Assignment 3 Daniel DeBonis

Question 1

First, let us import the list of majors from github

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats   1.0.0     ✔ readr     2.1.5
## ✔ ggplot2   3.5.1     ✔ stringr   1.5.1
## ✔ lubridate 1.9.4     ✔ tibble    3.2.1
## ✔ purrr     1.0.4     ✔ tidyr     1.3.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

majors <- read.csv("https://raw.githubusercontent.com/fivethirtyeight/data/refs/heads/master/college-majors/majors-list.csv")

Then we can use the filter function to return only majors that include the words “data” or “statistics”

data_stat <- majors |>
          filter(grepl("DATA|STATISTICS", Major))

There are three identified majors that include the word “data” or “statistics.”

Question 2

We need to transform this list of fruit

fruit <- c("bell pepper", "bilberry", "blackberry", "blood orange", "blueberry", "cantaloupe", "chili pepper", "cloudberry", "elderberry", "lime", "lychee", "mulberry", "olive", "salal berry")
print(fruit)

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

As it stands now, this list is not printed as a single line of text. We can transform it with the flatten string function. To correctly punctuate and space out the list, a comma and a space is added between each entry on the list.

vecfruit <- str_flatten(fruit, ", ")
print(vecfruit)

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

Question 3

If the following is entered into R, what do the expressions match? (.)\1\1 One character is presented 3 times in a row

“(.)(.)\2\1” Two characters followed by the same two characters reversed

(..)\1 A set of two characters repeating

“(.).\1.\1” A character, followed by another character, then the first character, another character, and finally the first one again.

“(.)(.)(.).*\3\2\1” Three characters followed by anything, followed by the first characters in reverse

Question 4

I will use the same words dataset used in the textbook in order to test the accuracy of the expressions.

Start and end with the same character.

words <- stringr::words
str_view(words, "^(.).*\\1$")

##  [36] │ <america>
##  [49] │ <area>
## [209] │ <dad>
## [213] │ <dead>
## [223] │ <depend>
## [258] │ <educate>
## [266] │ <else>
## [268] │ <encourage>
## [270] │ <engine>
## [278] │ <europe>
## [283] │ <evidence>
## [285] │ <example>
## [287] │ <excuse>
## [288] │ <exercise>
## [291] │ <expense>
## [292] │ <experience>
## [296] │ <eye>
## [386] │ <health>
## [394] │ <high>
## [450] │ <knock>
## ... and 16 more

Contain a repeated pair of letters (e.g. “church” contains “ch” repeated twice.)

str_view(words, "(..).*\\1")

##  [48] │ ap<propr>iate
## [152] │ <church>
## [181] │ c<ondition>
## [217] │ <decide>
## [275] │ <environmen>t
## [487] │ l<ondon>
## [598] │ pa<ragra>ph
## [603] │ p<articular>
## [617] │ <photograph>
## [638] │ p<repare>
## [641] │ p<ressure>
## [696] │ r<emem>ber
## [698] │ <repre>sent
## [699] │ <require>
## [739] │ <sense>
## [858] │ the<refore>
## [903] │ u<nderstand>
## [946] │ w<hethe>r

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

str_view(words, "(.).*\\1.*\\1")

##  [48] │ a<pprop>riate
##  [62] │ <availa>ble
##  [86] │ b<elieve>
##  [90] │ b<etwee>n
## [119] │ bu<siness>
## [221] │ d<egree>
## [229] │ diff<erence>
## [233] │ di<scuss>
## [265] │ <eleve>n
## [275] │ e<nvironmen>t
## [283] │ <evidence>
## [288] │ <exercise>
## [291] │ <expense>
## [292] │ <experience>
## [423] │ <indivi>dual
## [598] │ p<aragra>ph
## [684] │ r<eceive>
## [696] │ r<emembe>r
## [698] │ r<eprese>nt
## [845] │ t<elephone>
## ... and 2 more