按行值分组，同时聚合其他单元格值的总值

Question

我有一个看起来像这样的数据框

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

现在我想：

1. 按我完成的特定日期 (2018-11-02) 过滤

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

1. 按用户分组并将他们在门票上的总支出相加。

我尝试了几种使用 group_by 或聚合的方法，但我设法得到的只是一个额外的列，而不是对每个用户的值进行分组或聚合。

1. 理想情况下，我的最终输出可以让我提取出在门票上花费最多的 10 个用户，例如：

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

示例数据

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"))

关于如何解决这个问题的任何建议？

编辑： 最终解决方案

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]

这给了我

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

Answer 1

试试这个

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

或

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

或

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