Please deliver links to an R Markdown file (in GitHub and rpubs.com) with solutions to the problems below. You may work in a small group, but please submit separately with names of all group participants in your submission. #1. Using the 173 majors listed in fivethirtyeight.com’s College Majors dataset [https://fivethirtyeight.com/features/the-economic-guide-to-picking-a-college-major/], provide code that identifies the majors that contain either "DATA" or "STATISTICS" #2 Write code that transforms the data below: [1] "bell pepper" "bilberry" "blackberry" "blood orange" [5] "blueberry" "cantaloupe" "chili pepper" "cloudberry" [9] "elderberry" "lime" "lychee" "mulberry" [13] "olive" "salal berry" Into a format like this: c("bell pepper", "bilberry", "blackberry", "blood orange", "blueberry", "cantaloupe", "chili pepper", "cloudberry", "elderberry", "lime", "lychee", "mulberry", "olive", "salal berry") The two exercises below are taken from R for Data Science, 14.3.5.1 in the on-line version: #3 Describe, in words, what these expressions will match: (.)\1\1 "(.)(.)\\2\\1" (..)\1 "(.).\\1.\\1" "(.)(.)(.).*\\3\\2\\1" #4 Construct regular expressions to match words that: Start and end with the same character. Contain a repeated pair of letters (e.g. "church" contains "ch" repeated twice.) Contain one letter repeated in at least three places (e.g. "eleven" contains three "e"s.)

The current analysis focuses on the most frequent words found in the Blogs, News, and Twitter datasets, using a random sample of each.