R dplyr: filtering dataframe by combination of values-CodePudding

The following dataframe (dput see below) contains about 500 surface reflection observations from different points accross a time-series.

Each point belongs to a specific transect (either transsect 1 or 2)
each transect belongs to an arrangement out of two transsects, called spot (either spot 1 or 2, note: the dput contains only spot 1 because of size restrictions)
each spot belongs to a site (either site A or B)

From each transsect I filtered the out that 3 points that were mapped most frequently as a given class (in this case WW) in a preceding step and ranked each point by the number of observations.
So far just for understanding the df.

Now, I'd like to filter only those observations for which

at one date and
at one transect of same spot and site

all 3 ranks are existing, as e.g. for transect A-1-1 at 20190531 (rows 7 to 9 in the example).

I would like to use dplyr, but I am quite new to, as generally to R. I did my first experiences with dplyr, but at this point I was struggling for now. Could anybody help me please?

dput(aaa)
structure(list(Site_ID = c("A", "A", "A", "A", "A", "A", "A", 
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", 
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", 
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", 
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", 
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", 
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", 
"A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", 
"A", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", 
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", 
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", 
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", 
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", 
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", 
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", 
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", 
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", 
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", 
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", 
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", 
"B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", "B", 
"B", "B", "B"), Spot_Nr = c("1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1"), Transkt_Nr = c("1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "2", "2", "2", "2", "2", "2", "2", 
"2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", 
"2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", 
"2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", 
"2", "2", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", 
"1", "1", "1", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", 
"2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", 
"2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", 
"2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", 
"2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", 
"2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", 
"2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", 
"2", "2", "2", "2"), Point_Nr = c("14", "15", "14", "15", "14", 
"15", "14", "15", "13", "14", "15", "14", "15", "14", "15", "15", 
"14", "15", "14", "15", "14", "15", "14", "15", "14", "15", "14", 
"15", "14", "14", "15", "14", "15", "14", "15", "14", "15", "14", 
"15", "14", "15", "14", "15", "14", "15", "13", "14", "13", "14", 
"15", "13", "15", "14", "15", "14", "15", "15", "14", "15", "15", 
