Data607 Assignment 3

library(tidyverse)

## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --

## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.6     v dplyr   1.0.7
## v tidyr   1.2.0     v stringr 1.4.0
## v readr   2.1.2     v forcats 0.5.1

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(stringr)

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”

provide code that identifies the majors that contain either “DATA” or “STATISTICS”

collegeMajor <- read.csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/college-majors/majors-list.csv", header = TRUE)

collegeMajor$Major %>% str_subset("DATA|STATISTICS")

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

Questions 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:

vec.text = c('[1] "bell pepper"  "bilberry"     "blackberry"   "blood orange"
[5] "blueberry"    "cantaloupe"   "chili pepper" "cloudberry"  
[9] "elderberry"   "lime"         "lychee"       "mulberry"    
[13] "olive"        "salal berry"')

vec.text.char = gsub("(\\n\\[\\d+\\])|(^\\[\\d+\\])", "", vec.text)
vec.text.char = strsplit(vec.text.char, '\\"')
vec.text.char = unlist(vec.text.char)
vec.text.char = vec.text.char[grep("[a-z]", vec.text.char)]
vec.text.char

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

Question3: The two exercises below are taken from R for Data Science, 14.3.5.1 in the on-line version:

• Describe, in words, what these expressions will match:
  • “(.)\1\1”
  • “(.)(.)\2\1”
  • “(..)\1”
  • “(.).\1.\1”
  • “(.)(.)(.).*\3\2\1”

Answer3A: one chracter repeated three times in a row

x1<- "zzz"
str_detect(x1,"(.)\\1\\1")

## [1] TRUE

Answer3B: One character appears, followed by a second character twice, followed by first character

x1<- "peep"
str_detect(x1,"(.)(.)\\2\\1")

## [1] TRUE

Answer3C:

Two character repeated one time

x1<- "soso"
str_detect(x1,"(..)\\1")

## [1] TRUE

Answer3D: First character, followed by any character followed by the first character, followed by any character, and then the first character again.

x1<- "gpgeg"
str_detect(x1,"(.).\\1.\\1")

## [1] TRUE

Answer3E:

• The string must have three of any character, with any other characters afterwards as long as it ends with the original 3 backwards. Example
  • mopepom
  • moppom
  • mopecpom

x1<- "moppom"
str_detect(x1,"(.)(.)(.).*\\3\\2\\1")

## [1] TRUE

Construct regular expressions to match words that:

1. Start and end with the same character

str_view("pump", "^(.).*\\1$", match = T)

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

str_view("salsa", "(..).*\\1", match = T)

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

str_view("settlements", "(.).*\\1.*\\1", match = T)