Access via API and R

An application programming interface (API) is a way for two or more computer programs to communicate with each other. More information about how to amend API links can be found here. Useful introductions to using APIs in R can be found here.

There are three tables the user can pull from the API. Learn more about them on the FOSS data description page. Here, you can see them in their raw JSON format:

Here are some examples of how to use the data with R:

12.1 Ex. Load all rows of the catch, haul, and species data tables

Note that without specifying, a basic query to the API will only return 25 entries.

12.1.1 Load haul data

# link to the API
api_link_haul <- 'https://apps-st.fisheries.noaa.gov/ods/foss/afsc_groundfish_survey_haul/'

12.1.1.1 Load first 25 rows of data

res <- httr::GET(url = api_link_haul)
# res ## Test connection

## convert from JSON format
dat <- jsonlite::fromJSON(base::rawToChar(res$content))$items

# Find how many rows and columns are in the data pull
print(paste0("rows: ", nrow(dat), "; cols: ", ncol(dat)))

[1] "rows: 25; cols: 28"

12.1.1.2 Load all data:

Since the maxim number of rows a user can pull is 10,000 rows in a query, the user needs to cycle through by offsetting to the next 10,000 rows (as is shown here).

dat <- data.frame()
for (i in seq(0, 500000, 10000)){
  ## find how many iterations it takes to cycle through the data
  print(i)
  ## query the API link
  res <- httr::GET(url = paste0(api_link_haul, "?offset=",i,"&limit=10000"))
  ## convert from JSON format
  data <- jsonlite::fromJSON(base::rawToChar(res$content)) 
  
  ## if there are no data, stop the loop
  if (is.null(nrow(data$items))) {
    break
  }
  
  ## bind sub-pull to dat data.frame
  dat <- dplyr::bind_rows(dat, 
                          data$items |>
                            dplyr::select(-links)) # necessary for API accounting, but not part of the dataset)
}

[1] 0
[1] 10000
[1] 20000
[1] 30000
[1] 40000

Explore the data contents:

# Find how many rows and columns are in the data pull
print(paste0("rows: ", nrow(dat), "; cols: ", ncol(dat)))

[1] "rows: 34839; cols: 27"

# learn about the structure of the data
summary(dat)

      year          srvy              survey          survey_name       
 Min.   :1982   Length:34839       Length:34839       Length:34839      
 1st Qu.:1997   Class :character   Class :character   Class :character  
 Median :2006   Mode  :character   Mode  :character   Mode  :character  
 Mean   :2006                                                           
 3rd Qu.:2015                                                           
 Max.   :2025                                                           
                                                                        
 survey_definition_id     cruise         cruisejoin         hauljoin      
 Min.   : 47.00       Min.   :198201   Min.   :   -777   Min.   : -24955  
 1st Qu.: 47.00       1st Qu.:199701   1st Qu.:   -700   1st Qu.: -14898  
 Median : 78.00       Median :200601   Median :   -618   Median :  -4846  
 Mean   : 74.77       Mean   :200595   Mean   : 286597   Mean   : 281205  
 3rd Qu.: 98.00       3rd Qu.:201501   3rd Qu.: 827462   3rd Qu.: 802366  
 Max.   :143.00       Max.   :202502   Max.   :1225395   Max.   :1225635  
                                                                          
      haul          stratum        station            vessel_id  
 Min.   :  1.0   Min.   : 10.0   Length:34839       Min.   :  1  
 1st Qu.: 56.0   1st Qu.: 31.0   Class :character   1st Qu.: 88  
 Median :111.0   Median : 50.0   Mode  :character   Median : 94  
 Mean   :116.6   Mean   :129.4                      Mean   :109  
 3rd Qu.:169.0   3rd Qu.:141.0                      3rd Qu.:147  
 Max.   :355.0   Max.   :794.0                      Max.   :178  
                                                                 
 vessel_name         date_time         latitude_dd_start longitude_dd_start
 Length:34839       Length:34839       Min.   :51.19     Min.   :-180.0    
 Class :character   Class :character   1st Qu.:55.02     1st Qu.:-170.7    
 Mode  :character   Mode  :character   Median :57.24     Median :-165.3    
                                       Mean   :56.90     Mean   :-140.1    
                                       3rd Qu.:58.98     3rd Qu.:-154.5    
                                       Max.   :65.34     Max.   : 180.0    
                                                                           
 latitude_dd_end longitude_dd_end bottom_temperature_c surface_temperature_c
 Min.   :51.19   Min.   :-180.0   Min.   :-2.100       Min.   :-1.100       
 1st Qu.:55.02   1st Qu.:-170.7   1st Qu.: 2.700       1st Qu.: 5.800       
 Median :57.25   Median :-165.3   Median : 4.100       Median : 7.500       
 Mean   :56.90   Mean   :-140.1   Mean   : 3.843       Mean   : 7.809       
 3rd Qu.:58.99   3rd Qu.:-154.5   3rd Qu.: 5.200       3rd Qu.: 9.300       
 Max.   :65.35   Max.   : 180.0   Max.   :15.300       Max.   :18.100       
 NA's   :4       NA's   :4        NA's   :1599         NA's   :853          
    depth_m       distance_fished_km  duration_hr      net_width_m   
 Min.   :   9.0   Min.   :0.135      Min.   :0.0250   Min.   : 7.51  
 1st Qu.:  68.0   1st Qu.:1.498      1st Qu.:0.2710   1st Qu.:15.58  
 Median : 101.0   Median :2.537      Median :0.4900   Median :16.40  
 Mean   : 136.9   Mean   :2.208      Mean   :0.4009   Mean   :16.43  
 3rd Qu.: 155.0   3rd Qu.:2.834      3rd Qu.:0.5100   3rd Qu.:17.22  
 Max.   :1200.0   Max.   :4.334      Max.   :0.9800   Max.   :23.82  
                                                                     
  net_height_m    area_swept_km2      performance    
 Min.   : 0.000   Min.   :0.002314   Min.   :0.0000  
 1st Qu.: 2.378   1st Qu.:0.024317   1st Qu.:0.0000  
 Median : 5.816   Median :0.039604   Median :0.0000  
 Mean   : 4.794   Mean   :0.036397   Mean   :0.2782  
 3rd Qu.: 6.768   3rd Qu.:0.047225   3rd Qu.:0.0000  
 Max.   :11.038   Max.   :0.077795   Max.   :7.0000  
 NA's   :3270

# Print the first few lines of the data 
dat |> 
  head(3) |> 
  flextable::flextable() |>
  flextable::colformat_num(
      j = c("year", "cruise", "cruisejoin"), 
      big.mark = "") |> 
  flextable::theme_zebra()

year	srvy	survey	survey_name	survey_definition_id	cruise	cruisejoin	hauljoin	haul	stratum	station	vessel_id	vessel_name	date_time	latitude_dd_start	longitude_dd_start	latitude_dd_end	longitude_dd_end	bottom_temperature_c	surface_temperature_c	depth_m	distance_fished_km	duration_hr	net_width_m	area_swept_km2
1989	EBS	eastern Bering Sea	Eastern Bering Sea Crab/Groundfish Bottom Trawl Survey	98	198901	159	11,795	153	61	I-25	78	OCEAN HOPE 3	1989-07-15T09:00:00Z	57.65283	-172.7848	57.67733	-172.7922	3.4	7.8	121	2.759	0.50	17.47	0.048200
1989	EBS	eastern Bering Sea	Eastern Bering Sea Crab/Groundfish Bottom Trawl Survey	98	198901	159	11,796	154	61	J-25	78	OCEAN HOPE 3	1989-07-15T12:00:00Z	57.98600	-172.8603	58.01000	-172.8702	3.1	8.0	110	2.741	0.50	17.47	0.047885
1989	EBS	eastern Bering Sea	Eastern Bering Sea Crab/Groundfish Bottom Trawl Survey	98	198901	159	11,797	155	61	K-25	78	OCEAN HOPE 3	1989-07-15T14:00:00Z	58.32133	-172.9123	58.33733	-172.9138	3.1	8.4	110	1.796	0.33	17.47	0.031376

# save outputs for later comparison
dat_haul_api <- dat

12.1.2 Load catch data

# link to the API
api_link_catch <- 'https://apps-st.fisheries.noaa.gov/ods/foss/afsc_groundfish_survey_catch/'

12.1.2.1 Load first 25 rows of data

res <- httr::GET(url = api_link_catch)
# res ## Test connection

## convert from JSON format
dat <- jsonlite::fromJSON(base::rawToChar(res$content))$items

# Find how many rows and columns are in the data pull
print(paste0("rows: ", nrow(dat), "; cols: ", ncol(dat)))

[1] "rows: 25; cols: 8"

12.1.2.2 Load all data

Since the maxim number of rows a user can pull is 10,000 rows in a query, the user needs to cycle through by offsetting to the next 10,000 rows (as is shown here).

dat <- data.frame()
# for (i in seq(0, 100000, 10000)){
for (i in seq(0, 1000000, 10000)){
  ## find how many iterations it takes to cycle through the data
  # print(i)
  ## query the API link
  res <- httr::GET(url = paste0(api_link_catch, "?offset=",i,"&limit=10000"))
  ## convert from JSON format
  data <- jsonlite::fromJSON(base::rawToChar(res$content)) 
  
  ## if there are no data, stop the loop
  if (is.null(nrow(data$items))) {
    break
  }
  
  ## bind sub-pull to dat data.frame
  dat <- dplyr::bind_rows(dat, 
                          data$items |>
                            dplyr::select(-links)) # necessary for API accounting, but not part of the dataset)
}

Explore the data contents:

# Find how many rows and columns are in the data pull
print(paste0("rows: ", nrow(dat), "; cols: ", ncol(dat)))

[1] "rows: 917401; cols: 7"

# learn about the structure of the data
summary(dat)

    hauljoin        species_code     cpue_kgkm2          cpue_nokm2        
 Min.   : -24955   Min.   :    1   Min.   :      0.0   Min.   :      12.9  
 1st Qu.: -15284   1st Qu.:20510   1st Qu.:      5.6   1st Qu.:      58.5  
 Median :  -5742   Median :40500   Median :     48.9   Median :     214.8  
 Mean   : 271952   Mean   :45282   Mean   :   1236.9   Mean   :    4613.4  
 3rd Qu.: 802106   3rd Qu.:71890   3rd Qu.:    371.8   3rd Qu.:    1146.5  
 Max.   :1225635   Max.   :99999   Max.   :3226234.7   Max.   :21780780.3  
                                                       NA's   :91873       
     count            weight_kg         taxon_confidence  
 Min.   :     1.0   Min.   :    0.001   Length:917401     
 1st Qu.:     2.0   1st Qu.:    0.200   Class :character  
 Median :     8.0   Median :    1.814   Mode  :character  
 Mean   :   181.2   Mean   :   41.359                     
 3rd Qu.:    43.0   3rd Qu.:   13.800                     
 Max.   :867119.0   Max.   :18187.700                     
 NA's   :91873

# Print the first few lines of the data 
dat |> 
  head(3) |> 
  flextable::flextable() |>
  flextable::colformat_num(
      j = c("species_code"), 
      big.mark = "") |> 
  flextable::theme_zebra()

hauljoin	species_code	cpue_kgkm2	cpue_nokm2	count	weight_kg
-24,470	68578	84.5611	1,297.341	61	3.976
-24,470	68580	2,703.0414	24,798.350	1,166	127.095
-24,470	68590	348.6019	3,062.575	144	16.391

# save outputs for later comparison
dat_catch_api <- dat

12.1.3 Load species data

Since there are less than 10,000 rows of species data (and the maxim number of rows a user can pull from this API is 10,000 rows in a query), we can simply call ?offset=0&limit=10000 in our query call.

# link to the API
api_link_species <- 'https://apps-st.fisheries.noaa.gov/ods/foss/afsc_groundfish_survey_species/'

res <- httr::GET(url = paste0(api_link_species, "?offset=0&limit=10000"))

## convert from JSON format
data <- jsonlite::fromJSON(base::rawToChar(res$content))
dat <- data$items  |>
  dplyr::select(-links) # necessary for API accounting, but not part of the dataset

Explore the data contents:

# Find how many rows and columns are in the data pull
print(paste0("rows: ", nrow(dat), "; cols: ", ncol(dat)))

[1] "rows: 1014; cols: 6"

# learn about the structure of the data
summary(dat)

  species_code   scientific_name    common_name          id_rank         
 Min.   :    1   Length:1014        Length:1014        Length:1014       
 1st Qu.:22177   Class :character   Class :character   Class :character  
 Median :66868   Mode  :character   Mode  :character   Mode  :character  
 Mean   :50653                                                           
 3rd Qu.:75077                                                           
 Max.   :99999                                                           
                                                                         
     worms              itis        
 Min.   :     51   Min.   :  46861  
 1st Qu.: 127206   1st Qu.:  97781  
 Median : 254573   Median : 162045  
 Mean   : 293224   Mean   : 217907  
 3rd Qu.: 342060   3rd Qu.: 167487  
 Max.   :1699296   Max.   :1206057  
 NA's   :82        NA's   :132

# Print the first few lines of the data 
dat |> 
  head(3) |> 
  flextable::flextable() |>
  flextable::colformat_num(
      j = c("species_code", "worms", "itis"), # 
      big.mark = "") |> 
  flextable::theme_zebra()

species_code	scientific_name	common_name	id_rank	worms	itis
1		fish egg unid.
2		fish larvae unid.
3		fish unid.

# save outputs for later comparison
dat_species_api <- dat

12.2 Ex. Create zero-filled data using data loaded in last example

It is important to create and have access to zero-fill (presence and absence) so you can do simple analyses and plot data.

First prepare a table with all combinations of what species should be listed for what hauls/surveys. For zero-filled data, all species caught in a survey need to have zero or non-zero row entries for a haul

comb <- dplyr::full_join(
  # find all species that have been caught, by survey
  x = dplyr::left_join(dat_catch_api, dat_haul_api, by = "hauljoin") |>
    dplyr::select(survey_definition_id, species_code) |>
    dplyr::distinct(),
  # find all haul events (hauljoins), by survey
  y = dat_haul_api |>
    dplyr::select(survey_definition_id, hauljoin) |>
    dplyr::distinct(),
  relationship = "many-to-many",
  by = "survey_definition_id"
) |> 
  dplyr::select(-survey_definition_id) # now, redundant

Explore the data contents:

print(paste0("rows: ", nrow(comb), "; cols: ", ncol(comb)))

[1] "rows: 22070179; cols: 2"

comb |> head(3) |> 
  flextable::flextable()  |>
  flextable::colformat_num(
      j = c("species_code", "hauljoin"), 
      big.mark = "") |> 
  flextable::theme_zebra()

species_code	hauljoin
68578	11795
68578	11796
68578	11797

Now, using that table of combinations (here, called comb), join data to make a full zero-filled CPUE dataset. When all of the data have been full joined together, there should be the maximum number of rows in comb.

dat <- comb |>
  # add species data
  dplyr::left_join(dat_species_api) |> # , "species_code"
  # add haul data
  dplyr::left_join(dat_haul_api) |> # , c("hauljoin")
  # add catch data
  dplyr::left_join(dat_catch_api) |> # , c("species_code", "hauljoin")
  # modify/clean up zero-filled rows
  dplyr::mutate(
    cpue_kgkm2 = ifelse(is.na(cpue_kgkm2), 0, cpue_kgkm2),
    cpue_nokm2 = ifelse(is.na(cpue_nokm2), 0, cpue_nokm2),
    count = ifelse(is.na(count), 0, count),
    weight_kg = ifelse(is.na(weight_kg), 0, weight_kg))

TRUE Joining with `by = join_by(species_code)`
TRUE Joining with `by = join_by(hauljoin)`
TRUE Joining with `by = join_by(species_code, hauljoin)`

Explore the data contents:

# Find how many rows and columns are in the data pull
print(paste0("rows: ", nrow(dat), "; cols: ", ncol(dat)))

[1] "rows: 22070179; cols: 38"

# learn about the structure of the data
summary(dat)

  species_code      hauljoin       scientific_name    common_name       
 Min.   :    1   Min.   : -24955   Length:22070179    Length:22070179   
 1st Qu.:21800   1st Qu.: -14713   Class :character   Class :character  
 Median :66770   Median :  -5025   Mode  :character   Mode  :character  
 Mean   :50356   Mean   : 289418                                        
 3rd Qu.:74983   3rd Qu.: 816069                                        
 Max.   :99999   Max.   :1225635                                        
                                                                        
   id_rank              worms              itis              year     
 Length:22070179    Min.   :     51   Min.   :  46861   Min.   :1982  
 Class :character   1st Qu.: 126737   1st Qu.:  97160   1st Qu.:1997  
 Mode  :character   Median : 254508   Median : 160846   Median :2007  
                    Mean   : 266901   Mean   : 202211   Mean   :2006  
                    3rd Qu.: 291581   3rd Qu.: 167452   3rd Qu.:2015  
                    Max.   :1699296   Max.   :1206057   Max.   :2025  
                    NA's   :1614127   NA's   :2538236                 
     srvy              survey          survey_name        survey_definition_id
 Length:22070179    Length:22070179    Length:22070179    Min.   : 47.0       
 Class :character   Class :character   Class :character   1st Qu.: 47.0       
 Mode  :character   Mode  :character   Mode  :character   Median : 52.0       
                                                          Mean   : 69.1       
                                                          3rd Qu.: 98.0       
                                                          Max.   :143.0       
                                                                              
     cruise         cruisejoin           haul        stratum     
 Min.   :198201   Min.   :   -777   Min.   :  1   Min.   : 10.0  
 1st Qu.:199701   1st Qu.:   -700   1st Qu.: 59   1st Qu.: 31.0  
 Median :200701   Median :   -623   Median :116   Median : 61.0  
 Mean   :200607   Mean   : 294711   Mean   :122   Mean   :140.5  
 3rd Qu.:201501   3rd Qu.: 837799   3rd Qu.:176   3rd Qu.:211.0  
 Max.   :202502   Max.   :1225395   Max.   :355   Max.   :794.0  
                                                                 
   station            vessel_id     vessel_name         date_time        
 Length:22070179    Min.   :  1.0   Length:22070179    Length:22070179   
 Class :character   1st Qu.: 88.0   Class :character   Class :character  
 Mode  :character   Median : 94.0   Mode  :character   Mode  :character  
                    Mean   :111.2                                        
                    3rd Qu.:148.0                                        
                    Max.   :178.0                                        
                                                                         
 latitude_dd_start longitude_dd_start latitude_dd_end longitude_dd_end
 Min.   :51.19     Min.   :-180.0     Min.   :51.19   Min.   :-180.0  
 1st Qu.:54.73     1st Qu.:-169.9     1st Qu.:54.73   1st Qu.:-169.9  
 Median :56.99     Median :-163.4     Median :56.99   Median :-163.4  
 Mean   :56.65     Mean   :-137.1     Mean   :56.65   Mean   :-137.1  
 3rd Qu.:58.68     3rd Qu.:-152.1     3rd Qu.:58.68   3rd Qu.:-152.1  
 Max.   :65.34     Max.   : 180.0     Max.   :65.35   Max.   : 180.0  
                                      NA's   :2251    NA's   :2251    
 bottom_temperature_c surface_temperature_c    depth_m       distance_fished_km
 Min.   :-2.10        Min.   :-1.10         Min.   :   9.0   Min.   :0.135     
 1st Qu.: 3.10        1st Qu.: 5.90         1st Qu.:  71.0   1st Qu.:1.481     
 Median : 4.30        Median : 7.60         Median : 109.0   Median :1.675     
 Mean   : 4.12        Mean   : 8.06         Mean   : 141.3   Mean   :2.096     
 3rd Qu.: 5.40        3rd Qu.: 9.70         3rd Qu.: 166.0   3rd Qu.:2.804     
 Max.   :15.30        Max.   :18.10         Max.   :1200.0   Max.   :4.334     
 NA's   :1094226      NA's   :593432                                           
  duration_hr     net_width_m     net_height_m     area_swept_km2    
 Min.   :0.025   Min.   : 7.51   Min.   : 0.00     Min.   :0.002314  
 1st Qu.:0.269   1st Qu.:15.54   1st Qu.: 2.58     1st Qu.:0.023848  
 Median :0.305   Median :16.34   Median : 6.17     Median :0.028048  
 Mean   :0.380   Mean   :16.38   Mean   : 5.18     Mean   :0.034451  
 3rd Qu.:0.500   3rd Qu.:17.16   3rd Qu.: 6.88     3rd Qu.:0.046246  
 Max.   :0.980   Max.   :23.82   Max.   :11.04     Max.   :0.077795  
                                 NA's   :1723925                     
  performance       cpue_kgkm2          cpue_nokm2             count          
 Min.   :0.0000   Min.   :      0.0   Min.   :       0.0   Min.   :     0.00  
 1st Qu.:0.0000   1st Qu.:      0.0   1st Qu.:       0.0   1st Qu.:     0.00  
 Median :0.0000   Median :      0.0   Median :       0.0   Median :     0.00  
 Mean   :0.2917   Mean   :     51.4   Mean   :     172.6   Mean   :     6.78  
 3rd Qu.:0.0000   3rd Qu.:      0.0   3rd Qu.:       0.0   3rd Qu.:     0.00  
 Max.   :7.0000   Max.   :3226234.7   Max.   :21780780.3   Max.   :867119.00  
                                                                              
   weight_kg         taxon_confidence  
 Min.   :    0.000   Length:22070179   
 1st Qu.:    0.000   Class :character  
 Median :    0.000   Mode  :character  
 Mean   :    1.719                     
 3rd Qu.:    0.000                     
 Max.   :18187.700

# Print the first few lines of the data 
dat |> 
  head(3) |> 
  flextable::flextable() |>
  flextable::colformat_num(
      j = c("species_code", "hauljoin", "year", "cruise", "cruisejoin", "worms", "itis"), #
      big.mark = "") |> 
  flextable::theme_zebra()

species_code	hauljoin	scientific_name	common_name	id_rank	worms	itis	year	srvy	survey	survey_name	survey_definition_id	cruise	cruisejoin	haul	stratum	station	vessel_id	vessel_name	date_time	latitude_dd_start	longitude_dd_start	latitude_dd_end	longitude_dd_end	bottom_temperature_c	surface_temperature_c	depth_m	distance_fished_km	duration_hr	net_width_m	area_swept_km2	cpue_kgkm2	cpue_nokm2	count	weight_kg	taxon_confidence
68578	11795	Hyas lyratus	Pacific lyre crab	species	442167	98422	1989	EBS	eastern Bering Sea	Eastern Bering Sea Crab/Groundfish Bottom Trawl Survey	98	198901	159	153	61	I-25	78	OCEAN HOPE 3	1989-07-15T09:00:00Z	57.65283	-172.7848	57.67733	-172.7922	3.4	7.8	121	2.759	0.50	17.47	0.048200	0.00000	0.0000	0	0.000
68578	11796	Hyas lyratus	Pacific lyre crab	species	442167	98422	1989	EBS	eastern Bering Sea	Eastern Bering Sea Crab/Groundfish Bottom Trawl Survey	98	198901	159	154	61	J-25	78	OCEAN HOPE 3	1989-07-15T12:00:00Z	57.98600	-172.8603	58.01000	-172.8702	3.1	8.0	110	2.741	0.50	17.47	0.047885	14.20061	229.7157	11	0.680	High
68578	11797	Hyas lyratus	Pacific lyre crab	species	442167	98422	1989	EBS	eastern Bering Sea	Eastern Bering Sea Crab/Groundfish Bottom Trawl Survey	98	198901	159	155	61	K-25	78	OCEAN HOPE 3	1989-07-15T14:00:00Z	58.32133	-172.9123	58.33733	-172.9138	3.1	8.4	110	1.796	0.33	17.47	0.031376	14.46960	318.7137	10	0.454	High

# save outputs for later comparison
dat_zerofill_api <- dat

12.3 Ex. Visualize zero-filled data for 2023 eastern Bering Sea walleye pollock in CPUE data in distribution map

Using the zero-filled data from the previous example, we can make a few plots!

Here is some example data of 2023 through 2019 (year %in% 2019:2023) eastern and northern Bering Sea (srvy %in% c("EBS", "NBS)) walleye pollock (species_code == 21740).

dat <- dat_zerofill_api |> 
  dplyr::filter(year %in% 2019:2023 & 
                  srvy %in% c("EBS", "NBS") & 
                  species_code == 21740) |> 
  dplyr::select(year, common_name, longitude_dd_start, latitude_dd_start, cpue_kgkm2)

# Find how many rows and columns are in the data pull
print(paste0("rows: ", nrow(dat), "; cols: ", ncol(dat)))

[1] "rows: 2052; cols: 5"

# # learn about the structure of the data
# summary(dat)

# Print the first few lines of the data 
dat |> 
  head(3) |> 
  flextable::flextable() |>
  flextable::colformat_num(
      j = c("year"), 
      big.mark = "") |> 
  flextable::theme_zebra()

year	common_name	longitude_dd_start	latitude_dd_start	cpue_kgkm2
2021	walleye pollock	-172.1196	57.35342	4,836.466
2021	walleye pollock	-171.4952	57.32779	12,709.182
2021	walleye pollock	-171.3904	57.00871	18,944.559

12.3.1 Plot locations on map

library(ggplot2)

ggplot2::ggplot(data = dat |> dplyr::filter(cpue_kgkm2 != 0), 
                mapping = aes(x = longitude_dd_start, 
                              y = latitude_dd_start, 
                              size = cpue_kgkm2)) + 
  ggplot2::geom_point(alpha = .75) +
  ggplot2::geom_point(data = dat |> dplyr::filter(cpue_kgkm2 == 0), 
                      color = "red", 
                      shape = 17,
                      alpha = .75,
                      size = 3) +
  ggplot2::xlab("Longitude *W") +
  ggplot2::ylab("Latitude *N") +
  ggplot2::ggtitle(label = "CPUE (kg/km^2) of walleye pollock (Weight CPUE; kg/km2)", 
                   subtitle = "Eastern Bering Sea bottom trawl survey") +
  ggplot2::scale_size_continuous(name = "Weight (kg)") + 
  ggplot2::facet_wrap(facets = vars(year)) + 
  ggplot2::theme_bw()

12.3.2 Plot inverse-distance weighted plot of CPUE

This map is constructed using akgfmaps. To make IDW plots, you must have data from all stations surveyed, even if no fish of interest were found there.

These plots are similar to those published in the annual Bering Sea data reports.

# devtools::install_github("afsc-gap-products/akgfmaps", build_vignettes = TRUE)
library(akgfmaps)
idw <- akgfmaps::make_idw_stack(
  x = dat |> 
    dplyr::select(COMMON_NAME = common_name, 
                  CPUE_KGHA = cpue_kgkm2, 
                  LATITUDE = latitude_dd_start, 
                  LONGITUDE = longitude_dd_start, 
                  year), 
  grouping.vars = "year", 
  region = "bs.all", # 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
  extrapolation.grid.type = "sf")

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

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

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

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

shps <- akgfmaps::get_base_layers(
  select.region = "bs.all", 
  # include.corners = TRUE, 
  set.crs = "EPSG:3338")

shps$survey.area$SRVY <- c("EBS", "NBS")
shps$survey.area$SURVEY <- c("EBS", "NBS")

# set.breaks <- akgfmaps::eval_plot_breaks(CPUE = dat$cpue_kgkm2, n.breaks = 5)
# set.breaks <- as.vector(unlist(set.breaks[set.breaks$style == "pretty", -1]))
set.breaks <- c(0, 50000, 100000, 150000, 200000, 250000)

figure_print <- ggplot() +
  # add map of alaska
  ggplot2::geom_sf(data = shps$akland,
                   color = NA,
                   fill = "grey50") +
  # add IDW plots
  geom_sf(data = idw$extrapolation.stack,
          mapping = aes(fill = var1.pred),
          na.rm = FALSE,
          show.legend = TRUE, 
          color = NA) +
  ggplot2::scale_fill_manual(
    name = "walleye pollock\nCPUE (kg/km2)",
    values =  c("gray90",
                viridis::viridis(
                  option = "mako",
                  direction = -1,
                  n = length(set.breaks)-1,
                  begin = 0.20,
                  end = 0.80)),
    na.translate = FALSE, # Don't use NA
    drop = FALSE) + 
  # seperate plots by year
  ggplot2::facet_wrap(facets = vars(year), nrow = 2) + 
  # add survey area
  ggplot2::geom_sf(
    data = shps$survey.area, 
    mapping = aes(color = SURVEY, 
                  geometry = geometry), 
    fill = "transparent", 
    linewidth = 1, 
    show.legend = FALSE) +
  ggplot2::scale_color_manual(
    name = " ", 
    values = c("grey30", "grey50"),
    breaks = shps$survey.area$SURVEY,
    labels = shps$survey.area$SRVY) + 
  # lat/lon axis and map bounds
  ggplot2::scale_x_continuous(name = "Longitude °W",
                              breaks = seq(-180, -150, 5)) +
  ggplot2::scale_y_continuous(name = "Latitude °N",
                              breaks = seq(50, 65, 5)) + # seq(52, 62, 2)
  ggplot2::coord_sf(xlim = sf::st_bbox(shps$survey.area)[c(1,3)],
                    ylim = sf::st_bbox(shps$survey.area)[c(2,4)]) +
  # add theme aesthetics
  ggplot2::guides(
    fill = guide_legend(
      order = 1,
      title.position = "top",
      label.position = "bottom",
      title.hjust = 0.5,
      override.aes = list(color = NA),
      nrow = 1),
    color = "none") +
  ggplot2::theme( 
    panel.background = element_rect(fill = "white", colour = NA), 
    panel.border = element_rect(fill = NA, colour = "grey20"), 
    strip.background = element_blank(), 
    strip.text = element_text(size = 10, face = "bold"), 
    legend.text = element_text(size = 9),
    legend.background = element_rect(colour = "transparent", 
                                     fill = "transparent"),
    legend.key = element_rect(colour = "transparent", 
                              fill = "transparent"),
    legend.position = "bottom", 
    legend.box = "horizontal",
    legend.box.spacing = unit(0, "pt"), # reduce space between legend & plot
    legend.margin=margin(0, 0, 0, 0) )

figure_print

12.4 Ex. Show catch data for 2023 eastern Bering Sea Walleye Pollock (one species in one survey region in one year)

Data downloads and joins for just one species, survey, and year are much faster and easier to do.

First, because year is identified in the haul table, we need to identify all of the hauls (or more specifically, hauljoin codes) that were completed in the eastern Bering Sea ("srvy":"EBS") in 2023 ("year":2023).

Note: Check how many rows and columns are in the data pull. The eastern Bering Sea survey (before 2024) has 376 stations in it, and pollock are often found in throughout the region so this should have a similar number of rows.

## query the API link
res <- httr::GET(url = paste0(api_link_haul, '?limit=10000&q={"year":2023,"srvy":"EBS"}'))

## convert from JSON format
data <- jsonlite::fromJSON(base::rawToChar(res$content)) 
dat <- data$items |>
  dplyr::select(-links) # necessary for API accounting, but not part of the dataset

## show summary of data to make sure it is subset correctly
summary(dat |> dplyr::mutate(srvy = as.factor(srvy)))

      year       srvy        survey          survey_name       
 Min.   :2023   EBS:376   Length:376         Length:376        
 1st Qu.:2023             Class :character   Class :character  
 Median :2023             Mode  :character   Mode  :character  
 Mean   :2023                                                  
 3rd Qu.:2023                                                  
 Max.   :2023                                                  
 survey_definition_id     cruise         cruisejoin        hauljoin     
 Min.   :98           Min.   :202301   Min.   :-760.0   Min.   :-23019  
 1st Qu.:98           1st Qu.:202301   1st Qu.:-760.0   1st Qu.:-22776  
 Median :98           Median :202301   Median :-759.0   Median :-22539  
 Mean   :98           Mean   :202301   Mean   :-759.5   Mean   :-22552  
 3rd Qu.:98           3rd Qu.:202301   3rd Qu.:-759.0   3rd Qu.:-22333  
 Max.   :98           Max.   :202301   Max.   :-759.0   Max.   :-22110  
      haul           stratum        station            vessel_id    
 Min.   :  7.00   Min.   :10.00   Length:376         Min.   :134.0  
 1st Qu.: 65.75   1st Qu.:31.00   Class :character   1st Qu.:134.0  
 Median :114.00   Median :41.00   Mode  :character   Median :162.0  
 Mean   :114.16   Mean   :39.22                      Mean   :148.3  
 3rd Qu.:161.25   3rd Qu.:50.00                      3rd Qu.:162.0  
 Max.   :224.00   Max.   :90.00                      Max.   :162.0  
 vessel_name         date_time         latitude_dd_start longitude_dd_start
 Length:376         Length:376         Min.   :54.66     Min.   :-178.2    
 Class :character   Class :character   1st Qu.:57.00     1st Qu.:-172.7    
 Mode  :character   Mode  :character   Median :58.02     Median :-168.9    
                                       Mean   :58.26     Mean   :-168.8    
                                       3rd Qu.:59.50     3rd Qu.:-165.2    
                                       Max.   :62.01     Max.   :-158.3    
 latitude_dd_end longitude_dd_end bottom_temperature_c surface_temperature_c
 Min.   :54.68   Min.   :-178.2   Min.   :-1.600       Min.   : 1.700       
 1st Qu.:57.01   1st Qu.:-172.7   1st Qu.: 1.200       1st Qu.: 4.200       
 Median :58.02   Median :-168.9   Median : 2.700       Median : 6.550       
 Mean   :58.26   Mean   :-168.8   Mean   : 2.249       Mean   : 6.386       
 3rd Qu.:59.50   3rd Qu.:-165.2   3rd Qu.: 3.500       3rd Qu.: 8.525       
 Max.   :62.01   Max.   :-158.3   Max.   : 5.400       Max.   :11.000       
    depth_m       distance_fished_km  duration_hr      net_width_m   
 Min.   : 20.00   Min.   :1.065      Min.   :0.1890   Min.   :12.90  
 1st Qu.: 54.75   1st Qu.:2.805      1st Qu.:0.5100   1st Qu.:16.66  
 Median : 74.00   Median :2.889      Median :0.5180   Median :17.27  
 Mean   : 80.75   Mean   :2.854      Mean   :0.5129   Mean   :17.15  
 3rd Qu.:105.00   3rd Qu.:2.945      3rd Qu.:0.5260   3rd Qu.:17.83  
 Max.   :171.00   Max.   :3.849      Max.   :0.6560   Max.   :20.29  
  net_height_m   area_swept_km2     performance    
 Min.   :1.300   Min.   :0.02017   Min.   :0.0000  
 1st Qu.:1.875   1st Qu.:0.04725   1st Qu.:0.0000  
 Median :2.064   Median :0.04944   Median :0.0000  
 Mean   :2.107   Mean   :0.04892   Mean   :0.1075  
 3rd Qu.:2.343   3rd Qu.:0.05134   3rd Qu.:0.0000  
 Max.   :3.196   Max.   :0.06369   Max.   :6.2200

## Find how many rows and columns are in the data pull. 
print(paste0("rows: ", nrow(dat), "; cols: ", ncol(dat)))

[1] "rows: 376; cols: 27"

# save outputs for later comparison
dat_haul_ex <- dat

# Print the first few lines of the data 
dat_haul_ex |> 
  head(3) |> 
  flextable::flextable() |>
  flextable::colformat_num(
      j = c("year", "hauljoin", "cruise"), 
      big.mark = "") |> 
  flextable::theme_zebra()

Haul data filtered by `year = 2023` and `SRVY = 'EBS'`.
year	srvy	survey	survey_name	survey_definition_id	cruise	cruisejoin	hauljoin	haul	stratum	station	vessel_id	vessel_name	date_time	latitude_dd_start	longitude_dd_start	latitude_dd_end	longitude_dd_end	bottom_temperature_c	surface_temperature_c	depth_m	distance_fished_km	duration_hr	net_width_m	net_height_m	area_swept_km2	performance
2023	EBS	eastern Bering Sea	Eastern Bering Sea Crab/Groundfish Bottom Trawl Survey	98	202301	-759	-22283	64	31	G-08	162	ALASKA KNIGHT	2023-06-08T10:05:05Z	57.00863	-163.3957	56.98449	-163.4006	2.7	5.1	66	2.700	0.488	16.526	2.466	0.044620	0.0
2023	EBS	eastern Bering Sea	Eastern Bering Sea Crab/Groundfish Bottom Trawl Survey	98	202301	-759	-22284	63	10	H-08	162	ALASKA KNIGHT	2023-06-08T07:24:07Z	57.34861	-163.3868	57.32483	-163.3967	1.7	4.2	53	2.710	0.494	16.714	2.132	0.045295	0.0
2023	EBS	eastern Bering Sea	Eastern Bering Sea Crab/Groundfish Bottom Trawl Survey	98	202301	-759	-22285	66	31	E-08	162	ALASKA KNIGHT	2023-06-08T15:23:29Z	56.33826	-163.4162	56.31646	-163.4189	3.1	6.5	84	2.428	0.442	16.040	2.396	0.038945	1.1

12.4.1 Identify `species_code` for walleye pollock

In the catch data, we itemize species catches by species_code. To find out which species_code to use, you can check variations on the following code. Note that here the word pollock is case sensitive. All species common_name entries are lower case except for proper nouns (e.g., “Pacific”). The notation for finding a string is to use % around the phrase. Since % is a reserved character in a URL, you have to replace % with %25. Similarly, %20 needs to be used in place of a space (e.g., between “walleye” and “pollock”: "walleye%20pollock"}').

## query the API link. Use: 
res <- httr::GET(url = paste0(api_link_species, '?q={%22common_name%22:%22walleye%20pollock%22}'))
# OR
res <- httr::GET(url = paste0(api_link_species, '?q={"common_name":{"$like":"%25pollock%25"}}'))
# OR
res <- httr::GET(url = paste0(api_link_species, '?q={"common_name":"walleye%20pollock"}'))

## convert from JSON format
data <- jsonlite::fromJSON(base::rawToChar(res$content)) 

# save outputs for later comparison
dat_species_ex <- data$items |> dplyr::select(-links) # necessary for API accounting, but not part of the dataset

# Print the first few lines of the data
dat_species_ex |> 
  head(3) |> 
  flextable::flextable() |>
  flextable::colformat_num(
      j = c("species_code"), 
      big.mark = "") |> 
  flextable::theme_zebra()

Walleye pollock species information.
species_code	scientific_name	common_name	id_rank	worms	itis
21740	Gadus chalcogrammus	walleye pollock	species	300,735	934,083

12.4.2 Then, apply the `hauljoins` and `species_code` to catch query

We’ll use the data from the haul and species table we collected before to select 2023 eastern Bering Sea walleye pollock catch data.

## query the API link
# data for all walleye pollock caught in all 2023 eastern Bering Sea survey hauls
dat <- data.frame()
# there must be a better way to select multiple values for one parameter, 
# but saving that, we will loop through each hauljoin and collect the data of interest
for (i in 1:nrow(dat_haul_ex)) {
  res <- httr::GET(url = paste0(
    api_link_catch, 
    '?q={"species_code":21740,"hauljoin":', dat_haul_ex$hauljoin[i],'}'))
  ## convert from JSON format
  data <- jsonlite::fromJSON(base::rawToChar(res$content)) 
  if (length(data$items) != 0) {
    dat <- dplyr::bind_rows(
      dat,
      data$items |> 
        dplyr::select(-links)) # necessary for API accounting, but not part of the dataset
  }
}

Explore data:

# Find how many rows and columns are in the data pull
print(paste0("rows: ", nrow(dat), "; cols: ", ncol(dat)))

[1] "rows: 374; cols: 7"

# learn about the structure of the data
summary(dat)

    hauljoin       species_code     cpue_kgkm2          cpue_nokm2       
 Min.   :-23019   Min.   :21740   Min.   :    10.34   Min.   :    18.26  
 1st Qu.:-22777   1st Qu.:21740   1st Qu.:  1454.44   1st Qu.:  2281.20  
 Median :-22540   Median :21740   Median :  3286.76   Median :  5863.07  
 Mean   :-22553   Mean   :21740   Mean   :  6364.85   Mean   : 11540.65  
 3rd Qu.:-22324   3rd Qu.:21740   3rd Qu.:  6956.25   3rd Qu.: 12456.99  
 Max.   :-22110   Max.   :21740   Max.   :148679.68   Max.   :202321.08  
     count          weight_kg        taxon_confidence  
 Min.   :   1.0   Min.   :   0.492   Length:374        
 1st Qu.: 113.2   1st Qu.:  71.560   Class :character  
 Median : 284.0   Median : 162.310   Mode  :character  
 Mean   : 572.8   Mean   : 315.419                     
 3rd Qu.: 616.5   3rd Qu.: 350.399                     
 Max.   :9997.0   Max.   :7346.495

# Print the first few lines of the data 
dat |> 
  head(3) |> 
  flextable::flextable() |>
  flextable::colformat_num(
      j = c("hauljoin", "species_code"), 
      big.mark = "") |> 
  flextable::theme_zebra()

hauljoin	species_code	cpue_kgkm2	cpue_nokm2	count	weight_kg	taxon_confidence
-22283	21740	644.3718	963.6891	43	28.752	High
-22284	21740	3,554.5913	6,998.5742	317	161.005	High
-22285	21740	6,940.8696	9,808.6744	382	270.313	High

# save outputs for later comparison
dat_catch_ex <- dat

For reference and to help break down the above query, see these other query examples:

# data for haul -22775 (i.e., one specific haul)?
res <- httr::GET(url = paste0(api_link_catch, 
                              '?offset=',i,'&limit=10000&q={"hauljoin":-22775}'))

# data for all walleye pollock (i.e., one species) caught in all years and surveys
res <- httr::GET(url = paste0(api_link_catch, 
                              '?offset=',i,'&limit=10000&q={"species_code":21740}'))

12.4.3 Create zero-filled data for 2023 eastern Bering Sea walleye pollock and plot

It is important to create and have access to zero-fill (presence and absence) so you can do simple analyses and plot data.

dat <- dplyr::full_join(
  dat_haul_ex,
  dat_catch_ex) |> 
  dplyr::full_join(
    dat_species_ex)  |> 
  # modify zero-filled rows
  dplyr::mutate(
    cpue_kgkm2 = ifelse(is.na(cpue_kgkm2), 0, cpue_kgkm2),
    cpue_nokm2 = ifelse(is.na(cpue_nokm2), 0, cpue_nokm2),
    count = ifelse(is.na(count), 0, count),
    weight_kg = ifelse(is.na(weight_kg), 0, weight_kg))

Explore data

# Find how many rows and columns are in the data pull
print(paste0("rows: ", nrow(dat), "; cols: ", ncol(dat)))

[1] "rows: 376; cols: 38"

# learn about the structure of the data
summary(dat)

      year          srvy              survey          survey_name       
 Min.   :2023   Length:376         Length:376         Length:376        
 1st Qu.:2023   Class :character   Class :character   Class :character  
 Median :2023   Mode  :character   Mode  :character   Mode  :character  
 Mean   :2023                                                           
 3rd Qu.:2023                                                           
 Max.   :2023                                                           
                                                                        
 survey_definition_id     cruise         cruisejoin        hauljoin     
 Min.   :98           Min.   :202301   Min.   :-760.0   Min.   :-23019  
 1st Qu.:98           1st Qu.:202301   1st Qu.:-760.0   1st Qu.:-22776  
 Median :98           Median :202301   Median :-759.0   Median :-22539  
 Mean   :98           Mean   :202301   Mean   :-759.5   Mean   :-22552  
 3rd Qu.:98           3rd Qu.:202301   3rd Qu.:-759.0   3rd Qu.:-22333  
 Max.   :98           Max.   :202301   Max.   :-759.0   Max.   :-22110  
                                                                        
      haul           stratum        station            vessel_id    
 Min.   :  7.00   Min.   :10.00   Length:376         Min.   :134.0  
 1st Qu.: 65.75   1st Qu.:31.00   Class :character   1st Qu.:134.0  
 Median :114.00   Median :41.00   Mode  :character   Median :162.0  
 Mean   :114.16   Mean   :39.22                      Mean   :148.3  
 3rd Qu.:161.25   3rd Qu.:50.00                      3rd Qu.:162.0  
 Max.   :224.00   Max.   :90.00                      Max.   :162.0  
                                                                    
 vessel_name         date_time         latitude_dd_start longitude_dd_start
 Length:376         Length:376         Min.   :54.66     Min.   :-178.2    
 Class :character   Class :character   1st Qu.:57.00     1st Qu.:-172.7    
 Mode  :character   Mode  :character   Median :58.02     Median :-168.9    
                                       Mean   :58.26     Mean   :-168.8    
                                       3rd Qu.:59.50     3rd Qu.:-165.2    
                                       Max.   :62.01     Max.   :-158.3    
                                                                           
 latitude_dd_end longitude_dd_end bottom_temperature_c surface_temperature_c
 Min.   :54.68   Min.   :-178.2   Min.   :-1.600       Min.   : 1.700       
 1st Qu.:57.01   1st Qu.:-172.7   1st Qu.: 1.200       1st Qu.: 4.200       
 Median :58.02   Median :-168.9   Median : 2.700       Median : 6.550       
 Mean   :58.26   Mean   :-168.8   Mean   : 2.249       Mean   : 6.386       
 3rd Qu.:59.50   3rd Qu.:-165.2   3rd Qu.: 3.500       3rd Qu.: 8.525       
 Max.   :62.01   Max.   :-158.3   Max.   : 5.400       Max.   :11.000       
                                                                            
    depth_m       distance_fished_km  duration_hr      net_width_m   
 Min.   : 20.00   Min.   :1.065      Min.   :0.1890   Min.   :12.90  
 1st Qu.: 54.75   1st Qu.:2.805      1st Qu.:0.5100   1st Qu.:16.66  
 Median : 74.00   Median :2.889      Median :0.5180   Median :17.27  
 Mean   : 80.75   Mean   :2.854      Mean   :0.5129   Mean   :17.15  
 3rd Qu.:105.00   3rd Qu.:2.945      3rd Qu.:0.5260   3rd Qu.:17.83  
 Max.   :171.00   Max.   :3.849      Max.   :0.6560   Max.   :20.29  
                                                                     
  net_height_m   area_swept_km2     performance      species_code  
 Min.   :1.300   Min.   :0.02017   Min.   :0.0000   Min.   :21740  
 1st Qu.:1.875   1st Qu.:0.04725   1st Qu.:0.0000   1st Qu.:21740  
 Median :2.064   Median :0.04944   Median :0.0000   Median :21740  
 Mean   :2.107   Mean   :0.04892   Mean   :0.1075   Mean   :21740  
 3rd Qu.:2.343   3rd Qu.:0.05134   3rd Qu.:0.0000   3rd Qu.:21740  
 Max.   :3.196   Max.   :0.06369   Max.   :6.2200   Max.   :21740  
                                                    NA's   :2      
   cpue_kgkm2       cpue_nokm2         count          weight_kg      
 Min.   :     0   Min.   :     0   Min.   :   0.0   Min.   :   0.00  
 1st Qu.:  1431   1st Qu.:  2268   1st Qu.: 112.0   1st Qu.:  70.64  
 Median :  3273   Median :  5842   Median : 280.0   Median : 161.44  
 Mean   :  6331   Mean   : 11479   Mean   : 569.8   Mean   : 313.74  
 3rd Qu.:  6946   3rd Qu.: 12345   3rd Qu.: 611.5   3rd Qu.: 349.81  
 Max.   :148680   Max.   :202321   Max.   :9997.0   Max.   :7346.49  
                                                                     
 taxon_confidence   scientific_name    common_name          id_rank         
 Length:376         Length:376         Length:376         Length:376        
 Class :character   Class :character   Class :character   Class :character  
 Mode  :character   Mode  :character   Mode  :character   Mode  :character  
                                                                            
                                                                            
                                                                            
                                                                            
     worms             itis       
 Min.   :300735   Min.   :934083  
 1st Qu.:300735   1st Qu.:934083  
 Median :300735   Median :934083  
 Mean   :300735   Mean   :934083  
 3rd Qu.:300735   3rd Qu.:934083  
 Max.   :300735   Max.   :934083  
 NA's   :2        NA's   :2

# Print the first few lines of the data 
dat |> 
  head(3) |> 
  flextable::flextable() |>
  flextable::colformat_num(
      j = c("year", "cruise", "cruisejoin", "species_code"), 
      big.mark = "") |> 
  flextable::theme_zebra()

year	srvy	survey	survey_name	survey_definition_id	cruise	cruisejoin	hauljoin	haul	stratum	station	vessel_id	vessel_name	date_time	latitude_dd_start	longitude_dd_start	latitude_dd_end	longitude_dd_end	bottom_temperature_c	surface_temperature_c	depth_m	distance_fished_km	duration_hr	net_width_m	net_height_m	area_swept_km2	performance	species_code	cpue_kgkm2	cpue_nokm2	count	weight_kg	taxon_confidence	scientific_name	common_name	id_rank	worms	itis
2023	EBS	eastern Bering Sea	Eastern Bering Sea Crab/Groundfish Bottom Trawl Survey	98	202301	-759	-22,283	64	31	G-08	162	ALASKA KNIGHT	2023-06-08T10:05:05Z	57.00863	-163.3957	56.98449	-163.4006	2.7	5.1	66	2.700	0.488	16.526	2.466	0.044620	0.0	21740	644.3718	963.6891	43	28.752	High	Gadus chalcogrammus	walleye pollock	species	300,735	934,083
2023	EBS	eastern Bering Sea	Eastern Bering Sea Crab/Groundfish Bottom Trawl Survey	98	202301	-759	-22,284	63	10	H-08	162	ALASKA KNIGHT	2023-06-08T07:24:07Z	57.34861	-163.3868	57.32483	-163.3967	1.7	4.2	53	2.710	0.494	16.714	2.132	0.045295	0.0	21740	3,554.5913	6,998.5742	317	161.005	High	Gadus chalcogrammus	walleye pollock	species	300,735	934,083
2023	EBS	eastern Bering Sea	Eastern Bering Sea Crab/Groundfish Bottom Trawl Survey	98	202301	-759	-22,285	66	31	E-08	162	ALASKA KNIGHT	2023-06-08T15:23:29Z	56.33826	-163.4162	56.31646	-163.4189	3.1	6.5	84	2.428	0.442	16.040	2.396	0.038945	1.1	21740	6,940.8696	9,808.6744	382	270.313	High	Gadus chalcogrammus	walleye pollock	species	300,735	934,083

12.4.4 Visualize CPUE data in distribution map

Using the zero-filled data from the previous example, we can make a few plots!

12.5 Plot locations

library(ggplot2)

ggplot2::ggplot(data = dat |> dplyr::filter(cpue_kgkm2 != 0), 
                mapping = aes(x = longitude_dd_start, 
                              y = latitude_dd_start, 
                              size = cpue_kgkm2)) + 
  ggplot2::geom_point(alpha = .75) +
  ggplot2::geom_point(data = dat |> dplyr::filter(cpue_kgkm2 == 0), 
                      color = "red", 
                      shape = 17,
                      alpha = .75,
                      size = 3) +
  ggplot2::xlab("Longitude *W") +
  ggplot2::ylab("Latitude *N") +
  ggplot2::ggtitle(label = "Catches of walleye pollock (Weight CPUE; kg/km2)", 
                   subtitle = "2023 eastern Bering Sea bottom trawl survey") +
  ggplot2::scale_size_continuous(name = "Weight (kg)") + 
  ggplot2::theme_bw()

12.5.1 Plot inverse-distance weighted modeled product of locations

This map is constructed using akgfmaps

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

figure0 <- akgfmaps::make_idw_map(
  CPUE_KGHA = dat$cpue_kgkm2, # calculates the same, regardless of units.  
  LATITUDE = dat$latitude_dd_start, 
  LONGITUDE = dat$longitude_dd_start, 
  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
  extrapolation.grid.type = "sf")

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

figure0$plot + # 20x20km grid
  ggplot2::guides(fill=guide_legend(title = "walleye pollock\nCPUE (kg/km2)"))

12.1 Ex. Load all rows of the catch, haul, and species data tables

12.1.1 Load haul data

12.1.1.1 Load first 25 rows of data

12.1.1.2 Load all data:

12.1.2 Load catch data

12.1.2.1 Load first 25 rows of data

12.1.2.2 Load all data

12.1.3 Load species data

12.2 Ex. Create zero-filled data using data loaded in last example

12.3 Ex. Visualize zero-filled data for 2023 eastern Bering Sea walleye pollock in CPUE data in distribution map

12.3.1 Plot locations on map

12.3.2 Plot inverse-distance weighted plot of CPUE

12.4 Ex. Show catch data for 2023 eastern Bering Sea Walleye Pollock (one species in one survey region in one year)

12.4.1 Identify species_code for walleye pollock

12.4.2 Then, apply the hauljoins and species_code to catch query

12.4.3 Create zero-filled data for 2023 eastern Bering Sea walleye pollock and plot

12.4.4 Visualize CPUE data in distribution map

12.5 Plot locations

12.5.1 Plot inverse-distance weighted modeled product of locations

12.4.1 Identify `species_code` for walleye pollock

12.4.2 Then, apply the `hauljoins` and `species_code` to catch query