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Guide to Swift Strings Sample Code

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

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

values are considered equal if they are canonically equivalent, even if they comprise different Unicode scalar values.
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


values provide views to their UTF-8, UTF-16, and UTF-32 code units. This Playground shows the correspondence between the characters in a string and their various encoding forms.
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

values, which allow you to determine things like Unicode general category membership, whether a character has case mapping (lowercase / uppercase / titlecase), and whether the character has an associated number value.
("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

property on
values. For example, the
property does... well, exactly what you'd expect it to do.
("👏" as Unicode.Scalar).properties.isEmoji // true

Chapter 3

String as **___**

In Swift,

functionality is inherited from a complex hierarchy of interrelated protocols, including
, and others.

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


function can direct its output to a custom type conforming to the
protocol. This example implements a logger that prints the Unicode code points of the provided string.
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

destination. In this example, the
function is directed to write to
var standardError = StderrOutputStream()
print("Error!", to: &standardError)

Booking Class

Swift allows any type that conforms to

to be initialized from a string literal. This Playground provides a simple example through the
("J" as BookingClass) // Business Class

Flight Code

Types conforming to the

protocol can be initialized directly from
values. This Playground shows a
type that adopts both the
let flight: FlightCode = "AA 1"

flight.airlineCode flight.flightNumber


Unicode Styling

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

protocol. To demonstrate this, we implement a
type that allows interpolation segments to specify a style, such as bold, italic, and 𝔣𝔯𝔞𝔨𝔱𝔲𝔯.
let name = "Johnny"
let styled: StyledString = """
Hello, \(name, style: .fraktur(bold: true))!


Chapter 4

Range Conversion

Objective-C APIs that take

parameters or have
return values are imported by Swift to use
values instead. However, some of these APIs still specify ranges using the
type instead of
. This Playground demonstrates how to convert back and forth between the two range types.
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

type by providing localized string operations, including case mapping, searching, and comparison. Be sure to use localized string operations (ideally, the
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

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

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

inherits from
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"



is used in various string APIs, but it's perhaps most well-known for its role in the
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

import Foundation

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

String Format

When you import the Foundation framework,

-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

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 = .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

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


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 (?

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


The NaturalLanguage framework's

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

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

class with the
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

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

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>


This example demonstrates the

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


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


This Playground provides a Swift implementation of n-grams, which, combined with

, 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

// 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: " ") }



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] = [

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

// 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"]



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