"14", "15", "14", "15", "15", "14", "15", "14", "15", "14", "15", 
"15", "15", "14", "15", "14", "15", "15", "14", "15", "15", "14", 
"15", "14", "15", "14", "15", "14", "15", "14", "15", "14", "13", 
"15", "14", "13", "15", "14", "13", "13", "14", "15", "13", "14", 
"15", "13", "14", "15", "13", "14", "13", "14", "15", "13", "14", 
"15", "13", "14", "15", "13", "14", "15", "13", "14", "13", "14", 
"13", "14", "15", "13", "14", "15", "13", "14", "13", "14", "13", 
"14", "13", "14", "13", "14", "13", "14", "13", "14", "13", "14", 
"13", "14", "13", "13", "14", "13", "14", "13", "14", "13", "14", 
"13", "14", "15", "13", "14", "13", "14", "13", "14", "13", "14", 
"13", "14", "13", "14", "15", "13", "14", "15", "13", "14", "12", 
"13", "14", "12", "13", "14", "12", "13", "14", "13", "14", "12", 
"13", "14", "12", "13", "14", "12", "13", "14", "12", "13", "14", 
"13", "14", "12", "13", "14", "12", "13", "14", "12", "13", "14", 
"12", "13", "14", "12", "13", "14", "12", "13", "14", "12", "13", 
"14", "12", "13", "14", "13", "14", "12", "13", "12", "13", "14", 
"12", "13", "13", "12", "13", "14", "13", "13", "14", "12", "13", 
"14", "12", "13", "14", "12", "13", "14", "12", "13", "14", "12", 
"13", "14", "12", "13", "14", "12", "13", "14", "12", "13", "14", 
"12"), nobs = c(24L, 23L, 24L, 23L, 24L, 23L, 24L, 23L, 4L, 24L, 
23L, 24L, 23L, 24L, 23L, 23L, 24L, 23L, 24L, 23L, 24L, 23L, 24L, 
23L, 24L, 23L, 24L, 23L, 24L, 24L, 23L, 24L, 23L, 24L, 23L, 24L, 
23L, 24L, 23L, 24L, 23L, 24L, 23L, 24L, 23L, 4L, 24L, 4L, 24L, 
23L, 4L, 26L, 19L, 26L, 19L, 26L, 26L, 19L, 26L, 26L, 19L, 26L, 
19L, 26L, 26L, 19L, 26L, 19L, 26L, 19L, 26L, 26L, 26L, 19L, 26L, 
19L, 26L, 26L, 19L, 26L, 26L, 19L, 26L, 19L, 26L, 19L, 26L, 19L, 
26L, 19L, 26L, 19L, 3L, 26L, 19L, 3L, 26L, 19L, 3L, 34L, 33L, 
12L, 34L, 33L, 12L, 34L, 33L, 12L, 34L, 33L, 34L, 33L, 12L, 34L, 
33L, 12L, 34L, 33L, 12L, 34L, 33L, 12L, 34L, 33L, 34L, 33L, 34L, 
33L, 12L, 34L, 33L, 12L, 34L, 33L, 34L, 33L, 34L, 33L, 34L, 33L, 
34L, 33L, 34L, 33L, 34L, 33L, 34L, 33L, 34L, 33L, 34L, 34L, 33L, 
34L, 33L, 34L, 33L, 34L, 33L, 34L, 33L, 12L, 34L, 33L, 34L, 33L, 
34L, 33L, 34L, 33L, 34L, 33L, 34L, 33L, 12L, 34L, 33L, 12L, 34L, 
30L, 28L, 34L, 30L, 28L, 34L, 30L, 28L, 34L, 30L, 34L, 30L, 28L, 
34L, 30L, 28L, 34L, 30L, 28L, 34L, 30L, 28L, 34L, 30L, 34L, 30L, 
28L, 34L, 30L, 28L, 34L, 30L, 28L, 34L, 30L, 28L, 34L, 30L, 28L, 
34L, 30L, 28L, 34L, 30L, 28L, 34L, 30L, 28L, 34L, 30L, 34L, 30L, 
28L, 34L, 28L, 34L, 30L, 28L, 34L, 34L, 28L, 34L, 30L, 34L, 34L, 
30L, 28L, 34L, 30L, 28L, 34L, 30L, 28L, 34L, 30L, 28L, 34L, 30L, 
28L, 34L, 30L, 28L, 34L, 30L, 28L, 34L, 30L, 28L, 34L, 30L, 28L
), rank = c(1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 3L, 1L, 2L, 1L, 2L, 
1L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
3L, 1L, 3L, 1L, 2L, 3L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 
1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 
1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 3L, 
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
2L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 1L, 
2L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 3L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 1L, 2L, 
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 1L, 2L, 3L, 1L, 3L, 1L, 2L, 3L, 
1L, 1L, 3L, 1L, 2L, 1L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 
3L), Tile = c("1008", "1008", "1008", "1008", "1008", "1008", 
"1008", "1008", "1008", "1008", "1008", "1008", "1008", "1008", 
"1008", "1008", "1008", "1008", "1008", "1008", "1008", "1008", 
"1008", "1008", "1008", "1008", "1008", "1008", "1008", "1008", 
"1008", "1008", "1008", "1008", "1008", "1008", "1008", "1008", 
"1008", "1008", "1008", "1008", "1008", "1008", "1008", "1008", 
"1008", "1008", "1008", "1008", "1008", "1008", "1008", "1008", 
"1008", "1008", "1008", "1008", "1008", "1008", "1008", "1008", 
"1008", "1008", "1008", "1008", "1008", "1008", "1008", "1008", 
"1008", "1008", "1008", "1008", "1008", "1008", "1008", "1008", 
"1008", "1008", "1008", "1008", "1008", "1008", "1008", "1008", 
"1008", "1008", "1008", "1008", "1008", "1008", "1008", "1008", 
"1008", "1008", "1008", "1008", "1008", "0909", "0909", "0909", 
"0909", "0909", "0909", "0909", "0909", "0909", "0909", "0909", 
"0909", "0909", "0909", "0909", "0909", "0909", "0909", "0909", 
"0909", "0909", "0909", "0909", "0909", "0909", "0909", "0909", 
"0909", "0909", "0909", "0909", "0909", "0909", "0909", "0909", 
"0909", "0909", "0909", "0909", "0909", "0909", "0909", "0909", 
"0909", "0909", "0909", "0909", "0909", "0909", "0909", "0909", 
"0909", "0909", "0909", "0909", "0909", "0909", "0909", "0909", 
"0909", "0909", "0909", "0909", "0909", "0909", "0909", "0909", 
"0909", "0909", "0909", "0909", "0909", "0909", "0909", "0909", 
"0909", "0909", "0909", "0909", "0909", "0909", "0909", "0909", 
"0909", "0909", "0909", "0909", "0909", "0909", "0909", "0909", 
"0909", "0909", "0909", "0909", "0909", "0909", "0909", "0909", 
"0909", "0909", "0909", "0909", "0909", "0909", "0909", "0909", 
"0909", "0909", "0909", "0909", "0909", "0909", "0909", "0909", 
"0909", "0909", "0909", "0909", "0909", "0909", "0909", "0909", 
"0909", "0909", "0909", "0909", "0909", "0909", "0909", "0909", 
"0909", "0909", "0909", "0909", "0909", "0909", "0909", "0909", 
"0909", "0909", "0909", "0909", "0909", "0909", "0909", "0909", 
"0909", "0909", "0909", "0909", "0909", "0909", "0909", "0909", 
"0909", "0909", "0909", "0909", "0909", "0909", "0909", "0909", 
"0909", "0909", "0909", "0909", "0909", "0909", "0909", "0909"
), Date = c(20190501L, 20190501L, 20190506L, 20190506L, 20190524L, 
20190524L, 20190531L, 20190531L, 20190531L, 20190603L, 20190603L, 
20190620L, 20190620L, 20190625L, 20190625L, 20190628L, 20190630L, 
20190630L, 20190705L, 20190705L, 20190710L, 20190710L, 20190723L, 
20190723L, 20190730L, 20190730L, 20190809L, 20190809L, 20190814L, 
20190817L, 20190817L, 20190827L, 20190827L, 20190903L, 20190903L, 
20190911L, 20190911L, 20190913L, 20190913L, 20190916L, 20190916L, 
20190921L, 20190921L, 20191008L, 20191008L, 20191008L, 20191023L, 
20191023L, 20191026L, 20191026L, 20191026L, 20190501L, 20190501L, 
20190506L, 20190506L, 20190524L, 20190531L, 20190531L, 20190603L, 
20190620L, 20190620L, 20190625L, 20190625L, 20190628L, 20190630L, 
20190630L, 20190705L, 20190705L, 20190710L, 20190710L, 20190723L, 
20190725L, 20190730L, 20190730L, 20190809L, 20190809L, 20190814L, 
20190817L, 20190817L, 20190827L, 20190903L, 20190903L, 20190911L, 
20190911L, 20190913L, 20190913L, 20190916L, 20190916L, 20190921L, 
20190921L, 20191008L, 20191008L, 20191008L, 20191023L, 20191023L, 
20191023L, 20191026L, 20191026L, 20191026L, 20180414L, 20180414L, 
20180414L, 20180419L, 20180419L, 20180419L, 20180421L, 20180421L, 
20180421L, 20180429L, 20180429L, 20180506L, 20180506L, 20180506L, 
20180521L, 20180521L, 20180521L, 20180526L, 20180526L, 20180526L, 
20180531L, 20180531L, 20180531L, 20180605L, 20180605L, 20180608L, 
20180608L, 20180610L, 20180610L, 20180610L, 20180615L, 20180615L, 
20180615L, 20180620L, 20180620L, 20180623L, 20180623L, 20180630L, 
20180630L, 20180713L, 20180713L, 20180718L, 20180718L, 20180720L, 
20180720L, 20180725L, 20180725L, 20180728L, 20180728L, 20180730L, 
20180730L, 20180812L, 20180817L, 20180817L, 20180819L, 20180819L, 
20180822L, 20180822L, 20180827L, 20180827L, 20180908L, 20180908L, 
20180908L, 20180916L, 20180916L, 20180918L, 20180918L, 20180921L, 
20180921L, 20180926L, 20180926L, 20180928L, 20180928L, 20181011L, 
20181011L, 20181011L, 20181013L, 20181013L, 20181013L, 20180414L, 
20180414L, 20180414L, 20180419L, 20180419L, 20180419L, 20180421L, 
20180421L, 20180421L, 20180429L, 20180429L, 20180506L, 20180506L, 
20180506L, 20180521L, 20180521L, 20180521L, 20180526L, 20180526L, 
20180526L, 20180531L, 20180531L, 20180531L, 20180605L, 20180605L, 
20180608L, 20180608L, 20180608L, 20180610L, 20180610L, 20180610L, 
20180615L, 20180615L, 20180615L, 20180620L, 20180620L, 20180620L, 
20180623L, 20180623L, 20180623L, 20180630L, 20180630L, 20180630L, 
20180713L, 20180713L, 20180713L, 20180718L, 20180718L, 20180718L, 
20180720L, 20180720L, 20180725L, 20180725L, 20180725L, 20180728L, 
20180728L, 20180730L, 20180730L, 20180730L, 20180812L, 20180817L, 
20180817L, 20180819L, 20180819L, 20180822L, 20180827L, 20180827L, 
20180827L, 20180908L, 20180908L, 20180908L, 20180916L, 20180916L, 
20180916L, 20180918L, 20180918L, 20180918L, 20180921L, 20180921L, 
20180921L, 20180926L, 20180926L, 20180926L, 20180928L, 20180928L, 
20180928L, 20181011L, 20181011L, 20181011L, 20181013L, 20181013L, 
20181013L), id = c("14", "15", "14", "15", "14", "15", "14", 
"15", "13", "14", "15", "14", "15", "14", "15", "15", "14", "15", 
"14", "15", "14", "15", "14", "15", "14", "15", "14", "15", "14", 
"14", "15", "14", "15", "14", "15", "14", "15", "14", "15", "14", 
"15", "14", "15", "14", "15", "13", "14", "13", "14", "15", "13", 
"33", "32", "33", "32", "33", "33", "32", "33", "33", "32", "33", 
"32", "33", "33", "32", "33", "32", "33", "32", "33", "33", "33", 
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"445", "446", "445", "446", "445", "446", "445", "446", "445", 
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"463", "464", "462", "463", "464", "462", "463", "464", "462", 
"463", "464", "462", "463", "464", "462", "463", "464", "462", 
"463", "464", "462"), Point_ID = c("1014", "1015", "1014", "1015", 
"1014", "1015", "1014", "1015", "1013", "1014", "1015", "1014", 
"1015", "1014", "1015", "1015", "1014", "1015", "1014", "1015", 
"1014", "1015", "1014", "1015", "1014", "1015", "1014", "1015", 
"1014", "1014", "1015", "1014", "1015", "1014", "1015", "1014", 
"1015", "1014", "1015", "1014", "1015", "1014", "1015", "1014", 
"1015", "1013", "1014", "1013", "1014", "1015", "1013", "1033", 
"1032", "1033", "1032", "1033", "1033", "1032", "1033", "1033", 
"1032", "1033", "1032", "1033", "1033", "1032", "1033", "1032", 
"1033", "1032", "1033", "1033", "1033", "1032", "1033", "1032", 
"1033", "1033", "1032", "1033", "1033", "1032", "1033", "1032", 
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"2013", "2014", "2013", "2014", "2015", "2013", "2014", "2015", 
"2013", "2014", "2013", "2014", "2013", "2014", "2013", "2014", 
"2013", "2014", "2013", "2014", "2013", "2014", "2013", "2014", 
"2013", "2014", "2013", "2013", "2014", "2013", "2014", "2013", 
"2014", "2013", "2014", "2013", "2014", "2015", "2013", "2014", 
"2013", "2014", "2013", "2014", "2013", "2014", "2013", "2014", 
"2013", "2014", "2015", "2013", "2014", "2015", "2031", "2032", 
"2030", "2031", "2032", "2030", "2031", "2032", "2030", "2031", 
