need help understanding a Rmarkdown
Midterm Instructions This RMarkdown document is the entirety of the midterm exam. Like the problem sets in this class, you will be working with actual social science data and using the statistical and conceptual tools you’ve learned so far this term to answer social science questions. Please submit the midterm exam through the Canvas midterm submission portal by Monday, November 4th at 8pm. Late submissions can be submitted up to 24 hours late for a 10% reduction. Other key rules for midterm: • Submit a compiled PDF, not an .rmd document. If you are having trouble compiling/submitting your PDF, please email Prof. Waight before the deadline. • Prof. Waight will be available over email to answer clarifying questions only. If Prof. Waight deems a question important enough to share with the entire class, she will send out a class announcement over email during the midterm exam. Please check your email to make sure you don’t miss these. • Absolutely no collaboration with other students is allowed during the midterm. This means no discussing approaches, no sharing or comparing code/answers. We also do not allow consultation with Data Services. As with all class assignments we do not allow the use of generative AI or consulting with “friends on the internet.” • Please edit the header of this document to include your name. Also rename the compiled document with your name. We will take points off if you don’t do this. • We will take points off generally for any coding errors or poor coding conventions (for example not putting breaks in the code, so that it runs off the page). Please answer all text-based questions in full sentences. When answering interpretation-based questions, please make sure to include all elements we referenced in lecture: – Experiments: state assumption, justify assumption, state treatment in substantive language (i.e. don’t just say the name of the variable in the dataset, describe what the variable represents), state outcome in substantive language, indicate direction and size of causal effect, use appropriate unit of measurement, use causal language. – Descriptive (all other interpretations): state variable(s) in substantive language, include estimate(s), use appropriate units of measurement, indicate the sample the data is coming from. • For all visualizations, axis and title labels should be substantively interpretable (i.e. provide a substantive label for what a given variable represents, don’t just include the name of the variable). • Make sure to include all code in codeblocks and all text answers outside of codeblocks. We have included the RMarkdown guide as a reference in this Posit Cloud project. Part 1: Eviction, Debt, and Perceptions of Neighboorhood Safety As argued by sociologist Matthew Desmond and others, eviction has profound consequences for American life. Read More