DOCUMENTATION AND REPORT INTRODUCTION

Test your knowledge about documentation and report

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

2. 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?

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

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

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

6. Where in RStudio can you find the export menu for saving plots?

Test your knowledge on code chunks

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

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

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

4. 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?

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

Data Analysis with R Programming Weekly Challenge 5

1. R Markdown is a file format for making dynamic documents with R. What are the benefits of creating this kind of document? Select all that apply.

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

3. A data analyst writes two hashtags next to their header. What will this do to the header font in the .rmd file?

4. 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?

5. A data analyst wants to add a bulleted list to their R Markdown document. What symbol can they type to create this formatting?

6. Fill in the blank: Code added to an .rmd file is usually referred to as a code _____. This allows users to execute R code from within the .rmd file.

7. A data analyst adds specific characters before and after their code chunk to mark where the data item begins and ends in the .rmd file. What are these characters called?

8. 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.

GOOGLE DATA ANALYTICS COURSERA ANSWERS AND STUDY GUIDE

Weekly Breakdown | Google Study Guides | Back to Top

Data Analysis with R Programming Course Challenge

1. Scenario 1, questions 1-7

As part of the data science team at Gourmet Analytics, you use data analytics to advise companies in the food industry. You clean, organize, and visualize data to arrive at insights that will benefit your clients. As a member of a collaborative team, sharing your analysis with others is an important part of your job.

Your current client is Chocolate and Tea, an up-and-coming chain of cafes.

The eatery combines an extensive menu of fine teas with chocolate bars from around the world. Their diverse selection includes everything from plantain milk chocolate, to tangerine white chocolate, to dark chocolate with pistachio and fig. The encyclopedic list of chocolate bars is the basis of Chocolate and Tea’s brand appeal. Chocolate bar sales are the main driver of revenue.

Chocolate and Tea aims to serve chocolate bars that are highly rated by professional critics. They also continually adjust the menu to make sure it reflects the global diversity of chocolate production. The management team regularly updates the chocolate bar list in order to align with the latest ratings and to ensure that the list contains bars from a variety of countries.

They’ve asked you to collect and analyze data on the latest chocolate ratings. In particular, they’d like to know which countries produce the highest-rated bars of super dark chocolate (a high percentage of cocoa). This data will help them create their next chocolate bar menu.

Your team has received a dataset that features the latest ratings for thousands of chocolates from around the world. Click here to access the dataset. Given the data and the nature of the work you will do for your client, your team agrees to use R for this project.

Your supervisor asks you to write a short summary of the benefits of using R for the project. Which of the following benefits would you include in your summary? Select all that apply.

2. Scenario 1, continued

Before you begin working with your data, you need to import it and save it as a data frame. To get started, you open your RStudio workspace and load the tidyverse library. You upload a .csv file containing the data to RStudio and store it in a project folder named flavors_of_cacao.csv.

You use the read_csv() function to import the data from the .csv file. Assume that the name of the data frame is bars_df and the .csv file is in the working directory. What code chunk lets you create the data frame?

3. Scenario 1, continued

Now that you’ve created a data frame, you want to find out more about how the data is organized. The data frame has hundreds of rows and lots of columns.

Assume the name of your data frame is flavors_df. What code chunk lets you review the column names in the data frame?

4. Scenario 1, continued

Next, you begin to clean your data. When you check out the column headings in your data frame you notice that the first column is named Company…Maker.if.known. (Note: The period after known is part of the variable name.) For the sake of clarity and consistency, you decide to rename this column Maker (without a period at the end).

Assume the first part of your code chunk is:

flavors_df %>%

What code chunk do you add to change the column name?

5. After previewing and cleaning your data, you determine what variables are most relevant to your analysis. Your main focus is on Rating, Cocoa.Percent, and Bean.Type. You decide to use the select() function to create a new data frame with only these three variables.

Assume the first part of your code is:

trimmed_flavors_df <- flavors_df %>%

Add the code chunk that lets you select the three variables.

What bean type appears in row 6 of your tibble? 

6. Next, you select the basic statistics that can help your team better understand the ratings system in your data.

Assume the first part of your code is:

trimmed_flavors_df %>%

You want to use the summarize() and max() functions to find the maximum rating for your data. Add the code chunk that lets you find the maximum value for the variable Rating.

What is the maximum rating?

7. After completing your analysis of the rating system, you determine that any rating greater than or equal to 3.9 points can be considered a high rating. You also know that Chocolate and Tea considers a bar to be super dark chocolate if the bar’s cocoa percent is greater than or equal to 75%. You decide to create a new data frame to find out which chocolate bars meet these two conditions.

