Overview

#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”

#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 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" #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.)

Preparation Work

Source dataset

This step is to source the data set in R program from github location

library (readr)
dataUrl="https://raw.githubusercontent.com/rnivas2028/MSDS/Data607/Assignment3/majors-list.csv"
majorDataSet <- read.csv(dataUrl, header = TRUE, sep = ",", stringsAsFactors = FALSE)

Excercise 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”

key='DATA|STATISTICS'
majorSubDataSet <- majorDataSet$Major[grep(key, majorDataSet$Major)]
print(majorSubDataSet)
## [1] "MANAGEMENT INFORMATION SYSTEMS AND STATISTICS"
## [2] "COMPUTER PROGRAMMING AND DATA PROCESSING"     
## [3] "STATISTICS AND DECISION SCIENCE"

Excercise 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”)

dataSet <- data.frame(c("bell pepper", "bilberry", "blackberry","blood orange","blueberry","cantalope","chili pepper","cloudberry","elderberry","lime","lychee","mulberry","olive","salal berry"))
cat(paste(dataSet), collapse=",")
## c("bell pepper", "bilberry", "blackberry", "blood orange", "blueberry", "cantalope", "chili pepper", "cloudberry", "elderberry", "lime", "lychee", "mulberry", "olive", "salal berry") ,

Excercise 3

Describe, in words, what these expressions will match:

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

Answers: (.)\1\1 This regular expression is used to match a pattern in a strings with character repeats in it.

“(.)(.)\2\1” This regular expression is used to match strings with a set of 4 characters with 2 characters attached to the same 2 characters in reverse order(e.g: otto)

(..)\1 This regular expression is used to match any strings that have a repeated pair of letters

“(.).\1.\1” This regular expression is used to match any strings that has the same character repeat 3 times and they are all separated by one character. (ex: papaya)

"(.)(.)(.).*\3\2\1" This regular expression is used to match any strings with 3 characters followed by zero or more characters followed by the original 3 characters in reverse order.. (ex:abccba)

Excercise 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.)

dataSet <-c("cell","apple","dog","ada","bob","sense","church","banana","pepperoni","red","green","England","eleven","ten","twelve","soso","oso","bandana", "Louisiana", "Missouri", "Mississippi", "Connecticut", "google", "conscience","dalda","short","Evon","ele","Tort")
#4.1
expression="^(.)((.*\\1$)|\\1?$)"
result <- str_subset(dataSet,expression )
result
## [1] "ada" "bob" "oso" "ele"
#4.2
expression="([A-Za-z][A-Za-z]).*\\1"
result <- str_subset(dataSet,expression )
result
## [1] "sense"       "church"      "banana"      "pepperoni"   "soso"       
## [6] "bandana"     "Mississippi" "dalda"
#4.3
expression="([A-Za-z]).*\\1.*\\1"
result <- str_subset(dataSet,expression )
result
## [1] "banana"      "pepperoni"   "eleven"      "bandana"     "Mississippi"
## [6] "conscience"