R: calculate time difference between specific events - r

Question

I have the following dataset:
df = data.frame(cbind(user_id = c(rep(1, 4), rep(2,4)),
complete_order = c(rep(c(1,0,0,1), 2)),
order_date = c('2015-01-28', '2015-01-31', '2015-02-08', '2015-02-23', '2015-01-25', '2015-01-28', '2015-02-06', '2015-02-21')))
library(lubridate)
df$order_date = as_date(df$order_date)
user_id complete_order order_date
1 1 2015-01-28
1 0 2015-01-31
1 0 2015-02-08
1 1 2015-02-23
2 1 2015-01-25
2 0 2015-01-28
2 0 2015-02-06
2 1 2015-02-21
I'm trying to calculate the difference in days between only completed orders for each user. The desirable outcome would look like this:
user_id complete_order order_date complete_order_time_diff
<fctr> <fctr> <date> <time>
1 1 2015-01-28 NA days
1 0 2015-01-31 3 days
1 0 2015-02-08 11 days
1 1 2015-02-23 26 days
2 1 2015-01-25 NA days
2 0 2015-01-28 3 days
2 0 2015-02-06 12 days
2 1 2015-02-21 27 days
when I try this solution:
library(dplyr)
df %>%
group_by(user_id) %>%
mutate(complete_order_time_diff = order_date[complete_order==1]-lag(order_date[complete_order==1))
it returns the error:
Error: incompatible size (3), expecting 4 (the group size) or 1
Any help with this will be great, thank you!

Answer 1

try this
library(dplyr)
df %>% group_by(user_id, complete_order) %>%
mutate(c1 = order_date - lag(order_date)) %>%
group_by(user_id) %>% mutate(c2 = order_date - lag(order_date)) %>% ungroup %>%
mutate(complete_order_time_diff = ifelse(complete_order==0, c2, c1)) %>%
select(-c(c1, c2))
Update
for multiple cancelled orders
df %>% mutate(c3=cumsum( complete_order != "0")) %>% group_by(user_id, complete_order) %>%
mutate(c1 = order_date - lag(order_date)) %>%
group_by(user_id) %>% mutate(c2 = order_date - lag(order_date)) %>%
mutate(c2=as.numeric(c2)) %>% group_by(user_id, c3) %>%
mutate(c2=cumsum(ifelse(complete_order==1, 0, c2))) %>% ungroup %>%
mutate(complete_order_time_diff = ifelse(complete_order==0, c2, c1)) %>%
select(-c(c1, c2, c3))
logic
c3 is an id every time there is an order (i.e. complete_order not 0) to increment by 1.
c1 calculates the day difference bu user_id (but for non complete orders the result is wrong)
c2 fixes this inconsistency of c1 with respect to non complete orders.
hope this clears things.
I would suggest you work with combinations of group_by() and mutate(cumsum()) to better understand the results of having more than one grouped variable.

Answer 2

It seems that you're looking for the distance of each order from the last completed one. Having a binary vector, x, c(NA, cummax(x * seq_along(x))[-length(x)]) gives the indices of the last "1" seen before each element. Then, subtracting each element of "order_date" from the "order_date" at that respective index gives the desired output. E.g.
set.seed(1453); x = sample(0:1, 10, TRUE)
set.seed(1821); y = sample(5, 10, TRUE)
cbind(x, y,
last_x = c(NA, cummax(x * seq_along(x))[-length(x)]),
y_diff = y - y[c(NA, cummax(x * seq_along(x))[-length(x)])])
# x y last_x y_diff
# [1,] 1 3 NA NA
# [2,] 0 3 1 0
# [3,] 1 5 1 2
# [4,] 0 1 3 -4
# [5,] 0 3 3 -2
# [6,] 1 5 3 0
# [7,] 1 1 6 -4
# [8,] 0 3 7 2
# [9,] 0 4 7 3
#[10,] 1 5 7 4
On your data, first format df for convenience:
df$order_date = as.Date(df$order_date)
df$complete_order = df$complete_order == "1" # lose the 'factor'
And, then, either apply the above approach after a group_by:
library(dplyr)
df %>% group_by(user_id) %>%
mutate(time_diff = order_date -
order_date[c(NA, cummax(complete_order * seq_along(complete_order))[-length(complete_order)])])
, or, perhaps give a try on operations that avoid grouping (assuming ordered "user_id") after accounting for the indices where "user_id" changes:
# save variables to vectors and keep a "logical" of when "id" changes
id = df$user_id
id_change = c(TRUE, id[-1] != id[-length(id)])
compl = df$complete_order
dord = df$order_date
# accounting for changes in "id", locate last completed order
i = c(NA, cummax((compl | id_change) * seq_along(compl))[-length(compl)])
is.na(i) = id_change
dord - dord[i]
#Time differences in days
#[1] NA 3 11 26 NA 3 12 27

Answer 3

I think you can add a filter function in place of the subsetting with order_date[complete_order == 1] and make sure the order_date (and other variables) are the correct data types by adding stringsAsFactors = F to data.frame()):
df = data.frame(cbind(user_id = c(rep(1, 4), rep(2,4)),
complete_order = c(rep(c(1,1,0,1), 2)),
order_date = c('2015-01-28', '2015-01-31', '2015-02-08', '2015-02-23', '2015-01-25', '2015-01-28', '2015-02-06', '2015-02-21')),
stringsAsFactors = F)
df$order_date <- lubridate::ymd(df$order_date)
df %>%
group_by(user_id) %>%
filter(complete_order == 1) %>%
mutate(complete_order_time_diff = order_date - lag(order_date))
This returns the time until the next complete order (and NA if there is not one):
user_id complete_order order_date complete_order_time_diff
<chr> <chr> <date> <time>
1 1 1 2015-01-28 NA days
2 1 1 2015-01-31 3 days
3 1 1 2015-02-23 23 days
4 2 1 2015-01-25 NA days
5 2 1 2015-01-28 3 days
6 2 1 2015-02-21 24 days