RegEx Analysis

1. Using the 173 majors listed in fivethirtyeight.com’s College Majors

dataset, provide code that identifies the majors that contain either

“DATA” or “STATISTICS

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

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

data_statistics_majors <- majors %>% 
  filter(grepl('DATA', Major, ignore.case = TRUE) | 
         grepl('STATISTICS', Major, ignore.case = TRUE))

print(data_statistics_majors)

##   FOD1P                                         Major          Major_Category
## 1  6212 MANAGEMENT INFORMATION SYSTEMS AND STATISTICS                Business
## 2  2101      COMPUTER PROGRAMMING AND DATA PROCESSING Computers & Mathematics
## 3  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”)

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

print(fruits)

##  [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

“(.)(.)\2\1”

(..)\1

“(.).\1.\1”

“(.)(.)(.).*\3\2\1”

(.)\1\1

Three of the same character in sequence

I.e. ‘aaa’

“(.)(.)\2\1”

Two letters that repeat in reverse

I.e. ‘abba’ (like the band)

(..)\1

Two letters that repeat in sequence

I.e. ‘abab’

“(.).\1.\1”

A letter -> any letter -> the first letter -> any letter > first letter

I.e. ‘aXaYa’

“(.)(.)(.).*\3\2\1”

Three letters -> anything -> those three letters backwards

I.e. ‘abcXYZcba’

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.)

Words that start and end with the same letter:

# The ^ means start, $ means end
regex1 <- '^(.).*\1$'
print(regex1)  # Try it with words like 'anna' or 'eye'

## [1] "^(.).*\001$"

Words with a repeated pair of letters:

# Looking for any two letters that show up twice
regex2 <- '(..).*\1'
print(regex2)  # Works with words like 'murmur'

## [1] "(..).*\001"

Words with one letter showing up at least three times:

# Finding a letter that appears three times anywhere
regex3 <- '.*([A-Za-z]).*\1.*\1.*'
print(regex3)  # Try it with 'banana'

## [1] ".*([A-Za-z]).*\001.*\001.*"