Question

我有以下df（问题末尾的dput()）：

          link person   type      ID1   bin
1311752 793482 514677   departure 347 36900
1020897  66154 514677   arrival   379 36900
1376554 858880 514679   departure 374 42300
1374956 858880 514679   arrival   374 46800
1567933 992830 514680   arrival   140 31500
1568804 992830 514680   departure 140 32400
726546   87723 514681   departure  42 25200
726547  481929 514681   arrival   233 27900
723875  481929 514681   departure 233 45000
1396595  87723 514681   arrival    42 45900

我想要创建一个新的df，在其中我看到哪个person从（type=="departure"）哪个区域（ID1）到（type=="arrival"）哪个区域（ID1）和出发地bin。

从上面示例的最后4行可以看出，同一个person可能有多个arrival和departure。因此，重要的是将彼此最接近的两个组合（因此最小的增量bin）。

因此，新的df应该看起来像这样：

person  departure   arrival bin
514677  347         379     36900
514679  374         374     42300
514681  42          233     25200
514681  233         42      45000
...

如您所见，人514681在42至233之间来回走动。

我想您可以说我想基于person汇总数据。我不太确定该如何完成，并且会很高兴获得帮助！

structure(list(link = c("793482", "66154", "858880", "858880", 
"992830", "992830", "858880", "858880", "514143", "87723", "481929", 
"481929", "87723", "87723", "964586", "87723", "1013453", "pt_8591034", 
"973606", "pt_8591034", "481929", "481929", "87723", "87723", 
"399738", "399738", "87723", "738635", "481929", "738635", "481929", 
"738635", "301867", "89053", "92917", "92917", "981899", "981899", 
"89053", "92917", "89053", "89053", "699715", "699715", "89053", 
"452176", "738635", "452176", "738635", "641738", "712438", "699715", 
"215822", "621953", "634264", "362138", "634264", "634264", "763815", 
"763815", "631191", "631191", "763815", "763815", "634264", "634264", 
"10826", "10826", "634264", "999977", "1046826", "1046826", "634264", 
"999977", "848815", "848815", "999977", "999977", "848815", "848815", 
"999977", "261239", "993653", "287772", "763815", "763815", "287772", 
"494898", "824071", "594222", "494898", "494898", "452642", "452642", 
"494898", "494898", "452642", "494898", "787840", "442285"), 
    person = c(514677L, 514677L, 514679L, 514679L, 514680L, 514680L, 
    514680L, 514680L, 514680L, 514681L, 514681L, 514681L, 514681L, 
    514682L, 514682L, 514683L, 514683L, 514684L, 514684L, 514684L, 
    514684L, 514684L, 514684L, 514685L, 514685L, 514685L, 514685L, 
    514688L, 514688L, 514688L, 514688L, 514690L, 514690L, 514707L, 
    514707L, 514707L, 514707L, 514707L, 514707L, 514708L, 514708L, 
    514709L, 514709L, 514709L, 514709L, 514710L, 514710L, 514710L, 
    514710L, 514711L, 514711L, 514733L, 514733L, 514757L, 514757L, 
    514757L, 514757L, 514759L, 514759L, 514759L, 514759L, 514759L, 
    514759L, 514759L, 514759L, 514760L, 514760L, 514760L, 514760L, 
    514774L, 514774L, 514774L, 514774L, 514774L, 514774L, 514774L, 
    514774L, 514774L, 514774L, 514774L, 514774L, 514782L, 514782L, 
    514786L, 514786L, 514786L, 514786L, 514802L, 514802L, 514802L, 
    514802L, 514803L, 514803L, 514803L, 514803L, 514803L, 514803L, 
    514804L, 514804L, 514805L), type = c("departure", "arrival", 
    "departure", "arrival", "arrival", "departure", "arrival", 
    "departure", "arrival", "departure", "arrival", "departure", 
    "arrival", "departure", "arrival", "departure", "arrival", 
    "arrival", "departure", "departure", "arrival", "departure", 
    "arrival", "departure", "arrival", "departure", "arrival", 
    "departure", "arrival", "departure", "arrival", "departure", 
    "arrival", "departure", "arrival", "departure", "arrival", 
    "departure", "arrival", "departure", "arrival", "departure", 
    "arrival", "departure", "arrival", "departure", "arrival", 
    "arrival", "departure", "departure", "arrival", "departure", 
    "arrival", "departure", "arrival", "departure", "arrival", 
    "departure", "arrival", "departure", "arrival", "departure", 
    "arrival", "departure", "arrival", "departure", "arrival", 
    "departure", "arrival", "departure", "arrival", "departure", 
    "arrival", "departure", "arrival", "departure", "arrival", 
    "departure", "arrival", "departure", "arrival", "departure", 
    "arrival", "departure", "arrival", "departure", "arrival", 
    "departure", "arrival", "departure", "arrival", "departure", 
    "arrival", "departure", "arrival", "departure", "arrival", 
    "departure", "arrival", "departure"), ID1 = c(347L, 379L, 
    374L, 374L, 140L, 140L, 374L, 374L, 401L, 42L, 233L, 233L, 
    42L, 42L, 496L, 42L, 235L, 172L, 231L, 172L, 233L, 233L, 
    42L, 42L, 267L, 267L, 42L, 58L, 233L, 58L, 233L, 58L, 292L, 
    96L, 264L, 264L, 26L, 26L, 96L, 264L, 96L, 96L, 266L, 266L, 
    96L, 57L, 58L, 57L, 58L, 123L, 55L, 266L, 77L, 412L, 407L, 
    410L, 407L, 407L, 297L, 297L, 169L, 169L, 297L, 297L, 407L, 
    407L, 566L, 566L, 407L, 403L, 380L, 380L, 407L, 403L, 436L, 
    436L, 403L, 403L, 436L, 436L, 403L, 545L, 324L, 448L, 297L, 
    297L, 448L, 209L, 152L, 205L, 209L, 209L, 204L, 204L, 209L, 
    209L, 204L, 209L, 383L, 181L), bin = c(36900, 36900, 42300, 
    46800, 31500, 32400, 34200, 61200, 72000, 25200, 27900, 45000, 
    45900, 27900, 31500, 30600, 33300, 56700, 56700, 60300, 61200, 
    63900, 65700, 28800, 32400, 75600, 77400, 24300, 26100, 45900, 
    47700, 51300, 53100, 27900, 31500, 36900, 38700, 41400, 42300, 
    62100, 63900, 15300, 16200, 45900, 47700, 35100, 35100, 67500, 
    67500, 63900, 65700, 45900, 46800, 63000, 64800, 90000, 90900, 
    27000, 28800, 45000, 46800, 63000, 67500, 68400, 72000, 48600, 
    50400, 53100, 55800, 37800, 38700, 40500, 41400, 47700, 48600, 
    51300, 52200, 62100, 63000, 63900, 64800, 22500, 24300, 19800, 
    20700, 54000, 57600, 21600, 22500, 55800, 56700, 36900, 38700, 
    51300, 52200, 62100, 63900, 30600, 34200, 33300)), row.names = c(1311752L, 
1020897L, 1376554L, 1374956L, 1567933L, 1568804L, 1378922L, 1378604L, 
796332L, 1396610L, 726547L, 723875L, 1396595L, 1398179L, 1513826L, 
1396821L, 27568L, 1607272L, 1542798L, 1606758L, 721147L, 720198L, 
1397549L, 1398033L, 621908L, 626763L, 1397225L, 1184210L, 729418L, 
1185149L, 728038L, 1183207L, 465692L, 1426554L, 1460666L, 1457289L, 
1551709L, 1550925L, 1429217L, 1461183L, 1427235L, 1428930L, 1117203L, 
1119814L, 1431191L, 683362L, 1183236L, 684338L, 1184602L, 990483L, 
1142535L, 1125893L, 288323L, 947577L, 966966L, 544664L, 969372L, 
964351L, 1230180L, 1231933L, 960341L, 960438L, 1238195L, 1241438L, 
965853L, 964667L, 105034L, 106179L, 966724L, 1586724L, 72120L, 
76232L, 969048L, 1587092L, 1360314L, 1361152L, 1585499L, 1586919L, 
1361549L, 1359631L, 1587109L, 378600L, 1573640L, 428095L, 1236840L, 
1235995L, 430374L, 757402L, 1331227L, 916001L, 760182L, 757620L, 
686066L, 685765L, 759010L, 760140L, 687455L, 758617L, 1287055L, 
676520L), class = "data.frame")

