CODEWITHSUNDEEP
r
Yves
Yves

Reputation: 556

How to aggregate a data frame based on multiple columns in R?

I have the following df (dput() at the end of question):

          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

What I want is to create a new df, where I see which person goes from (type=="departure") which zone (ID1) to (type=="arrival") which zone (ID1) and at which departure bin.

As can be seen in the last 4 rows of the example above, it is possible that the same person has multiple arrival and departure. It is important therefore that the two, which are closest to one another are combined (therefore smallest delta bin).

Therefore the new df should look something like this:

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

As you can see, the person 514681 travels back and forth between 42 and 233.

I guess you can say that I want to aggregate the data based on the person. I am not quite sure how this can be accomplished and would be very happy for some help!

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")

Upvotes: 1

Views: 54

Answers (2)

Michael M
Michael M

Reputation: 1593

With dplyr, this could be something like this:

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

Here a refined version that tries to satisfy the additional logic. Since the problem is not too clear, I am quite certain that it is not yet 100% fine. 

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])

Upvotes: 1

Daniel O
Daniel O

Reputation: 4358

In base R we can split the data and only keep the person/bin combinations that have more than 1 row (meaning there is an arrival and a departure) then extract the data.

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])))

output:

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

Upvotes: 0

Related Questions