Google Data-Analysis-with-R-Programming Quizzes & respuestas – Coursera
Embark on a journey into the realm of análisis de los datos with R Programación through this insightful collection of Coursera cuestionarios y respuestas. Delve into the intricacies of Google Data Analysis con R, where each question serves as a stepping stone towards mastering this powerful tool.
Explore key concepts, sharpen your analytical habilidades, and enhance your understanding of data manipulation and visualization. Whether you are a seasoned data professional or an aspiring analyst, this curated content promises to enrich your knowledge and elevate your expertise in the dynamic field of análisis de los datos.
Weekly challenge 1
Q1. A data analyst uses words and symbols to give instructions to a computer. What are the words and symbols known as?
- Function language
- Lenguaje de programación
- Syntax language
- Coded language
Q2. What are the benefits of using a programming language for data analysis? Seleccione todas las que correspondan.
- Clarify the steps of the analysis
- Easily reproduce and share the analysis
- Efficiently save time
- Automatically choose a topic for analysis
Tercer trimestre. A data analyst wants to use a programming language that allows to create packages and share them freely. What type of programming language should they use?
- Open-source
- Visual
- Process-oriented
- Open-access
Cuarto trimestre. 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.
- structured query language
- the R programming language
- a dashboard
- a spreadsheet
Q5. A data analyst is searching for a single tool that will allow them to query massive amounts of data, reproduce their analysis, and create word-class visuals. Which one of the following tools is the best option for them?
- The R programming language
- A database
- A dashboard
- SQL
Q6. Which of the following statements about RStudio’s integrated development environment are correct? Seleccione todas las que correspondan.
- The layout of panes in R studio is fixed.
- R studio is built specifically for working with R.
- R studio is unable to produce visualizations.
- R studio helps with file management.
Q7. A data analyst writes the code summary(pingüinos) in order to display a summary of the penguins dataset. Where in RStudio can the analyst execute the code? Seleccione todas las que correspondan.
- Source editor pane
- Environment pane
- Files tab
- R console pane
Q8. In RStudio, where can you find a list of all of the R commands you have run in your current sessions?
- History tab
- Source editor
- Help tab
- Files tab
Weekly challenge 2
Q1. 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?
- -sales-2020
- sales_2020
- _2020sales
- 2020_sales
Q2. A data analyst wants to store a sequence of data elements that all have the same data type in a single variable. What R concept allows them to do this?
- Logical operator
- comentarios
- Vector
- Arithmetic operator
Tercer trimestre. A data analyst finds the code mdy(10211020) in an R script. What is the year of the date that is created?
- 1020
- 2120
- 1021
- 1102
Cuarto trimestre. A data analyst wants to combine values using mathematical operations. What type of operator would they use to do this?
- Asignación
- Aritmética
- Conditional
- Lógico
Q5. Which of the following is a best practice when naming functions in R?
- Function names should be capitalized
- Function names should be very long
- Function names should be verbs
- Function names should start with a special character
Q6. R packages include sample datasets. They also include reusable R functions and documentation about how to use the functions.
- Cierto
- Falso
Q7. What is the relationship between RStudio and CRAN?
- CRAN creates visualizations based on an analyst’s programming in RStudio.
- RStudio and CRAN are both environments where data analysts can program using R code.
- RStudio installs packages from CRAN that are not in Base R.
- CRAN contains all of the data that RStudio users need for analysis.
Q8. When programming in R, what is a pipe used as an alternative for?
- Variable
- Installed package
- Vector
- Nested function
Weekly challenge 3
Q1. What is an advantage of using data frames instead of tibbles?
- Data frames store never change variable names
- Data frames allow you to use column names
- Data frames make printing easier
- Data frames allow you to create row names
Q2. A data analyst is checking a script for one of their peers. They want to learn more about a specific data frame. What function(s) will allow them to see a subset of data values in the data frame? Sekect all that apply.
- colnames()
- str()
- Este es un curso breve mediante el cual puede integrar rápidamente la biblioteca CLI()
- cabeza()
Tercer trimestre. You are working with the ToothGrowth dataset. You want to use the glimpse() function to get a quick summary of the dataset. Write the code chunk that will give you this summary.
código: glimpse(ToothGrowth)
How many different data types are used for the column data types?
- 2
- 3
- 60
- 1
Cuarto trimestre. You have a data frame named employees with a column named Last_NAME. What will the name of the employees column be in the results of the function rename_with(empleados, tolower)?
- Last_NAME
- last_name
- last_nAME
- lAST_nAME
Q5. A data analyst is working with the penguins dataset in R. What code chunk will allow them to sort the penguins data by the variable bill_length_mm?
