I wish to label my data based on their week. This is my data:
df2 <- structure(list(Order_Date = structure(c(16735, 16805, 16753,
16830, 17075, 17009, 17085, 16740, 16891, 16750, 16820, 16849,
16906, 16929, 16746, 16731, 16786, 16873, 16895, 16931), class = "Date")), .Names = "Order_Date", row.names = c(NA,
-20L), class = "data.frame")
and I tried to label them based on the week (0th week, 1th week and ....) and I wish to group my data by the week later
And I tried this:
# order by data
library (dplyr)
df2<- arrange(df2, Order_Date)
# label them by week
$df2$week <- cumsum(weekdays(df2$Order_Date) == "Friday")
It doesn't give me the correct result and I have the following output, which is weird
Order_Date week
1 2015-10-27 0
2 2016-01-05 0
3 2015-11-14 0
4 2016-01-30 0
5 2016-10-01 0
6 2016-07-27 0
7 2016-10-11 0
8 2015-11-01 0
9 2016-03-31 0
10 2015-11-11 0
11 2016-01-20 0
12 2016-02-18 0
13 2016-04-15 1
14 2016-05-08 1
15 2015-11-07 1
16 2015-10-23 2
17 2015-12-17 2
18 2016-03-13 2
19 2016-04-04 2
20 2016-05-10 2
Ideally, I'd like to have this output:
Order_Date label
1 2015-10-23 0
2 2015-10-27 0
3 2015-11-01 1
4 2015-11-07 2
5 2015-11-11 2
6 2015-11-14 3
7 2015-12-17 8
8 2016-01-05 10
since row number 8 occurs 10 week after row number 1 but also a solution that generates the following is my second alternative that shows these data are not in the same week:
Order_Date label
1 2015-10-23 0
2 2015-10-27 0
3 2015-11-01 1
4 2015-11-07 2
5 2015-11-11 2
6 2015-11-14 3
7 2015-12-17 4
8 2016-01-05 5
cut.Date
takes an interval specification (see ?cut.Date
).
Your data covers a whole year, so unless you want to re-name those weeks, this will count up the actual number of weeks:
library(dplyr)
df2 %>%
mutate(week = cut.Date(Order_Date, breaks = "1 week", labels = FALSE)) %>%
arrange(Order_Date)
#> Order_Date week
#> 1 2015-10-23 1
#> 2 2015-10-27 2
#> 3 2015-11-01 2
#> 4 2015-11-07 3
#> 5 2015-11-11 4
#> 6 2015-11-14 4
#> 7 2015-12-17 9
#> 8 2016-01-05 12
#> 9 2016-01-20 14
#> 10 2016-01-30 15
#> 11 2016-02-18 18
#> 12 2016-03-13 21
#> 13 2016-03-31 24
#> 14 2016-04-04 25
#> 15 2016-04-15 26
#> 16 2016-05-08 29
#> 17 2016-05-10 30
#> 18 2016-07-27 41
#> 19 2016-10-01 50
#> 20 2016-10-11 52
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With