stringr_exercise.R

# load package
library(stringr)

# prepare the exercises data
addresses <- c("14 Pine Street, Los Angeles", "152 Redwood Street, Seattle", "8 Washington Boulevard, New York")
products <- c("TV  ", " laptop", "portable  charger", "Wireless Keybord", "   HeadPhones   ")
long_sentences <- stringr::sentences[1:10]
field_names <- c("order_number", "order_date", "customer_email", "product_title", "amount")
employee_skills <- c("John Bale (Beginner)", "Rita Murphy (Pro)", "Chris White (Pro)", "Sarah Reid (Medium)")

####################
#                  #
#    Exercise 1    #
#                  #
####################
str_to_lower(string = addresses)

## [1] "14 pine street, los angeles"      "152 redwood street, seattle"     
## [3] "8 washington boulevard, new york"

####################
#                  #
#    Exercise 2    #
#                  #
####################
str_extract(string = addresses, pattern = "[:digit:]+")

## [1] "14"  "152" "8"

####################
#                  #
#    Exercise 3    #
#                  #
####################
str_split(string = addresses, pattern = ", ", simplify = T)

##      [,1]                     [,2]         
## [1,] "14 Pine Street"         "Los Angeles"
## [2,] "152 Redwood Street"     "Seattle"    
## [3,] "8 Washington Boulevard" "New York"

####################
#                  #
#    Exercise 4    #
#                  #
####################
str_split(string = addresses, pattern = "(?<=[:digit:]) |, ", simplify = T)

##      [,1]  [,2]                   [,3]         
## [1,] "14"  "Pine Street"          "Los Angeles"
## [2,] "152" "Redwood Street"       "Seattle"    
## [3,] "8"   "Washington Boulevard" "New York"

####################
#                  #
#    Exercise 5    #
#                  #
####################
str_extract_all(string = long_sentences, pattern = "^T[A-z]+|[A-z]+s\\.$")

## [[1]]
## [1] "The"     "planks."
## 
## [[2]]
## character(0)
## 
## [[3]]
## character(0)
## 
## [[4]]
## [1] "These"
## 
## [[5]]
## [1] "bowls."
## 
## [[6]]
## [1] "The"
## 
## [[7]]
## [1] "The"
## 
## [[8]]
## [1] "The"
## 
## [[9]]
## [1] "us."
## 
## [[10]]
## character(0)

####################
#                  #
#    Exercise 6    #
#                  #
####################
str_trunc(string = long_sentences, width = 22, ellipsis = "..")

##  [1] "The birch canoe slid.." "Glue the sheet to th.."
##  [3] "It's easy to tell th.." "These days a chicken.."
##  [5] "Rice is often served.." "The juice of lemons .."
##  [7] "The box was thrown b.." "The hogs were fed ch.."
##  [9] "Four hours of steady.." "Large size in stocki.."

####################
#                  #
#    Exercise 7    #
#                  #
####################
str_to_upper(string = str_squish(string = products))

## [1] "TV"               "LAPTOP"           "PORTABLE CHARGER"
## [4] "WIRELESS KEYBORD" "HEADPHONES"

####################
#                  #
#    Exercise 8    #
#                  #
####################
str_to_title(string = str_replace_all(string = field_names, pattern = "_", replacement = " "))

## [1] "Order Number"   "Order Date"     "Customer Email" "Product Title" 
## [5] "Amount"

####################
#                  #
#    Exercise 9    #
#                  #
####################
str_pad(field_names, width = max(str_length(field_names)), side = "left")

## [1] "  order_number" "    order_date" "customer_email" " product_title"
## [5] "        amount"

####################
#                  #
#    Exercise 10   #
#                  #
####################
str_match(string = employee_skills, pattern = "([A-z ]*) \\((Pro|Medium)\\)")[, 2:3]

##      [,1]          [,2]    
## [1,] NA            NA      
## [2,] "Rita Murphy" "Pro"   
## [3,] "Chris White" "Pro"   
## [4,] "Sarah Reid"  "Medium"