Rimodellamento dei dati in R con "accesso" "uscita" orari

Question

Sono nuovo a R e sto lavorando a un progetto parallelo per i miei scopi. Ho questi dati (dput riproducibile di questo è alla fine della questione):

 X   datetime user state 
1 1 2016-02-19 19:13:26 User1 joined 
2 2 2016-02-19 19:21:18 User2 joined 
3 3 2016-02-19 19:21:33 User1 joined 
4 4 2016-02-19 19:35:38 User1 joined 
5 5 2016-02-19 19:44:15 User1 joined 
6 6 2016-02-19 19:48:55 User1 joined 
7 7 2016-02-19 19:52:40 User1 joined 
8 8 2016-02-19 19:53:15 User3 joined 
9 9 2016-02-19 20:02:34 User3 joined 
10 10 2016-02-19 20:13:48 User3 joined 
19 637 2016-02-19 19:13:32 User1 left 
20 638 2016-02-19 19:25:26 User1 left 
21 639 2016-02-19 19:30:30 User2 left 
22 640 2016-02-19 19:42:16 User1 left 
23 641 2016-02-19 19:47:59 User1 left 
24 642 2016-02-19 19:51:06 User1 left 
25 643 2016-02-19 20:02:26 User3 left 

voglio farlo sembrare come questo:

user joined    left 
1 User1 2016-02-19 19:13:26 2016-02-19 19:13:32 
2 User2 2016-02-19 19:21:18 2016-02-19 19:30:30 
3 User3 2016-02-19 19:53:15 2016-02-19 20:02:26 
4 User1 2016-02-19 19:21:33 2016-02-19 19:25:26 
. 
. 
. 

sto guardando tidyr come c'è un po 'di rimodellamento coinvolto ovviamente, ma non riesco a capire cosa si debba fare esattamente. È possibile anche questo (senza cicli/quantità massicce di codice procedurale)? Il problema che non riesco a capire come aggirare è che non c'è modo di sapere che un particolare record "di sinistra" dovrebbe essere unito a un particolare record "unito". Gli esempi che riesco a trovare riguardano tutti un mese o giorno statico su cui vengono raccolti altri valori. Devo aggiungere che non è necessariamente garantito che tutti i record abbiano un valore "sinistro" (un utente potrebbe ancora essere "unito").

Ecco l'output dput di un campione dei dati:

> dput(samp) 
structure(list(X = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 637L, 638L, 639L, 640L, 
641L, 642L, 643L, 644L, 645L, 646L, 647L, 648L, 649L, 650L, 651L 
), datetime = structure(c(1L, 3L, 4L, 7L, 9L, 11L, 13L, 14L, 
16L, 18L, 21L, 22L, 23L, 26L, 27L, 30L, 32L, 33L, 2L, 5L, 6L, 
8L, 10L, 12L, 15L, 17L, 19L, 20L, 24L, 25L, 28L, 29L, 31L), .Label = c("2016-02-19 19:13:26", 
"2016-02-19 19:13:32", "2016-02-19 19:21:18", "2016-02-19 19:21:33", 
"2016-02-19 19:25:26", "2016-02-19 19:30:30", "2016-02-19 19:35:38", 
"2016-02-19 19:42:16", "2016-02-19 19:44:15", "2016-02-19 19:47:59", 
"2016-02-19 19:48:55", "2016-02-19 19:51:06", "2016-02-19 19:52:40", 
"2016-02-19 19:53:15", "2016-02-19 20:02:26", "2016-02-19 20:02:34", 
"2016-02-19 20:13:38", "2016-02-19 20:13:48", "2016-02-19 20:42:27", 
"2016-02-19 20:48:22", "2016-02-19 20:49:31", "2016-02-19 20:59:58", 
"2016-02-19 21:06:20", "2016-02-19 21:10:43", "2016-02-19 21:11:13", 
"2016-02-19 21:11:15", "2016-02-19 21:11:22", "2016-02-19 21:17:33", 
"2016-02-19 22:02:45", "2016-02-19 22:05:18", "2016-02-19 22:05:37", 
"2016-02-19 22:05:47", "2016-02-19 22:30:30"), class = "factor"), 
    user = structure(c(1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 
    3L, 4L, 1L, 1L, 4L, 4L, 4L, 3L, 1L, 1L, 2L, 1L, 1L, 1L, 3L, 
    3L, 3L, 1L, 4L, 1L, 1L, 4L, 4L), .Label = c("User1", "User2", 
    "User3", "User4"), class = "factor"), state = structure(c(1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L), .Label = c("joined", "left"), class = "factor")), .Names = c("X", 
"datetime", "user", "state"), class = "data.frame", row.names = c(NA, 
-33L)) 

Answer 1

possiamo fare uso dell 'ordine di "sinistra" e "uniti", e partita quando uno segue l'altro per ogni utente.

