Midterm 1 Study Guide Solutions

1. Rapid-fire:

   1. every row is an observation and every column is a variable;
   2. given the data and the code, we can exactly reproduce all of the figures and tables in the analysis;
   3. both lists and vector are ways of bundling together multiple values in R. But a vector consists of values of all the same type, whereas a list can bundle different types;
   4. STAAAAAAAWANANAAAAA;
   5. It takes the object on the left and feeds it forward as the first argument to the command on the right;
   6. It tells the computer what columns in the data frame to use and how to use them;
   7. if_else handles two conditions; case_when handles multiple conditions;
   8. filter discards rows and select discards columns;
   9. adds a “grouping” attribute to a data frame so that subsequent operations are performed within the levels of a categorical variable;
   10. it’s the cutoff point on the number line that has 38% of your data values below and 62% of the values above;
       • yes I could;
       • smaller than.

2. e

3. d

4. b

5. a

6. c

7. f

8. f

9. a

10. b

11. e

12. c

13. d

14. Missing + between ggplot and geom_point;

15. vs is numeric type, but the shape aesthetic requires a factor;

16. mtcars needs to be overwritten to save the new variable heavy;

17. There’s a hanging comma inside the values argument to scale_color_manual;

18. color and shape have different labels. shape is defaulting to the column name “species”, and color is using the user-specified “Species”. It’s case sensitive. Add shape = "Species" inside labs, and the legends are consolidated;

19. n_people is not a column in the summary table. age_sum is a column in the summary table.

20. The wider data frame has a column titled Remain Against The Law, which is improper because of the spaces. To reference that column by name, you need to put it in back ticks, no quotes;

21. Inside the if_else, we are mixing types: characters and numbers;

22. We are supplying ggplot with the data twice, by piping it in and supplying the data frame directly. It needs to be one or the other;

23. We are assigning the entire pipeline to the variable name us_uk_tr_votes. The pipeline ends with the creation of a plot, so this new variable is not a data frame, it’s a plot object. You can’t filter that;

24. b, c, f, g -

-   The `blizzard_salary` dataset has 409 rows.

-   The `percent_incr` variable is numerical and continuous.

-   The `salary_type` variable is categorical.

25. Figure 1 - A shared x-axis makes it easier to compare summary statistics for the variable on the x-axis.

26. c - It’s a value higher than the median for hourly but lower than the mean for salaried.

27. b - There is more variability around the mean compared to the hourly distribution.

28. a, b, e - Pie charts and waffle charts are for visualizing distributions of categorical data only. Scatterplots are for visualizing the relationship between two numerical variables.

29. c - mutate() is used to create or modify a variable.

30. a - "Poor", "Successful", "High", "Top"

31. b - Option 2. The plot in Option 1 shows the number of employees with a given performance rating for each salary type while the plot in Option 2 gives the proportion of employees with a given performance rating for each salary type. In order to assess the relationship between these variables (e.g., how much more likely is a Top rating among Salaried vs. Hourly workers), we need the proportions, not the counts.

32. There may be some NAs in these two variables that are not visible in the plot.

33. The proportions under Hourly would go in the Hourly bar, and those under Salaried would go in the Salaried bar.

34. c - filter(salary_type != "Hourly" & performance_rating == "Poor") - There are 5 observations for “not Hourly” “and” Poor.

35. a - arrange() - The result is arranged in increasing order of annual_salary, which is the default for arrange().

36. c, d, e, f.

37. Part 1: The following should be fixed:

    • There should be a | after # before label

    • There should be a : after label, not =

    • There shouldn’t be a space in the chunk label, it should be plot-blizzard

    • There should be spaces after commas in the code

    • There should be spaces on both sides of = in the code

    • There should be a space before +

    • geom_boxplot() should be on the next line and indented

    • There should be a + at the end of the geom_boxplot() line

    • labs() should be indented

    Part 2: The warning is caused by NA in the data. It means that 39 observations were NAs and are not plotted/represented on the plot.

38. Part 1:

    1. Render: Run all of the code and render all of the text in the document and produce an output.
    2. Commit: Take a snapshot of your changes in Git with an appropriate message.
    3. Push: Send your changes off to GitHub.

    Part 2: c - Rendering or committing isn’t sufficient to send your changes to your GitHub repository, a push is needed. A pull is also not needed to view the changes in the browser.

39. d

40. a, d

41. a, d

42. c

43. b

44. a

45. b

47. a, d

48. b, c, e

49. a

50. a, c, e

51. a, b, c, d

52. a, d