Occasionally you may want to aggregate daily data to weekly, monthly, or yearly data in R.

This tutorial explains how to easily do so using the **lubridate** and **dplyr** packages.

**Example: Aggregate Daily Data in R**

Suppose we have the following data frame in R that shows the daily sales of some item over the course of 100 consecutive days:

#make this example reproducible set.seed(1) #create data frame df <- data.frame(date = as.Date("2020-12-01") + 0:99, sales = runif(100, 20, 50)) #view first six rows head(df) date sales 1 2020-12-01 27.96526 2 2020-12-02 31.16372 3 2020-12-03 37.18560 4 2020-12-04 47.24623 5 2020-12-05 26.05046 6 2020-12-06 46.95169

To aggregate this data, we can use the floor_date() function from the **lubridate **package which uses the following syntax:

floor_date(x, unit)

where:

**x:**A vector of date objects.**unit:**A time unit to round to. Options include second, minute, hour, day, week, month, bimonth, quarter, halfyear, and year.

The following code snippets show how to use this function along with the group_by() and summarize() functions from the **dplyr **package to find the mean sales by week, month, and year:

**Mean Sales by Week**

library(lubridate) library(dplyr) #round dates down to week df$week <- floor_date(df$date, "week") #find mean sales by week df %>% group_by(week) %>% summarize(mean = mean(sales)) # A tibble: 15 x 2 week mean 1 2020-11-29 33.9 2 2020-12-06 35.3 3 2020-12-13 39.0 4 2020-12-20 34.4 5 2020-12-27 33.6 6 2021-01-03 35.9 7 2021-01-10 37.8 8 2021-01-17 36.8 9 2021-01-24 32.8 10 2021-01-31 33.9 11 2021-02-07 34.1 12 2021-02-14 41.6 13 2021-02-21 31.8 14 2021-02-28 35.2 15 2021-03-07 37.1

**Mean Sales by Month**

library(lubridate) library(dplyr) #round dates down to week df$month <- floor_date(df$date, "month") #find mean sales by month df %>% group_by(month) %>% summarize(mean = mean(sales)) # A tibble: 4 x 2 month mean 1 2020-12-01 35.3 2 2021-01-01 35.6 3 2021-02-01 35.2 4 2021-03-01 37.0

**Mean Sales by Year**

library(lubridate) library(dplyr) #round dates down to week df$year <- floor_date(df$date, "year") #find mean sales by month df %>% group_by(year) %>% summarize(mean = mean(sales)) # A tibble: 2 x 2 year mean 1 2020-01-01 35.3 2 2021-01-01 35.7

Note that we chose to aggregate by the mean, but we could use any summary statistic we’d like such as the median, mode, max, min, etc.

**Additional Resources**

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

How to Calculate the Mean by Group in R

How to Calculate Cumulative Sums in R

How to Plot a Time Series in R