library(stringr)

Question 3

Part A

Use the tools of this chapter to rearrange the vector so that all elements conform to the standard first_name last_name.

raw.data <- "555-1239Moe Szyslak(636) 555-0113Burns, C. Montgomery555
-6542Rev. Timothy Lovejoy555 8904Ned Flanders636-555-3226Simpson,Homer5553642Dr. Julius Hibbert"
name <- unlist(str_extract_all(raw.data, "[[:alpha:]., ]{2,}"))
name
## [1] "Moe Szyslak"          "Burns, C. Montgomery" "Rev. Timothy Lovejoy"
## [4] "Ned Flanders"         "Simpson,Homer"        "Dr. Julius Hibbert"
# Change order
name2 <- str_replace_all(name, "(.+)(, .+)$", "\\2 \\1") 
name2 
## [1] "Moe Szyslak"           ", C. Montgomery Burns" "Rev. Timothy Lovejoy" 
## [4] "Ned Flanders"          "Simpson,Homer"         "Dr. Julius Hibbert"
# Remove commas
name3 <- str_replace_all(name2, "," , " ") 
name3
## [1] "Moe Szyslak"           "  C. Montgomery Burns" "Rev. Timothy Lovejoy" 
## [4] "Ned Flanders"          "Simpson Homer"         "Dr. Julius Hibbert"
#Remove titles
no_title <- str_replace(name3, "[[:alpha:]]{2,}\\. ", "")
no_title 
## [1] "Moe Szyslak"           "  C. Montgomery Burns" "Timothy Lovejoy"      
## [4] "Ned Flanders"          "Simpson Homer"         "Julius Hibbert"

Part B

Construct a logical vector indicating whether a character has a title (i.e., Rev. andDr.).

title_Check <- c(str_detect(name, "(^[A-z]{1,}\\. ).*"))
title_Check <- data.frame(name, title_Check)
names(title_Check) <- c("Name", "Has Title?")
title_Check
##                   Name Has Title?
## 1          Moe Szyslak      FALSE
## 2 Burns, C. Montgomery      FALSE
## 3 Rev. Timothy Lovejoy       TRUE
## 4         Ned Flanders      FALSE
## 5        Simpson,Homer      FALSE
## 6   Dr. Julius Hibbert       TRUE
Part C

Construct a logical vector indicating whether a character has a second name.

secondname_Check <- str_detect(name, " [A-z]{1,}\\. ")
secondname_Check <- data.frame(name, secondname_Check)
names(secondname_Check) <- c("Name", "Has Second Name?")
secondname_Check
##                   Name Has Second Name?
## 1          Moe Szyslak            FALSE
## 2 Burns, C. Montgomery             TRUE
## 3 Rev. Timothy Lovejoy            FALSE
## 4         Ned Flanders            FALSE
## 5        Simpson,Homer            FALSE
## 6   Dr. Julius Hibbert            FALSE

Question 4

Part A

[0-9]+\$ Strings that confirm to the above pattern are those that contain a series of numbers from 0 to 9 and ends with the $ sign.

Example:
example1 <- c("Juanelle1978$Anthony", "$123456", "987654321$123", "6789$", "$23sf")
example1 <- unlist(str_extract_all(example1, pattern = "[0-9]+\\$" ))
example1
## [1] "1978$"      "987654321$" "6789$"
Part B

\b[a-z]{1,4}\b Strings that confirm to the above pattern are those that matches one to four-character words in the input string ( the four character words words must not contain punctuation marks or digits and must be lowercase).

Example:
example2 <- c("Timothy", "i am a  boy", "123 abc doh rey me", "891 abcd efghi", "ABC abc XYZ xyz")
example2 <- str_extract_all(example2, pattern = "\\b[a-z]{1,4}\\b" )
example2
## [[1]]
## character(0)
## 
## [[2]]
## [1] "i"   "am"  "a"   "boy"
## 
## [[3]]
## [1] "abc" "doh" "rey" "me" 
## 
## [[4]]
## [1] "abcd"
## 
## [[5]]
## [1] "abc" "xyz"

Part C

.*?\.txt$ Strings that confirm to the above pattern are those that end with .txt. The above pattern is great for checking file types.

