Access data via Oracle and R

Access data via Oracle (AFSC only)

AFSC Oracle users can access the database via SQL developer to view and pull the production data directly from the GAP_PRODUCTS Oracle schema. The user can also use SQL developer to view and pull the GAP Products data directly from the GAP_PRODUCTS Oracle schema.

8.0.1 Connect to Oracle from R

Many users will want to access the data from Oracle using R. The user will need to install the RODBC R package and ask OFIS (IT) connect R to Oracle. Then, use the following code in R to establish a connection from R to Oracle:

Here, the user can establish the oracle connection by entering their username and password in the channel <- gapindex::oracle_connect() function. Never save usernames or passwords in scripts that may be intentionally or unintentionally shared with others. If no username and password is entered in the function, pop-ups will appear on the screen asking for the username and password.

After you connect to VPN, you’ll be able to log into Oracle.

library(RODBC)
channel <- gapindex::get_connected()

Data SQL Query Examples:

library(gapindex)
library(RODBC)
library(flextable)
library(ggplot2)
library(magrittr)
library(dplyr)

8.0.2 Ex. Select all data from tables

You can download all of the tables locally using a variation of the code below. Once connected, pull and save the tables of interest into the R environment.

locations <- c(
  "GAP_PRODUCTS.AKFIN_AGECOMP", 
  "GAP_PRODUCTS.AKFIN_AREA", 
  "GAP_PRODUCTS.AKFIN_BIOMASS", 
  "GAP_PRODUCTS.AKFIN_CATCH", 
  "GAP_PRODUCTS.AKFIN_CPUE", 
  "GAP_PRODUCTS.AKFIN_CRUISE", 
  "GAP_PRODUCTS.AKFIN_HAUL", 
  "GAP_PRODUCTS.AKFIN_LENGTH", 
  "GAP_PRODUCTS.AKFIN_METADATA_COLUMN", 
  "GAP_PRODUCTS.AKFIN_SIZECOMP", 
  "GAP_PRODUCTS.AKFIN_SPECIMEN", 
  "GAP_PRODUCTS.AKFIN_STRATUM_GROUPS", 
  "GAP_PRODUCTS.AKFIN_SURVEY_DESIGN", 
  "GAP_PRODUCTS.AKFIN_TAXONOMIC_CLASSIFICATION"
)

for (i in 1:length(locations)) {
  print(locations[i])
  a <- RODBC::sqlQuery(channel, paste0("SELECT * FROM ", locations[i]))
  write.csv(x = a, file = here::here("data", paste0(locations[i], ".csv")))
}

library(odbc)
library(RODBC)
library(dbplyr)

my_spp_codes <- c(
  30010, #  Sebastolobus sp.    thornyhead unid.
  30020, #  Sebastolobus alascanus  shortspine thornyhead
  30025, #  Sebastolobus macrochir  broadfin thornyhead
  30330, #  Sebastes melanops   black rockfish
  30430, #  Sebastes proriger   redstripe rockfish
  30470, #  Sebastes ruberrimus yelloweye rockfish
  30475, #  Sebastes babcocki   redbanded rockfish
  30535, #  Sebastes variegatus harlequin rockfish
  30560, # Sebastes zacentrus   sharpchin rockfish
  30600, # Sebastes reedi   yellowmouth rockfish
  30030, # Sebastolobus altivelis   longspine thornyhead
  30040, # Sebastes sp. rockfish unid.
  30100, # Sebastes brevispinis silvergray rockfish
  30150, # NA   dusky and dark rockfishes unid.
  30152, # Sebastes variabilis  dusky rockfish
  30170, # Sebastes crameri darkblotched rockfish
  30270) # Sebastes helvomaculatus  rosethorn rockfish

a <- dplyr::tbl(channel, dplyr::sql('gap_products.akfin_biomass')) |> 
  dplyr::rename_all(tolower) |> 
  dplyr::select(survey_definition_id, area_id, species_code, year, biomass_mt, biomass_var) |> 
  dplyr::filter(species_code %in% my_spp_codes & 
                  area_id %in% 99904 & 
                  year >= 1991) |> 
  dplyr::collect() 

flextable::flextable(head(a)) |> 
  flextable::fit_to_width(max_width = 6) |> 
  flextable::theme_zebra()

8.0.3 Ex. CPUE for all EBS and NBS stations with associated haul, cruise, and species information.

a <- RODBC::sqlQuery(channel = channel, # NOT RACEBASE.HAUL
                     query = paste0(
                       "
-- Select columns for output data
SELECT
cr.CRUISEJOIN,
cr.CRUISE,
cr.YEAR,
cr.SURVEY_DEFINITION_ID,
cr.SURVEY_NAME,
cr.VESSEL_ID,
cr.VESSEL_NAME,
cp.HAULJOIN,
cp.SPECIES_CODE,
tt.SPECIES_NAME,
tt.COMMON_NAME,
cp.WEIGHT_KG,
cp.COUNT,
cp.AREA_SWEPT_KM2,
cp.CPUE_KGKM2,
cp.CPUE_NOKM2,
hh.HAUL,
hh.STATION

-- Identify what tables to pull data from
FROM GAP_PRODUCTS.AKFIN_HAUL hh
LEFT JOIN GAP_PRODUCTS.AKFIN_CRUISE cr
ON hh.CRUISEJOIN = cr.CRUISEJOIN
LEFT JOIN GAP_PRODUCTS.AKFIN_CPUE cp
ON hh.HAULJOIN = cp.HAULJOIN
LEFT JOIN GAP_PRODUCTS.TAXONOMIC_CLASSIFICATION tt
ON cp.SPECIES_CODE = tt.SPECIES_CODE

-- Filter for EBS and NBS observations
WHERE SURVEY_DEFINITION_ID IN (143, 98) -- 143 NBS, 98 EBS
AND tt.SURVEY_SPECIES = 1

-- Only return the first 3 rows because otherwise this would be a huge table!
FETCH FIRST 3 ROWS ONLY;")) 

flextable::flextable(head(a[,2:8])) |> 
  flextable::fit_to_width(max_width = 6) |> 
  flextable::theme_zebra()

CPUE for all EBS and NBS stations with associated haul, cruise, and species information.
CRUISE	YEAR	SURVEY_DEFINITION_ID	SURVEY_NAME	VESSEL_ID	VESSEL_NAME	HAULJOIN
198,203	1,982	98	Eastern Bering Sea Crab/Groundfish Bottom Trawl Survey	1	CHAPMAN	877
198,203	1,982	98	Eastern Bering Sea Crab/Groundfish Bottom Trawl Survey	1	CHAPMAN	877
198,203	1,982	98	Eastern Bering Sea Crab/Groundfish Bottom Trawl Survey	1	CHAPMAN	877

8.0.4 Ex. CPUE for all stations contained in the INPFC Shumagin region (AREA_ID = 919) for Pacific cod.

dat <- RODBC::sqlQuery(channel = channel,
                       query =
                         "
-- Select columns for output data
SELECT 
HAULJOIN, 
SPECIES_CODE, 
STRATUM, 
LATITUDE_DD_START, 
LONGITUDE_DD_START,
CPUE_KGKM2, 
GEAR_TEMPERATURE_C

-- Identify what tables to pull data from
FROM GAP_PRODUCTS.AKFIN_CPUE cpue
LEFT JOIN GAP_PRODUCTS.AKFIN_HAUL haul
USING (HAULJOIN) 

-- Filter for P. Cod observations
WHERE SPECIES_CODE IN (21720)

-- Select all stratum within the area_id 919 (INPFC Shumagin region)
AND haul.STRATUM IN
(
SELECT 
STRATUM
FROM GAP_PRODUCTS.AKFIN_STRATUM_GROUPS 
WHERE AREA_ID = 919
);")

dat <- dat |> 
  dplyr::select(HAULJOIN, STRATUM, SPECIES_CODE, LATITUDE_DD_START, LONGITUDE_DD_START, CPUE_KGKM2, GEAR_TEMPERATURE_C) |> 
  dplyr::mutate(SPECIES_CODE = as.character(SPECIES_CODE), 
                STRATUM = as.character(STRATUM)) |> 
  dplyr::arrange(SPECIES_CODE)

flextable::flextable(head(dat)) |>   
  flextable::fit_to_width(max_width = 6) |> 
  flextable::theme_zebra()

CPUE for all stations contained in the Shumagin region (AREA_ID = 919).
HAULJOIN	STRATUM	SPECIES_CODE	LATITUDE_DD_START	LONGITUDE_DD_START	CPUE_KGKM2	GEAR_TEMPERATURE_C
-12,880	210	21720	52.55793	-169.7829	6,863.3672
-12,881	10	21720	52.63840	-169.7815	1,536.8594	4.9
-12,882	111	21720	52.67131	-169.4279	10,044.8409	4.7
-12,883	10	21720	53.24099	-168.0725	1,937.7294	5.2
-12,884	10	21720	53.16771	-167.9810	830.2039	5.1
-12,885	111	21720	53.06838	-167.6713	2,891.8092	4.9

8.0.5 Ex. EBS Pacific Ocean perch CPUE and `akgfmaps` map

Pacific Ocean perch catch-per-unit-effort estimates for EBS in 2021 from GAP_PRODUCTS.AKFIN_CPUE and map constructed using akgfmaps. Here, we’ll use AKFIN HAUL and CRUISES data also included in this repo, for convenience, though they are very similar to their RACEBASE analogs.

dat <- RODBC::sqlQuery(channel = channel, 
                       query = 
                         "
-- Select columns for output data
SELECT 
(cp.CPUE_KGKM2/100) CPUE_KGHA, -- akgfmaps is expecting hectares, but can take any units
hh.LATITUDE_DD_START LATITUDE,
hh.LONGITUDE_DD_START LONGITUDE

-- Use HAUL data to obtain LATITUDE & LONGITUDE and connect to cruisejoin
FROM GAP_PRODUCTS.AKFIN_CPUE cp
LEFT JOIN GAP_PRODUCTS.AKFIN_HAUL hh
ON cp.HAULJOIN = hh.HAULJOIN

-- Use CRUISES data to obtain YEAR and SURVEY_DEFINITION_ID
LEFT JOIN GAP_PRODUCTS.AKFIN_CRUISE cc
ON hh.CRUISEJOIN = cc.CRUISEJOIN

-- Filter data
WHERE cp.SPECIES_CODE = 30060 
AND cc.SURVEY_DEFINITION_ID = 98 
AND cc.YEAR = 2021;")

dat |> 
  dplyr::arrange(desc(CPUE_KGHA)) |> 
  head() |> 
  flextable::flextable() |>  
  flextable::fit_to_width(max_width = 6) |> 
  flextable::theme_zebra()

EBS Pacific Ocean perch CPUE and `akgfmaps` map.
CPUE_KGHA	LATITUDE	LONGITUDE
10.1768965	57.64871	-173.3735
6.2734470	56.36952	-169.4604
3.0252034	56.66253	-171.9549
1.8214628	57.98912	-173.4816
0.5535672	55.65865	-168.1804
0.2813533	57.32545	-173.3217

# devtools::install_github("afsc-gap-products/akgfmaps", build_vignettes = TRUE)
library(akgfmaps)

figure <- akgfmaps::make_idw_map(
  x = dat, # Pass data as a data frame
  region = "bs.south", # Predefined EBS area
  set.breaks = "jenks", # Gets Jenks breaks from classint::classIntervals()
  in.crs = "+proj=longlat", # Set input coordinate reference system
  out.crs = "EPSG:3338", # Set output coordinate reference system
  grid.cell = c(20000, 20000), # 20x20km grid
  key.title = "Pacific Ocean perch") # Include in the legend title

[inverse distance weighted interpolation]
[inverse distance weighted interpolation]

figure$plot

EBS Pacific Ocean perch CPUE and `akgfmaps` map.

8.0.6 Ex. GOA Pacific Ocean perch biomass and abundance

Biomass and abundance for Pacific Ocean perch from 1990 – 2023 for the western/central/eastern GOA management areas as well as for the entire region.

dat <- RODBC::sqlQuery(channel = channel, 
                       query = 
                         "
-- Manipulate data to join to
WITH FILTERED_STRATA AS (
SELECT AREA_ID, DESCRIPTION FROM GAP_PRODUCTS.AKFIN_AREA
WHERE AREA_TYPE in ('REGULATORY AREA', 'REGION') 
AND SURVEY_DEFINITION_ID = 47
-- Use the AREA records associated with the GOA stratification prior to 2025
AND DESIGN_YEAR = 1984)

-- Select columns for output data
SELECT 
BIOMASS_MT,
POPULATION_COUNT, 
YEAR, 
DESCRIPTION

