dplyr: keep empty levels of factor but not empty levels of a combination of factors that don't appear in data - r

Question

When grouping and summarising with dplyr, what is the correct way to keep empty levels of each grouping factor but not keep empty combinations from multiple grouping factors?
As an example, consider data recorded at different times at multiple sites. I might filter and then calculate something for each year in each site. I'd like to have the default value of the summary on an empty vector if the filter removes a year completely. So site "a" has 10 years and site "b" has 1 year so I'd always like 11 rows in the summary.
If I use .drop = TRUE in group_by I lose years:
library(dplyr)
library(zoo)
library(lubridate)
set.seed(1)
df <- data.frame(site = factor(c(rep("a", 120), rep("b", 12))),
date = c(seq.Date(as.Date("2000/1/1"), by = "month", length.out = 120), seq.Date(as.Date("2000/1/1"), by = "month", length.out = 12)),
value = rnorm(132, 50, 10))
df$year <- factor(lubridate::year(df$date))
df %>%
filter(value > 65) %>%
group_by(site, year, .drop = TRUE) %>%
summarise(f = first(date))
#> # A tibble: 6 x 3
#> # Groups: site [1]
#> site year f
#> <fct> <fct> <date>
#> 1 a 2000 2000-04-01
#> 2 a 2004 2004-08-01
#> 3 a 2005 2005-01-01
#> 4 a 2007 2007-11-01
#> 5 a 2008 2008-10-01
#> 6 a 2009 2009-02-01
and with .drop = FALSE I gain all the extra years for site "b" which were not in the original data:
df %>%
filter(value > 65) %>%
group_by(site, year, .drop = FALSE) %>%
summarise(f = first(date))
#> # A tibble: 20 x 3
#> # Groups: site [2]
#> site year f
#> <fct> <fct> <date>
#> 1 a 2000 2000-04-01
#> 2 a 2001 NA
#> 3 a 2002 NA
#> 4 a 2003 NA
#> 5 a 2004 2004-08-01
#> 6 a 2005 2005-01-01
#> 7 a 2006 NA
#> 8 a 2007 2007-11-01
#> 9 a 2008 2008-10-01
#> 10 a 2009 2009-02-01
#> 11 b 2000 NA
#> 12 b 2001 NA
#> 13 b 2002 NA
#> 14 b 2003 NA
#> 15 b 2004 NA
#> 16 b 2005 NA
#> 17 b 2006 NA
#> 18 b 2007 NA
#> 19 b 2008 NA
#> 20 b 2009 NA
The best way I could think of was to calculate counts, then merge then filter then drop the count variable, but that's pretty messy.
I know the .drop was only recently added to dplyr, which is very useful for one factor, but is there yet a clean way to do this for multiple factors?
df %>%
filter(value > 65) %>%
group_by(site, year, .drop = FALSE) %>%
summarise(f = first(date)) %>%
left_join(df %>% count(site, year, .drop = FALSE), by = c("site", "year")) %>%
filter(n > 0) %>%
select(-n)
#> # A tibble: 11 x 3
#> # Groups: site [2]
#> site year f
#> <fct> <fct> <date>
#> 1 a 2000 2000-04-01
#> 2 a 2001 NA
#> 3 a 2002 NA
#> 4 a 2003 NA
#> 5 a 2004 2004-08-01
#> 6 a 2005 2005-01-01
#> 7 a 2006 NA
#> 8 a 2007 2007-11-01
#> 9 a 2008 2008-10-01
#> 10 a 2009 2009-02-01
#> 11 b 2000 NA

Answer 1

Not sure if this is what you like.
If you replace dates with value < 65 with NA instead of filtering them out you can proceed as usual.
df %>%
mutate(date = replace(date, value < 65, NA)) %>%
group_by(site, year) %>%
summarise(f = first(date[!is.na(date)]))
# A tibble: 11 x 3
# Groups: site [2]
site year f
<fct> <fct> <date>
1 a 2000 NA
2 a 2001 NA
3 a 2002 2002-03-01
4 a 2003 NA
5 a 2004 NA
6 a 2005 NA
7 a 2006 2006-02-01
8 a 2007 NA
9 a 2008 2008-07-01
10 a 2009 2009-02-01
11 b 2000 2000-08-01