суммирование одного столбца в R на основе частотного интервала, полученного из второго столбца - PullRequest
Новая
       6

суммирование одного столбца в R на основе частотного интервала, полученного из второго столбца

0 голосов
/ 02 октября 2019

У меня есть данные из 3 столбцов. Столбец 3 необходимо суммировать на основе столбца 2, который имеет дискретные значения в диапазоне от 1 до 1000. Столбец 2 должен быть уменьшен до частотного интервала 10, то есть 10, 20, 30, 40, 50 ... 1000,Шаг 1 должен быть преобразованием столбца 2 в частотный интервал, а затем шаг 2 должен быть суммированием столбца 3 на основе частоты. Данные представлены следующим образом: 

    structure(list(date3 = c(20190913L, 20190913L, 20190913L, 20190913L, 
20190913L, 20190913L, 20190913L, 20190913L, 20190913L, 20190913L, 
20190913L, 20190913L, 20190913L, 20190913L, 20190913L, 20190913L, 
20190913L, 20190913L, 20190913L, 20190913L, 20190913L, 20190913L, 
20190913L, 20190913L, 20190913L, 20190913L, 20190913L, 20190913L, 
20190913L, 20190913L, 20190913L, 20190913L, 20190913L, 20190913L, 
20190913L, 20190913L, 20190913L, 20190913L, 20190913L, 20190913L, 
20190913L, 20190913L, 20190913L, 20190913L, 20190913L, 20190913L, 
20190913L, 20190913L, 20190913L, 20190913L, 20190913L, 20190913L, 
20190913L, 20190913L, 20190913L, 20190913L, 20190913L, 20190913L, 
20190913L, 20190913L, 20190914L, 20190914L, 20190914L, 20190914L, 
20190914L, 20190914L, 20190914L, 20190914L, 20190914L, 20190914L, 
20190914L, 20190914L, 20190914L, 20190914L, 20190914L, 20190914L, 
20190914L, 20190914L, 20190914L, 20190914L, 20190914L, 20190914L, 
20190914L, 20190914L, 20190914L, 20190914L, 20190914L, 20190914L, 
20190914L, 20190914L, 20190914L, 20190914L, 20190914L, 20190914L, 
20190914L, 20190914L, 20190914L, 20190914L, 20190914L, 20190914L, 
20190914L, 20190914L, 20190914L, 20190914L, 20190914L, 20190914L, 
20190914L, 20190914L, 20190914L, 20190914L, 20190914L, 20190914L, 
20190914L, 20190914L, 20190914L, 20190914L, 20190914L, 20190914L, 
20190914L, 20190914L, 20190915L, 20190915L, 20190915L, 20190915L, 
20190915L, 20190915L, 20190915L, 20190915L, 20190915L, 20190915L, 
20190915L, 20190915L, 20190915L, 20190915L, 20190915L, 20190915L, 
20190915L, 20190915L, 20190915L, 20190915L, 20190915L, 20190915L, 
20190915L, 20190915L, 20190915L, 20190915L, 20190915L, 20190915L, 
20190915L, 20190915L, 20190915L, 20190915L, 20190915L, 20190915L, 
20190915L, 20190915L, 20190915L, 20190915L, 20190915L, 20190915L, 
20190915L, 20190915L, 20190915L, 20190915L, 20190915L, 20190915L, 
20190915L, 20190915L, 20190915L, 20190915L, 20190915L, 20190915L, 
20190915L, 20190915L, 20190915L, 20190915L, 20190915L, 20190915L, 
20190915L, 20190915L, 20190916L, 20190916L, 20190916L, 20190916L, 
20190916L, 20190916L, 20190916L, 20190916L, 20190916L, 20190916L, 
20190916L, 20190916L, 20190916L, 20190916L, 20190916L, 20190916L, 
20190916L, 20190916L, 20190916L, 20190916L, 20190916L, 20190916L, 
20190916L, 20190916L, 20190916L, 20190916L, 20190916L, 20190916L, 
20190916L, 20190916L, 20190916L, 20190916L, 20190916L, 20190916L, 
20190916L, 20190916L, 20190916L, 20190916L, 20190916L, 20190916L, 
20190916L, 20190916L, 20190916L, 20190916L, 20190916L, 20190916L, 
20190916L, 20190916L, 20190916L, 20190916L, 20190916L, 20190916L, 
20190916L, 20190916L, 20190916L, 20190916L, 20190916L, 20190916L, 
20190916L, 20190916L, 20190917L, 20190917L, 20190917L, 20190917L, 
20190917L, 20190917L, 20190917L, 20190917L, 20190917L, 20190917L, 
20190917L, 20190917L, 20190917L, 20190917L, 20190917L, 20190917L, 
20190917L, 20190917L, 20190917L, 20190917L, 20190917L, 20190917L, 
20190917L, 20190917L, 20190917L, 20190917L, 20190917L, 20190917L, 
20190917L, 20190917L, 20190917L, 20190917L, 20190917L, 20190917L, 
20190917L, 20190917L, 20190917L, 20190917L, 20190917L, 20190917L, 
20190917L, 20190917L, 20190917L, 20190917L, 20190917L, 20190917L, 
20190917L, 20190917L, 20190917L, 20190917L, 20190917L, 20190917L, 
20190917L, 20190917L, 20190917L, 20190917L, 20190917L, 20190917L, 
20190917L, 20190917L, 20190918L, 20190918L, 20190918L, 20190918L, 
20190918L, 20190918L, 20190918L, 20190918L, 20190918L, 20190918L, 
20190918L, 20190918L, 20190918L, 20190918L, 20190918L, 20190918L, 
20190918L, 20190918L, 20190918L, 20190918L, 20190918L, 20190918L, 
20190918L, 20190918L, 20190918L, 20190918L, 20190918L, 20190918L, 
20190918L, 20190918L, 20190918L, 20190918L, 20190918L, 20190918L, 
20190918L, 20190918L, 20190918L, 20190918L, 20190918L, 20190918L, 
20190918L, 20190918L, 20190918L, 20190918L, 20190918L, 20190918L, 
20190918L, 20190918L, 20190918L, 20190918L, 20190918L, 20190918L, 
20190918L, 20190918L, 20190918L, 20190918L, 20190918L, 20190918L, 
20190918L, 20190918L, 20190919L, 20190919L, 20190919L, 20190919L, 
20190919L, 20190919L, 20190919L, 20190919L, 20190919L, 20190919L, 
20190919L, 20190919L, 20190919L, 20190919L, 20190919L, 20190919L, 
20190919L, 20190919L, 20190919L, 20190919L, 20190919L, 20190919L, 
20190919L, 20190919L, 20190919L, 20190919L, 20190919L, 20190919L, 
20190919L, 20190919L, 20190919L, 20190919L, 20190919L, 20190919L, 
20190919L, 20190919L, 20190919L, 20190919L, 20190919L, 20190919L, 
