Week 3 Assignment

#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(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── 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

library(RCurl)

## 
## Attaching package: 'RCurl'
## 
## The following object is masked from 'package:tidyr':
## 
##     complete

x <- getURL("https://raw.githubusercontent.com/isaias-soto/CUNY_DAT607/08e7bcca8ddd127ac68612a511c7d05f8d16261e/majors-list.csv") 
major_list <- read.csv(text = x)
major_list[146,]

##     FOD1P                             Major Major_Category
## 146 bbbb  N/A (less than bachelor's degree)           <NA>

major_list <- major_list[-146,] # remove missing value

str_view(major_list$Major,"DATA|STATISTICS")

## [44] │ MANAGEMENT INFORMATION SYSTEMS AND <STATISTICS>
## [52] │ COMPUTER PROGRAMMING AND <DATA> PROCESSING
## [59] │ <STATISTICS> AND DECISION SCIENCE

major_list[c(44,52,59),]

##    FOD1P                                         Major          Major_Category
## 44  6212 MANAGEMENT INFORMATION SYSTEMS AND STATISTICS                Business
## 52  2101      COMPUTER PROGRAMMING AND DATA PROCESSING Computers & Mathematics
## 59  3702               STATISTICS AND DECISION SCIENCE Computers & Mathematics

#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 following code sets up the problem as a character vector with 14 elements:

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

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

Next, it seems we are to shrink this into a one-element vector. This can be achieved by the following:

str_view(str_flatten(fruits, ", "))

## [1] │ 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:

|Header|Description|

(.)\1\1 “(.)(.)\\2\\1” (..)\1 “(.).\\1.\\1” “(.)(.)(.).*\\3\\2\\1”

Exercise	Description
(.)\1\1	This would match words that have the same letter repeated 3 times, for example, “bbb”.
“(.)(.)\\2\\1”	This would match words where the second letter is repeated and this first letter is the start and end, for example, “abba”.
(..)\1	This would match words with any two letters with the same two letters repeated, for example, “cdcd”.
“(.).\\1.\\1”	This would match words that begin with any letter, followed by a different letter, followed by the first letter repeated, followed by any letter, and finally with followed by the first letter. For example, “abaca”.
“(.)(.)(.).*\\3\\2\\1”	This matches words where there are 3 different letters, optionally followed by zero to any number of characters, and end with the first three letters in reverse order. For example, “abcdcba” or “abccba”.

#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.)

Exercise	regex
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

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