How to mutate a complex variable involving dates?

I have a tibble in which each row represents an image of an eye and contains the following relevant variables: patientId, laterality (left or right), date, imageId.

I would like to manipulate this to create another tibble showing the number of followUpYears for each eye (patientId, laterality). followUpYears is defined in a somewhat unusual way:

1. In order to meet the requirements for follow-up in a particular year, there must be two different imaging dates during that year i.e. between days 0-365 for year 1, days 366-730 for year 2 etc. The first image date is always the baseline and followUpYears is always an integer.
2. Only one image per date is considered.
3. Follow-up ceases as soon as the requirement for 2 imaging dates in a year is not met i.e. if there is only 1 imaging date in the first year, followUpYears is 0 regardless of how many images are taken subsequently.
4. There is no requirement for there to be at least n years between the first and last image date for an eye to have n followUpYears.

The following dummy data demonstrates these points:

data <- tibble(patientId = c('A','A','A','A','A','A','B','B','B','B','B','B','B'),
               laterality = c('L','L','L','L','L','L','R','R','R','R','L','L','L'),
               date = as.Date(c('2000-05-05','2000-05-05','2001-05-06','2001-05-07','2002-05-06','2002-05-07','2000-09-08','2001-09-07','2001-09-09','2001-09-10','2000-09-08','2001-09-07','2001-09-10')),
               imageId = 1:13)

expected_output <- tibble(patientId = c('A','B','B'),
                 laterality = c('L','R','L'),
                 followUpYears = c(0, 2, 1)) 

Patient A's left eye has 0 followUpYears because of points 2 and 3. Patient B's right eye has 2 followUpYears because of point 4 (despite the fact that there is only slightly more than 1 year between the first and last image date). Patient B's left eye only has 1 year of follow up since it doesn't meet the requirement for 2 image dates in year 2.

I am familiar with the basic dplyr verbs but I can't think of how to frame this type of variable. Note that patients might have one or both eyes included and some might have 10+ years of follow up. Finally, a solution that considers 1 year to be 365 days regardless of leap years is fine.

Thank you!

Here's a way with ifelse. diff_year is a helper function that computes the difference between two dates in year rounded to the value above.

library(dplyr)
diff_year <- function(date1, date2) ceiling(as.numeric(difftime(date1, date2)) / 365)
data %>% 
  group_by(patientId) %>% 
  summarise(followUpYears = ifelse(diff_year(date[date != first(date)][1], first(date)) <= 1,
                                   diff_year(max(date), min(date)), 0))

#A tibble: 2 × 2
#  patientId followUpYears
#  <chr>             <dbl>
#1 A                     0
#2 B                     2

Update with OP's comment. This should work with all conditions:

diff_year <- function(date1, date2) as.numeric((date1 - date2) / 365)
data %>%
  distinct(patientId, laterality, date, .keep_all = TRUE) %>% 
  group_by(patientId, laterality) %>% 
  mutate(diffYear = floor(diff_year(date, min(date)))) %>%
  add_count(count = diffYear) %>% 
  filter(!cumany(lag(n == 1, default = 0)) | row_number() == 1) %>% 
  summarise(followUpYears = ifelse(any(n > 1), ceiling(diff_year(max(date[n != 1]), min(date))), 0))


#  patientId laterality followUpYears
#1 A         L                      0
#2 B         L                      1
#3 B         R                      2

Below is my approach which should cover all four conditions, I'm not sure however, how you get:

#> # A tibble: 1 x 3
#>   patientId laterality followUpYears
#>   <chr>     <chr>              <dbl>
#> 1 B         L                      1

since according to your logic it should fall into the two year band from 2000-09-08 to 2001-09-10 are 367 days which equals two years.

The idea is that we first calculate a followup_flag which checks if the date is within 365 days of the former date, and then takes the cummin() so that the series breaks as soon there is no direct follow up year.

Then we can filter all rows which meet the followup_flag == 1.

And for this data set we check how many years are between the first and the last date, and since we want to count 367 as 2 years we have to take the ceiling().

library(dplyr)
library(lubridate)

data %>% 
  group_by(patientId, laterality) %>% 
  mutate(followup_flag = cummin(date - dplyr::lag(date, default = first(date)) <= 365)) %>% 
  filter(followup_flag == 1) %>% 
  summarise(followUpYears = as.numeric(
              difftime(last(date), first(date), units = "days") / 365) %>%
              ceiling()
            )

#> `summarise()` has grouped output by 'patientId'. You can override using the
#> `.groups` argument.
#> # A tibble: 3 x 3
#> # Groups:   patientId [2]
#>   patientId laterality followUpYears
#>   <chr>     <chr>              <dbl>
#> 1 A         L                      0
#> 2 B         L                      2
#> 3 B         R                      2

Data used:

data <- tibble(patientId = c('A','A','A','A','A','A','B','B','B','B','B','B','B'),
               laterality = c('L','L','L','L','L','L','R','R','R','R','L','L','L'),
               date = as.Date(c('2000-05-05','2000-05-05','2001-05-06','2001-05-07','2002-05-06','2002-05-07','2000-09-08','2001-09-07','2001-09-09','2001-09-10','2000-09-08','2001-09-07','2001-09-10')),
               imageId = 1:13)

^{Created on 2023-02-08 by the reprex package (v2.0.1)}