Aggregate daily level data to weekly level in R - r

Question

I have a huge dataset similar to the following reproducible sample data.
Interval value
1 2012-06-10 552
2 2012-06-11 4850
3 2012-06-12 4642
4 2012-06-13 4132
5 2012-06-14 4190
6 2012-06-15 4186
7 2012-06-16 1139
8 2012-06-17 490
9 2012-06-18 5156
10 2012-06-19 4430
11 2012-06-20 4447
12 2012-06-21 4256
13 2012-06-22 3856
14 2012-06-23 1163
15 2012-06-24 564
16 2012-06-25 4866
17 2012-06-26 4421
18 2012-06-27 4206
19 2012-06-28 4272
20 2012-06-29 3993
21 2012-06-30 1211
22 2012-07-01 698
23 2012-07-02 5770
24 2012-07-03 5103
25 2012-07-04 775
26 2012-07-05 5140
27 2012-07-06 4868
28 2012-07-07 1225
29 2012-07-08 671
30 2012-07-09 5726
31 2012-07-10 5176
I want to aggregate this data to weekly level to get the output similar to the following:
Interval value
1 Week 2, June 2012 *aggregate value for day 10 to day 14 of June 2012*
2 Week 3, June 2012 *aggregate value for day 15 to day 21 of June 2012*
3 Week 4, June 2012 *aggregate value for day 22 to day 28 of June 2012*
4 Week 5, June 2012 *aggregate value for day 29 to day 30 of June 2012*
5 Week 1, July 2012 *aggregate value for day 1 to day 7 of July 2012*
6 Week 2, July 2012 *aggregate value for day 8 to day 10 of July 2012*
How do I achieve this easily without writing a long code?

Answer 1

If you mean the sum of of ‘value’ by week I think the easiest way to do it is to convert the data into a xts object as GSee suggested:
data <- as.xts(data$value,order.by=as.Date(data$interval))
weekly <- apply.weekly(data,sum)
[,1]
2012-06-10 552
2012-06-17 23629
2012-06-24 23872
2012-07-01 23667
2012-07-08 23552
2012-07-10 10902
I leave the formatting of the output as an exercise for you :-)

Answer 2

If you were to use week from lubridate, you would only get five weeks to pass to by. Assume dat is your data,
> library(lubridate)
> do.call(rbind, by(dat$value, week(dat$Interval), summary))
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 24 552 4146 4188 3759 4529 4850
# 25 490 2498 4256 3396 4438 5156
# 26 564 2578 4206 3355 4346 4866
# 27 698 993 4868 3366 5122 5770
# 28 671 1086 3200 3200 5314 5726
This shows a summary for the 24th through 28th week of the year. Similarly, we can get the means with aggregate with
> aggregate(value~week(Interval), data = dat, mean)
# week(Interval) value
# 1 24 3758.667
# 2 25 3396.286
# 3 26 3355.000
# 4 27 3366.429
# 5 28 3199.500

