Reshaping data in R with “login” “logout” times

Question

I\'m new to R, and am working on a side project for my own purposes. I have this data (reproducable dput of this is at the end of the question):

     X              


        

Answer 1

Another way to do it:

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 2

Using rowid() from the data.table-package along with 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>

Answer 3

since tidyr 1.0.0 the following is possible :

suppressPackageStartupMessages(library(tidyverse))
pivot_wider(samp[-1], names_from = "state", values_from = "datetime", 
            values_fn = list(datetime = list)) %>%
  mutate(left = map2(left, lengths(joined),`length<-`)) %>%
  unchop(everything())

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

• values_fn is set to store multiple values for a given user in a list
• Because thes don't have the same length we complete the short ones with NAs using mutateand length<-
• Then we unnest vertically by using unchop

Answer 4

We need a sequence number that determines the order of datetime within each user+state group. The sequence number used here, in particular, it is a meaningful consecutive count of joined-[left] records in the reshaped data frame.

Using spread from 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>

This may also be written with dcast from reshape2

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

Answer 5

We can make use of the order of "left" and "joined", and match when one follows the other for each user.

For this I'm going to use 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 6

Base version:

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),]