-- Identify what tables to pull data from
FROM GAP_PRODUCTS.AKFIN_BIOMASS BIOMASS
JOIN FILTERED_STRATA STRATA 
ON STRATA.AREA_ID = BIOMASS.AREA_ID

-- Filter data results
WHERE BIOMASS.SPECIES_CODE = 30060
AND BIOMASS.YEAR BETWEEN 1990 AND 2023")

dat0 <- dat |> 
  janitor::clean_names() |> 
  dplyr::select(biomass_mt, population_count, year, area = description) |>
  pivot_longer(cols = c("biomass_mt", "population_count"), 
               names_to = "var", 
               values_to = "val") |> 
  dplyr::mutate(
    val = ifelse(var == "biomass_mt", val/1e6, val/1e9), 
    var = ifelse(var == "biomass_mt", "Biomass (Mmt)", "Population (B)"), 
    area = gsub(x = area, pattern = " - ", replacement = "\n"), 
    area = gsub(x = area, pattern = ": ", replacement = "\n"), 
    type = sapply(X = strsplit(x = area, split = "\n", fixed = TRUE), `[[`, 2))  |> 
  dplyr::arrange(type) |> 
  dplyr::mutate(
    area = factor(area, levels = unique(area), labels = unique(area), ordered = TRUE))

flextable::flextable(head(dat)) |> 
  flextable::fit_to_width(max_width = 6) |> 
  flextable::theme_zebra() |>
  flextable::colformat_num(j = "YEAR", big.mark = "")

GOA Pacific Ocean perch biomass and abundance.
BIOMASS_MT	POPULATION_COUNT	YEAR	DESCRIPTION
31,073.15	60,087,711	1990	CENTRAL GOA - INPFC
100,321.48	174,708,361	1990	EASTERN GOA - INPFC
24,435.56	79,343,919	1990	WESTERN GOA - INPFC
155,830.19	314,139,991	1990	GOA Region: All Strata
256,345.03	454,133,028	1993	CENTRAL GOA - INPFC
147,912.16	230,314,654	1993	EASTERN GOA - INPFC

# install.packages("scales")
library(scales)
figure <- ggplot2::ggplot(
  dat = dat0, 
  mapping = aes(x = year, y = val, color = type)) +
  ggplot2::geom_point(size = 3) + 
  ggplot2::facet_grid(cols = vars(area), rows = vars(var), scales = "free_y") + 
  ggplot2::scale_x_continuous(name = "Year", n.breaks = 3) +
  ggplot2::scale_y_continuous(name = "Estimate", labels = comma) +
  ggplot2::labs(title = 'GOA Pacific Ocean perch biomass and abundance 1990 – 2023')  + 
  ggplot2::guides(color=guide_legend(title = "Region Type"))+
  ggplot2::scale_color_grey() +
  ggplot2::theme_bw() +
  ggplot2::theme(legend.direction = "horizontal", 
                 legend.position = "bottom")

figure

GOA Pacific Ocean perch biomass and abundance.

8.0.7 Ex. AI rock sole size compositions and ridge plot

Northern and Southern rock sole size composition data from 1991 – 2022 for the Aleutian Islands, with Ridge plot from ggridges.

dat <- RODBC::sqlQuery(channel = channel, 
                       query = "
SELECT 
YEAR,
LENGTH_MM / 10 AS LENGTH_CM, 
SUM(POPULATION_COUNT) AS POPULATION_COUNT

-- Identify what tables to pull data from
FROM GAP_PRODUCTS.AKFIN_SIZECOMP 

-- 99904 is the AREA_ID that codes for the whole AI survey region
WHERE AREA_ID = 99904
-- including northern rock sole, southern rock sole, and rock sole unid.
AND SPECIES_CODE IN (10260, 10261, 10262)
-- remove the -9 LENGTH_MM code
AND LENGTH_MM > 0
-- sum over species_codes and sexes
GROUP BY (YEAR, LENGTH_MM)")

dat0 <- dat |> 
  janitor::clean_names() |> 
  head() |> 
  flextable::flextable() |> 
  flextable::fit_to_width(max_width = 6) |> 
  flextable::theme_zebra() |>
  flextable::colformat_num(j = "year", big.mark = "")
dat0

AI Rock sole size compositions and ridge plot.
year	length_cm	population_count
1991	23	4,625,236
1991	38	2,254,964
1991	42	820,614
1991	52	11,225
1994	16	741,246
1994	26	9,762,322

# install.packages("ggridges")
library(ggridges)
figure <- ggplot(dat, 
                 mapping = aes(x = LENGTH_CM, 
                               y = YEAR, 
                               height = POPULATION_COUNT, 
                               group = YEAR)) +
  ggridges::geom_density_ridges(stat = "identity", scale = 1) +
  ggplot2::ylab(label = "Year") +
  ggplot2::scale_x_continuous(name = "Length (cm)") +
  ggplot2::labs(title = paste0('Aleutian Islands Rock sole Size Compositions'), 
                subtitle = paste0(min(dat$YEAR), ' – ', max(dat$YEAR))) +
  ggplot2::theme_bw()

figure

AI Rock sole size compositions and ridge plot.

8.0.8 Ex. 2023 EBS Walleye Pollock Age Compositions and Age Pyramid

Walleye pollock age composition for the EBS standard + NW Area from 2023, with age pyramid plot.

dat <- RODBC::sqlQuery(channel = channel, 
                       query = "
-- Manipulate data to join to
WITH FILTERED_STRATA AS (
SELECT 
AREA_ID, 
DESCRIPTION 
FROM GAP_PRODUCTS.AKFIN_AREA
-- Filter for EBS Standard + NW Area 
WHERE AREA_ID = 99900)

-- Select columns for output data
SELECT 
AGECOMP.AGE, 
AGECOMP.POPULATION_COUNT, 
AGECOMP.SEX

-- Identify what tables to pull data from
FROM GAP_PRODUCTS.AKFIN_AGECOMP AGECOMP
JOIN FILTERED_STRATA STRATA 
ON STRATA.AREA_ID = AGECOMP.AREA_ID

-- Filter data results
WHERE SPECIES_CODE = 21740
AND YEAR = 2023
AND AGE >= 0")

dat0 <- dat |> 
  janitor::clean_names() |> 
  dplyr::filter(sex %in% c(1,2)) |>
  dplyr::mutate(
    sex = ifelse(sex == 1, "M", "F"),
    population_count = # change male population to negative
      ifelse(sex=="M", population_count*(-1), population_count*1)/1e9) 

flextable::flextable(head(dat)) |> 
  flextable::fit_to_width(max_width = 6) |> 
  flextable::theme_zebra()

EBS Walleye Pollock Age Compositions and Age Pyramid.
AGE	POPULATION_COUNT	SEX
1	22,060,172	1
2	123,165,369	1
3	136,542,625	1
4	252,538,747	1
5	964,790,939	1
6	242,135,720	1

figure <- ggplot2::ggplot(
  data = dat0, 
  mapping = 
    aes(x = age,
        y = population_count, 
        fill = sex)) +
  ggplot2::scale_fill_grey() +
  ggplot2::geom_bar(stat = "identity") +
  ggplot2::coord_flip() +
  ggplot2::scale_x_continuous(name = "Age") +
  ggplot2::scale_y_continuous(name = "Population (billions)", labels = abs) +
  ggplot2::ggtitle(label = "2023 EBS (Standard Area + NW) walleye pollock Age Composition")  + 
  ggplot2::guides(fill = guide_legend(title = "Sex"))+
  ggplot2::theme_bw()

figure

2023 EBS Walleye Pollock Age Compositions and Age Pyramid.

8.0.9 Ex. NBS Pacific cod biomass and abundance

Pacific cod biomass and abundance data for the NBS by stratum.

dat <- RODBC::sqlQuery(channel = channel, 
                       query = 
                         "
SELECT YEAR, AREA_ID AS STRATUM, AREA_NAME, BIOMASS_MT, POPULATION_COUNT 
FROM GAP_PRODUCTS.AKFIN_BIOMASS

JOIN ( -- join with area table
SELECT AREA_ID, AREA_NAME
FROM GAP_PRODUCTS.AKFIN_AREA
WHERE AREA_TYPE = 'STRATUM'
AND SURVEY_DEFINITION_ID = 143
AND DESIGN_YEAR = 2022)

USING (AREA_ID)

-- Filter data results to NBS Pacific cod
WHERE SURVEY_DEFINITION_ID IN 143 
AND SPECIES_CODE = 21720
ORDER BY YEAR, STRATUM")

dat0 <- dat |> 
  janitor::clean_names() |> 
  dplyr::select(year, area_name, biomass_mt, population_count) |>
  pivot_longer(cols = c("biomass_mt", "population_count"), 
               names_to = "var", 
               values_to = "val") |> 
  dplyr::mutate(
    val = ifelse(var == "biomass_mt", val/1e6, val/1e9), 
    var = ifelse(var == "biomass_mt", "Biomass (Mmt)", "Population (B)"), 
    area = factor(area_name, levels = unique(area_name), labels = unique(area_name), ordered = TRUE))
flextable::flextable(dat) |> 
  flextable::fit_to_width(max_width = 6) |> 
  flextable::theme_zebra() |>
  flextable::colformat_num(j = "YEAR", big.mark = "")

NBS Pacific cod biomass and abundance.
YEAR	STRATUM	AREA_NAME	BIOMASS_MT	POPULATION_COUNT
2010	70	Inner Domain	7,462.5586	4,724,153
2010	71	Inner Domain	20,983.3757	3,928,600
2010	81	Middle Domain	680.4357	250,837
2017	70	Inner Domain	132,490.1518	66,187,245
2017	71	Inner Domain	147,971.4542	65,078,489
2017	81	Middle Domain	7,089.8740	4,191,118
2019	70	Inner Domain	107,096.7296	102,734,142
2019	71	Inner Domain	194,846.7230	73,495,085
2019	81	Middle Domain	63,061.2786	25,926,805
2021	70	Inner Domain	95,849.9833	68,767,498
2021	71	Inner Domain	53,814.6332	17,941,471
2021	81	Middle Domain	77,917.1083	42,991,939
2022	70	Inner Domain	96,500.6975	60,433,135
2022	71	Inner Domain	26,747.0747	10,447,602
2022	81	Middle Domain	30,487.2782	15,157,597
2023	70	Inner Domain	76,708.4327	39,605,860
2023	71	Inner Domain	19,130.0046	8,459,469
2023	81	Middle Domain	12,507.8566	4,128,368
2025	70	Inner Domain	48,587.6745	31,324,540
2025	71	Inner Domain	11,129.2287	4,456,827
2025	81	Middle Domain	9,665.6621	7,262,957

figure <- ggplot2::ggplot(
  dat = dat0, 
  mapping = aes(y = val, x = year, fill = area))  + 
  ggplot2::geom_bar(position="stack", stat="identity") +  
  ggplot2::facet_grid(rows = vars(var), scales = "free_y") +
  ggplot2::scale_y_continuous(name = "Estimate", labels = comma) +
  ggplot2::scale_x_continuous(name = "Year", breaks = unique(dat0$year)) +
  ggplot2::labs(title = 'NBS Pacific cod biomass and abundance by stratum')  + 
  ggplot2::guides(fill=guide_legend(title = "Domain Type "))+
  ggplot2::scale_fill_grey() +
  ggplot2::theme_bw() +
  ggplot2::theme(legend.direction = "horizontal", 
                 legend.position = "bottom")

figure

8.0.10 Ex. GOA Pacific Ocean perch biomass and line plot

Pacific Ocean perch biomass totals for GOA between 1984-2021 from GAP_PRODUCTS.AKFIN_BIOMASS

dat <- RODBC::sqlQuery(channel = channel,
                       query = "
-- Select columns for output data
SELECT
SURVEY_DEFINITION_ID,
BIOMASS_MT / 1000000 AS BIOMASS_MMT,
(BIOMASS_MT - 2 * SQRT(BIOMASS_VAR)) / 1000000 AS BIOMASS_CI_DW,
(BIOMASS_MT + 2 * SQRT(BIOMASS_VAR)) / 1000000 AS BIOMASS_CI_UP,
YEAR

-- Identify what tables to pull data from
FROM GAP_PRODUCTS.AKFIN_BIOMASS

-- Filter data results
WHERE SPECIES_CODE = 30060
AND SURVEY_DEFINITION_ID = 47
AND AREA_ID = 99903
AND YEAR BETWEEN 1990 AND 2023" ) |> 
  janitor::clean_names()

flextable::flextable(head(dat)) |>
  flextable::fit_to_width(max_width = 6) |> 
  flextable::theme_zebra() |>
  flextable::colformat_num(j = "year", big.mark = "")

GOA Pacific Ocean perch biomass and line plot.
survey_definition_id	biomass_mmt	biomass_ci_dw	biomass_ci_up	year
47	0.1558302	0.06181370	0.2498467	1990
47	0.4796687	0.26596329	0.6933741	1993
47	0.7651705	0.36044598	1.1698950	1996
47	0.7243655	-0.05238029	1.5011113	1999
47	0.6723673	0.22903375	1.1157008	2001
47	0.4543899	0.31077353	0.5980063	2003

a_mean <- dat |> 
  dplyr::group_by(survey_definition_id) |> 
  dplyr::summarise(biomass_mmt = mean(biomass_mmt, na.rm = TRUE), 
                   minyr = min(year, na.rm = TRUE), 
                   maxyr = max(year, na.rm = TRUE)) 

figure <-
  ggplot(data = dat, 
         mapping = aes(x = year, 
                       y = biomass_mmt)) +
  ggplot2::geom_point(size = 2.5, color = "grey40") + 
  ggplot2::scale_x_continuous(
    name = "Year", 
    labels = scales::label_number(
      accuracy = 1, 
      big.mark = ""))   +
  ggplot2::scale_y_continuous(
    name = "Biomass (Mmt)", 
    labels = comma) +
  ggplot2::geom_segment(
    data = a_mean,
    mapping = aes(x = minyr, 
                  xend = maxyr, 
                  y = biomass_mmt, 
                  yend = biomass_mmt),
    linetype = "dashed", 
    linewidth = 2) +
  ggplot2::geom_errorbar(
    mapping = aes(ymin = biomass_ci_dw, ymax = biomass_ci_up),
    position = position_dodge(.9),
    alpha = 0.5, width=.2) +
  ggplot2::ggtitle(
    label = "GOA Pacific Ocean Perch Biomass 1984-2021", 
    subtitle = paste0("Mean = ", 
                      formatC(x = a_mean$biomass_mmt, 
                              digits = 2, 
                              big.mark = ",", 
                              format = "f"), 
                      " Mmt")) +
  ggplot2::theme_bw()

figure

GOA Pacific Ocean perch biomass and line plot.

8.0.11 Ex. 2022 AI Atka mackerel age specimen summary

All ages determined:

dat <- RODBC::sqlQuery(channel = channel,
                       query = "
-- Select columns for output data
SELECT SURVEY_DEFINITION_ID, YEAR, SPECIES_CODE, AGE

-- Identify what tables to pull data from
FROM GAP_PRODUCTS.AKFIN_SPECIMEN
JOIN (SELECT HAULJOIN, CRUISEJOIN FROM GAP_PRODUCTS.AKFIN_HAUL)
USING (HAULJOIN)
JOIN (SELECT CRUISEJOIN, YEAR, SURVEY_DEFINITION_ID FROM GAP_PRODUCTS.AKFIN_CRUISE)
USING (CRUISEJOIN)

-- Filter data results
WHERE GAP_PRODUCTS.AKFIN_SPECIMEN.SPECIMEN_SAMPLE_TYPE = 1
AND SPECIES_CODE = 21921
AND YEAR = 2022
AND SURVEY_DEFINITION_ID = 52") |> 
  janitor::clean_names()

flextable::flextable(head(dat) |> 
                       dplyr::arrange(age)) |>
  flextable::fit_to_width(max_width = 6) |> 
  flextable::theme_zebra() |>
  flextable::colformat_num(j = c("year", "species_code"), big.mark = "")

2022 AI Atka mackerel age specimen summary: all ages determined.
survey_definition_id	year	species_code	age
52	2022	21921	3
52	2022	21921	3
52	2022	21921	4
52	2022	21921	4
52	2022	21921	4
52	2022	21921	7

How many of each age was found:

dat <- RODBC::sqlQuery(channel = channel,
                       query = "
-- Select columns for output data
SELECT SURVEY_DEFINITION_ID, YEAR, SPECIES_CODE, AGE, 
COUNT(AGE) AS COUNTAGE

-- Identify what tables to pull data from
FROM GAP_PRODUCTS.AKFIN_SPECIMEN
JOIN (SELECT HAULJOIN, CRUISEJOIN FROM GAP_PRODUCTS.AKFIN_HAUL)
USING (HAULJOIN)
JOIN (SELECT CRUISEJOIN, YEAR, SURVEY_DEFINITION_ID FROM GAP_PRODUCTS.AKFIN_CRUISE)
USING (CRUISEJOIN)

