Guide to Swift Strings Sample Code

This repository contains sample code used in the Flight School Guide to Swift Strings.

• Chapter 2: Working with Strings in Swift
• Chapter 3: Swift String Protocols and Supporting Types
• Chapter 4: Working with Foundation String APIs
• Chapter 5: Binary-to-Text Encoding
• Chapter 6: Parsing Data From Text
• Chapter 7: Natural Language Processing

Chapter 2

String Literals

You can construct string values in Swift using string literals. This Playground has examples of each variety, from the conventional, single-line to the raw, multi-line.

let multilineRawString = #"""
 \-----------------------\
  \                       \
   \      ___              \
    \    (_  /'_ /_/        \        __
     \   /  (/(//)/          \       | \
      >      _/               >------|  \       ______
     /     __                /       --- \_____/**|_|_\____  |
    /     (  _ /     /      /          \_______ --------- __>-}
   /     __)( /)()()(      /              /  \_____|_____/   |
  /                       /               *         |
 /-----------------------/                         {o}
"""#

String Indexes

Swift strings have opaque index types. One consequence of this is that you can't access character by integer position directly, as you might in other languages. This Playground shows various strategies for working with string indices and ranges.

let string = "Hello"

string[string.startIndex] // "H"
string[string.index(after: string.startIndex)] // "e"
string[string.index(string.startIndex, offsetBy: 4)] // "o"

Canonical Equivalence

In Swift, two

String

let precomposed = "expos\u{00E9}" // é LATIN SMALL LETTER E WITH ACUTE
let decomposed = "expose\u{0301}" // ´ COMBINING ACUTE ACCENT

precomposed == decomposed
precomposed.elementsEqual(decomposed) // true
precomposed.unicodeScalars.elementsEqual(decomposed.unicodeScalars) // false

Unicode Views

Swift

String

let string = "東京 🇯🇵"
for unicodeScalar in character.unicodeScalars {
    print(unicodeScalar.codePoint, terminator: "\t")
}

Character Number Values

In Swift 5, you can access several Unicode properties of

Character

// U+2460 CIRCLED DIGIT ONE
("①" as Character).isNumber // true
("①" as Character).isWholeNumber // true
("①" as Character).wholeNumberValue // 1

Emoji Detection

For more direct access to the aforementioned character information, you can do so through the

properties

Unicode.Scalar

isEmoji

("👏" as Unicode.Scalar).properties.isEmoji // true

Chapter 3

String as **___**

In Swift,

String

Sequence

Collection

BidirectionalCollection

RangeReplaceableCollection

StringProtocol

Each of the protocols mentioned has their own Playground demonstrating the specific functionality they provide.

"Boeing 737-800".filter { $0.isCased }
                .map { $0.uppercased() }
["B", "O", "E", "I", "N", "G"]

Unicode Logger

The

print

TextOutputStream

var logger = UnicodeLogger()
print("👨‍👩‍👧‍👧", to: &logger)

// 0: 👨 U+1F468  MAN
// 1: ‍ U+200D    ZERO WIDTH JOINER
// 2: 👩 U+1F469  WOMAN
// 3: ‍ U+200D    ZERO WIDTH JOINER
// 4: 👧 U+1F467  GIRL
// 5: ‍ U+200D    ZERO WIDTH JOINER
// 6: 👧 U+1F467  GIRL

Stderr Output Stream

Text output streams can also be used to direct print statements from the default

stdout

print

stderr

var standardError = StderrOutputStream()
print("Error!", to: &standardError)

Booking Class

Swift allows any type that conforms to

ExpressibleByStringLiteral

BookingClass

("J" as BookingClass) // Business Class

Flight Code

Types conforming to the

LosslessStringConvertible

String

FlightCode

LosslessStringConvertible

ExpressibleByStringLiteral

let flight: FlightCode = "AA 1"

flight.airlineCode
flight.flightNumber
FlightCode(String(flight))

Unicode Styling

Swift 5 makes it possible to customize the behavior of interpolation in string literals by way of the

ExpressibleByStringInterpolation

StyledString

let name = "Johnny"
let styled: StyledString = """
Hello, \(name, style: .fraktur(bold: true))!
"""

print(styled)

Chapter 4

Range Conversion

Objective-C APIs that take

NSString

NSString

String

NSRange

Range

import Foundation

let string = "Hello, world!"
let nsRange = NSRange(string.startIndex..<string.endindex in: string let range="Range(nsRange,">
Localized String Operations

Foundation augments the Swift 
String
 type
by providing localized string operations,
including
case mapping,
searching,
and comparison.
Be sure to use localized string operations
(ideally, the 
standard
 variant, if applicable)
when working with text written or read by users.
import Foundation

"Éclair".contains("E") // false
"Éclair".localizedStandardContains("E") // true

Numeric String Sorting

Another consideration for localized string sorting is how to handle numbers.
By default, strings sort digits lexicographically;
7 follows 3, but 7 also follows 36.
This Playground demonstrates proper use of the

localizedStandardCompare
 comparator,
which is what Finder uses to sort filenames.
import Foundation

let files: [String] = [
    "File 3.txt",
    "File 7.txt",
    "File 36.txt"
]
let order: ComparisonResult = .orderedAscending
files.sorted { lhs, rhs in
    lhs.localizedStandardCompare(rhs) == order
}
// ["File 3.txt", "File 7.txt", "File 36.txt"]

Normalization Forms

Foundation provides APIs for accessing normalization forms for strings,
including NFC and NFD,
as demonstrated in this example.
import Foundation

let string = "ümlaut"
let nfc = string.precomposedStringWithCanonicalMapping
nfc.unicodeScalars.first
let nfd = string.decomposedStringWithCanonicalMapping
nfd.unicodeScalars.first

String Encoding Conversion

Foundation offers support for many different legacy string encodings,
as shown in this example.
import Foundation

"Hello, Macintosh!".data(using: .macOSRoman)

String from Data

Foundation provides APIs to read and write 
String
 values
from data values and files.
import Foundation

let url = Bundle.main.url(forResource: "file", withExtension: "txt")!
try String(contentsOf: url) // "Hello!"
let data = try Data(contentsOf: url)
String(data: data, encoding: .utf8) // "Hello!"

String Transformation

Another cool bit of functionality 
String
 inherits from 
NSString
is the ability to apply
ICU string transforms,
as seen in this example.
import Foundation

"Avión".applyingTransform(.stripDiacritics, reverse: false)
// "Avion"
"©".applyingTransform(.toXMLHex, reverse: false)
// "©"
"🛂".applyingTransform(.toUnicodeName, reverse: false)
// "\N{PASSPORT CONTROL}"
"マット".applyingTransform(.fullwidthToHalfwidth, reverse: false)
// "ﾏｯﾄ"

Trimming

Foundation's 
CharacterSet
 is used in various string APIs,
but it's perhaps most well-known for its role in the
trimmingCharacters(in:)
 method,
as shown in this Playground.
import Foundation

"""
        ✈️
""".trimmingCharacters(in: .whitespacesAndNewlines) // "✈️"

URL Encoding

Only certain characters are allowed in certain positions of a URLs.
By importing Foundation,
you can encode URL query parameters with confidence
with the 
addingPercentEncoding(withAllowedCharacters:)
 method.
import Foundation

"q=lax to jfk".addingPercentEncoding(withAllowedCharacters: .urlQueryAllowed)
// q=lax%20to%20jfk

String Format

When you import the Foundation framework,

String
 gets 
sprintf
-style initializers.
This Playground serves as an exhaustive reference
for all of the available
formatting specifiers, modifiers, flags, and arguments.
import Foundation

String(format: "%X", 127) // "7F"

Chapter 5

Base2 and Base16 Encoding

These examples show you how to
use the 
String(_:radix:uppercase:)
 initializer to
produce binary and hexadecimal representations of binary integer values.
let byte: UInt8 = 0xF0

String(byte, radix: 2) // "11110000"
String(byte, radix: 16, uppercase: true) // "F0"

Base64 Encoding

Foundation provides APIs for base64 encoding and decoding data,
which are demonstrated in this Playground.
import Foundation

let string = "Hello!"
let data = string.data(using: .utf8)!
let encodedString = data.base64EncodedString() // "SGVsbG8h"

Base🧑 Encoding

Anticipating emoji's role in the forthcoming collapse of human communication,
we present a novel binary-to-text encoding format
that represents data using human face emoji
combined with skin tone and hair style modifiers.
let data = "Fly".data(using: .utf8)!
let encodedString = data.base🧑EncodedString() // "👨🏽‍🦱👩🏻‍🦲👩🏽‍🦳👩🏿‍🦱"

Human Readable Encoding

In this example,
we implement the 11-bit binary-to-text encoding described in
RFC 1751:
"A Convention for Human-Readable 128-bit Keys".
"Why?" you ask?
Why indeed!
import Foundation

let data = Data(bytes: [0xB2, 0x03, 0xE2, 0x8F,
                        0xA5, 0x25, 0xBE, 0x47])
data.humanReadableEncodedString()
// "LONG IVY JULY AJAR BOND LEE"

Chapter 6

Parsing with Scanner

One of Foundation's many offerings is the 
Scanner
 class:
a sort of lexer/parser combo deal with some convenient features.
This Playground demonstrates how to make it even more convenient in Swift,
and how to use it to parse information from an AFTN message.
import Foundation

let scanner = Scanner(string: string)
scanner.charactersToBeSkipped = .whitespacesAndNewlines
try scanner.scan("ZCZC")
let transmission = try scanner.scan(.alphanumerics)
let additionalServices = try scanner.scan(.decimalDigits)
let priority = try scanner.scan(.uppercaseLetters)
let destination = try scanner.scan(.uppercaseLetters)
let time = try scanner.scan(.decimalDigits)
let origin = try scanner.scan(.uppercaseLetters)
let text = try scanner.scan(upTo: "NNNN")

Parsing with Regular Expressions

Foundation's 
NSRegularExpression
 offers the closest thing to
built-in regex support in Swift.
Underneath the hood, it wraps the
ICU regular expression engine;
we take advantage of a bunch of its advanced features in this Playground
to parse the same message as before using a different approach.
import Foundation

let pattern = #"""
(?x-i)
\A
ZCZC \h
(?[A-Z]{3}[0-9]{3}) \h (?[0-9]{0,8}) \n
(?[A-Z]{2}) \h (?[A-Z]{8}) \n
(?[0-9]{6}) \h (?[A-Z]{8}) \n+
(?[[A-Z][0-9]\h\n]+) \s*
NNNN
\Z
"""#
let regex = try NSRegularExpression(pattern: pattern,
                                    options: [])

Parsing with ANTLR4

ANTLR is a parser generator
with support for Swift code generation.
This example provides a functional integration between ANTLR4
and the Swift Package Manager to demonstrate yet another approach
to parsing the same AFTN message from the previous examples.
import AFTN

let message = try Message(string)!
message.priority
message.destination.location
message.destination.organization
message.destination.department
message.filingTime
message.text

Chapter 7

Tokenization

The NaturalLanguage framework's 
NLTokenizer
 class
can tokenize text by word, sentence, and paragraph,
as demonstrated in this example.
import NaturalLanguage

let string = "Welcome to New York, where the local time is 9:41 AM."
let tokenizer = NLTokenizer(unit: .word)
tokenizer.string = string
let stringRange = string.startIndex..<string.endindex tokenizer.enumeratetokens stringrange _ in let token="string[tokenRange]" print terminator: return true continue processing prints: to new york where the local time is am>
Language Tagging

