Efficient and accurate age calculation (in years, months, or weeks) in R given birth date and an arbitrary date

Question

I am facing the common task of calculating the age (in years, months, or weeks) given the date of birth and an arbitrary date. The thing is that quite often I have to do this over many many records (>300 millions), so performance is a key issue here.

After a quick search in SO and Google I found 3 alternatives:

• A common arithmetic procedure (/365.25) (link)
• Using functions new_interval() and duration() from package lubridate (link)
• Function age_calc() from package eeptools (link, link, link)

So, here's my toy code:

# Some toy birthdates birthdate <- as.Date(c("1978-12-30", "1978-12-31", "1979-01-01",                         "1962-12-30", "1962-12-31", "1963-01-01",                         "2000-06-16", "2000-06-17", "2000-06-18",                         "2007-03-18", "2007-03-19", "2007-03-20",                         "1968-02-29", "1968-02-29", "1968-02-29"))  # Given dates to calculate the age givendate <- as.Date(c("2015-12-31", "2015-12-31", "2015-12-31",                         "2015-12-31", "2015-12-31", "2015-12-31",                         "2050-06-17", "2050-06-17", "2050-06-17",                        "2008-03-19", "2008-03-19", "2008-03-19",                         "2015-02-28", "2015-03-01", "2015-03-02"))  # Using a common arithmetic procedure ("Time differences in days"/365.25) (givendate-birthdate)/365.25  # Use the package lubridate require(lubridate) new_interval(start = birthdate, end = givendate) /                       duration(num = 1, units = "years")  # Use the package eeptools library(eeptools) age_calc(dob = birthdate, enddate = givendate, units = "years") 

Let's talk later about accuracy and focus first on performance. Here's the code:

# Now let's compare the performance of the alternatives using microbenchmark library(microbenchmark) mbm <- microbenchmark(     arithmetic = (givendate - birthdate) / 365.25,     lubridate = new_interval(start = birthdate, end = givendate) /                                      duration(num = 1, units = "years"),     eeptools = age_calc(dob = birthdate, enddate = givendate,                          units = "years"),     times = 1000 )  # And examine the results mbm autoplot(mbm) 

Here the results:

Bottom line: performance of lubridate and eeptools functions is much worse than the arithmetic method (/365.25 is at least 10 times faster). Unfortunately, the arithmetic method is not accurate enough and I cannot afford the few mistakes that this method will make.

"because of the way the modern Gregorian calendar is constructed, there is no straightforward arithmetic method that produces a person’s age, stated according to common usage — common usage meaning that a person’s age should always be an integer that increases exactly on a birthday". (link)

As I read on some posts, lubridate and eeptools make no such mistakes (though, I haven't looked at the code/read more about those functions to know which method they use) and that's why I wanted to use them, but their performance does not work for my real application.

Any ideas on an efficient and accurate method to calculate the age?

EDIT

Ops, it seems lubridate also makes mistakes. And apparently based on this toy example, it makes more mistakes than the arithmetic method (see lines 3, 6, 9, 12). (am I doing something wrong?)

toy_df <- data.frame(     birthdate = birthdate,     givendate = givendate,     arithmetic = as.numeric((givendate - birthdate) / 365.25),     lubridate = new_interval(start = birthdate, end = givendate) /         duration(num = 1, units = "years"),     eeptools = age_calc(dob = birthdate, enddate = givendate,                         units = "years") ) toy_df[, 3:5] <- floor(toy_df[, 3:5]) toy_df      birthdate  givendate arithmetic lubridate eeptools 1  1978-12-30 2015-12-31         37        37       37 2  1978-12-31 2015-12-31         36        37       37 3  1979-01-01 2015-12-31         36        37       36 4  1962-12-30 2015-12-31         53        53       53 5  1962-12-31 2015-12-31         52        53       53 6  1963-01-01 2015-12-31         52        53       52 7  2000-06-16 2050-06-17         50        50       50 8  2000-06-17 2050-06-17         49        50       50 9  2000-06-18 2050-06-17         49        50       49 10 2007-03-18 2008-03-19          1         1        1 11 2007-03-19 2008-03-19          1         1        1 12 2007-03-20 2008-03-19          0         1        0 13 1968-02-29 2015-02-28         46        47       46 14 1968-02-29 2015-03-01         47        47       47 15 1968-02-29 2015-03-02         47        47       47 

Answer 1

The reason lubridate appears to be making mistakes above is that you are calculating duration (the exact amount of time that occurs between two instants, where 1 year = 31536000s), rather than periods (the change in clock time that occurs between two instants).

To get the change in clock time (in years, months, days, etc) you need to use

as.period(interval(start = birthdate, end = givendate)) 

which gives the following output

 "37y 0m 1d 0H 0M 0S"     "37y 0m 0d 0H 0M 0S"     "36y 11m 30d 0H 0M 0S"   ...  "46y 11m 30d 1H 0M 0S"   "47y 0m 0d 1H 0M 0S"     "47y 0m 1d 1H 0M 0S"  

To just extract years, you can use the following

as.period(interval(start = birthdate, end = givendate))$year  [1] 37 37 36 53 53 52 50 50 49  1  1  0 46 47 47 

Note sadly appears even slower than the methods above!

> mbm Unit: microseconds        expr       min        lq       mean    median         uq        max neval cld  arithmetic   116.595   138.149   181.7547   184.335   196.8565   5556.306  1000  a    lubridate 16807.683 17406.255 20388.1410 18053.274 21378.8875 157965.935  1000   b