 3 Answers

Q1. A data analyst is creating a new data frame. Their dataset has dates, currency, and text strings. What characteristic of data frames is this an instance of?

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Data stored can be many different types

Q2. A data analyst is considering using tibbles instead of basic data frames. What are some of the limitations of tibbles? Select all that apply.

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1.Tibbles can never create row names
2.Tibbles won’t automatically change the names of variables
3.Tibbles can never change the input type of the data

Q3. A data analyst is working with a large data frame. It contains so many columns that they don’t all fit on the screen at once. The analyst wants a quick list of all of the column names to get a better idea of what is in their data. What function should they use?

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colnames()

Q4. A data analyst is working with the ToothGrowth dataset in R. What code chunk will allow them to get a quick summary of the dataset?

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1.glimpse(ToothGrowth)
2.min(ToothGrowth)
3.separate(ToothGrowth)
4.colnames(ToothGrowth)

Q5. A data analyst is working with the penguins dataset. What code chunk does the analyst write to make sure all the column names are unique and consistent and contain only letters, numbers, and underscores?

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1.drop_na(penguins)
2.clean_names(penguins)
3.rename(penguins)
4.select(penguins)

Q6. A data analyst is working with the penguins data. They write the following code:

penguins %>%

The variable species includes three penguin species: Adelie, Chinstrap, and Gentoo. What code chunk does the analyst add to create a data frame that only includes the Gentoo species?

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1.filter(Gentoo == species)
2.filter(species <- "Gentoo") 3.filter(species == "Gentoo") 4.filter(species == "Adelie") [/expand]

Q7. A data analyst is working with the penguin’s dataset. They write the following code:

penguins %>%
    group_by(species) %>% 

What code chunk does the analyst add to find the mean value for the variable body_mass_g?

View 1.summarize(=body_mass_g)
2.summarize(max(body_mass_g))
3.summarize(mean(body_mass_g))
4.summarize(body_mass_g(mean))

Q8. A data analyst is working with a data frame named salary_data. They want to create a new column named wages that includes data from the rate column multiplied by 40. What code chunk lets the analyst create the wages column?

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1.mutate(salary_data, rate = wages * 40)
2.mutate(wages = rate * 40)
3.mutate(salary_data, wages = rate * 40)
4.mutate(salary_data, wages = rate + 40)

Q9. A data analyst is working with a data frame named customers. It has separate columns for area code (area_code) and phone number (phone_num). The analyst wants to combine the two columns into a single column called phone_number, with the area code and phone number separated by a hyphen. What code chunk lets the analyst create the phone_number column?

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1.unite(customers, area_code, phone_num, sep=”-“)
2.unite(customers, “phone_number”, area_code, phone_num)
3.unite(customers, “phone_number”, area_code, sep=”-“)
4.unite(customers, “phone_number”, area_code, phone_num, sep=”-“)

Q10. A data analyst wants to summarize their data with the sd(), cor(), and mean(). What kind of measures are these?

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Statistical

Q11. In R, which statistical measure demonstrates how strong the relationship is between two variables?

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Correlation

Q12. A data analyst is studying weather data. They write the following code chunk:

bias(actual_temp, predicted_temp)

What will this code chunk calculate?

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1.The minimum difference between the actual and predicted values
2.The maximum difference between the actual and predicted values
3.The average difference between the actual and predicted values
4.The total average of the values

Week 04: Data Analysis with R Programming Coursera Quiz Answers

Test your knowledge of data visualizations in R

Q1. In ggplot2, you can use the _____ function to specify the data frame to use for your plot.

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ggplot()

Q2. In ggplot2, you use the plus sign (+) to add a layer to your plot.

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True

Q3. In ggplot2, what function do you use to map variables in your data to visual features of your plot?

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The aes() function

Q4. What type of plot will the following code create?

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Scatterplot
ggplot(data = penguins) +
     geom_point(mapping = aes(x = flipper_length_mm, y = body_mass_g))

Test your knowledge of aesthetics in analysis

Q1. Which of the following aesthetic attributes can you map to the data in a scatterplot? Select all that apply.

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1.Color
2.Size
3.Shape

Q2. Which of the following functions lets you display smaller groups, or subsets, of your data?

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facet_wrap()

Q3. You can use the color aesthetic to add color to the outline of each bar in a bar chart.

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True

Q4. What is the role of the x argument in the following code?

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An aesthetic
ggplot(data = diamonds) +
     geom_bar(mapping = aes(x = cut))

Q5. A data analyst creates a scatterplot with a lot of data points. It is difficult for the analyst to distinguish the individual points on the plot because they overlap. What function could the analyst use to make the points easier to find?

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geom_jitter()

Test your knowledge of annotating and saving visualizations

Q1. Which of the following are the benefits of adding labels and annotations to your plot? Select all that apply.

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1.Indicating the main purpose of your plot
2.Helping stakeholders quickly understand your plot
3.Highlighting important data in your plot

Q2. A data analyst is creating a plot for a presentation to stakeholders. The analyst wants to add a title, subtitle, and caption to the plot to help communicate important information. What function could the analyst use?

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The labs() function

Q3. What function can you use to put a text label inside the grid of your plot to call out specific data points?

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The annotate() function

Q4. A data analyst wants to add the title “Penguins” to a plot that visualizes the penguins dataset. What is the correct syntax for the argument of the labs() function?

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1.labs(title <- “Penguins”)) 2.labs(title = “Penguins”) 3.labs("Penguins") 4.labs("Penguins" = title) [/expand]

Q5. Which of the following functions can you use to save your plots in ggplot2?

View The ggsave() function

Data Analysis with R Programming Weekly Challenge 4 Answers

Q1. Which of the following are the benefits of using ggplot2? Select all that apply.

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1.Easily add layers to your plot
2.Combine data manipulation and visualization
3.Customize the look and feel of your plot

Q2. In ggplot2, what symbol do you use to add layers to your plot?

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The plus sign (+)

Q3. A data analyst creates a plot using the following code chunk:

ggplot(data = penguins) + 
    geom_point(mapping = aes(x = flipper_length_mm, y = body_mass_g))

Which of the following represents a variable in the code chunk? Select all that apply.

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1.body_mass_g
2.x
3.flipper_length_mm
4.y

Q4. A data analyst uses the aes() function to define the connection between their data and the plots in their visualization. What argument is used to refer to matching up a specific variable in your data set with a specific aesthetic?

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Mapping

Q5. A data analyst is working with the penguin’s data. The analyst creates a scatterplot with the following code:

ggplot(data = penguins) + 
    geom_point(mapping = aes(x = flipper_length_mm, y = body_mass_g,alpha = species))

What does the alpha aesthetic do to the appearance of the points on the plot?

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Makes some points on the plot more transparent

Q6. You are working with the penguin’s dataset. You create a scatterplot with the following code chunk:

ggplot(data = penguins) + 
    geom_point(mapping = aes(x = flipper_length_mm, y = body_mass_g))

How do you change the second line of code to map the aesthetic size to the variable species?

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1.geom_point(mapping = aes(x = flipper_length_mm, y = body_mass_g, species = size)
2.geom_point(mapping = aes(x = flipper_length_mm, y = body_mass_g, size = species))
3.geom_point(mapping = aes(x = flipper_length_mm, y = body_mass_g, species + size)
4.geom_point(mapping = aes(x = flipper_length_mm, y = body_mass_g, size + species))

Q7. Fill in the blank: The _____ creates a scatterplot and then adds a small amount of random noise to each point in the plot to make the points easier to find.

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geom_jitter() function

Q8. You have created a plot based on data in the diamonds dataset. What code chunk can be added to your existing plot to create wrap-around facets based on the variable color?

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1.facet_wrap(~color)
2.facet_wrap(color)
3.facet_wrap(color~)
4.facet(~color)

Q9. A data analyst uses the annotate() function to create a text label for a plot. Which attributes of the text can the analyst change by adding code to the argument of the annotate() function? Select all that apply.

