Editing Statistics and Statistical Programming (Winter 2021)/Problem set 11 (section)

== Programming Challenges ==

For the programming challenges, we'll re-analyze data from the following (Halloween-appropriate!) study:
:: Aronow PM, Karlan D, Pinson LE. (2018). The effect of images of Michelle Obama’s face on trick-or-treaters’ dietary choices: A randomized control trial. ''PLoS ONE'' 13(1): e0189693. [https://doi.org/10.1371/journal.pone.0189693 https://doi.org/10.1371/journal.pone.0189693]

===PC1. Access and import the data ===
* '''Download the dataset''' from this URL at the [https://dataverse.harvard.edu/ Harvard Dataverse]: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/2NJV2P
* Once you have it, you will want to familiarize yourself with the experimental treatment and key details of the research design from the (short) article linked above (and included on the required tasks for today on the syllabus).
* Import the data into R. Depending on the file format you encounter, you may need to install the <code>readstata13</code> package and identify an appropriate function with which to do so.

===PC2. Explore and cleanup the data=== 
Get to know your dataset. Take a look at the codebook if necessary and make sure you have the two columns of the dataset that correspond to the experimental treatment (being presented with Michelle Obama's face or not) and the outcome (whether or not trick-or-treaters picked up fruit). Don't worry about any of the other measures for now.
===PC3. Summarize key variables===
Create a two-way contingency table summarizing these two variables. Make sure your table has easily understandable column and row names.
===PC4. Test for differences between groups ===
Construct and perform a statistical hypothesis test to determine whether or not the two groups are dependent. State your hypotheses clearly. Report and interpret the results of your test and be prepared to discuss your findings. Please note that the paper uses a variety of techniques including linear regression and incorporates other variables, but you should use estimators and tests we read about in ''OpenIntro'' §6 last week.
===PC5. Replicate a figure===
Try to reproduce the top panel of Figure 1 using the same two columns of the dataset (by ignoring year and the other variables we are, in effect, working with the "pooled" sample). If you cannot reproduce that portion of the figure (or something like it), try to at least reproduce the values presented in it.
===PC6. Export a table===
We've used RMarkdown to handle reproducible data analysis and export thus far, but it's also often important to export tables directly into your word processor or typesetting software without cutting and pasting the contents of individual cells by hand. Write R code that exports the ''output'' of your table from PC4. There are a bunch of functions you can use to do this. I would likely use the <code>xtable</code> package to generate HTML and/or LaTeX output, but I think that the Base-R <code>write.table()</code> function for export into Excel could do the job just as well.