CODEWITHSUNDEEP
rdate-arithmeticweek-number
MFR
MFR

Reputation: 2077

Group dates by week in R

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

Upvotes: 9

Views: 19869

Answers (3)

Tom
Tom

Reputation: 31

Alternatively you could use the ISOweek package to convert the dates to ISOweek format and then use that for filtering your output.

Example code using ISOweek package:

library(ISOweek)
x <- paste0(2000:2017, "-01-01")
x <- as.Date(x)
y <- ISOweek(x)
print(y)

Upvotes: 2

eipi10
eipi10

Reputation: 93811

The code below calculates the current week relative to the minimum week in the data. week2 uses modular arithmetic to make the code more concise, although the week numbers don't always line up exactly with the direct calculation of years and week numbers using lubridate functions.

library(dplyr)
library(lubridate)

df2 %>% mutate(week = (year(Order_Date) - year(min(Order_Date)))*52 + 
                 week(Order_Date) - week(min(Order_Date)),
               week2 = (as.numeric(Order_Date) %/% 7) - (as.numeric(min(Order_Date)) %/% 7)) %>%
  arrange(Order_Date)

   Order_Date week week2
1  2015-10-23    0     0
2  2015-10-27    0     0
3  2015-11-01    1     1
4  2015-11-07    2     2
5  2015-11-11    2     2
6  2015-11-14    3     3
7  2015-12-17    8     8
8  2016-01-05   10    10
9  2016-01-20   12    12
10 2016-01-30   14    14
11 2016-02-18   16    17
12 2016-03-13   20    20
13 2016-03-31   22    23
14 2016-04-04   23    23
15 2016-04-15   25    25
16 2016-05-08   28    28
17 2016-05-10   28    28
18 2016-07-27   39    39
19 2016-10-01   49    49
20 2016-10-11   50    50

Upvotes: 6

Jonathan Carroll
Jonathan Carroll

Reputation: 3947

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

Upvotes: 10

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