dplyr, lubridate : how to aggregate a dataframe by week?

Question

Consider the following example

library(tidyverse)
library(lubridate)
time <- seq(from =ymd(\"2014-02-24\"),to= ymd(\"2014-03-20\"), by=\"days\")
set.seed(         


        

Answer 1

In the tidyverse,

df2 %>% group_by(week = week(time)) %>% summarise(value = mean(values))

## # A tibble: 5 × 2
##    week    value
##       
## 1     8 37.50000
## 2     9 38.57143
## 3    10 38.57143
## 4    11 36.42857
## 5    12 45.00000

Or use isoweek instead:

df2 %>% group_by(week = isoweek(time)) %>% summarise(value = mean(values))

## # A tibble: 4 × 2
##    week    value
##       
## 1     9 37.14286
## 2    10 40.71429
## 3    11 35.00000
## 4    12 42.50000

Or cut.Date:

df2 %>% group_by(week = cut(time, "week")) %>% summarise(value = mean(values))

## # A tibble: 4 × 2
##         week    value
##           
## 1 2014-02-24 37.14286
## 2 2014-03-03 40.71429
## 3 2014-03-10 35.00000
## 4 2014-03-17 42.50000

which you can tell to start on Sunday, if you prefer:

df2 %>% group_by(week = cut(time, "week", start.on.monday = FALSE)) %>% 
    summarise(value = mean(values))

## # A tibble: 4 × 2
##         week    value
##           
## 1 2014-02-23 37.50000
## 2 2014-03-02 40.00000
## 3 2014-03-09 33.57143
## 4 2014-03-16 44.00000

If you want to shift to, say, Tuesday start, add one to your dates:

df2 %>% group_by(week = cut(time + 1, "week")) %>% summarise(value = mean(values))

## # A tibble: 4 × 2
##         week    value
##           
## 1 2014-02-24 37.50000
## 2 2014-03-03 40.00000
## 3 2014-03-10 33.57143
## 4 2014-03-17 44.00000

Labels will be off, though. If using cut, consider the implications of its include.lowest and right parameters, documented at ?cut.