Фильтровать данные с помощью функции l oop в R - PullRequest
1 голос
/ 05 мая 2020

Я новичок в R, и у меня есть некоторые основные c проблемы.

У меня есть набор данных, и я отфильтрую некоторые данные и сохраню их в новой таблице. Мне нужно сделать это для многих разных наборов данных, поэтому я хотел бы использовать al oop. Итак, у меня есть таблица с разными возрастами (от 0 до 100), и мне нужна таблица для каждого возраста. Основная c идея была такой:

n<-100    
for( i in 0:n){Age[i] <- subset(Data, Data$AGE == [i], select = c(XXX, XX, XXX))

Получает меня Ошибка: объект не найден?

структура (list (PAT_ALTER = c (3, 0, 8, 1, 8 , 17, 11, 12, 2, 6, 3, 6, 5, 5, 1, 12, 9, 11, 0, 6, 6, 10, 6, 9, 2, 16, 3, 4, 3, 6 , 14, 3, 0, 4, 2, 3, 3, 3, 2, 8, 0, 7, 0, 1, 1, 1, 7, 8, 14, 2, 0, 4, 0, 2, 3 , 0, 0, 0, 2, 5, 4, 3, 12, 8, 0, 12, 11, 2, 0, 0, 0, 1, 0, 9, 12, 2, 4, 4, 5, 16 , 0, 10, 8, 0, 4, 3, 2, 7, 5, 4, 0, 11, 1, 3, 5, 0, 8, 2, 0, 9), SEX = c («w "," w "," w "," m "," m "," w "," m "," m "," m "," m "," w "," m "," m ", «m», «m», «w», «m», «w», «m», «w», «w», «m», «w», «w», «w», «m» "," m "," w "," w "," w "," m "," m "," m "," w "," m "," m "," m "," m ", «w», «w», «m», «m», «w», «m», «m», «m», «w», «w», «w», «w», «w» "," w "," m "," m "," m "," w "," m "," w "," w "," m "," w "," w "," w ", «w», «w», «w», «m», «m», «w», «m», «w», «m», «w», «m», «m», «w» "," m "," w "," w "," m "," m "," m "," m "," m "," w "," m "," m "," m ", «w», «w», «m», «w», «w», «m», «w», «m», «w», «m», «w», «w») ICD_KAPITEL = c (тест1, тест2, тест3, NA_c haracter_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_ NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_), ICD_KODE = c (test1, test2, test3, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_ , NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_ NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_), ICD_KODE_TEXT = c (Тест1, test2, test3, test4, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_ , NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_ character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, NA_character_), BETT_REAL = c (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, , 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), ДРИНГЛИЧКЕЙТ = c (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 , 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 , 1, 1, 1, 3, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 , 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 , 1, 1, 1, 1, 1), ZUBRING_REAL = c (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)

Спасибо за вашу помощь, Алекс.

1 Ответ

0 голосов
/ 05 мая 2020

Вот пример использования набора данных Iris.

Вы можете разделить набор данных по переменной, сохранить его как список, а затем экспортировать содержимое списка в .GlobalEnv.

#split data frame by a variable
list_df <- split(iris, iris$Species)
#export contents of list to .GlovalEnv
list2env(list_df, envir = .GlobalEnv)

В вашем случае это может выглядеть примерно так (я не могу проверить без образца набора данных)

list_df2 <- split(df, df$age)
namesoflist <- paste0("age_",1:100)
setNames(list_df2,namesoflist)
list2env(list_df2,envir=.GlovalEnv)
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