25. A data analyst is working with a data frame called salary_data. They want to create a new column named hourly_salary that includes data from the wages column divided by 40. What code chunk lets the analyst create the hourly_salarycolumn?
- mutate(salary_data, hourly_salary = wages / 40)
- mutate(salary_data, hourly_salary = wages * 40)
- mutate(hourly_salary = wages / 40)
- mutate(hourly_salary, salary_data = wages / 40)
26. In R, which statistical measure demonstrates how strong the relationship is between two variables?
- Correlation
- Maximum
- Standard deviation
- Average
27. A data analyst creates two different predictive models for the same dataset. They use the bias() function on both models. The first model has a bias of -40. The second model has a bias of 1. Which model is less biased?
- The second model
- It can’t be determined from this information
- The first model
28. What scenarios would prevent you from being able to use a tibble?
- You need to create column names
- You need to store numerical data
- You need to create row names
- You need to change the data types of inputs
29. A data analyst is working with a data frame named salary_data. They want to create a new column named wagesthat includes data from the rate column multiplied by 40. What code chunk lets the analyst create the wages column?
- mutate(salary_data, wages = rate * 40)
- mutate(salary_data, wages = rate + 40)
- mutate(wages = rate * 40)
- mutate(salary_data, rate = wages * 40)
30. 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()
- cor()
- sd()
- mean()
31. A data analyst is considering using tibbles instead of basic data frames. What are some of the limitations of tibbles? Select all that apply.
- Tibbles can overload a console
- Tibbles can never change the input type of the data
- Tibbles won’t automatically change the names of variables
- Tibbles won’t automatically change the names of variables
32. A data analyst wants a high level summary of the structure of their data frame, including the column names, the number of rows and variables, and type of data within a given column. What function should they use?
- colnames()
- head()
- rename_with()
- str()
33. 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.
How many variables does the ToothGrowth dataset contain?
- 5
- 4
- 2
- 3
34. 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(penguins, bill_length_mm)
- arrange(bill_length_mm, penguins)
- arrange(=bill_length_mm)
- arrange(=bill_length_mm)
Shuffle Q/A 2
35. A data analyst is working with a data frame called sales. In the data frame, a column named location represents data in the format “city, state”. The analyst wants to split the city into an individual city column and state into a new countrycolumn. What code chunk lets the analyst split the location column?
- separate(sales, location, into=c(“country”, “city” ), sep=”, “)
- separate(sales, location, into=c(“city”, “country”), sep=”, “)
- untie(sales, location, into=c(“city”, “country”), sep=”, “)
- separate(sales, location, into=c(“country”, “city” ), sep=” “)
36. A data analyst is working with the penguins data. The variable species includes three penguin species: Adelie, Chinstrap, and Gentoo. The analyst wants to create a data frame that only includes the Adelie species. The analyst receives an error message when they run the following code:
penguins %>%
filter(species <- “Adelie”)
How can the analyst change the second line of code to correct the error?
- filter(Adelie == species)
- filter(“Adelie”)
- filter(“Adelie” <- species)
- filter(species == “Adelie”)
