In noaa-garfo/discaRd: Cochran bycatch estimation

# evaluate groundfish results

# which geartypes have no discard source?

final_table %>%
    filter(is.na(DISCARD_SOURCE)) %>% 
    group_by(CAMS_GEAR_GROUP) %>% 
    dplyr::summarise(nvtr = n_distinct(VTRSERNO))


## primarily lobster pots. some shrimp trawls, then handlines and unknowns

# what is the discard rate for these trips?
res_list[[1]] %>%
    filter(is.na(DISCARD_SOURCE)) %>% 
    dplyr::select(
        DISCARD_SOURCE,
        CAMS_GEAR_GROUP,
        ACTIVITY_CODE_1,
        VTRSERNO,
        ARATE,
        CRATE,
        DISC_RATE,
        STRATA,
        STRATA_ASSUMED,
        LINK1,
        EST_DISCARD,
        DISCARD,
        OBS_DISCARD
    )

## no assumed rate because no obsevations
## changing the make these DISCARD_SOURCE = 'A' 



# make a smaller output table
dplyr::select(
    SPECIES_ITIS_EVAL,
    DISCARD_SOURCE,
    ACTIVITY_CODE_1,
    VTRSERNO,
    ARATE,
    CRATE,
    DISC_RATE,
    STRATA,
    STRATA_ASSUMED,
    LINK1,
    EST_DISCARD,
    DISCARD,
    OBS_DISCARD,
    eval(stratvars)
)

# --------------------------------------------
# Check Caless's results for EGB

# re-ran the haddock estimate
# recall the GEAR mathcing table had been updated

hadd = readRDS('discard_est_164744_gftrips_only.RDS')

hadd %>% 
    filter(substr(ACTIVITY_CODE_1, 1, 3) == 'NMS' &
                                    SPECIES_STOCK == 'EGB') %>% 
    group_by(DISCARD_SOURCE) %>% 
    dplyr::summarise(
                                        OBS_DISCARD = sum(OBS_DISCARD, na.rm = T),
                                        FINAL_DISCARD = sum(DISCARD, na.rm = T)
                                    )

# look by STRATA
hadd %>% 
    filter(substr(ACTIVITY_CODE_1, 1, 3) == 'NMS' &
                    SPECIES_STOCK == 'EGB' & 
                    SECTID == 3) %>% 
    group_by(STRATA) %>% 
    dplyr::summarise(
        OBS_DISCARD = sum(OBS_DISCARD, na.rm = T),
        FINAL_DISCARD = sum(DISCARD, na.rm = T)
    ) %>% 
    knitr::kable()

# difference now much smaller

# A tibble: 3 x 3
# DISCARD_SOURCE OBS_DISCARD FINAL_DISCARD
# <chr>                <dbl>         <dbl>
#   1 A                        0          880.
# 2 E                        0        66779.
# 3 O                    33926        33926

# original table
c_o_dat2 %>% 
    filter(SECTID == 3) %>% 
    group_by(GEARCODE, MESHGROUP, PERMIT_EFP_1, PERMIT_EFP_2, PERMIT_EFP_3, PERMIT_EFP_4, REDFISH_EXEMPTION
                     , SNE_SMALLMESH_EXEMPTION
                     , XLRG_GILLNET_EXEMPTION) %>% 
    dplyr::summarise(nobs = n_distinct(LINK1))

ddat_focal %>% 
    filter(SECTID == 3) %>% 
    group_by(CAMS_GEAR_GROUP, MESHGROUP, PERMIT_EFP_1, PERMIT_EFP_2, PERMIT_EFP_3, PERMIT_EFP_4, REDFISH_EXEMPTION
                     , SNE_SMALLMESH_EXEMPTION
                     , XLRG_GILLNET_EXEMPTION) %>% 
    dplyr::summarise(nobs = n_distinct(LINK1), ntrips = n_distinct(VTRSERNO))

# haddock output
hadd %>% 
    filter(SECTID == 3) %>% 
    group_by(eval(stratvars)) %>% 
#   group_by(CAMS_GEAR_GROUP, MESHGROUP, PERMIT_EFP_1, PERMIT_EFP_2, PERMIT_EFP_3, PERMIT_EFP_4, REDFISH_EXEMPTION
#                                               , SNE_SMALLMESH_EXEMPTION
#                                               , XLRG_GILLNET_EXEMPTION) %>% 
    dplyr::summarise(nobs = n_distinct(LINK1)) %>% 
    knitr::kable()