You are currently in the GitHub repository (repo) for HW-2. You must have completed all the steps in Setting Up.

• You will perform an Exploratory Data Analysis (EDA) using visualizations and tables. What do I mean by EDA? As explained here, it is used as a preliminary analysis tool for
  • the detection of mistakes
  • checking of assumptions
  • preliminary selection of appropriate models
  • determining relationships among the explanatory variables, and
  • assessing the direction and rough size of relationships between explanatory and outcome variables.
• Loosely speaking, any method of looking at data that does not include formal statistical modeling and inference falls under the term EDA. It is just as, if not more, important as an actual analysis.
• Working with categorical variables, especially when the cateogories are super messy.
• Answering open-ended questions with no explicit "finish line". I have set some boundaries however so that you can't get too lost.

1. Follow the same workflow as in HW-0 for HW-2.
2. Do not submit a HW-2.Rmd file that does not knit.
3. I anticipate you spending between 8-12 total (across all submissions) on this homework.

Result of a Python script that scraped the OkCupid website. We consider 59K users who were:

• members on 2012/06/26
• within 25 miles of SF
• online in the last year
• have at least one photo

Their public profiles were pulled on 2012/06/30. i.e. only data that’s visible to the public. Also included are essay question response data in variables essay0 through essay9. The codebook for the data is available here. Note:

• Usernames were excluded from data set. However, this is not a guarantee of de-anonymization.
• Permission to use this data was granted from OkCupid.

Run the following code once to download the OkCupid data from GitHub into your HW-2 RStudio project i.e. into your HW-2 folder. After you run this, there should be a file profiles.csv in the same directory as all your HW-2 files.

url <- "https://github.com/rudeboybert/JSE_OkCupid/blob/master/profiles.csv.zip?raw=true"
temp_zip_file <- tempfile()
download.file(url, temp_zip_file)
unzip(temp_zip_file, "profiles.csv")