Передача внешней функции (и аргументов) для суммирования или преобразования dplyr - PullRequest
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Передача внешней функции (и аргументов) для суммирования или преобразования dplyr

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/ 20 декабря 2018

Я пытаюсь обобщить большой набор данных с помощью внешней функции (sii пакет).

Что мне нужно сделать, это рассчитать SII для каждого предмета, с каждой системой, на каждом уровне представления.

Пример данных: 

data <- structure(list(Subject = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L), .Label = c("1", "2"), class = "factor"), Ear = structure(c(1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L), .Label = c("Left", "Right"), class = "factor"), 
    System = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
    2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
    1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
    1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
    2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
    2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
    1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
    1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
    2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
    2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
    1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
    1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
    2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
    2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
    1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
    1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
    2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
    2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
    1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
    1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
    2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
    2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
    1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
    1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
    2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
    2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
    1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
    1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
    2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
    2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
    1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
    1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
    2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 
    2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L
    ), .Label = c("A", "B"), class = "factor"), Pres_Level = structure(c(1L, 
    1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 
    3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 
    1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 
    3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 
    1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 
    3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 
    1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 
    3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 
    1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 
    3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 
    1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 
    3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 
    1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 
    3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 
    1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 
    3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 
    1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 
    3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 
    1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 
    3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 
    1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 
    3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 
    1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 
    3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 
    1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 
    3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 