20190919L, 20190919L, 20190919L, 20190919L, 20190919L, 20190919L, 
20190919L, 20190919L, 20190919L, 20190919L, 20190919L, 20190919L, 
20190919L, 20190919L, 20190919L, 20190919L, 20190919L, 20190919L, 
20190919L, 20190919L, 20190920L, 20190920L, 20190920L, 20190920L, 
20190920L, 20190920L, 20190920L, 20190920L, 20190920L, 20190920L, 
20190920L, 20190920L, 20190920L, 20190920L, 20190920L, 20190920L, 
20190920L, 20190920L, 20190920L, 20190920L, 20190920L, 20190920L, 
20190920L, 20190920L, 20190920L, 20190920L, 20190920L, 20190920L, 
20190920L, 20190920L, 20190920L, 20190920L, 20190920L, 20190920L, 
20190920L, 20190920L, 20190920L, 20190920L, 20190920L, 20190920L, 
20190920L, 20190920L, 20190920L, 20190920L, 20190920L, 20190920L, 
20190920L, 20190920L, 20190920L, 20190920L, 20190920L, 20190920L, 
20190920L, 20190920L, 20190920L, 20190920L, 20190920L, 20190920L, 
20190920L, 20190920L), length = c(7L, 13L, 15L, 16L, 17L, 18L, 
19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 
32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 
45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 
58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 
71L, 72L, 8L, 11L, 12L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 
22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 
35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 
48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 
61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 7L, 8L, 9L, 
11L, 12L, 13L, 14L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 
25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 
38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 
51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 
64L, 65L, 66L, 67L, 68L, 4L, 11L, 12L, 13L, 15L, 16L, 17L, 18L, 
19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 
32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 
45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 
58L, 59L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L, 70L, 
2L, 6L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 
21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 
34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 
47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 
60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 5L, 7L, 8L, 10L, 11L, 
12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 
25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 
38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 
51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 
64L, 65L, 66L, 2L, 3L, 4L, 5L, 7L, 10L, 11L, 12L, 13L, 14L, 15L, 
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 
29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 
42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 
55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 64L, 1L, 3L, 7L, 
11L, 12L, 13L, 14L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 
25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 37L, 
38L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 47L, 48L, 49L, 50L, 
51L, 52L, 53L, 54L, 55L, 56L, 57L, 58L, 59L, 60L, 61L, 62L, 63L, 
64L, 65L, 66L, 67L, 68L), talib = c(2, 0, 1, 1, 1, 0, 0, 0, 0, 
0, 0, 0, 2, 2, 0, 3, 2, 1, 2, 3, 3, 5, 2, 1, 3, 1, 5, 1, 5, 4, 
3, 6, 3, 4, 5, 8, 4, 4, 2, 18, 2, 3, 4, 7, 7, 2, 8, 3, 3, 10, 
6, 7, 8, 9, 9, 3, 5, 10, 6, 8, 2, 0, 0, 0, 0, 1, 1, 3, 2, 4, 
2, 5, 2, 9, 5, 1, 5, 6, 4, 5, 4, 16, 6, 11, 8, 11, 12, 7, 14, 
12, 18, 19, 14, 18, 18, 22, 16, 18, 11, 18, 24, 39, 13, 26, 22, 
21, 18, 31, 24, 31, 28, 27, 26, 43, 25, 26, 31, 37, 39, 28, 1, 
0, 0, 0, 0, 1, 1, 1, 1, 0, 5, 3, 5, 5, 6, 4, 6, 3, 8, 10, 2, 
8, 17, 8, 8, 6, 17, 20, 9, 11, 13, 16, 10, 9, 24, 29, 25, 14, 
14, 20, 20, 28, 24, 29, 27, 29, 24, 19, 29, 27, 30, 24, 30, 80, 
32, 65, 26, 45, 40, 46, 0, 0, 0, 0, 0, 2, 4, 5, 5, 4, 1, 1, 2, 
6, 7, 5, 2, 10, 3, 5, 9, 11, 6, 9, 6, 5, 12, 17, 10, 16, 11, 
12, 9, 6, 12, 17, 14, 22, 10, 13, 18, 11, 33, 8, 9, 9, 20, 18, 
12, 23, 20, 46, 22, 35, 26, 21, 12, 27, 26, 27, 0, 0, 1, 0, 0, 
2, 3, 1, 3, 1, 3, 3, 6, 3, 1, 4, 7, 2, 4, 10, 6, 8, 11, 13, 7, 
13, 5, 7, 10, 10, 13, 8, 13, 9, 15, 7, 14, 22, 16, 19, 20, 17, 
13, 21, 19, 29, 22, 15, 17, 26, 20, 26, 24, 19, 25, 29, 26, 18, 
34, 20, 0, 1, 1, 0, 0, 0, 1, 2, 2, 1, 3, 1, 2, 2, 1, 0, 1, 3, 
4, 5, 5, 5, 1, 5, 9, 7, 9, 8, 14, 5, 11, 11, 12, 13, 10, 9, 11, 
16, 198, 13, 13, 17, 8, 9, 26, 14, 19, 16, 17, 10, 22, 24, 20, 
22, 33, 17, 26, 16, 35, 49, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 
3, 6, 10, 5, 4, 0, 3, 0, 3, 7, 5, 7, 7, 6, 6, 7, 7, 8, 11, 7, 
9, 14, 15, 24, 11, 13, 13, 13, 16, 28, 20, 9, 15, 11, 13, 5, 
11, 14, 12, 12, 20, 16, 20, 23, 30, 18, 20, 20, 33, 0, 0, 0, 
1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 2, 7, 1, 1, 4, 3, 9, 6, 
4, 6, 5, 11, 7, 7, 16, 8, 7, 7, 8, 7, 12, 11, 12, 4, 5, 17, 13, 
14, 5, 15, 9, 6, 6, 15, 24, 8, 8, 13, 9, 11, 20, 7, 18, 16)), row.names = c(NA, 
-480L), class = c("grouped_df", "tbl_df", "tbl", "data.frame"
), groups = structure(list(date3 = 20190913:20190920, .rows = list(
    1:60, 61:120, 121:180, 181:240, 241:300, 301:360, 361:420, 
    421:480)), row.names = c(NA, -8L), class = c("tbl_df", "tbl", 
"data.frame"), .drop = TRUE))

Данные были разбиты на 60 строк для каждого уникального значения в столбце 1. Поэтому частота столбца 2 не достигает 1000, ее можно развернуть только в том случае, есливсе данные 500000 строк размещены здесь;что неосуществимо.

1 Ответ

1 голос
/ 02 октября 2019

Мы можем использовать cut или findInterval для разделения данных на группы, а затем sum. Также обратите внимание, что dput shared уже сгруппирован по date, следовательно, мы сначала ungroup. 

library(dplyr)
df %>%
  ungroup() %>%
  mutate(temp = findInterval(length, seq(min(length), max(length), 10))) %>%
  group_by(date3, temp) %>%
  dplyr::summarise(sum_talib  = sum(talib))

#      date3  temp sum_talib
#      <int> <int>     <dbl>
# 1 20190913     1         2
# 2 20190913     2         3
# 3 20190913     3        10
# 4 20190913     4        26
# 5 20190913     5        46
# 6 20190913     6        56
# 7 20190913     7        70
# 8 20190913     8        14
# 9 20190914     1         2
#10 20190914     2        11
# … with 47 more rows

В базе R мы можем использовать transform для добавления нового столбца, а затем aggregate

aggregate(talib~date3+temp, transform(df, 
     temp = findInterval(length, seq(min(length), max(length), 10))), sum)
Добро пожаловать на сайт PullRequest, где вы можете задавать вопросы и получать ответы от других членов сообщества.

Нет похожих вопросов

...