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Coursera Google Data Analytics Professional Certificate Course 7: Data Analysis with R Programming quiz answers to all weekly questions (weeks 1 – 5): Show
You may also be interested in Google Data Analytics Professional Certificate Course 1: Foundations – Cliffs Notes. Week 1: Programming and data analyticsR is a programming language that can help you in your data analysis process. In this part of the course, you’ll learn about R and RStudio, the environment you’ll use to work in R. You’ll explore the benefits of using R and RStudio as well as the components of RStudio that will help you get started. Learning Objectives
Answers to week 1 quiz questionsL2 Programming languagesQuestion 1Fill in the blank: Programming involves _ a computer to perform an action or set of actions.
Question 2What are Python, JavaScript, SAS, Scala, and Julia?
Question 3What are the benefits of using a programming language to work with your data? Select all that apply.
L3 R programming languageQuestion 1Open-source code is only available to people who pay a subscription fee.
Question 2The R programming language can be used for which of the following tasks? Select all that apply.
Question 3Which of the following terms best describes the R programming language?
L4 Programming with RStudioQuestion 1What type of software application is RStudio?
Question 2RStudio includes which of the following panes? Select all that apply.
Question 3If you write code directly in the R console, RStudio will automatically save your code when you close your current session.
Weekly challenge 1Question 1A data analyst uses words and symbols to give instructions to a computer. What are the words and symbols known as?
Question 2Many data analysts prefer to use a programming language for which of the following reasons? Select all that apply.
Question 3Which of the following are benefits of open-source code? Select all that apply.
Question 4Fill in the blank: The benefits of using _ for data analysis include the ability to quickly process lots of data and create high quality visualizations.
Question 5A data analyst needs to quickly create a series of scatterplots to visualize a very large dataset. What should they use for the analysis?
Question 6RStudio’s integrated development environment lets you perform which of the following actions? Select all that apply.
Question 7In which two parts of RStudio can you execute code? Select all that apply.
Question 8Fill 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.
Week 2: Programming using RStudioUsing R can help you complete your analysis efficiently and effectively. In this part of the course, you’ll explore the fundamental concepts associated with R. You’ll learn about functions and variables for calculations and other programming. In addition, you’ll discover R packages, which are collections of R functions, code and sample data that you’ll use in RStudio. Learning Objectives
Answers to week 2 quiz questionsL2 Programming conceptsQuestion 1Why do analysts use comments In R programming? Select all that apply.
Question 2What should you use to assign a value to a variable in R?
Question 3Which of the following examples is the proper syntax for calling a function in R?
Question 4Which of the following examples can you use in R for date/time data? Select all that apply.
L3 Coding in RQuestion 1An analyst includes the following calculation in their R programming: 3 Which variable will the total from this calculation be assigned to?
Question 2An analyst is checking the value of the variable x using a logical operator, so they run the following code: 8Which values of x would return 9 when the analyst runs the code? Select all that apply.
Question 3Which of the following functions can analysts use to create conditional statements in their R programming? Select all that apply.
L4 R PackagesQuestion 1When using RStudio, what does the installed.packages() function do?
Question 2In data analytics, what is CRAN?
Question 3What are ggplot2, tidyr, dplyr, and forcats all a part of?
L5 Explore the tidyverseQuestion 1When working in R, for which part of the data analysis process do analysts use the tidyr package?
Question 2Which tidyverse package contains a set of functions, such as select(), that help with data manipulation?
Question 3An analyst is organizing a dataset in RStudio using the following code: 8Which of the following examples is a nested function in the code?
Weekly challenge 2Question 1Which of the following is an example of a piece of R code that contains both a function and an argument?
Question 2A data analyst is assigning a variable to a value in their company’s sales dataset for 2020. Which variable name uses the correct syntax?
Question 3You 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?
Question 4An 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?
Question 5A data analyst inputs the following code in RStudio: 4Which of the following types of operators does the analyst use in the code? Select all that apply.
Question 6A data analyst is deciding on naming conventions for an 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.
Question 7Which of the following are included in R packages? Select all that apply.
Question 8Packages installed in RStudio are called from CRAN. CRAN is an online archive with R packages and other R-related resources.
Question 9When programming in R, what is a pipe used as an alternative for?
Week 3: Working with data in RThe R programming language was designed to work with data at all stages of the data analysis process. In this part of the course, you’ll examine how R can help you structure, organize, and clean your data using functions and other processes. You’ll learn about data frames and how to work with them in R. You’ll also revisit the issue of data bias and how R can help. Learning Objectives
Answers to week 3 quiz questionsL2 Explore data and RQuestion 1Which of the following are best practices for creating data frames? Select all that apply.
Question 2Why are tibbles a useful variation of data frames?
Question 3Tidy data is a way of standardizing the organization of data within R.
Question 4Which R function can be used to make changes to a data frame?
L3 Cleaning dataQuestion 1A 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?
Question 2A data analyst is trying to sort the penguins bill_length_mm data in descending order. They input the following code: 8What code does the analyst add to organize the column bill_length_mm in descending order?
Question 3A 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?
L4 R functionsQuestion 1Which of the following functions can a data analyst use to get a statistical summary of their dataset? Select all that apply.
