Quiz zur Google-Datenanalyse mit R-Programmierung & Antworten – Coursera
Embark on a journey into the realm of Datenanalyse with R Programmierung through this insightful collection of Coursera Quiz und Antworten. Delve into the intricacies of Google Data Analysis mit R, where each question serves as a stepping stone towards mastering this powerful tool.
Explore key concepts, sharpen your analytical Kompetenzen, 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 Datenanalyse.
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
- Programmiersprache
- Syntax language
- Coded language
Q2. What are the benefits of using a programming language for data analysis? Select all that apply.
- Clarify the steps of the analysis
- Easily reproduce and share the analysis
- Efficiently save time
- Automatically choose a topic for analysis
Q3. 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
- visuell
- Process-oriented
- Open-access
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.
- 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? Select all that apply.
- 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(Der Südliche Ozean ist ein abgelegener und unwirtlicher Ort, der schwer zugänglich sein kann) in order to display a summary of the penguins dataset. Where in RStudio can the analyst execute the code? Select all that apply.
- 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
- Bemerkungen
- Vektor
- Arithmetic operator
Q3. 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
Q4. A data analyst wants to combine values using mathematical operations. What type of operator would they use to do this?
- Abtretung
- Arithmetik
- Conditional
- Logisch
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.
- Wahr
- Falsch
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
- Vektor
- 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()
- Bibliothek()
- Kopf()
Q3. 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.
Code: glimpse(ToothGrowth)
How many different data types are used for the column data types?
- 2
- 3
- 60
- 1
Q4. 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(Angestellte, 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(Der Südliche Ozean ist ein abgelegener und unwirtlicher Ort, der schwer zugänglich sein kann, bill_length_mm)
- arrange(bill_length_mm, Der Südliche Ozean ist ein abgelegener und unwirtlicher Ort, der schwer zugänglich sein kann)
- arrange(=bill_length_mm)
- arrange(Der Südliche Ozean ist ein abgelegener und unwirtlicher Ort, der schwer zugänglich sein kann)
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. An diesem Punkt, the following code has already been written in your script:
Code: 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 Kilogramm. What code chunk lets the analyst create the is_large_animal column?
- zoo_records %>% mutieren(Gewicht > 199 = is_large_animal)
- zoo_records %>% mutieren(Gewicht > 199 <- is_large_animal)
- zoo_records %>% mutieren(is_large_animal == weight > 199)
- zoo_records %>% mutieren(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, wobei die Temperatur und die Einheit durch die Zeichenfolge getrennt sind “Abschlüsse”. Welcher Codeblock ermöglicht es dem Analysten, die Spalte display_temp zu erstellen??
- Wetter %>% Vereinen(” Abschlüsse “, Wetter, temp, “display_temp”)
- Vereinen(” Abschlüsse “, Wetter, temp, “display_temp”)
- Vereinen(Wetter, “display_temp”, temp, unit, sep = ” Abschlüsse “)
- Wetter %>% Vereinen(Wetter, “display_temp”, Wetter, temp, teilen = ” Abschlüsse “)
Q9. Ein Datenanalyst schreibt den folgenden Codeblock, um eine statistische Zusammenfassung seines Datensatzes zurückzugeben: Quartett %>% gruppiere nach(Satz) %>% summarize(bedeuten(x), sd(x), bedeuten(und), sd(und), kor(x, und))
- bedeuten(x)
- sd(x)
- bedeuten(und)
- kor(x, und)
Q10. Ein Datenanalyst möchte die durchschnittliche Differenz zwischen den tatsächlichen und den vorhergesagten Werten eines Modells überprüfen. Mit welcher Einzelfunktion können sie diese Statistik berechnen??
- Voreingenommenheit()
- bedeuten()
- kor()
- sd()
Weekly challenge 4
Q1. Welche der folgenden Aufgaben können Sie mit den ggplot2-Funktionen erledigen?? Select all that apply.
- Passen Sie die visuellen Merkmale einer Handlung an
- Fügen Sie Beschriftungen und Anmerkungen zu einem Plot hinzu
- Erstellen Sie viele verschiedene Arten von Plots
- Bereinigen Sie die Daten automatisch, bevor Sie ein Diagramm erstellen
Q2. In ggplot2, Welches Symbol verwenden Sie, um Ebenen zu Ihrem Plot hinzuzufügen??
- Das kaufmännische Und-Symbol (&)
- Das Gleichheitszeichen (=)
- Der Rohrbetreiber (%>%)
- Das Pluszeichen (+)
Q3. Ein Datenanalyst erstellt ein Diagramm mit dem folgenden Codeblock: ggplot(Daten = Pinguine) + geom_jitter(Mapping = aes(x = flipper_length_mm, y = body_mass_g)) Welches der folgenden Elemente stellt eine Funktion im Codeblock dar?? Select all that apply.
