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”

library(stringr)
contains_data <- str_detect(college_majors$Major, fixed("DATA"))
college_majors[contains_data, ]

##    FOD1P                                    Major          Major_Category
## 52  2101 COMPUTER PROGRAMMING AND DATA PROCESSING Computers & Mathematics

There is one major wih ‘data’ in the major name.

contains_stats <- str_detect(college_majors$Major, fixed("STATISTICS"))
college_majors[contains_stats, ]

##    FOD1P                                         Major          Major_Category
## 44  6212 MANAGEMENT INFORMATION SYSTEMS AND STATISTICS                Business
## 59  3702               STATISTICS AND DECISION SCIENCE Computers & Mathematics

There are two majors with ‘statistics’ in the major name.

#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”)

fruits_orig <- '[1] "bell pepper"  "bilberry"     "blackberry"   "blood orange"

[5] "blueberry"    "cantaloupe"   "chili pepper" "cloudberry"  

[9] "elderberry"   "lime"         "lychee"       "mulberry"    

[13] "olive"        "salal berry"'
fruits_orig

## [1] "[1] \"bell pepper\"  \"bilberry\"     \"blackberry\"   \"blood orange\"\n\n[5] \"blueberry\"    \"cantaloupe\"   \"chili pepper\" \"cloudberry\"  \n\n[9] \"elderberry\"   \"lime\"         \"lychee\"       \"mulberry\"    \n\n[13] \"olive\"        \"salal berry\""

fruits_split <- unlist(str_extract_all(fruits_orig, pattern = "\"([a-z]+.[a-z]+)\""))
fruits_split

##  [1] "\"bell pepper\""  "\"bilberry\""     "\"blackberry\""   "\"blood orange\""
##  [5] "\"blueberry\""    "\"cantaloupe\""   "\"chili pepper\"" "\"cloudberry\""  
##  [9] "\"elderberry\""   "\"lime\""         "\"lychee\""       "\"mulberry\""    
## [13] "\"olive\""        "\"salal berry\""

fruits_clean <- str_remove_all(fruits_split, "\"")
fruits_clean

##  [1] "bell pepper"  "bilberry"     "blackberry"   "blood orange" "blueberry"   
##  [6] "cantaloupe"   "chili pepper" "cloudberry"   "elderberry"   "lime"        
## [11] "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

This will match any one character, followed by two repetitions, like “aaa” or “555”. The correct expression would be “(.)\1\1”.

“(.)(.)\2\1”

This will search for two characters repeated, except reverse. Like “abba” or “1331”.

(..)\1

This will search for two characters, repeated once, like “coco” or “1212”. The correct expression would be “(..)\1”.

“(.).\1.\1”

This will search for a five character term, three of which are the same, like “rarer” or “51525”.

"(.)(.)(.).*\3\2\1"

This will construct a set of characters that begin and end with the same three characters, except the second instance is reversed, like “racecar” or “12378724321”.

#4 Construct regular expressions to match words that:

Start and end with the same character.

__"(.).*\1"__

Contain a repeated pair of letters (e.g. “church” contains “ch” repeated twice.) __".([A-Za-z][A-Za-z]).\1.*"__

Contain one letter repeated in at least three places (e.g. “eleven” contains three “e”s.) “.([A-Za-z]).\1.\1.”