You can use the 
NLTagger
 class to detect the language and script
for a piece of natural language text,
as seen in this Playground.
import NaturalLanguage

let string = """
Sehr geehrte Damen und Herren,
herzlich willkommen in Frankfurt.
"""
let tagSchemes: [NLTagScheme] = [.language, .script]
let tagger = NLTagger(tagSchemes: tagSchemes)
tagger.string = string
for scheme in tagSchemes {
    if case let (tag?, _) = tagger.tag(at: string.startIndex,
                                       unit: .word,
                                       scheme: scheme) {
        print(scheme.rawValue, tag.rawValue)
    }
}
// Prints:
// "Language de"
// "Script Latn"

Part of Speech Tagging

To tag part of speech for words (noun, verb, etc.)
use the 
NLTagger
 class with the 
.lexicalClass
 tag scheme.
import NaturalLanguage

let string = "The sleek white jet soars over the hazy fog."
let tagger = NLTagger(tagSchemes: [.lexicalClass])
tagger.string = string
let stringRange = string.startIndex..<string.endindex let options: nltagger.options="[.omitWhitespace," .omitpunctuation tagger.enumeratetags stringrange unit: .word scheme: .lexicalclass options tagrange in if partofspeech="tag?.rawValue" print return true continue processing prints: determiner adjective noun ...>
Named Entity Recognition


NLTagger
 can also be used to detect named entities,
including people, places, and organizations.
This example shows how to do just that.
import NaturalLanguage

let string = """
Fang Liu of China is the current Secretary General of ICAO.
"""
let tagger = NLTagger(tagSchemes: [.nameType])
tagger.string = string
let stringRange = string.startIndex..<string.endindex let options: nltagger.options="[.omitWhitespace," .omitpunctuation .joinnames tagger.enumeratetags stringrange unit: .word scheme: .nametype options tagrange in if nametype="tag?.rawValue," tag .otherword print return true continue processing prints: liu: personalname placename organizationname>
Keyword Extraction

Short of implementing a more complete natural language parser,
you can use 
NLTagger
 to extract keywords by part of speech
as a first approximation for interpreting commands.
import NaturalLanguage

let string = "What's the current temperature in Tokyo?"
let tagger = NLTagger(tagSchemes: [.nameTypeOrLexicalClass])
tagger.string = string
var taggedKeywords: [(NLTag, String)] = []
let stringRange = string.startIndex..<string.endindex let options: nltagger.options="[.omitWhitespace," .omitpunctuation .joinnames tagger.enumeratetags stringrange unit: .word scheme: .nametypeorlexicalclass options tagrange in guard tag="tag" else return true switch case .noun .placename: print string default: break continue processing prints: temperature tokyo>
Lemmatization

This example demonstrates the 
.lemma
 tag scheme
and how it resolves conjugations of various words.
import NaturalLanguage

let string = """
Flying flights fly flyers flown.
"""
let tagger = NLTagger(tagSchemes: [.lemma])
tagger.string = string
tagger.enumerateTags(in: string.startIndex..<string.endindex unit: .word scheme: .lemma options: tagrange in if let lemma="tag?.rawValue" print return true continue processing prints: fly flight flyer>
Language Recognizer

The 
NLLanguageRecognizer
 provides a configurable classifier
for determining the language used in a piece of text.
Here, we demonstrate how to use the 
languageHints
 property
to resolve a sentence that could be understood in either
Norwegian Bokmål (
nb
) or Danish (
da
).
import NaturalLanguage

let string = """
God morgen mine damer og herrer.
"""
let languageRecognizer = NLLanguageRecognizer()
languageRecognizer.processString(string)
languageRecognizer.dominantLanguage // da
languageRecognizer.languageHints = [.norwegian: 0.75,
                                    .swedish: 0.25]
languageRecognizer.dominantLanguage // nb

Naive Bayes Classifier

This example provides a reference implementation for a
Naive Bayes "bag of words" classifier in Swift.
enum Sentiment: String, Hashable {
    case positive, negative
}

let classifier = NaiveBayesClassifier()
classifier.trainText("great flight", for: .positive)
classifier.trainText("flight was late and turbulent", for: .negative)
classifier.classifyText("I had a great flight") // positive

