Группировка по значению строки при агрегировании общего значения другого значения ячейки

Question

У меня есть фрейм данных, который выглядит примерно так

user booking_date origin destination  price  sale_channel
1 user5   2018-11-01    MAD         PMI  58.20        online
2 user7   2018-11-01    DUB         MAD 147.50        online
3 user4   2018-11-02    TFS         MAD  24.05        online
4 user7   2018-11-01    LPA         MAD  37.30   call center
5 user1   2018-11-01    AMS         MAD 149.74 travel agency
6 user1   2018-11-01    MAD         PMI  19.95        online

Теперь я хочу:

1. Фильтр по указанной дате c (2018-11-02 ) что я сделал по

df <- df[df$booking_date == '2018-11-02',]

Группируйте по пользователю и суммируйте их общие расходы на билеты.

Я пробовал несколько подходов с использованием group_by или агрегата, но все, что мне удалось получить, это дополнительный столбец, но не группирование или агрегирование значения на пользователя.

Мой конечный результат в идеале позволил бы мне выделить 10 пользователей, которые больше всего тратят на билеты, например, так:

'user1' 'user10' 'user 7' etc.

Пример данных

structure(list(user = c("user4", "user5", "user3", "user10", 
"user1", "user2", "user7", "user6", "user5", "user6", "user6", 
"user7", "user1", "user7", "user4", "user4", "user1", "user7", 
"user7", "user8", "user4", "user10", "user4", "user8", "user3", 
"user9", "user5", "user2", "user5", "user3", "user3", "user9", 
"user6", "user10", "user9", "user5", "user3", "user5", "user7", 
"user9", "user2", "user2", "user7", "user10", "user7", "user3", 
"user1", "user2", "user8", "user6", "user6", "user10", "user4", 
"user7", "user4", "user1", "user4", "user2", "user1", "user7", 
"user5", "user4", "user4", "user7", "user10"), booking_date = structure(c(17837, 
17837, 17837, 17837, 17837, 17837, 17837, 17837, 17837, 17837, 
17837, 17837, 17837, 17837, 17837, 17837, 17837, 17837, 17837, 
17837, 17837, 17837, 17837, 17837, 17837, 17837, 17837, 17837, 
17837, 17837, 17837, 17837, 17837, 17837, 17837, 17837, 17837, 
17837, 17837, 17837, 17837, 17837, 17837, 17837, 17837, 17837, 
17837, 17837, 17837, 17837, 17837, 17837, 17837, 17837, 17837, 
17837, 17837, 17837, 17837, 17837, 17837, 17837, 17837, 17837, 
17837), class = "Date"), origin = c("TFS", "MAD", "TFN", "MAD", 
"LYS", "LPA", "MAD", "MAD", "AMS", "MAD", "MAD", "MAD", "MAD", 
"LPA", "MAD", "MAD", "MAD", "TXL", "MAD", "MAD", "TXL", "TXL", 
"TFS", "MAD", "NAP", "TFS", "PMI", "TLS", "TFS", "NTE", "AMS", 
"FUE", "TFN", "CPH", "TFN", "MAD", "SVQ", "SCQ", "SVQ", "MAD", 
"PMI", "MAD", "PMI", "MAD", "MAD", "MAD", "MAD", "MAD", "SVQ", 
"NCE", "CDG", "MAD", "MAD", "MAD", "MAD", "MAD", "TFN", "LGW", 
"LGW", "MAD", "TFN", "MAD", "RNS", "AGP", "CDG"), destination = c("MAD", 
"DUB", "MAD", "TFS", "MAD", "MAD", "NAP", "TLS", "MAD", "SCQ", 
"LPA", "TFN", "TXL", "MAD", "TFN", "SVQ", "ACE", "MAD", "TLS", 
"SCQ", "MAD", "MAD", "MAD", "LPA", "MAD", "MAD", "MAD", "MAD", 
"MAD", "MAD", "MAD", "MAD", "MAD", "MAD", "MAD", "CAG", "MAD", 
"MAD", "MAD", "LPA", "MAD", "CDG", "MAD", "LPA", "TFS", "TFN", 
"PMI", "NAP", "MAD", "MAD", "MAD", "LPA", "LGW", "LPA", "CDG", 
"SPC", "MAD", "MAD", "MAD", "SCQ", "MAD", "SVQ", "MAD", "MAD", 
"MAD"), price = c(24.0499992371, 41.5400009155, 251.199996948, 
15.6000003815, 44.0099983215, 73.8499984741, 115.470001221, 69.4400024414, 
81.3899993896, 15.2399997711, 41.1199989319, 274.559997559, 150, 
29.3199996948, 332.440002441, 94.9100036621, 97.9800033569, 55.1199989319, 
81.7399978638, 4.86000013351, 39.0299987793, 53.6300010681, 39.3199996948, 
114.559997559, 65.4000015259, 96.2900009155, 41.75, 28.9099998474, 
25.1900005341, 14, 50.3100013733, 47.2999992371, 53.8199996948, 
91.3199996948, 77.6800003052, 17.8099994659, 96.5400009155, 27.6900005341, 
34.1399993896, 34.3300018311, 15.5600004196, 158.449996948, 45.2999992371, 
36.8100013733, 50.6800003052, 62.9000015259, 13.7399997711, 49.4399986267, 
66.1100006104, 95.4400024414, 41.8400001526, 69.8300018311, 60.6599998474, 
34.3300018311, 97.1399993896, 84.3099975586, 25.8099994659, 185.899993896, 
34.8899993896, 304.380004883, 15.1300001144, 29.1399993896, 133.529998779, 
208.910003662, 152.960006714), sale_channel = c("online", "online", 
"travel agency", "online", "online", "online", "travel agency", 
"online", "travel agency", "travel agency", "travel agency", 
"online", "travel agency", "online", "online", "travel agency", 
"online", "travel agency", "travel agency", "call center", "online", 
"online", "online", "travel agency", "travel agency", "travel agency", 
"online", "online", "online", "online", "online", "travel agency", 
"travel agency", "online", "travel agency", "call center", "online", 
"travel agency", "online", "online", "travel agency", "travel agency", 
"online", "travel agency", "online", "online", "online", "travel agency", 
"online", "travel agency", "travel agency", "online", "online", 
"online", "online", "online", "online", "online", "online", "travel agency", 
"travel agency", "travel agency", "online", "travel agency", 
"online"), total = c(876.0300006858, 250.5000009536, 540.3500022886, 
420.1500110628, 424.9299983977, 512.1099882118, 1233.9500045785, 
316.9000034332, 250.5000009536, 316.9000034332, 316.9000034332, 
1233.9500045785, 424.9299983977, 1233.9500045785, 876.0300006858, 
876.0300006858, 424.9299983977, 1233.9500045785, 1233.9500045785, 
185.52999830291, 876.0300006858, 420.1500110628, 876.0300006858, 
185.52999830291, 540.3500022886, 255.6000022889, 250.5000009536, 
512.1099882118, 250.5000009536, 540.3500022886, 540.3500022886, 
255.6000022889, 316.9000034332, 420.1500110628, 255.6000022889, 
250.5000009536, 540.3500022886, 250.5000009536, 1233.9500045785, 
255.6000022889, 512.1099882118, 512.1099882118, 1233.9500045785, 
420.1500110628, 1233.9500045785, 540.3500022886, 424.9299983977, 
512.1099882118, 185.52999830291, 316.9000034332, 316.9000034332, 
420.1500110628, 876.0300006858, 1233.9500045785, 876.0300006858, 
424.9299983977, 876.0300006858, 512.1099882118, 424.9299983977, 
1233.9500045785, 250.5000009536, 876.0300006858, 876.0300006858, 
1233.9500045785, 420.1500110628)), row.names = c(3L, 
26L, 37L, 42L, 48L, 82L, 89L, 100L, 112L, 124L, 133L, 144L, 148L, 
150L, 166L, 167L, 173L, 182L, 217L, 243L, 259L, 285L, 300L, 304L, 
306L, 336L, 341L, 366L, 388L, 397L, 413L, 417L, 423L, 452L, 457L, 
473L, 474L, 478L, 482L, 483L, 486L, 496L, 499L, 504L, 510L, 513L, 
529L, 531L, 558L, 605L, 615L, 628L, 629L, 664L, 669L, 672L, 684L, 
722L, 730L, 752L, 766L, 767L, 779L, 804L, 819L), class = c("data.table", 
"data.frame"))

Любые рекомендации о том, как подойти к этому?

Редактировать: Окончательное решение

df <- df[df$booking_date == '2018-11-02',]
total <- aggregate(price ~ user, df, sum)
top_10 <- total[order(total$price, decreasing = T), ]
top_10[1:5, 1]

Это дает мне

"user7" "user4" "user3" "user2" "user1"

Answer 1

Попробуйте это

total <- aggregate(price ~ user, df, sum)
total[order(total$price, decreasing = T), ]

или

dplyr::arrange(total, desc(price))

или

sort(tapply(df$price, df$user, sum), decreasing = T)