Assume the first part of your code is:

best_trimmed_flavors_df <- trimmed_flavors_df %>%

You want to apply the filter() function to the variables Cocoa.Percent and Rating. Add the code chunk that lets you filter the data frame for chocolate bars that contain at least 75% cocoa and have a rating of at least 3.9 points.

filter(Cocoa.Percent >= 75, Rating >= 3.9)

What value for cocoa percent appears in row 1 of your tibble?

8. Now that you’ve cleaned and organized your data, you’re ready to create some useful data visualizations. Your team assigns you the task of creating a series of visualizations based on requests from the Chocolate and Tea management team. You decide to use ggplot2 to create your visuals.

Assume your first line of code is:

ggplot(data = best_trimmed_flavors_df) +

You want to use the geom_bar() function to create a bar chart. Add the code chunk that lets you create a bar chart with the variable Company.Location on the x-axis.

How many bars does your bar chart display?

9. Your bar chart reveals the locations that produce the highest rated chocolate bars. To get a better idea of the specific rating for each location, you’d like to highlight each bar.

Assume that you are working with the following code:

ggplot(data = best_trimmed_flavors_df) +

  geom_bar(mapping = aes(x = Company.Location))

Add a code chunk to the second line of code to map the aesthetic fill to the variable Rating.

NOTE: the three dots (…) indicate where to add the code chunk.

According to your bar chart, which two company locations produce the highest rated chocolate bars?

10. Scenario 2, continued

A teammate creates a new plot based on the chocolate bar data. The teammate asks you to make some revisions to their code.

Assume your teammate shares the following code chunk:

ggplot(data = best_trimmed_flavors_df) +

     geom_bar(mapping = aes(x = Company)) +

What code chunk do you add to the third line to create wrap around facets of the variable Company?

11. Scenario 2, continued

Your team has created some basic visualizations to explore different aspects of the chocolate bar data. You’ve volunteered to add titles to the plots. You begin with a scatterplot.

Assume the first part of your code chunk is:

ggplot(data = trimmed_flavors_df) +

     geom_point(mapping = aes(x = Cocoa.Percent, y = Rating)) +

What code chunk do you add to the third line to add the title Best Chocolates to your plot?

12. Scenario 2, continued

Next, you create a new scatterplot to explore the relationship between different variables. You want to save your plot so you can access it later on. You know that the ggsave() function defaults to saving the last plot that you displayed in RStudio, so you’re ready to write the code to save your scatterplot.

Assume your first two lines of code are:

ggplot(data = trimmed_flavors_df) +

     geom_point(mapping = aes(x = Cocoa.Percent, y = Rating))

What code chunk do you add to the third line to save your plot as a png file with chocolate as the file name?

13. Scenario 2, continued

As a final step in the analysis process, you create a report to document and share your work. Before you share your work with the management team at Chocolate and Tea, you are going to meet with your team and get feedback. Your team wants the documentation to include all your code and display all your visualizations.

You want to record and share every step of your analysis, let teammates run your code, and display your visualizations. What do you use to document your work?

14.  You use the read_csv() function to import the data from the .csv file. Assume that the name of the data frame is flavors_df and the .csv file is in the working directory. What code chunk lets you create the data frame?

15. Now that you’ve created a data frame, you want to find out more about how the data is organized. The data frame has hundreds of rows and lots of columns.

Assume the name of your data frame is flavors_df. What code chunk lets you get a glimpse of the contents of the data frame?

16. After previewing and cleaning your data, you determine what variables are most relevant to your analysis. Your main focus is on Rating, Cocoa.Percent, and Company.Location. You decide to use the select() function to create a new data frame with only these three variables.

Assume the first part of your code is:

trimmed_flavors_df <- flavors_df %>%

Add the code chunk that lets you select the three variables.

select(Rating, Cocoa.Percent, Company.Location)

What company location appears in row 1 of your tibble?

17. Now that you’ve cleaned and organized your data, you’re ready to create some useful data visualizations. Your team assigns you the task of creating a series of visualizations based on requests from the Chocolate and Tea management team. You decide to use ggplot2 to create your visuals.

Assume your first line of code is:

ggplot(data = best_trimmed_flavors_df) +

You want to use the geom_bar() function to create a bar chart. Add the code chunk that lets you create a bar chart with the variable Rating on the x-axis.

geom_bar(mapping = aes(x = Rating))

How many bars does your bar chart display?

18. Your team has created some basic visualizations to explore different aspects of the chocolate bar data. You’ve volunteered to add titles to the plots. You begin with a scatterplot.

Assume the first part of your code chunk is:

ggplot(data = trimmed_flavors_df) +

    geom_point(mapping = aes(x = Cocoa.Percent, y = Rating)) +

What code chunk do you add to the third line to add the title Recommended Bars to your plot?

20. Next, you create a new scatterplot to explore the relationship between different variables. You want to save your plot so you can access it later on. You know that the ggsave() function defaults to saving the last plot that you displayed in RStudio, so you’re ready to write the code to save your scatterplot.