    1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 
    3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 
    1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 
    3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 
    1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 
    3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 
    1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 
    3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L), .Label = c("55", "65", "75"
    ), class = "factor"), Frequency = c(125, 125, 125, 125, 125, 
    125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 125, 
    125, 125, 125, 125, 125, 125, 125, 160, 160, 160, 160, 160, 
    160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 160, 
    160, 160, 160, 160, 160, 160, 160, 200, 200, 200, 200, 200, 
    200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200, 
    200, 200, 200, 200, 200, 200, 200, 250, 250, 250, 250, 250, 
    250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 250, 
    250, 250, 250, 250, 250, 250, 250, 315, 315, 315, 315, 315, 
    315, 315, 315, 315, 315, 315, 315, 315, 315, 315, 315, 315, 
    315, 315, 315, 315, 315, 315, 315, 400, 400, 400, 400, 400, 
    400, 400, 400, 400, 400, 400, 400, 400, 400, 400, 400, 400, 
    400, 400, 400, 400, 400, 400, 400, 500, 500, 500, 500, 500, 
    500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 500, 
    500, 500, 500, 500, 500, 500, 500, 630, 630, 630, 630, 630, 
    630, 630, 630, 630, 630, 630, 630, 630, 630, 630, 630, 630, 
    630, 630, 630, 630, 630, 630, 630, 800, 800, 800, 800, 800, 
    800, 800, 800, 800, 800, 800, 800, 800, 800, 800, 800, 800, 
    800, 800, 800, 800, 800, 800, 800, 1000, 1000, 1000, 1000, 
    1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 
    1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 
    1250, 1250, 1250, 1250, 1250, 1250, 1250, 1250, 1250, 1250, 
    1250, 1250, 1250, 1250, 1250, 1250, 1250, 1250, 1250, 1250, 
    1250, 1250, 1250, 1250, 1600, 1600, 1600, 1600, 1600, 1600, 
    1600, 1600, 1600, 1600, 1600, 1600, 1600, 1600, 1600, 1600, 
    1600, 1600, 1600, 1600, 1600, 1600, 1600, 1600, 2000, 2000, 
    2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 
    2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 
    2000, 2000, 2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500, 
    2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500, 2500, 
    2500, 2500, 2500, 2500, 2500, 2500, 3000, 3000, 3000, 3000, 
    3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 
    3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 3000, 
    3150, 3150, 3150, 3150, 3150, 3150, 3150, 3150, 3150, 3150, 
    3150, 3150, 3150, 3150, 3150, 3150, 3150, 3150, 3150, 3150, 
    3150, 3150, 3150, 3150, 4000, 4000, 4000, 4000, 4000, 4000, 
    4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 
    4000, 4000, 4000, 4000, 4000, 4000, 4000, 4000, 5000, 5000, 
    5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 
    5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 5000, 
    5000, 5000, 6000, 6000, 6000, 6000, 6000, 6000, 6000, 6000, 
    6000, 6000, 6000, 6000, 6000, 6000, 6000, 6000, 6000, 6000, 
    6000, 6000, 6000, 6000, 6000, 6000, 6300, 6300, 6300, 6300, 
    6300, 6300, 6300, 6300, 6300, 6300, 6300, 6300, 6300, 6300, 
    6300, 6300, 6300, 6300, 6300, 6300, 6300, 6300, 6300, 6300, 
    8000, 8000, 8000, 8000, 8000, 8000, 8000, 8000, 8000, 8000, 
    8000, 8000, 8000, 8000, 8000, 8000, 8000, 8000, 8000, 8000, 
    8000, 8000, 8000, 8000), REM_SPL = c(43.68, 38.85, 51.43, 
    48.71, 59.22, 58.62, 38.51, 38.45, 48.33, 48.44, 58.18, 58.34, 
    52.51, 39.6, 58.89, 49.06, 64.63, 58.7, 40.42, 38.81, 49.03, 
    48.52, 58.3, 58.31, 54.92, 49.44, 62.59, 59.24, 70.32, 68.97, 
    48.43, 48.19, 58.21, 58.16, 68.17, 67.99, 63.83, 51.47, 68.49, 
    59.93, 73.25, 69.09, 48.69, 48.3, 58.35, 58.1, 68.19, 68.01, 
    60.9, 50.42, 68.57, 61.23, 76.59, 71.69, 53.6, 52.7, 63.7, 
    62.63, 73.63, 72.59, 69.31, 50.73, 73.51, 60.53, 78.59, 71.52, 
    52.12, 51.55, 62.76, 61.94, 73.15, 72.33, 60.74, 57.26, 68.37, 
    66.47, 76.5, 75.78, 52.44, 49.21, 62.49, 58.96, 72.44, 69.11, 
    68.65, 60.55, 72.73, 67.42, 78.12, 75.97, 50.62, 58.85, 58.9, 
    61.92, 70.33, 68.8, 55.39, 50.46, 62.96, 59.28, 71.08, 68.47, 
