Как можно использовать `dplyr` для создания новой группы при каждом изменении значения столбца? - PullRequest
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Как можно использовать `dplyr` для создания новой группы при каждом изменении значения столбца?

1 голос
/ 30 октября 2019

У меня есть дата-кадр событий обнаружения со столбцами, в которых указаны индивидуальный тег (ID), время начала обнаружения (StartDateTime_UTC), время окончания обнаружения (EndDateTime_UTC) и местоположение.

Я хочу объединитьи суммируйте этот фрейм данных для каждого тега и каждый раз при изменении местоположения. Для каждого тега и каждого изменения я хотел бы взять min (StartDateTime_UTC) и max (EndDateTime_UTC). К сожалению, для каждого изменения не задано количество записей, поэтому простые сводные сводки не работают.

Существует 3 возможных местоположения, если это имеет значение "IN", "OUT", "OVERLAP"

Я бы предпочел решение с использованием пакета dplyr, если это возможно.

Для приведенных ниже данных результат будет выглядеть следующим образом: 

      Tag   StartDateTime_UTC     EndDateTime_UTC location
1 5004.24 2014-10-15 15:26:16 2014-10-17 07:12:19       IN
2 5004.24 2014-10-17 07:15:43 2014-10-21 02:12:29      OUT

пример данных: 

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1 Ответ

3 голосов
/ 30 октября 2019

Как я понимаю ваш вопрос, вам интересны строки, в которых до предыдущей строки не было одинакового местоположения. Это можно сделать с помощью location != lag(location), который просто сообщает вам, изменилось ли местоположение. Для первой строки значение будет отсутствовать, поэтому нам нужно указать значение по умолчанию, которое является просто первым значением в векторе. Теперь, чтобы назначить идентификаторы блокам наблюдений между изменениями местоположения, мы можем просто использовать cumsum в нашем недавно построенном логическом векторе. Окончательный код выглядит следующим образом: 

example_data %>% 
  mutate(
    LocationChange = cumsum(location != lag(location, def = first(location)))
  ) %>% 
  group_by(Tag, LocationChange, location) %>% 
  summarise(
    StartDateTime_UTC = min(StartDateTime_UTC),
    EndDateTime_UTC = max(EndDateTime_UTC)
  ) %>% 
  ungroup()

, что дает ожидаемый результат.

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