Answer 3

I just came across this old question because it was used as a dupe target.
Unfortunately, all the upvoted answers (except the one by konvas and a now deleted one) present solutions for aggregating the data by week of the year while the OP has requested to aggregate by week of the month.
The definition of week of the year and week of the month is ambiguous as discussed here, here, and here.
However, the OP has indicated that he wants to count the days 1 to 7 of each month as week 1 of the month, days 8 to 14 as week 2 of the month, etc. Note that week 5 is a stub for most of the months consisting of only 2 or 3 days (except for the month of February if no leap year).
Having prepared the ground, here is a data.table solution for this kind of aggregation:
library(data.table)
DT[, .(value = sum(value)),
by = .(Interval = sprintf("Week %i, %s",
(mday(Interval) - 1L) %/% 7L + 1L,
format(Interval, "%b %Y")))]
Interval value
1: Week 2, Jun 2012 18366
2: Week 3, Jun 2012 24104
3: Week 4, Jun 2012 23348
4: Week 5, Jun 2012 5204
5: Week 1, Jul 2012 23579
6: Week 2, Jul 2012 11573
We can verify that we have picked the correct intervals by
DT[, .(value = sum(value),
date_range = toString(range(Interval))),
by = .(Week = sprintf("Week %i, %s",
(mday(Interval) -1L) %/% 7L + 1L,
format(Interval, "%b %Y")))]
Week value date_range
1: Week 2, Jun 2012 18366 2012-06-10, 2012-06-14
2: Week 3, Jun 2012 24104 2012-06-15, 2012-06-21
3: Week 4, Jun 2012 23348 2012-06-22, 2012-06-28
4: Week 5, Jun 2012 5204 2012-06-29, 2012-06-30
5: Week 1, Jul 2012 23579 2012-07-01, 2012-07-07
6: Week 2, Jul 2012 11573 2012-07-08, 2012-07-10
which is in line with OP's specification.
Data
library(data.table)
DT <- fread(
"rn Interval value
1 2012-06-10 552
2 2012-06-11 4850
3 2012-06-12 4642
4 2012-06-13 4132
5 2012-06-14 4190
6 2012-06-15 4186
7 2012-06-16 1139
8 2012-06-17 490
9 2012-06-18 5156
10 2012-06-19 4430
11 2012-06-20 4447
12 2012-06-21 4256
13 2012-06-22 3856
14 2012-06-23 1163
15 2012-06-24 564
16 2012-06-25 4866
17 2012-06-26 4421
18 2012-06-27 4206
19 2012-06-28 4272
20 2012-06-29 3993
21 2012-06-30 1211
22 2012-07-01 698
23 2012-07-02 5770
24 2012-07-03 5103
25 2012-07-04 775
26 2012-07-05 5140
27 2012-07-06 4868
28 2012-07-07 1225
29 2012-07-08 671
30 2012-07-09 5726
31 2012-07-10 5176", drop = 1L)
DT[, Interval := as.Date(Interval)]

Answer 4

If you are using a data frame, you can easily do this with the tidyquant package. Use the tq_transmute function, which applies a mutation and returns a new data frame. Select the "value" column and apply the xts function apply.weekly. The additional argument FUN = sum will get the aggregate by week.
library(tidyquant)
df
#> # A tibble: 31 x 2
#> Interval value
#> <date> <int>
#> 1 2012-06-10 552
#> 2 2012-06-11 4850
#> 3 2012-06-12 4642
#> 4 2012-06-13 4132
#> 5 2012-06-14 4190
#> 6 2012-06-15 4186
#> 7 2012-06-16 1139
#> 8 2012-06-17 490
#> 9 2012-06-18 5156
#> 10 2012-06-19 4430
#> # ... with 21 more rows
df %>%
tq_transmute(select = value,
mutate_fun = apply.weekly,
FUN = sum)
#> # A tibble: 6 x 2
#> Interval value
#> <date> <int>
#> 1 2012-06-10 552
#> 2 2012-06-17 23629
#> 3 2012-06-24 23872
#> 4 2012-07-01 23667
#> 5 2012-07-08 23552
#> 6 2012-07-10 10902

Answer 5

When you say "aggregate" the values, you mean take their sum? Let's say your data frame is d and assuming d$Interval is of class Date, you can try
# if d$Interval is not of class Date d$Interval <- as.Date(d$Interval)
formatdate <- function(date)
paste0("Week ", (as.numeric(format(date, "%d")) - 1) + 1,
", ", format(date, "%b %Y"))
# change "sum" to your required function
aggregate(d$value, by = list(formatdate(d$Interval)), sum)
# Group.1 x
# 1 Week 1, Jul 2012 3725.667
# 2 Week 2, Jul 2012 3199.500
# 3 Week 2, Jun 2012 3544.000
# 4 Week 3, Jun 2012 3434.000
# 5 Week 4, Jun 2012 3333.143
# 6 Week 5, Jun 2012 3158.667