Introduction

The objective of this assignment is to use regular expressions to manipulate and
analyze strings in R, as well as to become familiar with functions/packages utilized
in string manipulation.

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”

Answer:

There are three majors which include the words ‘Data’ or ‘Statistics’. To find these three majors I first loaded the data from github and then use ‘filter’ to obtain desired results.

Majors<- read_csv("https://raw.githubusercontent.com/fivethirtyeight/data/master/college-majors/majors-list.csv", show_col_types = FALSE) 
Majors_like_data_or_stats <- filter(Majors, Major %like% "STATISTICS" | Major %like% "DATA") 
Majors_like_data_or_stats

Question 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:

answer <-c(“bell pepper”, “bilberry”, “blackberry”, “blood orange”, “blueberry”, “cantaloupe”, “chili pepper”, “cloudberry”, “elderberry”, “lime”, “lychee”, “mulberry”, “olive”, “salal berry”) dput(answer)

Answer

#removing the '\'
df_fruit2 <-grep("\\[\\d+", invert = TRUE, value = TRUE, scan(text = 
'[1] "bell pepper"  "bilberry"     "blackberry"   "blood orange"
[5] "blueberry"    "cantaloupe"   "chili pepper" "cloudberry"  
[9] "elderberry"   "lime"         "lychee"       "mulberry"    
[13] "olive"        "salal berry"' , what = ""))

#removing the spaces
updated_string <- gsub("", "", df_fruit2)
dput(updated_string)
## c("bell pepper", "bilberry", "blackberry", "blood orange", "blueberry", 
## "cantaloupe", "chili pepper", "cloudberry", "elderberry", "lime", 
## "lychee", "mulberry", "olive", "salal berry")

Question 3

Describe, in words, what these expressions will match: Please note pattern1 ‘(.)\1\1’ and pattern3 ‘(..)\1’ did not return anything so i modified the expression by including an additional ’' where appropriate.

#patterns 
pattern1 <- "(.)\\1\\1"
pattern2 <- "(.)(.)\\2\\1"
pattern3 <- "(..)\\1"
pattern4 <- "(.).\\1.\\1"
pattern5 <- "(.)(.)(.).*\\3\\2\\1"

#data to search for patterns
character<-c('lll','he','m','nnn','123','10101','1239321','pep1pep','pepper','lll','lll')

Answer 3a
(.)\1\1
Defines a group (.).
Matches any single character and the repeats a character twice(\1\1).
The end result will be same character repeated three instances, ie XXX

character %>% 
  str_subset(pattern1)
## [1] "lll" "nnn" "lll" "lll"

Answer #3b
“(.)(.)\2\1”
Defines two groups fo any characters (.)(.)
Refers to a pair of two characters except the second pair is in reverse order.
The end result will be the original two characters followed by the original two characters is reverse order, ie eppe.

character %>% 
  str_subset(pattern2)
## [1] "pepper"

Answer #3c
(..)\1
Defines a group of two characters (..).
Repeats a the group of in the original order ‘\1’, ie olol.

character %>% 
  str_subset(pattern3)
## [1] "10101"

Answer #3d
“(.).\1.\1”
Defines a group of any character (.).
Followed by a random character ‘.’
Followed by the original character’\1’
Followed by a random character ‘.’
Followed by the original character’\1’

character %>% 
  str_subset(pattern4)
## [1] "10101"   "pep1pep"

Answer #3e
“(.)(.)(.).\3\2\1”
Defines a group of two characters (..).
Followed by zero or more characters of any kind ’’.
Followed by the same three characters in reverse order ‘\3\2\1’.

character %>% 
  str_subset(pattern5)
## [1] "1239321" "pep1pep"

QUESTION 4

Construct regular expressions to match words that:

Answer #4a
Start and end with the same character.

#data to search for patterns
characterfinal<-data.frame('kedccdck', 'aaaa', 'abbb', 'ZuZ','abba',     
                           'pep','pepe','1212','church','eleven')

pattern6 <- "^k.+k$|^a.+a$|^b.+b$|^c.+c$|^d.+d$|^e.+e$|^f.+f$|^g.+g$|^h.+h$
|^i.+i$|^j.+j$|^k.+k$|^l.+l$|^m.+m$|^n.+n$|^o.+o$|^p.+p$|^q.+q$|^r.+r$|^s.+s$
|^t.+t$|^u.+u$|^v.+v$|^w.+w$|^x.+x$|^y.+y$|^z.+z$"

characterfinal %>% 
  str_subset(pattern6)
## [1] "kedccdck" "aaaa"     "abba"     "pep"

Answer #4b
Contain a repeated pair of letters (e.g. “church” contains “ch” repeated twice.)

pattern7 <- "([A-Za-z][A-Za-z]).*\\1"

characterfinal %>% 
  str_subset(pattern7)
## [1] "kedccdck" "aaaa"     "pepe"     "church"

Answer #4c
Contain one letter repeated in at least three places (e.g. “eleven” contains three “e”s.)

pattern8 <- "([A-Za-z]).*\\1.*\\1"

characterfinal %>% 
  str_subset(pattern8)
## [1] "kedccdck" "aaaa"     "abbb"     "eleven"

Conclusion

String manipulation utilizing regular expression is a powerful tool.
When applied correctly, regular expressions can streamline code, be used in a
variety of different languages, ie r, python, and have a large number of use cases.