Interview and Thesis


   I missed the first half of thesis because I went to a college interview at Starbucks. At the beginning of the week, a Stanford alum emailed me about scheduling an interview sometime during the week. We agreed to have it on Thursday at 8:30 a.m. She had to fill out a report by Sunday.

   I arrived ten minutes early. I bought us both two water bottles. When she arrived, she immediately went straight to the interview. At first, I was nervous, but as the interview progressed, I felt more confident about my answers. It transformed from an interview to a conversation. When it ended, she informed me that I would be a great candidate for the school, although they do reject a lot of good candidates, but that she would write me the best report she can. She liked my answers and the specificity of them, and my kindness for giving her a water bottle (she was feeling a little under the weather).
 
   I discussed the topics that I did not get to address much in my application: my thesis project, my bible study, and some of the hardships I have encountered over the past couple of years. I felt confident about my interview, and I will find out the decision on December 15.

   Now onto thesis...

   My mentor advised me to become very familiar with R. She recommended I read up on the "dplyr" and "tidyr" packages. Both packages have functions that facilitate data cleaning and data manipulation. Operators like "%>%" forward a value into the function, but it is very efficient for if I want to send values through multiple functions.

   I familiarized myself with the tidyr package. It has four operations:
          gather()- shapes the columns from a "wide" format to a "long" format
          spread()-shapes the columns from a "long" format to a "wide" format
          separate()-creates multiple columns from a single column
          unite()-creates a single column from multiple columns

   There are seven operations for the dplyr package:
          select()-select variables
          filter()-filters data by certain conditions
          group_by()- groups the data by qualitative variables
          summarise()-"performs summary statistics on the variables"
          arrange()-orders the data
          join()-"joins two datasets together"
          mutate()-"creates new variables"

   This weekend, I am going to have a meeting with my mentor about the direction for my thesis project. Hopefully, I can get access to the data.

 



Sources Used:
https://www.computerworld.com/article/2921176/business-intelligence/great-r-packages-for-data-import-wrangling-visualization.html

https://www.computerworld.com/article/2497143/business-intelligence/business-intelligence-beginner-s-guide-to-r-introduction.html#tk.ctw-infsb

https://www.computerworld.com/resources/106345/advanced-beginners-guide-to-r.html#tk.ctw-infsb

https://rpubs.com/bradleyboehmke/data_wrangling
 

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