Assume your first two lines of code are:

ggplot(data = trimmed_flavors_df) +

    geom_point(mapping = aes(x = Cocoa.Percent, y = Rating)) +

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

21. A teammate creates a new plot based on the chocolate bar data. The teammate asks you to make some revisions to their code.

Assume your teammate shares the following code chunk:

ggplot(data = best_trimmed_flavors_df) +

geom_bar(mapping = aes(x = Cocoa.Percent)) +

What code chunk do you add to the third line to create wrap around facets of the variable Coca.Percent?

22. You finish working with an R Markdown notebook and now you need to distribute your work. How can you export your analysis as a styled report?

23. Fill in the blank: A delimiter is a character that indicates the beginning or end of _____.

24. A data analyst wants to change the default file format that gets exported by the Knit button in RStudio. Where should they change the output format?

25.  What delimiter is used to indicate the YAML metadata in an R Markdown notebook?

26. What type of export document should you use while you are working and don’t need to worry about adding page breaks in the correct places?

27. 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?

28. Fill in the blank: A data analyst includes _____ in their R Markdown notebook so that they can refer to it directly in their explanation of their analysis.

29. A data analyst wants to add a bulleted list to their R Markdown document. What symbol can they type to create this formatting?

30. A data analyst is working in a .rmd file and comes across the text “`{r analysis}. What is the purpose of the text “analysis”?

31. What does the — delimiter (three hyphens) indicate in an R Markdown notebook?

32. A data analyst is regularly exporting documents from a .rmd file and manually customizing the appearance of the document they give to stakeholders. What would allow them to automatically customize the appearance of the document?

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

34. A data analyst works with an .rmd file in RStudio and wants the ability to quickly find a code chunk using the label “analysis”. Which code example would allow the analyst to quickly access the code chunk using this label?

35. A data analyst wants to make a word in their markdown stand out by making it bold. What characters should they surround the text with to achieve the bold style?

36. Scenario 1, questions 1-7

As part of the data science team at Gourmet Analytics, you use data analytics to advise companies in the food industry. You clean, organize, and visualize data to arrive at insights that will benefit your clients. As a member of a collaborative team, sharing your analysis with others is an important part of your job.

Your current client is Chocolate and Tea, an up-and-coming chain of cafes.

The eatery combines an extensive menu of fine teas with chocolate bars from around the world. Their diverse selection includes everything from plantain milk chocolate, to tangerine white chocolate, to dark chocolate with pistachio and fig. The encyclopedic list of chocolate bars is the basis of Chocolate and Tea’s brand appeal. Chocolate bar sales are the main driver of revenue.

Chocolate and Tea aims to serve chocolate bars that are highly rated by professional critics. They also continually adjust the menu to make sure it reflects the global diversity of chocolate production. The management team regularly updates the chocolate bar list in order to align with the latest ratings and to ensure that the list contains bars from a variety of countries.

They’ve asked you to collect and analyze data on the latest chocolate ratings. In particular, they’d like to know which countries produce the highest-rated bars of super dark chocolate (a high percentage of cocoa). This data will help them create their next chocolate bar menu.

Your team has received a dataset that features the latest ratings for thousands of chocolates from around the world. Click here to access the dataset. Given the data and the nature of the work you will do for your client, your team agrees to use R for this project.

You create a short document about the benefits of using R for the project and share the document with your team. You write that the benefits include R’s ability to quickly process lots of data and easily reproduce and share an analysis. What is another benefit of using R for the project?

37. Scenario 1, continued

Before you begin working with your data, you need to import it and save it as a data frame. To get started, you open your RStudio workspace and load all the necessary libraries and packages. You upload a .csv file containing the data to RStudio and store it in a project folder named flavors_of_cacao.csv.

You use the read_csv() function to import the data from the .csv file. Assume that the name of the data frame is flavors_df and the .csv file is in the working directory. What code chunk lets you create the data frame?

38. After previewing and cleaning your data, you determine what variables are most relevant to your analysis. Your main focus is on Rating, Cocoa.Percent, and Company. You decide to use the select() function to create a new data frame with only these three variables.

Assume the first part of your code is: 

trimmed_flavors_df <- flavors_df %>%

Add the code chunk that lets you select the three variables.

1

What company appears in row 1 of your tibble?

39. Next, you select the basic statistics that can help your team better understand the ratings system in your data. 

Assume the first part of your code is:

trimmed_flavors_df %>%

You want to use the summarize() and mean() functions to find the mean rating for your data. Add the code chunk that lets you find the mean value for the variable Rating.

1

Run

Reset

What is the mean rating?

40. After completing your analysis of the rating system, you determine that any rating greater than or equal to 3.9 points can be considered a high rating. You also know that Chocolate and Tea considers a bar to be super dark chocolate if the bar’s cocoa percent is greater than or equal to 75%. You decide to create a new data frame to find out which chocolate bars meet these two conditions.

Assume the first part of your code is:

best_trimmed_flavors_df <- trimmed_flavors_df %>% 

You want to apply the filter() function to the variables Cocoa.Percent and Rating. Add the code chunk that lets you filter the data frame for chocolate bars that contain at least 75% cocoa and have a rating of at least 3.9 points.

1

What value for cocoa percent appears in row 1 of your tibble?

41. Now that you’ve cleaned and organized your data, you’re ready to create some useful data visualizations. Your team assigns you the task of creating a series of visualizations based on requests from the Chocolate and Tea management team. You decide to use ggplot2 to create your visuals. 

Assume your first line of code is:

ggplot(data = best_trimmed_flavors_df) +

You want to use the geom_bar() function to create a bar chart. Add the code chunk that lets you create a bar chart with the variable Rating on the x-axis.

1

How many bars does your bar chart display?

42. Your bar chart reveals the locations that produce the highest rated chocolate bars. To get a better idea of the specific rating for each location, you’d like to highlight each bar.

Assume that you are working with the code chunk:

ggplot(data = best_trimmed_flavors_df) +

  geom_bar(mapping = aes(x = Company.Location))

Add a code chunk to the second line of code to map the aesthetic color to the variable Rating.

NOTE: the three dots (…) indicate where to add the code chunk.

1

geom_bar(mapping = aes(x = Company.Location, …))

According to your bar chart, which two company locations produce the highest rated chocolate bars?

43. Scenario 2, continued

A teammate creates a new plot based on the chocolate bar data. The teammate asks you to make some revisions to their code.

Assume your teammate shares the following code chunk:

ggplot(data = best_trimmed_flavors_df) +

  geom_bar(mapping = aes(x = Company)) +

What code chunk do you add to the third line to create wrap around facets of the variable Company? 

44. Scenario 2, continued

Your team has created some basic visualizations to explore different aspects of the chocolate bar data. You’ve volunteered to add titles to the plots. You begin with a scatterplot.

Assume the first part of your code chunk is:

ggplot(data = trimmed_flavors_df) +

    geom_point(mapping = aes(x = Cocoa.Percent, y = Rating)) +

What code chunk do you add to the third line to add the title Recommended Bars to your plot?

45. Scenario 1, continued

Before you begin working with your data, you need to import it and save it as a data frame. To get started, you open your RStudio workspace and load the tidyverse library. You upload a .csv file containing the data to RStudio and store it in a project folder named flavors_of_cacao.csv.

You use the read_csv() function to import the data from the .csv file. Assume that the name of the data frame is bars_df and the .csv file is in the working directory. What code chunk lets you create the data frame?

46. Next, you select the basic statistics that can help your team better understand the ratings system in your data. 

Assume the first part of your code is:

trimmed_flavors_df %>%

You want to use the summarize() and sd() functions to find the standard deviation of the rating for your data. Add the code chunk that lets you find the standard deviation for the variable Rating.

1

 What is the standard deviation of the rating?

47. After completing your analysis of the rating system, you determine that any rating greater than or equal to 3.5 points can be considered a high rating. You also know that Chocolate and Tea considers a bar to be super dark chocolate if the bar’s cocoa percent is greater than or equal to 70%. You decide to create a new data frame to find out which chocolate bars meet these two conditions. 

Assume the first part of your code is:

best_trimmed_flavors_df <- trimmed_flavors_df %>% 

You want to apply the filter() function to the variables Cocoa.Percent and Rating. Add the code chunk that lets you filter the data frame for chocolate bars that contain at least 70% cocoa and have a rating of at least 3.5 points.

1

What rating appears in row 1 of your tibble?

48. Now that you’ve cleaned and organized your data, you’re ready to create some useful data visualizations. Your team assigns you the task of creating a series of visualizations based on requests from the Chocolate and Tea management team. You decide to use ggplot2 to create your visuals. 

Assume your first line of code is:

ggplot(data = best_trimmed_flavors_df) +

You want to use the geom_bar() function to create a bar chart. Add the code chunk that lets you create a bar chart with the variable Rating on the x-axis.

1

How many bars does your bar chart display?

49. Scenario 2, continued

Your team has created some basic visualizations to explore different aspects of the chocolate bar data. You’ve volunteered to add titles to the plots. You begin with a scatterplot.

Assume the first part of your code chunk is:

ggplot(data = trimmed_flavors_df) +

    geom_point(mapping = aes(x = Cocoa.Percent, y = Rating)) +

What code chunk do you add to the third line to add the title Best Chocolates to your plot?