- arrange(pingüinos, bill_length_mm)
- arrange(bill_length_mm, pingüinos)
- arrange(=bill_length_mm)
- arrange(pingüinos)
Q6. You are working with the penguins dataset. You want to use the summarize() and mean() functions to find the mean value for the variable bill_mass_g. En este punto, the following code has already been written in your script:
código: summarize(body_mass_g_m = mean(body_mass_g))
What is the mean body mass in g for the Adelie species?
- 5092.437
- 3706.164
- 4207.433
- 3733.088
Q7. A data analyst is working with a data frame called zoo_records. They want to create a new column named is_large_animal that signifies if an animal has a weight of more than 199 kilogramos. What code chunk lets the analyst create the is_large_animal column?
- zoo_records %>% mudar(peso > 199 = is_large_animal)
- zoo_records %>% mudar(peso > 199 <- is_large_animal)
- zoo_records %>% mudar(is_large_animal == weight > 199)
- zoo_records %>% mudar(is_large_animal = weight > 199)
Q8. A data analyst is working with a data frame named weather. It has separate columns for temperatures (temp) and measurment units (unit). The analyst wants to combine into a single column called display_temp, with the temperature and unit separated by the string “Degrees”. What code chunk lets the analyst create the display_temp column?
- weather %>% unite(” Degrees “, clima, temp, “display_temp”)
- unite(” Degrees “, clima, temp, “display_temp”)
- unite(clima, “display_temp”, temp, unit, sep = ” Degrees “)
- weather %>% unite(clima, “display_temp”, clima, temp, delim = ” Degrees “)
Q9. A data analyst writes the following code chunk to return a statistical summary of their dataset: quartet %>% group_by(colocar) %>% summarize(significar(X), sd(X), significar(y), sd(y), cor(X, y))
- significar(X)
- sd(X)
- significar(y)
- cor(X, y)
Q10. A data analyst wants to check the average difference between the actual and predicted values of a model. What single function can they use to calculate this statistic?
- bias()
- significar()
- cor()
- sd()
Weekly challenge 4
Q1. Which of the following tasks can you complete with ggplot2 features? Seleccione todas las que correspondan.
- Customize the visual features of a plot
- Add labels and annotations to a plot
- Create many different types of plots
- Automatically clean data before creating a plot
Q2. In ggplot2, what symbol do you use to add layers to your plot?
- The ampersand symbol (&)
- The equals sign (=)
- The pipe operator (%>%)
- The plus sign (+)
Tercer trimestre. A data analyst creates a plot using the following code chunk: ggplot(data = penguins) + geom_jitter(mapping = aes(x = flipper_length_mm, y = body_mass_g)) Which of the following represents a function in the code chunk? Seleccione todas las que correspondan.
- The geom_point function
- The data function
- The aes function
- The ggplot function
Cuarto trimestre. Which code snippet will make all of the bars in the plot purple?
- ggplot(data = buildings) + geom_bar(mapping = aes(x = construction_year, color=”purple”))
- ggplot(data = buildings) + geom_bar(mapping = aes(x = construction_year, color=height))
- ggplot(data = buildings) + geom_bar(mapping = aes(x = construction_year)) + color(“purple”)
- ggplot(data = buildings) + geom_bar(mapping = aes(x = construction_year), color=”purple”)
Q5. A data analyst is working with the penguins 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, alphar=species)) What does the alpha aesthetic do to the appereance of the points on the plot?
- Makes the points on the plot smaller
- Makes the points on the plot larger
- Makes the points on the plot more colorful
- Makes some points on the plot more transparent
Q6. You are working with the penguins dataset. You create a scatterplot with the following code: ggplot(data = penguins) + geom_point(mapping = aes(x = flipper_length_mm, y = body_mass_g)) You want to highlight the different penguin species on your plot. Add a code chunk to the second line of code to map the aesthetic shape to the variable species.
código: geom_point(mapping = aes(x = flipper_length_mm, y = body_mass_g, color=species))
Which penguin species does visualization display?
- Adelie, Chinstrap, Gentoo
- Adelie, Emperor, Gentoo
- Adelie, Chinstrap, Macaroni
- Chinstrap, Emperor, Gentoo
Q7. Which aesthetic of the geom_smooth function can be used to change the style of the line?
- línea
- linestyle
- linetype
- linelook
Q8. Which of the following statements best describes a facet in ggplot?
- Facets are the text used in and around plots.
- Facets are the visual characteristics of geometry objects.
- Facets are subplots that display data for each value of a variable.
- Facets are the ggplot terminology for a chart axis.
Q9. What argument of the label() function can a data analyst use to add text outside of the grid area of a plot?
- Nota
- título
- texto
- annotate
Q10. Por defecto, what plot does the ggsve() function export?
- The plot define the plots.config file
- The last displayed plot
- The plot defined in the Plots Tab of R Studio
- The first plot displayed
Weekly challenge 5
Q1. R Markdown is a file format for making dynamic documents with R. What are benefits of creating this kind of document? Seleccione todas las que correspondan.
- Generate a report with executable code chunks
- Perform calculations for analysis more efficiently
- Create a record of your cleaning process
- Salvar, organizar, and document code
Q2. A data analyst wants to export their R Markdown notebook as a text document. What are the text document formats they can use to share their R Markdown notebook? Seleccione todas las que correspondan.
- HTML
- Palabra
- Bloc
Tercer trimestre. A data analyst is reading through an R Markdown notebook and finds the text esta is important. What is the purpose of the underscore characters in this text?
- They wrap the text in a clickable link
- They style the text as bold
- They add the text as an image caption
- They style the text as italics
Cuarto trimestre. 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?
- Use the Contents Menu
- Use two hashtags
- Use the Knit Button
- Use markdown text
Q5. A data analyst comes across <www.cooursera.com> in a piece of markdown text. What effect do the angle brackets (<>) have on their inner text?
- They create a piece of inline code
- They create a clickable link
- They create bold text
- They create a bullet list
Q6. A data analyst is working in a .rmd file and comes across the text ”'{r nalysis}. What is purpose of the text “análisis”?
- It alters the output file format of Knit
- It changes the way the code gets exported
- It runs the code in analysis mode
- It is a label for the code chunk
Q7. Lo que hace el ”'{r} delimetr (three backticks followed by an r contained inside curly brackets) indicate in an R Markdown notebook?
- The end of YAML metadata
- The start of a code chunk
- The start of YAML metadata
- The end of a code chunk
Q8. Fill in the blank: If an analyst creates the same kind of document over and over or customizes the appereance of a final report, they can use ______ to save them time.
- a filter
- a code chunk
- an .rmd file
- a template
Course challenge
Guión 1, preguntas 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, organizar, 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. En 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.
Q1. 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? Seleccione todas las que correspondan.
- Easily reproduce and share the analysis
- Define a problem and ask the right questions
- Create high-quality data visualizations
- Quickly process lots of data
Q2. 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?
- flavors_df <- read_csv(“flavors_of_cacao.csv”)
- flavors_df + read_csv(“flavors_of_cacao.csv”)
- read_csv(“flavors_of_cacao.csv”) <- flavors_df
- read_csv(flavors_df <- “flavors_of_cacao.csv”)
Tercer trimestre. Assume the name of your data frame is flavors_df. What code chunk lets you review the column names in the data frame?
- colnames(flavors_df)
- arrange(flavors_df)
- col(flavors_df)
- rebautizar(flavors_df)
Cuarto trimestre. Assume the first part of your code chunk is: flavors_df %>% What code chunk do you add to change the column name?
- rebautizar(Compañía…Maker.if.known %<% Maker)
- rebautizar(Maker %<% Compañía…Maker.if.known.)
- rebautizar(Maker = Company…Maker.if.known.)
- rebautizar(Compañía…Maker.if.known. = Maker)
Q5. Assume the first part of your code is: trimmed_flavors_df <- flavors_df %>% Add the code chunk that lets you select the three variables.
Código: Seleccione(Clasificación, Cocoa.Percent, Compañía)
- Rogue
- Videri
- UNA. Morin
- Soma
Q6. Siguiente, 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.
Código: summarize(sd(Clasificación))
- 0.3720475
- 0.2951794
- 0.4780624
- 0.4458434
Q7. 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 puntos.
- 75%
- 88%
- 80%
- 78%
Q8. 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.
- 2
- 5
- 6
- 3
Q9. 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.
- Canada and France
- Scotland and U.S.A
- Scotland and Canada
- Amsterdam and France
Q10. 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 Cocoa.Percent?
- facet_wrap(Cocoa.Percent~)
- facet_wrap(~Cocoa.Percent)
- facet(=Cocoa.Percent)
- facet_wrap(%>%Cocoa.Percent)
tecnicos. 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?
- laboratorios(title = “Recommended Bars”)
- laboratorios(title = Recommended Bars)
- laboratorios(“Recommended Bars”)
- laboratorios(título + “Recommended Bars”)
Q12. 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 jpeg file with chocolate as the file name?
- ggsave(“chocolate.jpeg”)
- ggsave(“chocolate.png”)
- ggsave(“jpeg.chocolate”)
- ggsave(chocolate.jpeg)’
Q12. You decide to create an R Markdown notebook to document your work. What are your reasons for choosing an R Markdown notebook? Seleccione todas las que correspondan.
- It lets you record and share every step of your analysis
- It allows users to run your code
- It automatically creates a website to show your work
- It displays your data visualizations
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