R Language Quiz

1. Which of these accurately describes piping?

2. To combine functions, use the ————.

3. Suppose you have a data frame named “dat” with two numeric columns, $value1$ and $value2$. You want to add a third column called $my\_value$, where the value in each row is the product of multiplying the other two values in the row by one another. Which is the correct line of code?

4. The tidyverse replaces the techniques for manipulating data with base R.

5. Which of these are advantages of Tibbles over data frames?

6. Looking at tidyverse.org, how many core packages are included in the tidyverse?

7. Suppose you have a dataset that looks like this:

colors <- c("red","green","yellow")
speeds <- c("slow","fast","medium")
my_dat <- data.frame(colors,speeds)

What is the correct code to recode the “colors” column so that red equals 0, green equals 2, and yellow equals 1?

8. Functions contained in packages such as dplyr are used to:

9. Suppose you have a tibble saved to the object $my\_dat$ with two columns, $alpha$ and $beta$. These are filled with numeric data. Which of these will arrange the data in descending order by $alpha$?

10. What do you need to do to use tidyverse commands in R?

11. Which is NOT a principle of tidy data?

12. Which one of these libraries is widely used for data manipulation in R?

13. When you run the line: dat <- read_csv("my_data.csv"). What kind of object is dat?

14. Suppose you have a tibble called “cities” with columns including population (“population”), a measure of economic activity (“gdp”), and the state in which the city is located (“state”).

Which of these commands would select rows from the dataset where the value for population is more than 3,000, the value for economic activity is less than 120,000, and the city is not located in Alabama?

15. Which of the following is NOT one of the four groups in the tidyverse library?

16. Suppose you have a tibble saved into your R environment as “$my_dat$” with two columns named “$alpha$” and “$beta$”. You want to rename the “$beta$” column and call it “$gamma$”. Which of these will create a new tibble with the renamed column?

17. Which of the following are steps in the data-wrangling process?

18. Which tidyverse package is used for data import and management?

19. Suppose you have a tibble named “dat” that has a time, date, employee, and sales column.
You are reviewing someone’s R code and see the following lines:

my_time <- filter(dat,time == 1)
my_time_and_date <- filter(my_time,date>5)


group_by_employee_my_time_and_date <- group_by(my_time_and_date, employee)
summarise(group_by_employee_my_time_and_date, average=mean(sales))

Which of these would do the same thing using piping?

20. If there is missing data in a .csv file that you import, what should you do?