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1.Change the size of the text
2.Change the font style of the text
3.Change the color of the text

Q10. You are working with the penguin’s dataset. You create a scatterplot with the following lines of code:

ggplot(data = penguins) + 
    geom_point(mapping = aes(x = flipper_length_mm, y = body_mass_g)) + 

What code chunk do you add to the third line to save your plot as a JPEG file with “penguins” as the file name?

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1.ggsave(penguins)
2.ggsave(“penguins.jpeg”)
3.ggsave(penguins.jpeg)
4.ggsave(“jpeg.penguins”)

Week 05: Data Analysis with R Programming Coursera Quiz Answers

Test your knowledge of documentation and reports

Q1. R Markdown allows you to create a record of the steps you took to complete your analysis directly in RStudio.

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True

Q2. Fill in the blank: Markdown is a _____ for formatting plain text files.

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syntax

Q3. A data analyst creates an interactive version of their R Markdown document to share with other users that allows them to execute code the analyst wrote. What did they create?

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An R notebook

Q4. A data analyst wants to convert their R Markdown file into another format. What are their options? Select all that apply.

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1.HTML, PDF, and Word
2.Slide presentation
3.Dashboard

Q5. A data analyst has finished editing their R Markdown file and wants to save it as an HTML report. What tool will they use?

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Knit

Test your knowledge about creating R Markdown documents

Q1. What information does a data analyst usually find in the header section of an RMarkdown document? Select all that apply.

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1.Title and author
2.File type
3.Date

Q2. While formatting their R Markdown document, a data analyst decides to make one of the headers smaller. What do they type into the document to do this?

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Hashtags

Q3. Inline code can be inserted directly into a .rmd file.

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True

Q4. To create bullet points to their output document, a data analyst adds _____ to their RMarkdown document.

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asterisks

Q5. A data analyst wants to embed a link in their RMarkdown document. They write (click here!)(www.rstudio.com) but it doesn’t work. What should they write instead?

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1.[click here!](www.rstudio.com)
2.(www.rstudio.com)
3.”click here!”(www.rstudio.com)
4.click here!(www.rstudio.com)

Test your knowledge of code chunks

Q1. A data analyst includes a section of code in their RMarkdown file so they can add comments and allow stakeholders to run it. What is the term for this section of code?

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Code chunk

Q2. Fill in the blank: A delimiter is a character that marks the beginning and end of _____.

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a data item

Q3. Data analysts put three backticks at the end of their code chunks to act as a delimiter.

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True

Q4. A data analyst has to create a monthly report for their stakeholders. What can they create to help them save time generating these reports?

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Template

Q5. A data analyst wants to mark the beginning of their code chunk. What delimiter should they type in their .rmd file?

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1.+++{r }
2.“`{r }
3.==={r }
4.***{r }

Data Analysis with R Programming Weekly Challenge 5 Answers

Q1. A data analyst wants to create a shareable report of their analysis with documentation of their process and notes explaining their code to stakeholders. What tool can they use to generate this?

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R Markdown

Q2. Fill in the blank: R Markdown notebooks can be converted into HTML, PDF, and Word documents, slide presentations, and _____.

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dashboards

Q3. A data analyst notices that their header is much smaller than they wanted it to be. What happened?

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They have too many hashtags

Q4. A data analyst wants to include a line of code directly in their .rmd file in order to explain their process more clearly. What is this code called?

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Inline code

Q5. What symbol can be used to add bullet points in R Markdown?

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Asterisks

Q6. A data analyst adds a section of executable code to their .rmd file so users can execute it and generate the correct output. What is this section of code called?

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Code chunk

Q7. A data analyst is inserting a line of code directly into their .rmd file. What will they use to mark the beginning and end of the code?

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Delimiters

Q8. If an analyst creates the same kind of document over and over or customizes the appearance of a final report, they can use _____ to save them time.

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a template
Conclusion:

Completing the Data Analysis with R Programming Coursera quiz is a significant step toward mastering data analysis using R. These answers provide valuable insights and solutions to help you excel in your course and deepen your understanding of data analysis techniques.

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