    48.88, 61.78, 58.9, 71.68, 68.8, 80.89, 64.34, 56.79, 68.32, 
    61.46, 73.3, 68.84, 62.02, 68.72, 68.09, 74.56, 73.49, 80.8, 
    56.69, 59.3, 64.04, 67.76, 72.05, 76.68, 51.93, 69.98, 61.96, 
    79.52, 71.84, 88.99, 67, 65.08, 70.76, 70.07, 75.18, 77.28, 
    71.03, 77.65, 75.63, 82.24, 79.81, 88.9, 51.92, 57.93, 59.21, 
    66.1, 67.46, 75.01, 56.59, 68.41, 65.69, 77.82, 75.5, 87.89, 
    63.7, 64.53, 67.67, 69.39, 71.81, 76.13, 69.27, 76.85, 73.64, 
    81.51, 78.2, 87.85, 48.87, 53.9, 55.78, 61.65, 63.82, 70.4, 
    58.82, 65.38, 67.34, 74.02, 76.93, 83.84, 61.42, 61.24, 65.35, 
    65.97, 69.53, 72.23, 68.71, 74.23, 73.86, 79.7, 79.37, 85.62, 
    48.01, 50.44, 54.41, 57.41, 61.56, 65.25, 55.58, 60.89, 63.1, 
    68.84, 71.74, 77.46, 60.05, 58.6, 63.73, 62.9, 67.59, 68.1, 
    66.15, 68.8, 70.82, 74.09, 76.38, 79.82, 47.18, 48.5, 53.45, 
    55.01, 60.08, 61.96, 50.95, 55.74, 57.98, 63.25, 65.43, 71.02, 
    59.17, 56.77, 63.17, 61.08, 67.06, 65.64, 62.25, 64.22, 66.68, 
    69.38, 71.26, 74.48, 45.35, 46.41, 51.51, 52.74, 57.89, 59.15, 
    49.19, 51.76, 55.54, 58.63, 61.76, 65.99, 57.58, 54.92, 61.84, 
    59.05, 65.64, 63.46, 61.35, 60.78, 64.83, 65.47, 68.72, 69.57, 
    46.65, 47.33, 51.94, 53.13, 57.31, 59, 49.36, 51.67, 55.5, 
    57.69, 60.82, 63.09, 60.43, 56.86, 64.43, 60.78, 68.14, 64.65, 
    64.16, 60.72, 66.77, 64.85, 70.22, 68.47, 52.05, 52.4, 57.11, 
    57.74, 62.04, 63.19, 54.05, 54.49, 58.79, 59.71, 62.52, 63.23, 
    66.43, 61.57, 70.32, 65.49, 73.94, 69.19, 68.14, 62.49, 70.3, 
    66.13, 72.44, 68.77, 58.81, 58.52, 63.77, 63.85, 68.05, 68.88, 
    60.06, 60.46, 64.5, 64.38, 67.55, 67.18, 70.69, 66.81, 74.87, 
    70.92, 78.38, 74.41, 72.09, 66.25, 74.33, 69.39, 76.22, 71.49, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, 60.18, 59.65, 64.79, 
    64.88, 68.88, 70.03, 67.33, 66.6, 71.46, 71.19, 73.76, 73.17, 
    69.65, 67.08, 73.98, 71.05, 77.77, 74.67, 75.44, 69.74, 78.49, 
    73.39, 79.65, 75.22, 58.4, 59.01, 63.43, 64.86, 67.39, 69.91, 
    68.71, 66.86, 73.05, 75.1, 74.7, 76.83, 65.9, 65.64, 71.34, 
    70.31, 75.86, 73.99, 71.73, 67.53, 77.7, 75.64, 78.59, 77.33, 
    58.44, 58.86, 63.4, 64.32, 67.55, 69.36, 66.91, 66.78, 71.71, 
    75.09, 73.4, 76.7, 66.48, 64.59, 71.47, 68.87, 75.38, 72.33, 
    68.31, 66.76, 75.47, 75.42, 76.96, 76.82, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, 55.72, 55.8, 60.74, 61.53, 65.31, 66.82, 
    67.76, 66.76, 71.6, 72.51, 73.49, 74.22, 63.62, 62.1, 68.72, 
    66.45, 72.44, 69.93, 68.97, 67.19, 75.25, 73, 76.63, 74.49, 
    52.18, 51.25, 57.97, 57.54, 62.94, 63.07, 67.82, 67.65, 71.4, 
    72, 73.4, 73.92, 60.18, 58.02, 66.23, 63.48, 70.87, 67.6, 
    68.92, 68.42, 75.79, 72.7, 77.23, 74.33), Thresh_SPL = c(40, 
    40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 65, 60, 65, 60, 
    65, 60, 65, 60, 65, 60, 65, 60, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 33, 33, 33, 33, 
    33, 33, 33, 33, 33, 33, 33, 33, 58, 53, 58, 53, 58, 53, 58, 
    53, 58, 53, 58, 53, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, 34.6, 34.6, 34.6, 34.6, 34.6, 
    34.6, 34.6, 34.6, 34.6, 34.6, 34.6, 34.6, 59.6, 54.6, 59.6, 
    54.6, 59.6, 54.6, 59.6, 54.6, 59.6, 54.6, 59.6, 54.6, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, 35, 30, 35, 30, 35, 30, 35, 30, 35, 30, 35, 30, 50, 
    50, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 44.6, 
    44.6, 44.6, 44.6, 44.6, 44.6, 44.6, 44.6, 44.6, 44.6, 44.6, 
    44.6, 69.6, 59.6, 69.6, 59.6, 69.6, 59.6, 69.6, 59.6, 69.6, 
    59.6, 69.6, 59.6, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 72.5, 
    77.5, 72.5, 77.5, 72.5, 77.5, 72.5, 77.5, 72.5, 77.5, 72.5, 
    77.5, 87.5, 77.5, 87.5, 77.5, 87.5, 77.5, 87.5, 77.5, 87.5, 
    77.5, 87.5, 77.5, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 76.5, 
    81.5, 76.5, 81.5, 76.5, 81.5, 76.5, 81.5, 76.5, 81.5, 76.5, 
    81.5, 96.5, 81.5, 96.5, 81.5, 96.5, 81.5, 96.5, 81.5, 96.5, 
    81.5, 96.5, 81.5, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 77.5, 
    82.5, 77.5, 82.5, 77.5, 82.5, 77.5, 82.5, 77.5, 82.5, 77.5, 
    82.5, 82.5, 77.5, 82.5, 77.5, 82.5, 77.5, 82.5, 77.5, 82.5, 
    77.5, 82.5, 77.5, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 94, 
    104, 94, 104, 94, 104, 94, 104, 94, 104, 94, 104, 104, 89, 
    104, 89, 104, 89, 104, 89, 104, 89, 104, 89)), row.names = c(NA, 
-504L), class = "data.frame")

Функция sii принимает несколько аргументов: 

sii(speech = speech, threshold = threshold, freq = frequency, method = "one-third octave", interpolate = T)

Я хочу исправить аргумент freq как: freq =c (125, 250, 500, 1000, 2000, 3150, 4000, 6300, 8000)

Одна особенно сложная часть заключается в том, что мне нужно поднастроить аргументы speech и threshold для слегка разных значенийFrequency:

Для speech: c (125, 250, 500, 1000, 2000, 3150, 4000, 6300, 8000)

Для threshold: c (125,250, 500, 1000, 2000, 3000, 4000, 6000, 8000)

Другие аргументы должны быть рассчитаны на основе группировки.То, что я пробовал до сих пор, не работает: 

library(tidyverse)
library(sii)

data %>%
      group_by(Subject, Ear, System, Pres_Level) %>%
        summarize(SII = sii(speech = . %>% 
                            filter(Frequency %in% c(125, 250, 500, 1000, 2000, 3150, 4000, 6300, 8000)) %>%
                            pull(REM_SPL),
                          threshold = . %>% 
                            filter(Frequency %in% c(125, 250, 500, 1000, 2000, 3000, 4000, 6000, 8000)) %>%
                            pull(Thresh_SPL), 
                          freq = frequency, method = "one-third octave", interpolate = T))

    Error in sii(speech = . %>% filter(Frequency %in% c(125, 250, 500, 1000,  : 
      `speech' must have the same length as `freq'.

Попытка сохранить группировку для аргументов: 

data %>%
      select(-REM_Level) %>%
      filter(Frequency >= 125, Frequency <= 8000) %>%
      group_by(Subject, Ear, System, Pres_Level) %>%
      mutate(Speech = tibble(REM_SPL)  %>%
      filter(Frequency %in% c(125, 250, 500, 1000, 2000, 3150, 4000, 6300, 8000))) %>%
      pull(REM_SPL)), 
             threshold = tibble(Thresh_SPL) %>% 
                     filter(Frequency %in% c(125, 250, 500, 1000, 2000, 3000, 4000, 6000, 8000) %>%
                     pull(Thresh_SPL))) %>%
      mutate(SII = sii(speech = speech, threshold = threshold, freq = c(125, 250, 500, 1000, 2000, 3000, 4000, 6000, 8000),
                   method = "one-third octave", interpolate = T))

Error in mutate_impl(.data, dots) : 
  Column `Speech` is of unsupported class data.frame

Я пытался использовать несколько вложенных циклов, но это не помоглоработал на всех.

Мой желаемый результат примерно такой (это поддельные значения SII): 

   Subject System Pres_Level  SII
1        1      A         55 0.65
2        1      B         55 0.60
3        1      C         55 0.60
4        1      A         65 0.70
5        1      B         65 0.75
6        1      C         65 0.80
7        1      A         75 0.76
8        1      B         75 0.78
9        1      C         75 0.74
10       2      A         55 0.55
11       2      B         55 0.58
12       2      C         55 0.57
13       2      A         65 0.74
14       2      B         65 0.72
15       2      C         65 0.82
16       2      A         75 0.80
17       2      B         75 0.82
18       2      C         75 0.76
19       3      A         55 0.58
20       3      B         55 0.62
21       3      C         55 0.64
22       3      A         65 0.74
23       3      B         65 0.76
24       3      C         65 0.78
25       3      A         75 0.80
26       3      B         75 0.76
27       3      C         75 0.74

Кто-нибудь может подсказать, как мне добиться того, что я ищу?

1 Ответ

0 голосов
/ 20 декабря 2018

Я считаю, что это то, что вы ищете ... Одна вещь, на которую вам нужно обратить внимание, это то, что возвращает sii (объект длины 10, а не вектор длины 1).Вам необходимо дополнительно извлечь значение SII из результата, следовательно, sii(...)$sii в вызове summarize.

После редактирования с новыми данными: 

data %>%
  group_by(Subject, Ear, System, Pres_Level) %>%
  summarize(SII = sii(speech      =    REM_SPL[Frequency %in% c(125, 250, 500, 1000, 2000, 3150, 4000, 6300, 8000)],
                      threshold   = Thresh_SPL[Frequency %in% c(125, 250, 500, 1000, 2000, 3000, 4000, 6000, 8000)],
                      freq        =  Frequency[Frequency %in% c(125, 250, 500, 1000, 2000, 3150, 4000, 6300, 8000)],
                      method      = "one-third octave",
                      interpolate = T)$sii)

# A tibble: 24 x 5
# Groups:   Subject, Ear, System [?]
   Subject Ear   System Pres_Level   SII
   <fct>   <fct> <fct>  <fct>      <dbl>
 1 1       Left  A      55         0.788
 2 1       Left  A      65         0.782
 3 1       Left  A      75         0.759
 4 1       Left  B      55         0.806
 5 1       Left  B      65         0.774
 6 1       Left  B      75         0.742
 7 1       Right A      55         0.749
 8 1       Right A      65         0.749
 9 1       Right A      75         0.737
10 1       Right B      55         0.765
# ... with 14 more rows
...