Question 2A data analyst inputs the following command: 4Which of the functions in this command can help them determine how strongly related their variables are?
Question 3Fill 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.
Weekly challenge 3Question 1A 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?
Question 2A data analyst is considering using tibbles instead of basic data frames. What are some of the limitations of tibbles? Select all that apply.
Question 3A 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?
Question 4A data analyst is working with the ToothGrowth dataset in R. What code chunk will allow them to get a quick summary of the dataset?
Question 5A 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?
Question 6A data analyst is working with the penguins data. They write the following code: 8The 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?
Question 7A data analyst is working with the penguins dataset. They write the following code:
What code chunk does the analyst add to find the mean value for the variable body_mass_g?
Question 8A 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?
Question 9A 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?
Question 10A data analyst wants to summarize their data with the sd(), cor(), and mean(). What kind of measures are these?
Question 11In R, which statistical measure demonstrates how strong the relationship is between two variables?
Question 12A data analyst is studying weather data. They write the following code chunk:
What will this code chunk calculate?
Week 4: More about visualizations, aesthetics, and annotationsR is a tool well-suited for creating detailed visualizations. In this part of the course, you’ll learn how to use R to generate and troubleshoot visualizations. You’ll also explore the features of R and RStudio that will help you with the aesthetics of your visualizations and for annotating and saving them. Learning Objectives
Answers to week 4 quiz questionsL2 Aesthetics in analysisQuestion 1In ggplot2, you can use the _ function to specify the data frame to use for your plot.
Question 2In ggplot2, you use the plus sign (+) to add a layer to your plot.
Question 3In ggplot2, what function do you use to map variables in your data to visual features of your plot?
Question 4What type of plot will the following code create?
L3 Aesthetics in analysisQuestion 1Which of the following aesthetics attributes can you map to the data in a scatterplot? Select all that apply.
Question 2Which of the following functions let you display smaller groups, or subsets, of your data?
Question 3You can use the color aesthetic to add color to the outline of each bar in a bar chart.
Question 4What is the role of the x argument in the following code?
Question 5A 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?
L4 Annotating and saving visualizationsQuestion 1Which of the following are benefits of adding labels and annotations to your plot? Select all that apply.
Question 2A 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?
Question 3What function can you use to put a text label inside the grid of your plot to call out specific data points?
Question 4A 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?
Question 5Which of the following functions can you use to save your plots in ggplot2?
Weekly challenge 4Question 1Which of the following are benefits of using ggplot2? Select all that apply.
Question 2In ggplot2, what symbol do you use to add layers to your plot?
Question 3A data analyst creates a plot using the following code chunk:
Which of the following represents a variable in the code chunk? Select all that apply.
Question 4A 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?
Question 5A data analyst is working with the penguins data. The analyst creates a scatterplot with the following code:
What does the alpha aesthetic do to the appearance of the points on the plot?
Question 6You are working with the penguins dataset. You create a scatterplot with the following code chunk:
How do you change the second line of code to map the aesthetic size to the variable species?
Question 7Fill 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.
Question 8You 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?
Question 9A 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.
Question 10You are working with the penguins dataset. You create a scatterplot with the following lines of code:
What code chunk do you add to the third line to save your plot as a jpeg file with “penguins” as the file name?
Week 5: Documentation and reportsWhen you’re ready to save and present your analysis, R has different options to consider. In this part of the course, you’ll explore R Markdown, a file format for making dynamic documents with R. You’ll find out how to format and export R Markdown, including how to incorporate R code chunks in your documents. Learning Objectives
Answers to week 5 quiz questionsL2 Documentation and reportsQuestion 1R Markdown allows you to create a record of the steps you took to complete your analysis directly in RStudio.
Question 2Fill in the blank: Markdown is a _ for formatting plain text files.
Question 3A 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?
Question 4A data analyst wants to convert their R Markdown file into another format. What are their options? Select all that apply.
Question 5A data analyst has finished editing their R Markdown file and wants to save it as an HTML report. What tool will they use?
L3 Creating R Markdown documentsQuestion 1What information does a data analyst usually find in the header section of an RMarkdown document? Select all that apply.
Question 2While 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?
Question 3Inline code can be inserted directly into a .rmd file.
Question 4To create bullet points to their output document, a data analyst adds _ to their RMarkdown document.
Question 5A 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?
L4 Code chunksQuestion 1A data analyst includes a section of code in their RMarkdown file so they can add comments and allow stakeholders to run it. What is this the term for this section of code?
Question 2Fill in the blank: A delimiter is a character that marks the beginning and end of _.
Question 3Data analysts put three backticks at the end of their code chunks to act as a delimiter.
Question 4A data analyst has to create a monthly report for their stakeholders. What can they create to help them save time generating these reports?
Question 5A data analyst wants to mark the beginning of their code chunk. What delimiter should they type in their .rmd file?
Weekly challenge 5Question 1A 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?
Question 2Fill in the blank: R Markdown notebooks can be converted into HTML, PDF, and Word documents, slide presentations, and _.
Question 3A data analyst notices that their header is much smaller than they wanted it to be. What happened?
Question 4A 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?
Question 5What symbol can be used to add bullet points in R Markdown?
Question 6A 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?
Question 7A 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?
Question 8If 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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