lubridate::ceiling_date, lubridate::floor_date, lubridate::round_date

BSTA 526 Functions of the Week

Rounding dates and time object to a specified unit of time

library(lubridate)

1 lubridate::ceiling_date, lubridate::floor_date, & lubridate::round_date

What function(s) is being presented and what package is it from?

ceiling_date, floor_date, and round_date are rounding date function from the lubridate package.

2 What is it for?

Discuss what the function(s) does.

ceiling_date rounds up date or time (of choice) to the closest boundary of specified time unit.

floor_date rounds down the date or time (of choice) to the closest boundary of the specified time unit.

round_date rounds the date or time (of choice) to the nearest boundary of the specified time unit.

3 Examples

Provide at least two examples that you have created yourself for your dataset of choice that show how to use the function(s).

data_dates <- tibble(
  messy_dates = c("05/05/1987", "11/23/1994", "03/17/2025",
                  "03/05/2026","01/17/2019", "04,15,2023",
                  "04,16,2023"))

These functions operate on Date or date-time objects. If dates are stored as character strings, they must first be parsed using functions like mdy() or ymd() to convert them into Date or POSIX date-time formats.

ceiling_date

data_dates %>%
  mutate(
    dates = mdy(messy_dates),
    ceiling_month = ceiling_date(dates, "month")
  )

# A tibble: 7 × 3
  messy_dates dates      ceiling_month
  <chr>       <date>     <date>       
1 05/05/1987  1987-05-05 1987-06-01   
2 11/23/1994  1994-11-23 1994-12-01   
3 03/17/2025  2025-03-17 2025-04-01   
4 03/05/2026  2026-03-05 2026-04-01   
5 01/17/2019  2019-01-17 2019-02-01   
6 04,15,2023  2023-04-15 2023-05-01   
7 04,16,2023  2023-04-16 2023-05-01   

Works on units: “.1 sec”, “second”, ” minute”, “5 mins”, “hour”, “day”, “week”, “month”, “bimonth”, “quarter”, “season”, “halfyear”, “year”

floor_date

data_dates %>%
  mutate(
    dates = mdy(messy_dates),
    floor_month = floor_date(dates, "month")
  )

# A tibble: 7 × 3
  messy_dates dates      floor_month
  <chr>       <date>     <date>     
1 05/05/1987  1987-05-05 1987-05-01 
2 11/23/1994  1994-11-23 1994-11-01 
3 03/17/2025  2025-03-17 2025-03-01 
4 03/05/2026  2026-03-05 2026-03-01 
5 01/17/2019  2019-01-17 2019-01-01 
6 04,15,2023  2023-04-15 2023-04-01 
7 04,16,2023  2023-04-16 2023-04-01 

Works on units: “.1s”, “second”, “minute”, “hour”, “day”, “week”, “month”, “bimonth”, “quarter”, “season”, “halfyear”, “year”

round_date

data_dates %>%
  mutate(
    dates = mdy(messy_dates),
    round_month = round_date(dates, "month")
  )

# A tibble: 7 × 3
  messy_dates dates      round_month
  <chr>       <date>     <date>     
1 05/05/1987  1987-05-05 1987-05-01 
2 11/23/1994  1994-11-23 1994-12-01 
3 03/17/2025  2025-03-17 2025-04-01 
4 03/05/2026  2026-03-05 2026-03-01 
5 01/17/2019  2019-01-17 2019-02-01 
6 04,15,2023  2023-04-15 2023-04-01 
7 04,16,2023  2023-04-16 2023-05-01 

Works on units: “.5s”, “sec”, “second”, “minute”, “hour”, “2 hours”, “day”, “week”, “month”, “bimonth”, “quarter”, “halfyear”, “year”

4 Is it helpful?

Discuss whether you think this function(s) is useful for you and your work. Is it the best thing since sliced bread, or is it not really relevant to your work? If not relevant to you, can you think of examples of when it would be useful?

I think the functions would be useful, especially when working with datasets that include many dates. Since data is often collected over time, so being able to round dates to the nearest month or year would make it easier to organize and summarize trends. For example, grouping patient visits by month to look at changes in outcomes over time.

I don’t think these functions are something I would use every single day, but they are very helpful when working with time-based data. They make organizing and analyzing dates much easier and more efficient.