Git configuration / password caching

Configure your Git user name and email. If you cannot remember the instructions, refer to an earlier lab. Also remember that you can cache your password for a limited amount of time.

Project name:

Update the name of your project to match the lab’s title.

YAML:

Open the R Markdown (Rmd) file in your project, change the author name to your team name, and knit the document.

Commiting and pushing changes:

• Go to the Git pane in your RStudio.
• View the Diff and confirm that you are happy with the changes.
• Add a commit message like “Update team name” in the Commit message box and hit Commit.
• Click on Push. This will prompt a dialogue box where you first need to enter your user name, and then your password.

Pulling changes:

Now, the remaining team members who have not been concurrently making these changes on their projects should click on the Pull button in their Git pane and observe that the changes are now reflected on their projects as well.

Codebook

Part 1: Data Manipulation

⊕The rowwise function is useful for applying mathematical operations to each row.

1. Create a new variable called bty_avg that is the average attractiveness score of the six students for each professor (bty_f1lower through bty_m2upper). Add this new variable to the evals data frame. Do this in one pipe, using the rowwise function. Incomplete code is given below to guide you in the right direction, however you will need to fill in the blanks.

___ <- evals %>%
  rowwise() %>%
  ___(bty_avg = mean( c( ___ ) )) %>%
  ungroup()

Note that we end the pipeline with ungroup() to remove the effect of the rowwise function from earlier in the pipeline. The rowwise function works a lot like group_by, except it groups the data frame one row at a time so that any operations applied to the data frame is done once per each row. This is helpful for finding the mean beauty score for each row. However in the remainder of the analysis we don’t want to, say, calculate summary statistics for each row, or fit a model for each row. Hence we need to undo the effect of rowwise, which we can do with ungroup.

Part 2: Exploratory Data Analysis

2. Visualize the distribution of score. Is the distribution skewed? What does that tell you about how students rate courses? Is this what you expected to see? Why, or why not? Include any summary statistics and visualizations you use in your response.

3. Visualize and describe the relationship between score and the new variable you created, bty_avg.

⊕Hint: See the help page for the function at http://ggplot2.tidyverse.org/reference/index.html.

4. Replot the scatterplot from Exercise 3, but this time use
   geom_jitter()? What does “jitter” mean? What was misleading about the initial scatterplot?

Part 3: Linear regression with a numerical predictor

⊕Linear model is in the form \(\hat{y} = b_0 + b_1 x\).

5. Let’s see if the apparent trend in the plot is something more than natural variation. Fit a linear model called m_bty to predict average professor evaluation score by average beauty rating (bty_avg). Based on the regression output, write the linear model.

6. Replot your visualization from Exercise 3, and add the regression line to this plot in orange color. Turn off the shading for the uncertainty of the line.

7. Interpret the slope of the linear model in context of the data.

8. Interpret the intercept of the linear model in context of the data. Comment on whether or not the intercept makes sense in this context.

9. Determine the \(R^2\) of the model and interpret it in context of the data.

Part 4: Linear regression with a categorical predictor

10. Fit a new linear model called m_gen to predict average professor evaluation score based on gender of the professor. Based on the regression output, write the linear model and interpret the slope and intercept in context of the data.

11. What is the equation of the line corresponding to male professors? What is it for female professors?

12. Fit a new linear model called m_rank to predict average professor evaluation score based on rank of the professor. Based on the regression output, write the linear model and interpret the slopes and intercept in context of the data.

⊕See the course slides on using the forcats package for changing the order of levels.

13. Create a new variable called rank_relevel where "tenure track" is the baseline level.

14. Fit a new linear model called m_rank_relevel to predict average professor evaluation score based on rank_relevel of the professor. This is the new (releveled) variable you created in Exercise 13. Based on the regression output, write the linear model and interpret the slopes and intercept in context of the data. Also determine and interpret the \(R^2\) of the model.

15. Create another new variable called tenure_eligible that labels "teaching" faculty as "no" and labels "tenure track" and "tenured" faculty as "yes".

16. Fit a new linear model called m_tenure_eligible to predict average professor evaluation score based on tenure_eligibleness of the professor. This is the new (regrouped) variable you created in Exercise 15. Based on the regression output, write the linear model and interpret the slopes and intercept in context of the data. Also determine and interpret the \(R^2\) of the model.