Answer 1

对于dplyr，可能是这样的：

library(dplyr)
data %>% 
  group_by(person) %>% 
  summarize(departure = ID1[type == "departure"][1],
            arrival = ID1[type == "arrival"][1],
            bin = bin[type == "departure"][1])

这里是试图满足附加逻辑的改进版本。由于问题还不太清楚，所以我可以肯定它还不是100％罚款。

data %>%
  arrange(person, bin) %>%
  group_by(person) %>%
  mutate(pair = rep(row_number(), each = 2, length.out = n())) %>%
  group_by(person, pair) %>%
  summarize(departure = ID1[type == "departure"][1],
            arrival = ID1[type == "arrival"][1],
            bin = bin[type == "departure"][1])

Answer 2

在基数R中，我们可以拆分数据，仅保留具有超过1行（表示有到达和离开）的人员/箱位组合，然后提取数据。

splitdata <- split(df,with(df,paste0(person,bin)))
splitdata <- splitdata[sapply(splitdata,nrow)>1]

do.call(rbind,
    lapply(splitdata, function(x) 
        data.frame(
            person = x[1,2], 
            departure = x$ID1[x$type == "departure"],
            arrival = x$ID1[x$type == "arrival"],
            bin = x[1,5])))

输出：

            person departure arrival   bin
51467736900 514677       347     379 36900
51468456700 514684       231     172 56700
51471035100 514710        57      58 35100
51471067500 514710        58      57 67500

如何基于R中的多个列聚合数据帧？

2 个答案: