[Java Study] 11-x. 스트림으로 데이터 수집

// 통화별로 트랜잭션을 그룹화한 코드 Map<Currency, List<Transaction>> transactionsByCurrencies = transactions.stream().collect(Collectors.groupingBy(Transaction::getCurrency));

// Comparator 생성 Comparator<Dish> dishCaloriesComparator = Comparator.comparingInt(Dish::getCalories); // maxBy Optional<Dish> mostCaloriesDish = menu.stream() .collect(maxBy(dishCaloriesComparator)); // max(dishCaloriesComparator) // minBy Optional<Dish> leastCaloriesDish = menu.stream() .collect(minBy(dishCaloriesComparator)); // min(dishCaloriesComparator)

// summingXXX Integer collect1 = Dish.menu.stream() .collect(Collectors.summingInt(Dish::getCalories)); // averagingXXX Double collect2 = Dish.menu.stream() .collect(Collectors.averagingInt(Dish::getCalories)); // summarizingXXX IntSummaryStatistics collect3 = Dish.menu.stream() .collect(Collectors.summarizingInt(Dish::getCalories));

String shortMenu = Dish.menu.stream() .map(Dish::getName) .collect(Collectors.joining(", "));

// 최대 칼로리값 반환 Dish.menu.stream() .collect(Collectors.reducing((d1, d2) -> d1.getCalories() > d2.getCalories() ? d1 : d2)); // 칼로리값의 합 반환 Dish.menu.stream() .collect(Collectors.reducing(0, Dish::getCalories, (i, j) -> i + j);

// collect로 만든 리스트 List<Integer> collectedList = stream.collect(toList()); // reduce로 만든 리스트 List<Integer> reducedList = stream.reduce( new ArrayList<>(), (List<Integer> l, Integer e) -> { // 누적자 l.add(e); return l;}, (List<Integer> l1, List<Integer> l2) -> { // 결합자 l1.addAll(l2); return l1;});

Map<Dish.Type, List<Dish>> dishesByType = menu.stream().collect(groupingBy(Dish::getType));

// 400칼로리 이하를 diet, 400~700칼로리를 normal, 700칼로리 초과를 fat으로 분류 Map<CaloricLevel, List<Dish>> dishesByCaloricLevel = menu.stream().collect( groupingBy(dish -> { if (dish.getCalories() <= 400) return CaloricLevel.DIET; else if (dish.getCalories() <= 700) return CaloricLevel.NORMAL; else return CaloricLevel.FAT; }));

Map<Dish.Type, List<Dish>> calDish = Dish.menu.stream() .collect(groupingBy(Dish::getType, Collectors.filtering(dish -> dish.getCalories() > 500, Collectors.toList())));

Map<Dish.Type, List<String>> nameDish = Dish.menu.stream() .collect(groupingBy(Dish::getType, Collectors.mapping(Dish::getName, Collectors.toList())));

// Map<Dish.Type, Set<String>> dishNamesByType = Dish.menu.stream() .collect(groupingBy(Dish::getType, Collectors.flatMapping(dish -> dishTags.get(dish.getName()).stream(), Collectors.toSet())));

// 타입으로 그룹화 한 다음, 칼로리 크기별로 나눈다. Map<Dish.Type, Map<CaloricLevel, List<Dish>>> twoGrouping = Dish.menu.stream().collect( groupingBy(Dish::getType, groupingBy(dish -> { if (dish.getCalories() <= 400) return CaloricLevel.DIET; else if (dish.getCalories() <= 700) return CaloricLevel.NORMAL; else return CaloricLevel.FAT; })) );

// 각 타입별로 칼로리가 가장 높은 음식 Map<Dish.Type, Dish> mostCaloricByType = Dish.menu.stream().collect( groupingBy( Dish::getType, collectingAndThen( maxBy(Comparator.comparingInt(Dish::getCalories)), Optional::get)));

// 채식주의자를 위한 음식인가 아닌가? Map<Boolean, List<Dish>> partitionMenu = Dish.menu.stream() .collect(Collectors.partitioningBy(Dish::isVegetarian)); // filter로도 동일하게 뽑을 수 있음 List<Dish> vegetarianDishes = Dish.menu.stream() .filter(Dish::isVegetarian).collect(toList());

Map<Boolean, Map<Dish.Type, List<Dish>>> vegetarianDishByType = Dish.menu.stream().collect( Collectors.partitioningBy(Dish::isVegetarian, groupingBy(Dish::getType)) );

public static boolean isPrime(int candidate) { int candidateRoot = ((int) Math.sqrt((double) candidate)); return IntStream.rangeClosed(2, candidateRoot).noneMatch(i -> candidate % i == 0); }

public static Map<Boolean, List<Integer>> partitionPrimes(int n) { return IntStream.rangeClosed(2, n) .boxed() .collect(Collectors.partitioningBy(candidate -> isPrime(candidate))); } System.out.println(partitionPrimes(10)); // {false=[4, 6, 8, 9, 10], true=[2, 3, 5, 7]}