EXample:
example3<- c("Juanelle.txt", "911.txt",".txtMarks", "1234.txt", "Anthony Marks xyz.txt")
example3 <- str_extract_all(example3, pattern = ".*?\\.txt$" )
example3
## [[1]]
## [1] "Juanelle.txt"
## 
## [[2]]
## [1] "911.txt"
## 
## [[3]]
## character(0)
## 
## [[4]]
## [1] "1234.txt"
## 
## [[5]]
## [1] "Anthony Marks xyz.txt"
Part D

\d{2}/\d{2}/\d{4} Strings that confirm to the above pattern are those that begin with two digits immediately followed by a forward slash then immediately followed by two digits and a forward slash, then immediately followed by four digits. The output resembles a date format. Great for checking for the presence of dates.

Example
example4 <- c("09/12/2018", "ab/cd/ efgh", "09/ab/2018", "111/2/19654")
example4 <- str_extract_all(example4, pattern = "\\d{2}/\\d{2}/\\d{4}")
example4
## [[1]]
## [1] "09/12/2018"
## 
## [[2]]
## character(0)
## 
## [[3]]
## character(0)
## 
## [[4]]
## character(0)
Part E

<(.+?)>.+?</\1> Strings that confirm to the above pattern are those that confirm to the same pattern as open and close html tags.

Example:
example5<-c("<p> This is a paragraph</p>", "<p> This is a paragraph</z>", "<p> This is a paragraph", "This is a paragraph</p>", "<h1> This is a paragraph</h1>", "123abc")
example5 <- str_extract_all(example5, pattern = "<(.+?)>.+?</\\1>")
example5
## [[1]]
## [1] "<p> This is a paragraph</p>"
## 
## [[2]]
## character(0)
## 
## [[3]]
## character(0)
## 
## [[4]]
## character(0)
## 
## [[5]]
## [1] "<h1> This is a paragraph</h1>"
## 
## [[6]]
## character(0)

Question 9

The following code hides a secret message. Crack it with R and regular expressions. Hint: Some of the characters are more revealing than others!

“clcopCow1zmstc0d87wnkig7OvdicpNuggvhryn92Gjuwczi8hqrfpRxs5Aj5dwpn0TanwoUwisdij7Lj8kpf03AT5Idr3coc0bt7yczjatOaootj55t3Nj3ne6c4Sfek.r1w1YwwojigOd6vrfUrbz2.2bkAnbhzgv4R9i05zEcrop.wAgnb.SqoU65fPa1otfb7wEm24k6t3sR9zqe5fy89n6Nd5t9kc4fE905gmc4Rgxo5nhDk!gr”

A message was detected when all uppercase characters were extracted from the code. See below:

code<-"clcopCow1zmstc0d87wnkig7OvdicpNuggvhryn92Gjuwczi8hqrfpRxs5Aj5dwpn0TanwoUwisdij7Lj8kpf03AT5Idr3coc0bt7yczjatOaootj55t3Nj3ne6c4Sfek.r1w1YwwojigOd6vrfUrbz2.2bkAnbhzgv4R9i05zEcrop.wAgnb.SqoU65fPa1otfb7wEm24k6t3sR9zqe5fy89n6Nd5t9kc4fE905gmc4Rgxo5nhDk!gr"

secret_message<-unlist(str_extract_all(code, "[[:upper:].]{1,}"))

secret_message <-  str_replace_all(string =  secret_message, pattern =  "\\.", replacement = " ")

paste(secret_message, collapse = "")
## [1] "CONGRATULATIONS YOU ARE A SUPERNERD"