"2032", "2031", "2032", "2030", "2031", "2032", "2030", "2031", 
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"2030", "2031", "2032", "2030", "2031", "2032", "2030", "2031", 
"2032", "2031", "2032", "2030", "2031", "2030", "2031", "2032", 
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"2031", "2032", "2030", "2031", "2032", "2030", "2031", "2032", 
"2030", "2031", "2032", "2030", "2031", "2032", "2030", "2031", 
"2032", "2030"), RED = c(1719, 1773, 1865, 1906, 1885, 1733, 
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1328, 897, 1197, 776, 1007, 846, 1143, 1247, 1102, 1016, 1314, 
835, 1115, 2048, 1125, 1467, 1797, 964, 1055, 1088, 1204, 975, 
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1259, 1206, 1189, 1348, 1290, 1308, 1288, 1428, 1195, 1232, 1544, 
1124, 1081, 1538, 1231, 1241, 1444, 1553, 1297, 1320, 1331, 1391, 
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1260, 1378, 1250, 1200, 1276, 1386, 1226, 1291, 1273, 1268, 1538, 
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1203, 1333, 1278, 1159, 1321, 2507, 2522, 2474, 2137, 2104, 2105, 
1860, 1841, 1824, 1644, 1679, 1228, 1250, 1285, 1014, 1025, 1133, 
936, 963, 1046, 1148, 1240, 1305, 1246, 1347, 1302, 1467, 1233, 
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1441, 1210, 1397, 1309, 1341, 1511, 1347, 1478, 1598, 1364, 1265, 
1367, 1181, 1251, 1213, 1508, 1435, 1149, 1319, 1256, 1402, 1255, 
1232, 1094, 1279, 1182, 1213, 1384, 1320, 1196, 1292, 1220, 1250, 
1392, 1173, 1098, 1315, 1213, 1090, 1375, 1056, 1038, 1278, 980, 
921, 1145, 992, 1137, 1222, 1122, 1127, 1175, 1240), SWIR1 = c(775, 
1026, 737, 1111, 1421, 1328, 730, 671, 970, 1375, 1012, 1276, 
1108, 1167, 1059, 1239, 1236, 1069, 986, 910, 1128, 961, 1443, 
1209, 1387, 1218, 1076, 868, 945, 1201, 1336, 1217, 1228, 1096, 
1178, 1097, 1087, 1070, 1057, 1195, 1324, 1157, 1211, 978, 1378, 
1132, 1003, 766, 1044, 1127, 904, 1074, 921, 1082, 761, 1357, 
591, 808, 982, 970, 1330, 994, 1221, 1191, 947, 1478, 778, 1012, 
944, 1401, 1220, 1230, 1109, 1469, 930, 1323, 1171, 1293, 1426, 
1116, 1012, 1110, 1025, 1203, 905, 1133, 1147, 1196, 1145, 1324, 
1243, 995, 1423, 1041, 994, 922, 1102, 1094, 1111, 557, 722, 
729, 203, 207, 220, 221, 189, 155, 926, 847, 588, 570, 614, 675, 
363, 775, 664, 640, 737, 835, 826, 1017, 1075, 1040, 688, 933, 
941, 753, 1180, 760, 748, 1068, 910, 863, 930, 1116, 1006, 970, 
937, 1010, 1009, 1046, 1032, 1037, 975, 938, 1034, 1248, 1007, 
969, 1193, 1021, 1251, 1096, 1025, 1136, 1333, 717, 753, 891, 
782, 1134, 916, 1036, 1089, 1041, 902, 998, 923, 1007, 840, 845, 
926, 837, 1113, 1006, 935, 1181, 677, 828, 467, 196, 189, 213, 
150, 147, 167, 803, 802, 410, 460, 481, 475, 328, 572, 476, 571, 
635, 707, 792, 963, 901, 970, 628, 948, 865, 635, 760, 877, 562, 
655, 854, 684, 839, 957, 824, 1108, 1040, 857, 1044, 1059, 722, 
971, 936, 963, 1136, 1109, 862, 1008, 901, 1035, 1229, 907, 1064, 
836, 1000, 1087, 1049, 1070, 1023, 909, 1128, 948, 577, 674, 
608, 630, 830, 785, 809, 980, 1007, 794, 996, 973, 787, 1187, 
860, 796, 968, 901, 616, 788, 885, 806, 867, 932, 823, 909, 1000
), PdKeyT = c(54L, 54L, 53L, 53L, 40L, 40L, 60L, 60L, 60L, 43L, 
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3L, 2L, 2L, 2L, 109L, 109L, 109L, 98L, 98L, 98L, 80L, 80L, 80L, 
37L, 37L, 46L, 46L, 46L, 54L, 54L, 54L, 36L, 36L, 36L, 26L, 26L, 
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45L, 5L, 5L, 5L, -3L, -3L, -3L, 5L, 5L, 5L, -2L, -2L, -2L, 7L, 
7L, 7L, 7L, 7L, 7L, 18L, 18L, 18L, 12L, 12L, 12L)), row.names = c(NA, 
-270L), class = "data.frame")