Sentiment Classification

Using Create ML, we can build a Core ML classifier model
that can be used by the Natural Language framework
to determine if a piece of natural language text expresses
positive, negative, or neutral sentiment.
import NaturalLanguage

let url = Bundle.main.url(forResource: "SentimentClassifier",
                          withExtension: "mlmodelc")!
let model = try NLModel(contentsOf: url)
model.predictedLabel(for: "Nice, smooth flight") // positive

N-Grams

This Playground provides a Swift implementation of n-grams,
which, combined with 
NLTokenizer
,
can produce bigrams and trigrams of words in a piece of natural language text.
import NaturalLanguage

let string = """
Please direct your attention to flight attendants
as we review the safety features of this aircraft.
"""
let tokenizer = NLTokenizer(unit: .word)
tokenizer.string = string
let words = tokenizer.tokens(for: string.startIndex..<string.endindex .map string bigrams ...>
Markov Chain

Using n-grams to determine the conditional probability of
transitions from one word to another,
we can construct a model that randomly generates text
that trivially resembles the provided source.
In this example,
we feed in a corpus of Air Traffic Control transcripts.
import Foundation
import NaturalLanguage

// https://catalog.ldc.upenn.edu/LDC94S14A
let url = Bundle.main.url(forResource: "LDC94S14A-sample",
                          withExtension: "txt")!
let text = try String(contentsOf: url)
var markovChain = MarkovChain(sentencesAndWords(for: text))
for word in markovChain {
    print(word, terminator: " ")
}
// Prints: "CACTUS EIGHT OH EIGHT TURN LEFT HEADING ONE SEVENTY HEAVY"

Soundex

Soundex is a classic phonetic coding system
used to resolve ambiguity in the spelling of surnames.
This example provides a Swift implementation of the standard algorithm.
let names: [String] = [
    "Washington",
    "Lee",
    "Smith",
    "Smyth"
]

for name in names {
    print("(name): (soundex(name))")
}
// Prints:
// "Washington: W252"
// "Lee: L000"
// "Smith: S530"
// "Smyth: S530"

Levenshtein Distance

You can use a string metric like Levenshtein edit distance
to quantify the similarity between two sequences.
/*
 |     |     |  S  |  a  |  t  |  u  |  r  |  d  |  a  |  y  |
 |-----|-----|-----|-----|-----|-----|-----|-----|-----|-----|
 |     | _0_ |  1  |  2  |  3  |  4  |  5  |  6  |  7  |  8  |
 |   S |  1  | _0_ | _1_ | _2_ |  3  |  4  |  5  |  6  |  7  |
 |   u |  2  |  1  |  1  |  2  | _2_ |  3  |  4  |  5  |  6  |
 |   n |  3  |  2  |  2  |  2  |  3  | _3_ |  4  |  5  |  6  |
 |   d |  4  |  3  |  3  |  3  |  3  |  4  | _3_ |  4  |  5  |
 |   a |  5  |  4  |  3  |  4  |  4  |  4  |  4  | _3_ |  4  |
 |   y |  6  |  5  |  4  |  4  |  5  |  5  |  5  |  4  | _3_ |
*/
levenshteinDistance(from: "Saturday", to: "Sunday") // 3

Spell Checker

Using the Levenshtein distance function from the previous example,
and combining it with a corpus of frequently-used words,
you can create a reasonably effective spell checker
with very little additional code.
import Foundation

// https://catalog.ldc.upenn.edu/LDC2006T13
guard let url = Bundle.main.url(forResource: "LDC2006T13-sample",
                                withExtension: "txt")
else {
    fatalError("Missing required resource")
}
let spellChecker = try SpellChecker(contentsOf: url)
spellChecker.suggestions(for: "speling")
// ["spelling", "spewing", "sperling"]



License

MIT

About Flight School

Flight School is a book series for advanced Swift developers
that explores essential topics in iOS and macOS development
through concise, focused guides.

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or email us at [email protected].