Regular Expressions And R

library(tidyverse)
library(stringr)
library(DT)

Question 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”

majors <- read_csv('https://raw.githubusercontent.com/greerda/Data607/main/majors-list.csv')

## Rows: 174 Columns: 3
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): FOD1P, Major, Major_Category
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

View(majors)
datatable(majors)

str_subset(majors$Major, "DATA|STATISTICS")

## [1] "MANAGEMENT INFORMATION SYSTEMS AND STATISTICS"
## [2] "COMPUTER PROGRAMMING AND DATA PROCESSING"     
## [3] "STATISTICS AND DECISION SCIENCE"

str_detect(majors$Major_Category,"(.).*\1")

##   [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
##  [97] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [109] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [121] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [133] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [145] FALSE    NA FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [157] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [169] FALSE FALSE FALSE FALSE FALSE FALSE

Question 2

Write code that transforms the data below:

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

extracted_str <- str_extract_all(input_str, pattern = "([a-z]+.[a-z]+)", simplify = FALSE)
values<-c(unlist(extracted_str, use.names = FALSE))
new_string <- toString(values)
c(new_string)

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

Question 3

Describe, in words, what these expressions will match:

“(.)(.)\\2\\1” two single character groups and the reverse order of the character groups.This example has escape characters “\”

(..)\1 finds a repeating group of 2 different numbers

(.)\\1\\1 one character group repeated once and then repeated again 3 time in total

“(.).\\1.\\1” any character in the first group must be repeated twice in the second group. This example has escape characters “\”

“(.)(.)(.).*\\3\\2\\1” three of groups of any number of characters groups. The first and last groups having their individual characters in reverse order. There can be any number of values between two sets of three groups. This example has escape characters “\”

Question 4

Construct regular expressions to match words that:

A. Start and end with the same character

^(.)((.*\1$)|\1$)

B. Contain repeated pairs of letters

([A-Za-z][A-Za-z]).*\1

C. Contain that one letter repeated in at least three places (e.g. “eleven” contains three “e”s.)

([a-z]).*\1.*\1