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

现在我要：

按我完成的特定日期（2018-11-02）过滤

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

按用户分组，并汇总他们在门票上的总支出。

我尝试了使用group_by或aggregate的几种方法，但是我设法获得的只是一个附加的列，而不是对每个用户的值进行分组或汇总。

理想的最终输出是使我能够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"))

关于如何处理此问题的任何建议？

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]

那给了我

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

Answer 1

尝试一下

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)

按行值分组，同时汇总其他单元格值的总值

1 个答案: