Data 607 Assignment Week 3

Please deliver links to an R Markdown file (in GitHub and rpubs.com) with solutions to the problems below. You may work in a small group, but please submit separately with names of all group participants in your submission.

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”

#Install package
#install.packages("stringr")

#Import libraries
library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✓ ggplot2 3.3.5     ✓ purrr   0.3.4
## ✓ tibble  3.1.4     ✓ dplyr   1.0.7
## ✓ tidyr   1.1.3     ✓ stringr 1.4.0
## ✓ readr   2.0.1     ✓ forcats 0.5.1

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

library(stringr)
library(dplyr)

#Load the dataset

theUrl <- "https://raw.githubusercontent.com/fivethirtyeight/data/master/college-majors/majors-list.csv"

majors_df <- read.csv(file = theUrl, header = TRUE, sep = ",")

head(majors_df)

##   FOD1P                                 Major                  Major_Category
## 1  1100                   GENERAL AGRICULTURE Agriculture & Natural Resources
## 2  1101 AGRICULTURE PRODUCTION AND MANAGEMENT Agriculture & Natural Resources
## 3  1102                AGRICULTURAL ECONOMICS Agriculture & Natural Resources
## 4  1103                       ANIMAL SCIENCES Agriculture & Natural Resources
## 5  1104                          FOOD SCIENCE Agriculture & Natural Resources
## 6  1105            PLANT SCIENCE AND AGRONOMY Agriculture & Natural Resources

#Use str_detect to filter out majors containing "DATA" and "STATISTICS"
df <- majors_df
df %>%
  filter(str_detect(Major, "DATA|STATISTICS"))

##   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”)

#Transform from text string to character vector using str_c
fruits <- str_c("bell pepper", "bilberry", "blackberry", "blood orange", "blueberry", "cantaloupe", "chili pepper", "cloudberry", "elderberry", "lime", "lychee", "mulberry", "olive", "salal berry", sep = ",")
fruits

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

# This is using str_extract_all

fruits <- c("bell pepper", "bilberry", "blackberry", "blood orange", "blueberry", "cantaloupe", "chili pepper", "cloudberry", "elderberry", "lime", "lychee", "mulberry", "olive", "salal berry") 
fruits <- unlist(str_extract_all(fruits, pattern = "(.+?)+", simplify = FALSE))
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"

#Bringing up the fruits data frame to show the regex patterns
head(fruit)

## [1] "apple"       "apricot"     "avocado"     "banana"      "bell pepper"
## [6] "bilberry"

• (.)\1\1: This first regex won’t return anything.

#Assigning the pattern and using the fruit data frame to see what it returns with str_subset and str_match


#pattern <- (.)\1\1 
#fruit %>% 
  #str_subset(pattern)%>%
  #str_match(pattern)

• “(.)(.)\2\1”: This second regex returns fruits that have repeated pair of letters and returns the second letter first followed by the first letter.

#Assigning the pattern and using the fruit data frame to see what it returns with str_subset and str_match


#pattern <- "(.)(.)\\2\\1"
#fruit %>% 
  #str_subset(pattern)%>%
  #str_match(pattern)

• (..)\1: This regex does not return anything.

#Assigning the pattern and using the fruit data frame to see what it returns with str_subset and str_match


#pattern <- (..)\1
#fruit %>% 
  #str_subset(pattern)%>%
  #str_match(pattern)

• “(.).\1.\1”: This regex returns the fruit that end and start with the same letter and returns that letter once.

#Assigning the pattern and using the fruit data frame to see what it returns with str_subset and str_match


#pattern <- "(.).\\1.\\1"
#fruit %>% 
  #str_subset(pattern)%>%
  #str_match(pattern)

• **_"(.)(.)(.).*\3\2\1": This last regex does not return anything._**

#Assigning the pattern and using the fruit data frame to see what it returns with str_subset and str_match


#pattern <- "(.)(.)(.).*\\3\\2\\1"
#fruit %>% 
  #str_subset(pattern)%>%
  #str_match(pattern)

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.)

str_view("anemia","(.)(.*)\\1$")

str_view("church", "([A-Za-z][A-Za-z]).*\\1" )

str_view("eleven", "([A-Za-z]).*\\1.*\\1.")