CodePudding user response：

I think this is what you want - we group by (look at unique combinations of) Site, Spot, Transect, and Date, and then keep the whole group if ranks 1:3 are all present, and otherwise discard the whole group.

df %>%
  group_by(Site_ID, Spot_Nr, Transkt_Nr, Date) %>%
  filter(all(1:3 %in% rank))
# # A tibble: 132 × 13
# # Groups:   Site_ID, Spot_Nr, Transkt_Nr, Date [44]
# Site_ID Spot_Nr Transkt_Nr Point_Nr  nobs  rank Tile      Date id    Point_ID   RED SWIR1 PdKeyT
# <chr>   <chr>   <chr>      <chr>    <int> <int> <chr>    <int> <chr> <chr>    <dbl> <dbl>  <int>
# 1 A       1       1          14          24     1 1008  20190531 14    1014      2221   730     60
# 2 A       1       1          15          23     2 1008  20190531 15    1015      2252   671     60
# 3 A       1       1          13           4     3 1008  20190531 13    1013      2212   970     60
# 4 A       1       1          14          24     1 1008  20191008 14    1014       864   978      2
# 5 A       1       1          15          23     2 1008  20191008 15    1015      1421  1378      2
# 6 A       1       1          13           4     3 1008  20191008 13    1013      1097  1132      2
# 7 A       1       1          14          24     1 1008  20191026 14    1014       799  1044      2
# 8 A       1       1          15          23     2 1008  20191026 15    1015      1252  1127      2
# 9 A       1       1          13           4     3 1008  20191026 13    1013       978   904      2
#10 A       1       2          15          26     1 1008  20191008 33    1033      1174  1243      2
# # … with 122 more rows

It's hard to know how you might want to generalize this. What I've show is good to check that all of a particular set of rank values are present. You could alternately do a test like n_distinct(rank) >= 3) if you wanted to keep a group if it had at least 3 distinct ranks.