Per questo ho intenzione di utilizzare library(data.table)

library(data.table) 
setDT(df) 

## order the data by user and datetime 
df <- df[order(user, datetime)] 
## add an 'order' column, which is a sequence from 1 to lenght() 
## for each user 
df[, order := seq(1:.N), by=user] 

## split the left and joins 
dt_left <- df[state == "left"] 
dt_joined <- df[state == "joined"] 

## assuming 'left' is after 'joined', shift the 'order' back for left 
dt_left[, order := order - 1] 

## join user an dorder (and subsetting relevant columns) 
## keeping when there's a 'joined' but not a 'left' 
dt <- dt_left[, .(user, order, datetime)][dt_joined[, .(user, order, datetime)], on=c("user", "order"), nomatch=NA] 

## rename columns 
setnames(dt, c("datetime", "i.datetime"), c("left", "joined")) 

    user order    left    joined 
1: User1  1 2016-02-19 19:13:32 2016-02-19 19:13:26 
2: User1  3 2016-02-19 19:25:26 2016-02-19 19:21:33 
3: User1  5 2016-02-19 19:42:16 2016-02-19 19:35:38 
4: User1  7 2016-02-19 19:47:59 2016-02-19 19:44:15 
5: User1  9 2016-02-19 19:51:06 2016-02-19 19:48:55 
6: User1 11 2016-02-19 20:48:22 2016-02-19 19:52:40 
7: User1 13 2016-02-19 21:11:13 2016-02-19 21:06:20 
8: User1 15 2016-02-19 21:17:33 2016-02-19 21:11:15 
9: User2  1 2016-02-19 19:30:30 2016-02-19 19:21:18 
10: User3  1 2016-02-19 20:02:26 2016-02-19 19:53:15 
11: User3  3 2016-02-19 20:13:38 2016-02-19 20:02:34 
12: User3  5 2016-02-19 20:42:27 2016-02-19 20:13:48 
13: User3  7     NA 2016-02-19 20:49:31 
14: User3  8     NA 2016-02-19 22:30:30 
15: User4  1 2016-02-19 21:10:43 2016-02-19 20:59:58 
16: User4  3 2016-02-19 22:02:45 2016-02-19 21:11:22 
17: User4  5 2016-02-19 22:05:37 2016-02-19 22:05:18 
18: User4  7     NA 2016-02-19 22:05:47 

Answer 2

versione Base:

samp$count <- with(samp, ave(as.character(user),list(state,user),FUN=seq_along)) 

out <- merge(
    samp[samp$state=="joined",c("user","datetime","count")], 
    samp[samp$state=="left",c("user","datetime","count")], 
    by=c("user","count"), all.x=TRUE 
) 

out[order(out$count),] 

Answer 3

Un altro modo per farlo:

library(tidyr) 
df <- df %>% spread(state, datetime) 

df_joined <- df[!is.na(df$joined), 2:3] 
df_joined <- df_joined[with(df_joined, order(user, joined)), ] 

df_left <- df[!is.na(df$left), c(2, 4)] 
df_left <- df_left[with(df_left, order(user, left)), ] 

merge(df_joined, df_left, all = TRUE, by = 'user') 

Answer 4

Abbiamo bisogno di un numero di sequenza che determina l'ordine di data/ora all'interno di ciascun utente + gruppo di stati. Il numero di sequenza usato qui, in particolare, è un conteggio consecutivo significativo di record uniti a sinistra nel frame di dati rimodellati.

Utilizzando spread da tidyr

spread(within(samp[,-1],seq<-ave(as.numeric(datetime),user,state,FUN=order)), 
    state,datetime) 



    user seq    joined    left 
1 User1 1 2016-02-19 19:13:26 2016-02-19 19:13:32 
2 User1 2 2016-02-19 19:21:33 2016-02-19 19:25:26 
3 User1 3 2016-02-19 19:35:38 2016-02-19 19:42:16 
4 User1 4 2016-02-19 19:44:15 2016-02-19 19:47:59 
5 User1 5 2016-02-19 19:48:55 2016-02-19 19:51:06 
6 User1 6 2016-02-19 19:52:40 2016-02-19 20:48:22 
7 User1 7 2016-02-19 21:06:20 2016-02-19 21:11:13 
8 User1 8 2016-02-19 21:11:15 2016-02-19 21:17:33 
9 User2 1 2016-02-19 19:21:18 2016-02-19 19:30:30 
10 User3 1 2016-02-19 19:53:15 2016-02-19 20:02:26 
11 User3 2 2016-02-19 20:02:34 2016-02-19 20:13:38 
12 User3 3 2016-02-19 20:13:48 2016-02-19 20:42:27 
13 User3 4 2016-02-19 20:49:31    <NA> 
14 User3 5 2016-02-19 22:30:30    <NA> 
15 User4 1 2016-02-19 20:59:58 2016-02-19 21:10:43 
16 User4 2 2016-02-19 21:11:22 2016-02-19 22:02:45 
17 User4 3 2016-02-19 22:05:18 2016-02-19 22:05:37 
18 User4 4 2016-02-19 22:05:47    <NA> 

Questo può anche essere scritto con dcast da reshape2

dcast(within(samp,seq<-ave(as.numeric(datetime),user,state,FUN=order)), 
    user+seq~state, value.var="datetime") 

Answer 5

Utilizzando rowid() dal data.table-pacchetto insieme al dcast:

require(data.table) 
dcast(dt, user + rowid(user, state) ~ state, value.var="datetime") 

#  user user_1    joined    left 
# 1: User1  1 2016-02-19 19:13:26 2016-02-19 19:13:32 
# 2: User1  2 2016-02-19 19:21:33 2016-02-19 19:25:26 
# 3: User1  3 2016-02-19 19:35:38 2016-02-19 19:42:16 
# 4: User1  4 2016-02-19 19:44:15 2016-02-19 19:47:59 
# 5: User1  5 2016-02-19 19:48:55 2016-02-19 19:51:06 
# 6: User1  6 2016-02-19 19:52:40    <NA> 
# 7: User2  1 2016-02-19 19:21:18 2016-02-19 19:30:30 
# 8: User3  1 2016-02-19 19:53:15 2016-02-19 20:02:26 
# 9: User3  2 2016-02-19 20:02:34    <NA> 
# 10: User3  3 2016-02-19 20:13:48    <NA>