-- Filter data results
WHERE AGE >= 0
AND SPECIES_CODE = 21921
AND YEAR = 2022
AND SURVEY_DEFINITION_ID = 52
GROUP BY (YEAR, SURVEY_DEFINITION_ID, SPECIES_CODE, AGE)

ORDER BY AGE") |> 
  janitor::clean_names()

flextable::flextable(dat) |> 
  flextable::fit_to_width(max_width = 6) |> 
  flextable::theme_zebra() |>
  flextable::colformat_num(j = c("year", "species_code"), big.mark = "")

Ex.: 2022 AI Atka mackerel age specimen summary: how many of each age were determined.
survey_definition_id	year	species_code	age	countage
52	2022	21921	1	1
52	2022	21921	2	40
52	2022	21921	3	295
52	2022	21921	4	119
52	2022	21921	5	130
52	2022	21921	6	116
52	2022	21921	7	108
52	2022	21921	8	61
52	2022	21921	9	88
52	2022	21921	10	73
52	2022	21921	11	20
52	2022	21921	12	9
52	2022	21921	13	1

How many otoliths were aged:

Using SQL

dat <- RODBC::sqlQuery(channel = channel,
                       query = "
-- Select columns for output data
SELECT SURVEY_DEFINITION_ID, YEAR, SPECIES_CODE, 
COUNT(AGE) AS COUNTAGE

-- Identify what tables to pull data from
FROM GAP_PRODUCTS.AKFIN_SPECIMEN
JOIN (SELECT HAULJOIN, CRUISEJOIN FROM GAP_PRODUCTS.AKFIN_HAUL)
USING (HAULJOIN)
JOIN (SELECT CRUISEJOIN, YEAR, SURVEY_DEFINITION_ID FROM GAP_PRODUCTS.AKFIN_CRUISE)
USING (CRUISEJOIN)

-- Filter data results
WHERE GAP_PRODUCTS.AKFIN_SPECIMEN.SPECIMEN_SAMPLE_TYPE = 1
AND SPECIES_CODE = 21921
AND YEAR = 2022
AND SURVEY_DEFINITION_ID = 52
GROUP BY (YEAR, SURVEY_DEFINITION_ID, SPECIES_CODE)") |> 
  janitor::clean_names()

Using dbplyr:

library(odbc)
library(keyring)
library(dplyr)
library(dbplyr)

channel <- DBI::dbConnect(odbc::odbc(), "akfin", uid = keyring::key_list("akfin")$username,
                        pwd = keyring::key_get("akfin", keyring::key_list("akfin")$username))

dat <- dplyr::tbl(src = channel, dplyr::sql('gap_products.akfin_specimen')) |> 
    dplyr::rename_all(tolower) |> 
    dplyr::select(hauljoin, specimen = specimen_id, species_code, length = length_mm, 
                  weight = weight_g, age, sex, age_method = age_determination_method) |> 
    dplyr::left_join(dplyr::tbl(akfin, dplyr::sql('gap_products.akfin_haul')) |>
                       dplyr::rename_all(tolower) |> 
                       dplyr::select(cruisejoin, hauljoin, haul, date_collected = date_time_start, 
                                     latitude = latitude_dd_start, longitude = longitude_dd_start),
                     by = join_by(hauljoin)) |> 
    dplyr::left_join(dplyr::tbl(akfin, dplyr::sql('gap_products.akfin_cruise')) |>
                       dplyr::rename_all(tolower) |> 
                       dplyr::select(cruisejoin, year, vessel = vessel_id, survey_definition_id),
                     by = join_by(cruisejoin)) |> 
    dplyr::filter(year == YEAR &
             survey_definition_id == 52 & 
             species_code %in% spp_codes &
             !is.na(age)) |> 
    dplyr::collect()

Both scripts will produce this table:

flextable::flextable(head(dat)) |>
  flextable::fit_to_width(max_width = 6) |> 
  flextable::theme_zebra() |>
  flextable::colformat_num(j = c("year", "species_code"), big.mark = "")

2022 AI Atka mackerel age specimen summary: how many otoliths were aged. This quiery was created using SQL.
survey_definition_id	year	species_code	countage
52	2022	21921	1,061