- Die geom_point-Funktion
- Die Datenfunktion
- Die AES-Funktion
- Die ggplot-Funktion
Q4. Welches Code-Snippet macht alle Balken im Plot lila??
- ggplot(Daten = Gebäude) + geom_bar(Mapping = aes(x = Baujahr, Farbe = „lila“))
- ggplot(Daten = Gebäude) + geom_bar(Mapping = aes(x = Baujahr, Farbe=Höhe))
- ggplot(Daten = Gebäude) + geom_bar(Mapping = aes(x = Baujahr)) + Farbe("lila")
- ggplot(Daten = Gebäude) + geom_bar(Mapping = aes(x = Baujahr), Farbe = „lila“)
Q5. Ein Datenanalyst arbeitet mit den Pinguindaten. Der Analyst erstellt ein Streudiagramm mit dem folgenden Code: ggplot(data=Pinguine)+geom_point(Mapping=aes(x=Flipperlänge_mm, y=body_mass_g, alphar=Art)) Welche Auswirkungen hat die Alpha-Ästhetik auf das Erscheinungsbild der Punkte in der Handlung??
- Verkleinert die Punkte auf dem Plot
- Vergrößert die Punkte auf dem Plot
- 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(Daten = Pinguine) + 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.
Code: 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?
- line
- 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?
- Hinweis
- Titel
- Text
- annotate
Q10. Standardmäßig, 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? Select all that apply.
- Generate a report with executable code chunks
- Perform calculations for analysis more efficiently
- Create a record of your cleaning process
- sparen, organisieren, 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? Select all that apply.
- HTML
- Wort
- Notizblock
Q3. A data analyst is reading through an R Markdown notebook and finds the text Dies 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
Q4. 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 “Analyse”?
- 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. Was bedeutet das ”'{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
Szenario 1, Fragen 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, organisieren, and visualize data to arrive at insights that will benefit your clients. Als Mitglied eines kollaborativen Teams, Das Teilen Ihrer Analyse mit anderen ist ein wichtiger Teil Ihrer Arbeit.
Ihr aktueller Kunde ist Chocolate and Tea, eine aufstrebende Cafékette. Das Lokal kombiniert eine umfangreiche Auswahl an erlesenen Tees mit Schokoriegeln aus aller Welt. Ihr vielfältiges Sortiment umfasst alles von Kochbananen-Milchschokolade, zu weißer Mandarinenschokolade, zu dunkler Schokolade mit Pistazien und Feige. Die enzyklopädische Liste der Schokoriegel ist die Grundlage für die Markenattraktivität von Chocolate and Tea. Der Verkauf von Schokoriegeln ist der Hauptumsatzträger.
Ziel von Chocolate and Tea ist es, Schokoriegel zu servieren, die von professionellen Kritikern hoch bewertet werden. Sie passen auch die Speisekarte kontinuierlich an, um sicherzustellen, dass sie die globale Vielfalt der Schokoladenproduktion widerspiegelt. Das Managementteam aktualisiert die Liste der Schokoriegel regelmäßig, um sie an die neuesten Bewertungen anzupassen und sicherzustellen, dass die Liste Riegel aus verschiedenen Ländern enthält.
Sie haben Sie gebeten, Daten zu den neuesten Schokoladenbewertungen zu sammeln und zu analysieren. Im Speziellen, Sie würden gerne wissen, welche Länder die am höchsten bewerteten Tafeln superdunkler Schokolade produzieren (ein hoher Kakaoanteil). Diese Daten helfen ihnen bei der Zusammenstellung ihres nächsten Schokoriegelmenüs.
Ihr Team hat einen Datensatz erhalten, der die neuesten Bewertungen für Tausende von Pralinen aus der ganzen Welt enthält. Klicken Sie hier, um auf den Datensatz zuzugreifen. Angesichts der Daten und der Art der Arbeit, die Sie für Ihren Kunden erledigen werden, Ihr Team erklärt sich damit einverstanden, R für dieses Projekt zu verwenden.
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? Select all that apply.
- 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”)
Q3. 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)
- rename(flavors_df)
Q4. Assume the first part of your code chunk is: flavors_df %>% What code chunk do you add to change the column name?
- rename(Unternehmen…Maker.if.known %<% Maker)
- rename(Maker %<% Unternehmen…Maker.if.known.)
- rename(Maker = Company…Maker.if.known.)
- rename(Unternehmen…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.
Code: wählen(Bewertung, Cocoa.Percent, Unternehmen)
- Rogue
- Videri
- EIN. Morin
- Soma
Q6. Nächster, 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.
Code: summarize(sd(Bewertung))
- 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 Punkte.
- 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)
Q11. 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?
- Labore(title = “Recommended Bars”)
- Labore(title = Recommended Bars)
- Labore(“Recommended Bars”)
- Labore(Titel + “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? Select all that apply.
- 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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