(using current master; 9a066b9)
> mhq_terr_assessments %>%
filter(assessment_date == max(assessment_date), .by = point_code) %>%
filter(!is_present) %>%
select(point_code, assessment_date, is_present, type) %>%
inner_join(
mhq_terr_popunits %>%
select(point_code, grts_ranking_draw, grts_ranking, type, source),
join_by(point_code, type),
relationship = "many-to-many",
unmatched = "drop"
) %>%
arrange(type, grts_ranking_draw)
# A tibble: 15 × 7
point_code assessment_date is_present type grts_ranking_draw grts_ranking source
<chr> <date> <lgl> <chr> <dbl> <dbl> <chr>
1 1830705_1 2020-09-23 FALSE 2190_overig 1830705 1830705 assessment
2 105682_1 2022-09-07 FALSE 2310 105682 105682 assessment/habitatmap 2023
3 115422_1 2023-06-15 FALSE 2310 115422 115422 assessment/habitatmap 2023
4 8786_1 2022-09-02 FALSE 4030 8786 8786 assessment/habitatmap 2023
5 40146_1 2022-09-06 FALSE 4030 40146 40146 assessment/habitatmap 2023
6 46006_1 2019-08-22 FALSE 4030 46006 46006 assessment/habitatmap 2023
7 606390_1 2024-07-10 FALSE 6230_hn 606390 606390 assessment/habitatmap 2023
8 2943734_2 2022-06-22 FALSE 6410_mo 2943734 53275382 assessment
9 143382_1 2023-06-01 FALSE 6510_hu 143382 143382 assessment
10 398529_3 2022-06-02 FALSE 6510_hu 398529 398529 assessment
11 659794_2 2022-06-09 FALSE 6510_hu 659794 659794 assessment/habitatmap 2023
12 366225_2 2022-05-31 FALSE 6510_hua 366225 45463185 assessment/habitatmap 2023
13 589698_4 2022-06-07 FALSE 6510_huk 589698 589698 assessment/habitatmap 2023
14 4390190_1 2019-05-14 FALSE 6510_hus 4390190 4390190 assessment
15 6201542_1 2024-06-17 FALSE 6510_hus 6201542 6201542 assessment/habitatmap 2023
(using current
master; 9a066b9)