In 2022, there appear to be many extra NAs. E.g. looking at the set counts for dogfish:
# from my gfsynosis cache:
gfiphc_dat <- readRDS("report/data-cache-2024-05/iphc/north-pacific-spiny-dogfish.rds")$set_counts
dplyr::filter(gfiphc_dat, year == 2022, is.na(N_it20), is.na(N_it))
# A tibble: 174 × 12
year station lat lon E_it N_it C_it E_it20 N_it20 C_it20 usable standard
<dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <fct>
1 2022 2324 54.0 -131. NA NA NA NA NA NA Y N
2 2022 2150 53.8 -131. NA NA NA NA NA NA Y Y
3 2022 2318 53.8 -131. NA NA NA NA NA NA Y N
4 2022 2147 53.7 -131. NA NA NA NA NA NA Y Y
5 2022 2148 53.7 -131. NA NA NA NA NA NA Y Y
6 2022 2145 53.5 -131. NA NA NA NA NA NA Y Y
7 2022 2142 53.3 -131. NA NA NA NA NA NA Y Y
8 2022 2139 53.2 -131. NA NA NA NA NA NA Y Y
9 2022 2297 53.2 -131. NA NA NA NA NA NA Y N
10 2022 2295 53.2 -132. NA NA NA NA NA NA Y N
# ℹ 164 more rows
These appear to just be from a bad join. The 'real' data are also there with non-NA data for those same stations.
E.g.
filter(gfiphc_dat, year == 2022, station == 2324)
# A tibble: 2 × 12
year station lat lon E_it N_it C_it E_it20 N_it20 C_it20 usable standard
<dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <fct>
1 2022 2324 54.0 -131. NA NA NA 1.61 0 0 Y N
2 2022 2324 54.0 -131. NA NA NA NA NA NA Y N
In 2022, there appear to be many extra NAs. E.g. looking at the set counts for dogfish:
These appear to just be from a bad join. The 'real' data are also there with non-NA data for those same stations.
E.g.