You can use the strftime() function in base R with the “%V” argument to group data by week in R.

This function uses the following basic syntax:

df$week_num <- strftime(df$date, format = "%V")

The following example shows how to use this function in practice.

Example: Group Data by Week in R

Suppose we have the following data frame in R that shows the total sales of some item on various dates:

#create data frame 
df <- data.frame(date=as.Date(c('1/8/2022', '1/9/2022', '2/10/2022', '2/15/2022',
                                '3/5/2022', '3/22/2022', '3/27/2022'), '%m/%d/%Y'),
                 sales=c(8, 14, 22, 23, 16, 17, 23))

#view data frame
df

        date sales
1 2022-01-08     8
2 2022-01-09    14
3 2022-02-10    22
4 2022-02-15    23
5 2022-03-05    16
6 2022-03-22    17
7 2022-03-27    23

We can use the following code to add a column that shows the week number of each date:

#add column to show week number
df$week_num <- strftime(df$date, format = "%V")

#view updated data frame
df

        date sales week_num
1 2022-01-08     8       01
2 2022-01-09    14       01
3 2022-02-10    22       06
4 2022-02-15    23       07
5 2022-03-05    16       09
6 2022-03-22    17       12
7 2022-03-27    23       12

Note: From the documentation, here is how %V% calculates date numbers:  “the week number of the year (Monday as the first day of the week) as a decimal number [01,53]. If the week containing 1 January has four or more days in the new year, then it is considered week 1. Otherwise, it is the last week of the previous year, and the next week is week 1.”

Once we’ve created this new column, we can aggregate values based on week number.

For example, we can use the following code to calculate the sum of sales, grouped by week:

library(dplyr)

#calculate sum of sales, grouped by week
df %>%
  group_by(week_num) %>%
  summarize(total_sales = sum(sales))

# A tibble: 6 x 2
  week_num total_sales
            
1 01                22
2 06                22
3 07                23
4 09                16
5 12                40

From the output we can see:

• The sum of sales during week 1 was 22.
• The sum of sales during week 6 was 22.
• The sum of sales during week 7 was 23.

And so on.

We can also use another metric to aggregate the data.

library(dplyr)

#calculate mean of sales, grouped by week
df %>%
  group_by(week_num) %>%
  summarize(mean_sales = mean(sales))

# A tibble: 5 x 2
  week_num mean_sales
           
1 01               11
2 06               22
3 07               23
4 09               16
5 12               20

From the output we can see:

• The mean of sales during week 1 was 11.
• The mean of sales during week 6 was 22.
• The mean of sales during week 7 was 23.

And so on.

Additional Resources

The following tutorials explain how to perform other common tasks in R: