Scallop Example

Introduction

This vignette is an example of an exploratory data analysis using FishSET. It utilizes a range of FishSET functions for importing and upload data, performing quality assessment/quality control, and summarizing and visualizing data.


Packages

library(FishSET)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following object is masked from 'package:FishSET':
#> 
#>     select_vars
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(tidyr)
library(ggplot2)
library(maps)


Project Setup

The chunk below defines the location of the FishSET Folder. A temporary directory is used in this vignette example; for actual use, set the folderpath to a location that is not temporary.

folderpath <- tempdir()

proj <- "scallop"


Data Import

Upload the northeast scallop data that is included with the FishSET package.

load_maindata(dat = FishSET::scallop, project = proj)
#> Table saved to database
#> 
#> ! Data saved to database as scallopMainDataTable20250116 (raw) and scallopMainDataTable (working). 
#> Table is also in the working environment. !

The northeast scallop dataset is an obscured version of trip-level data from the scallop fishery from 2008-2015. We have retained 10,000 observations from the Limited Access Fishery that were landed at “large” ports. We have added random noise to the point locations, landings, and value. We have also replaced the TRIPID and PERMIT variables with identifiers that cannot be linked to originals.

Column Name Type Description
TRIPID integer An identifier variable for a trip (O)
DATE_TRIP Date The date of landing (O)
PERMIT.y int An identifier variable for a vessel. Rows with the same value indicate repeated observations of trips by the same vessel (O)
TRIP_LENGTH num length of the trip, in days
GEARCODE chr the type of gear used
port_lat num latitude of the landing port, decimal degrees
port_lon num longitude of the landing port, decimal degrees
previous_port_lat num latitude of the previous landing port, decimal degrees
previous_port_lon num longitude of the previous landing port, decimal degrees
Plan Code chr =SES, indicating these trips are in the Scallop Fishery
Program Code chr =SAA indicates a trip to a scallop access area; =SCA indicates a Limited Access DAYS-at-Sea Trip
TRIP_COST_WINSOR_2020_DOL num Predicted trip costs,in 2020 dollars, winsorized to remove outliers
DDLAT num latitude of fishing location, decimal degrees (O)
DDLON num longitude of fishing location, decimal degrees (O)
ZoneID num Fishing zone ID that corresponds to the TEN_ID variable of the tenMNSQR spatial table
LANDED_OBSCURED num Landed meat weights of scallops, in pounds (O)
DOLLAR_OBSCURED num Value of landed scallops, nominal dollars (O)
DOLLAR_2020_OBSCURED num Value of landed scallops, 2020 dollars (O)
DOLLAR_ALL_SP_2020_OBSCURED num Value of all species landed on a trip, 2020 dollars (O)

(O) indicates the values in the columns have been obscured.


This data contains 10000 rows and 19 variables.


A commonly used grid in the Northeast United States is the 10 minute grid. Each grid cell is 10’ latitude by 10’ longitude. This may or may not be the appropriate grid for your research question.

Upload and view the ten minute squares map from the FishSET package.

load_spatial(spat = FishSET::tenMNSQR, project = proj, name = "TenMNSQR")
#> Writing layer `scallopTenMNSQRSpatTable' to data source 
#>   `/tmp/Rtmp0lMQPP/scallop/data/spat/scallopTenMNSQRSpatTable.geojson' using driver `GeoJSON'
#> Writing 5267 features with 9 fields and geometry type Polygon.
#> Writing layer `scallopTenMNSQRSpatTable20250116' to data source 
#>   `/tmp/Rtmp0lMQPP/scallop/data/spat/scallopTenMNSQRSpatTable20250116.geojson' using driver `GeoJSON'
#> Writing 5267 features with 9 fields and geometry type Polygon.
#> Spatial table saved to project folder as scallopTenMNSQRSpatTable
plot_spat(tenMNSQR)

We have included the locations of prospective Wind Lease areas in the Northeast United States as of early 2023. These are intended to illustrate FishSET capabilities.

Upload and view the WindClose areas from the FishSET package.


load_spatial(spat = FishSET::windLease, project = proj, name = "WindClose")
#> Writing layer `scallopWindCloseSpatTable' to data source 
#>   `/tmp/Rtmp0lMQPP/scallop/data/spat/scallopWindCloseSpatTable.geojson' using driver `GeoJSON'
#> Writing 32 features with 1 fields and geometry type Multi Polygon.
#> Writing layer `scallopWindCloseSpatTable20250116' to data source 
#>   `/tmp/Rtmp0lMQPP/scallop/data/spat/scallopWindCloseSpatTable20250116.geojson' using driver `GeoJSON'
#> Writing 32 features with 1 fields and geometry type Multi Polygon.
#> Spatial table saved to project folder as scallopWindCloseSpatTable
plot_spat(windLease)


Assign the the 10 minute squares cells (scallopTenMNSQRSpatTable) and Wind areas (scallopWindCloseSpatTable) to variables for the working environment.

scallopTenMNSQRSpatTable <- table_view("scallopTenMNSQRSpatTable", proj)
#> Reading layer `scallopTenMNSQRSpatTable' from data source 
#>   `/tmp/Rtmp0lMQPP/scallop/data/spat/scallopTenMNSQRSpatTable.geojson' 
#>   using driver `GeoJSON'
#> Simple feature collection with 5267 features and 9 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -77 ymin: 33 xmax: -64 ymax: 46.00139
#> Geodetic CRS:  NAD83
scallopWindCloseSpatTable <- table_view("scallopWindCloseSpatTable", proj)
#> Reading layer `scallopWindCloseSpatTable' from data source 
#>   `/tmp/Rtmp0lMQPP/scallop/data/spat/scallopWindCloseSpatTable.geojson' 
#>   using driver `GeoJSON'
#> Simple feature collection with 32 features and 1 field
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -75.90347 ymin: 36.14111 xmax: -70.02155 ymax: 41.71859
#> Geodetic CRS:  WGS 84


Fleet assignment

FishSET can operate on distinct fleets. To do so, create a fleet table and use FishSET’s ``fleet_assign’’ function.

Assign all observations to either “Access Area” or “Days at Sea” fleets.

fleet_tab <- 
  data.frame(
    condition = c('`Plan Code` == "SES" & `Program Code` == "SAA"',
                  '`Plan Code` == "SES" & `Program Code` == "SCA"'),
    fleet = c("Access Area", "Days at Sea"))
# save fleet table to FishSET DB
fleet_table(scallopMainDataTable, 
            project = proj,
            table = fleet_tab, save = TRUE)
#> Table saved to fishset_db database
#>                                        condition       fleet
#> 1 `Plan Code` == "SES" & `Program Code` == "SAA" Access Area
#> 2 `Plan Code` == "SES" & `Program Code` == "SCA" Days at Sea
# grab tab name
fleet_tab_name <- list_tables(proj, type = "fleet")
# create fleet column
scallopMainDataTable <- 
  fleet_assign(scallopMainDataTable, project = proj, 
               fleet_tab = fleet_tab_name)


Bin Gears

The data contain several types of fishing gear. For simplicity, the GEARCODE column is re-binned to include three categories: "DREDGE", "TRAWL-BOTTOM", and "OTHER".

scallopMainDataTable$GEARCODE_OLD <- scallopMainDataTable$GEARCODE
#Anything with "DREDGE" in the GEARCODE will be rebinned to "DREDGE" 
pat_match <- "*DREDGE*"
reg_pat <- glob2rx(pat_match)
scallopMainDataTable$GEARCODE[grep(reg_pat, scallopMainDataTable$GEARCODE)] <- 'DREDGE'
#Look at the GEARCODE NOW, there should be 'DREDGE', 'TRAWL-BOTTOM', and some funky stuff
table(scallopMainDataTable$GEARCODE)
#> 
#>       DREDGE        OTHER TRAWL-BOTTOM 
#>         9916            1           83
scallopMainDataTable$GEARCODE[!(scallopMainDataTable$GEARCODE %in% c('DREDGE','TRAWL-BOTTOM'))] <- 'OTHER'


Operating Profit

Calculate operating profit of a trip by subtracting trip costs from revenues.

scallopMainDataTable <- 
  scallopMainDataTable %>% 
  mutate(OPERATING_PROFIT_2020 = DOLLAR_ALL_SP_2020_OBSCURED - TRIP_COST_WINSOR_2020_DOL)


Summary Table

FishSET can construct a table of summary statistics. To do so, use FishSET’s ``summary_stats’’ function.

summary_stats(scallopMainDataTable, proj) %>% 
  pretty_tab_sb()
TRIPID PERMIT.y TRIP_LENGTH port_lat port_lon previous_port_lat previous_port_lon TRIP_COST_WINSOR_2020_DOL DDLAT DDLON ZoneID LANDED_OBSCURED DOLLAR_OBSCURED DOLLAR_2020_OBSCURED DOLLAR_ALL_SP_2020_OBSCURED fleetAssignPlaceholder OPERATING_PROFIT_2020 DATE_TRIP GEARCODE Plan Code Program Code fleet GEARCODE_OLD
Min. : 6 Min. : 1 Min. : 0.15 Min. :37 Min. :-76 Min. :35 Min. :-77 Min. : 484 Min. :35 Min. :-76 Min. : 0 Min. : 22 Min. : 174 Min. : 204 Min. : 204 Min. :1 Min. : -12390 First: 2007-05-01 First: DREDGE First: SES First: SCA First: Days at Sea First: DREDGE-SCALLOP
Median :18836 Median :218 Median : 7.17 Median :42 Median :-71 Median :42 Median :-71 Median :12668 Median :40 Median :-73 Median :406712 Median :15639 Median :130938 Median :146722 Median : 148013 Median :1 Median : 134354 NA NA NA NA NA NA
Mean :19076 Mean :236 Mean : 7.47 Mean :40 Mean :-73 Mean :40 Mean :-73 Mean :13886 Mean :40 Mean :-72 Mean :400562 Mean :14822 Mean :137458 Mean :151992 Mean : 156171 Mean :1 Mean : 142285 NA NA NA NA NA NA
Max. :38503 Max. :456 Max. :24.58 Max. :42 Max. :-71 Max. :44 Max. :-70 Max. :30596 Max. :43 Max. :-66 Max. :427066 Max. :76507 Max. :648601 Max. :721698 Max. :2412282 Max. :1 Max. :2396500 NA NA NA NA NA NA
NA’s: 0 NA’s: 0 NA’s: 0 NA’s: 0 NA’s: 0 NA’s: 18 NA’s: 18 NA’s: 0 NA’s: 0 NA’s: 0 NA’s: 0 NA’s: 0 NA’s: 0 NA’s: 0 NA’s: 0 NA’s: 0 NA’s: 0 NA’s: 0 NA’s: 0 NA’s: 0 NA’s: 0 NA’s: 0 NA’s: 0
Unique Obs: 10000 Unique Obs: 130 Unique Obs: 3923 Unique Obs: 6 Unique Obs: 6 Unique Obs: 41 Unique Obs: 41 Unique Obs: 9651 Unique Obs: 2039 Unique Obs: 2212 Unique Obs: 465 Unique Obs: 10000 Unique Obs: 10000 Unique Obs: 10000 Unique Obs: 10000 Unique Obs: 1 Unique Obs: 10000 Unique Obs: 3544 Unique Obs: 3 Unique Obs: 1 Unique Obs: 2 Unique Obs: 2 Unique Obs: 4
No. 0’s: 0 No. 0’s: 0 No. 0’s: 0 No. 0’s: 0 No. 0’s: 0 No. 0’s: NA No. 0’s: NA No. 0’s: 0 No. 0’s: 0 No. 0’s: 0 No. 0’s: 1 No. 0’s: 0 No. 0’s: 0 No. 0’s: 0 No. 0’s: 0 No. 0’s: 0 No. 0’s: 0 No. Empty: 0 No. Empty: 0 No. Empty: 0 No. Empty: 0 No. Empty: 0 No. Empty: 0


QAQC

FishSET can do Quality Assurance checks on your data for NA, NaN, duplicates, and formatting.

NA Check

na_filter(scallopMainDataTable, 
          project = proj, 
          replace = FALSE, remove = FALSE, 
          rep.value = NA, over_write = FALSE)
#> The following columns contain NAs: previous_port_lat, previous_port_lon. Consider using na_filter to replace or remove NAs.

The na_filter() shows that some columns have missing values. By setting remove=TRUE, these can be dropped, although other methods of handling missing data may be better.


NaN Check

nan_filter(scallopMainDataTable, 
           project = proj, 
           replace = FALSE, remove = FALSE, 
           rep.value = NA, over_write = FALSE)
#> No NaNs found.

The nan_filter() shows the data does not have any NaNs


Unique Rows

unique_filter(scallopMainDataTable, project = proj, remove = FALSE)
#> Unique filter check for scallopMainDataTable dataset on 20250116 
#> Each row is a unique choice occurrence. No further action required.

The unique_filter() shows that all rows are unique.


Empty Variables

“Empty” variables contain only NAs.

empty_vars_filter(scallopMainDataTable, project = proj, remove = FALSE)
#> Empty vars check for scallopMainDataTable dataset on 20250116 
#> No empty variables identified.

The empty_vars_filter() shows that none of the columns are exclusively filled with NAs.


Lon/Lat Format

degree(scallopMainDataTable, project = proj,
       lat = "DDLAT", lon = "DDLON", 
       latsign = NULL, lonsign = NULL,
       replace = FALSE)
#> Latitude and longitude variables in decimal degrees. No further action required.


Spatial QAQC

FishSET can perform some spatial QAQC. The object scallopTenMNSQRSpatTable defines areas of the ocean. The spatial_qaqc function is used to check for rows of data that are on land (not in scallopTenMNSQRSpatTable) and on the boundary between cells.

spat_qaqc_out <- spatial_qaqc(dat = scallopMainDataTable, 
                              project = proj,
                              spat = scallopTenMNSQRSpatTable, 
                              lon.dat = "DDLON", 
                              lat.dat = "DDLAT")
#> Warning: Spatial reference EPSG codes for the spatial and primary datasets do
#> not match. The detected projection in the spatial file will be used unless epsg
#> is specified.
#> Spherical geometry (s2) switched off
#> although coordinates are longitude/latitude, st_intersects assumes that they
#> are planar
#> although coordinates are longitude/latitude, st_intersects assumes that they
#> are planar
#> Warning: 10 observations (0.1%) occur on land.
#> although coordinates are longitude/latitude, st_intersects assumes that they
#> are planar
#> although coordinates are longitude/latitude, st_intersects assumes that they
#> are planar
#> Warning: 698 observations (7%) occur on boundary line between regulatory zones.
#> 10 observations (0.1%) occur on land.
#> 698 observations (7%) occur on boundary line between regulatory zones.

spat_qaqc_out$dataset <- NULL # drop dataset

spat_qaqc_out$spatial_summary %>%
  pretty_lab(cols = "n") %>%
  pretty_tab()
YEAR n EXPECTED_LOC ON_LAND ON_ZONE_BOUNDARY perc
2007 773 728 2 43 7.73
2008 822 771 0 51 8.22
2009 861 820 1 40 8.61
2010 864 809 1 54 8.64
2011 818 761 0 57 8.18
2012 784 745 0 39 7.84
2013 603 562 1 40 6.03
2014 523 487 1 35 5.23
2015 601 551 0 50 6.01
2016 682 618 0 64 6.82
2017 788 727 0 61 7.88
2018 896 830 0 66 8.96
2019 985 883 4 98 9.85 

spat_qaqc_out[2:4]
#> $land_plot

#> 
#> $boundary_plot

#> 
#> $expected_plot

There are a few rows where the reported fishing location is on land.

There are also a few rows where the fishing location is exactly on the boundary between 10 minute squares. The regular spacing suggests that these points are located on vertices of cells (not the lines connecting the vertices). This is not particularly surprising. Each of the points on a vertex could reasonably be assigned to one of the four cells that share that vertex.


Data Creation

Landing Year

scallopMainDataTable <- 
  scallopMainDataTable %>% 
  mutate(DB_LANDING_YEAR = as.numeric(format(date_parser(DATE_TRIP), "%Y")))


Scale LANDED_OBSCURED to thousands of pounds

Scaling catch data to ‘smaller’ numbers can often improve model fit and computing efficiency for discrete choice models.

scallopMainDataTable$LANDED_OBSCURED <- scallopMainDataTable$LANDED_OBSCURED / 1000


Catch per Unit Effort (CPUE)

FishSET can create Catch per Unit Effort variables.

Create CPUE variable using TRIP_LENGTH and LANDED_OBSCURED. Filter out any infinite values.

scallopMainDataTable <- 
  cpue(scallopMainDataTable, proj,
       xWeight = "LANDED_OBSCURED",
       xTime = "TRIP_LENGTH", 
       name = "CPUE")
#> Warning: xWeight must a measurement of mass. CPUE calculated.
#> Warning: xTime should be a measurement of time. Use the create_duration
#> function. CPUE calculated.

scallopMainDataTable <- 
  scallopMainDataTable %>% 
  filter(!is.infinite(CPUE))


CPUE Percent Rank

Add a percent rank column to filter outliers.

outlier_table(scallopMainDataTable, proj, x = "CPUE") %>% 
  pretty_lab() %>% 
  pretty_tab()
Vector outlier_check N mean median SD min max NAs skew
CPUE None 10,000 2.01 1.95 2 0.01 149.98 0 45.53
CPUE 5_95_quant 9,000 1.95 1.95 0.73 0.49 3.47 0 0.01
CPUE 25_75_quant 5,000 1.95 1.95 0.35 1.31 2.57 0 -0.03
CPUE mean_2SD 9,973 1.96 1.95 0.9 0.01 5.79 0 0.23
CPUE mean_3SD 9,984 1.96 1.95 0.91 0.01 7.92 0 0.4
CPUE median_2SD 9,973 1.96 1.95 0.9 0.01 5.79 0 0.23
CPUE median_3SD 9,984 1.96 1.95 0.91 0.01 7.92 0 0.4 

scallopMainDataTable <- 
  scallopMainDataTable %>% 
  mutate(CPUE_p = percent_rank(CPUE))


Value Per Unit Effort

Similar to catch per unit effort, FishSET can create Value (or Revenue) per Unit Effort. Here, we used revenue (DOLLAR_OBSCURED) instead of Landed

scallopMainDataTable <- 
  cpue(scallopMainDataTable, proj,
       xWeight = "DOLLAR_OBSCURED",
       xTime = "TRIP_LENGTH", 
       name = "VPUE")
#> Warning: xWeight must a measurement of mass. CPUE calculated.
#> Warning: xTime should be a measurement of time. Use the create_duration
#> function. CPUE calculated.

scallopMainDataTable <- 
  scallopMainDataTable %>% 
  filter(!is.infinite(VPUE))


VPUE Percent Rank

Add a percent rank column to filter outliers.

outlier_table(scallopMainDataTable, proj, x = "VPUE") %>% 
  pretty_lab() %>% 
  pretty_tab()
Vector outlier_check N mean median SD min max NAs skew
VPUE None 10,000 18,675.14 17,691.99 15,505.59 92.36 933,919.8 0 26.74
VPUE 5_95_quant 9,000 18,026.09 17,691.99 7,670.15 4,199.84 35,185.46 0 0.2
VPUE 25_75_quant 5,000 17,704.31 17,691.99 3,802.8 11,073.26 24,564.41 0 0.02
VPUE mean_2SD 9,947 18,203.17 17,639.59 9,281 92.36 49,607.4 0 0.39
VPUE mean_3SD 9,978 18,315.2 17,670.87 9,483.24 92.36 64,302.48 0 0.51
VPUE median_2SD 9,944 18,193.81 17,635.59 9,266.73 92.36 48,637.5 0 0.39
VPUE median_3SD 9,977 18,310.59 17,670.19 9,472.53 92.36 59,843.36 0 0.5 

scallopMainDataTable <- 
  scallopMainDataTable %>% 
  mutate(VPUE_p = percent_rank(VPUE))


Fleet Tabulation

scallopMainDataTable %>% 
  count(fleet) %>% 
  mutate(perc = round(n/sum(n) * 100, 1)) %>% 
  pretty_lab(cols = "n") %>% 
  pretty_tab()
fleet n perc
Access Area 5,678 56.8
Days at Sea 4,322 43.2


Zone Assignment

Assign each observation to a ten minute square.

scallopMainDataTable <- 
  assignment_column(scallopMainDataTable, project = proj,
                    spat = scallopTenMNSQRSpatTable, 
                    lon.dat = "DDLON",
                    lat.dat = "DDLAT", 
                    cat = "TEN_ID",
                    name = "ZONE_ID",
                    closest.pt = FALSE,
                    hull.polygon = FALSE)
#> Warning: Projection does not match. The detected projection in the spatial file
#> will be used unless epsg is specified.
#> although coordinates are longitude/latitude, st_intersects assumes that they
#> are planar
#> although coordinates are longitude/latitude, st_intersects assumes that they
#> are planar
#> Warning: At least one observation assigned to multiple regulatory zones.
#> Assigning observations to nearest polygon.
#> although coordinates are longitude/latitude, st_nearest_feature assumes that
#> they are planar


Closure Area Assignment

Assign each observation to a closure area. An observation will have an NA if it does not occur within a closure area.

scallopMainDataTable <- 
  assignment_column(scallopMainDataTable, project = proj,
                    spat = scallopWindCloseSpatTable, 
                    lon.dat = "DDLON",
                    lat.dat = "DDLAT", 
                    cat = "NAME",
                    name = "closeID",
                    closest.pt = FALSE,
                    hull.polygon = FALSE) 
#> although coordinates are longitude/latitude, st_intersects assumes that they
#> are planar
#> although coordinates are longitude/latitude, st_intersects assumes that they
#> are planar

scallopMainDataTable <- 
  scallopMainDataTable %>% 
  mutate(in_closure = !is.na(closeID))


62 observations (0.62%) occurred inside a closure area.

agg_helper(scallopMainDataTable, 
           value = "in_closure", 
           count = TRUE, 
           fun = NULL) %>% 
  pivot_wider(names_from = "in_closure", values_from = "n") %>% 
  rename("Outside Closure(s)" = "FALSE", "Inside Closure(s)" = "TRUE") %>% 
  pretty_lab() %>% 
  pretty_tab()
Outside Closure(s) Inside Closure(s)
9,938 62


Observations inside/outside closures by fleet.

agg_helper(scallopMainDataTable, group = "fleet", 
            value = "in_closure", count = TRUE, fun = NULL) %>% 
  pivot_wider(names_from = "in_closure", values_from = "n") %>% 
  rename("Outside Closure(s)" = "FALSE", "Inside Closure(s)" = "TRUE") %>% 
  pretty_lab() %>% 
  pretty_tab()
fleet Outside Closure(s) Inside Closure(s)
Access Area 5,668 10
Days at Sea 4,270 52


Observations inside/outside closures by year.

agg_helper(scallopMainDataTable, value = "in_closure", 
            group = "DB_LANDING_YEAR", 
            count = TRUE, fun = NULL) %>% 
  pivot_wider(names_from = "in_closure", values_from = "n",
              values_fill = 0) %>% 
  arrange(DB_LANDING_YEAR) %>% 
  rename("Outside closure(s)" = "FALSE", "Inside closure(s)" = "TRUE") %>% 
  pretty_lab(ignore = "DB_LANDING_YEAR") %>% 
  pretty_tab()
DB_LANDING_YEAR Outside closure(s) Inside closure(s)
2007 773 0
2008 814 8
2009 855 6
2010 855 9
2011 807 11
2012 780 4
2013 599 4
2014 521 2
2015 598 3
2016 677 5
2017 785 3
2018 894 2
2019 980 5


Observations inside/outside closures by year and fleet.

agg_helper(scallopMainDataTable, value = "in_closure", 
            group = c("DB_LANDING_YEAR", "fleet"), 
            count = TRUE, fun = NULL) %>% 
  pivot_wider(names_from = "in_closure", values_from = "n",
              values_fill = 0) %>% 
  arrange(DB_LANDING_YEAR) %>% 
  rename("Outside closure(s)" = "FALSE", "Inside closure(s)" = "TRUE") %>% 
  pretty_lab(ignore = "DB_LANDING_YEAR") %>% 
  pretty_tab_sb(width = "60%")
DB_LANDING_YEAR fleet Outside closure(s) Inside closure(s)
2007 Access Area 423 0
2007 Days at Sea 350 0
2008 Access Area 471 0
2008 Days at Sea 343 8
2009 Access Area 479 2
2009 Days at Sea 376 4
2010 Access Area 454 4
2010 Days at Sea 401 5
2011 Access Area 500 1
2011 Days at Sea 307 10
2012 Access Area 438 0
2012 Days at Sea 342 4
2013 Days at Sea 353 4
2013 Access Area 246 0
2014 Days at Sea 348 2
2014 Access Area 173 0
2015 Access Area 304 0
2015 Days at Sea 294 3
2016 Access Area 354 2
2016 Days at Sea 323 3
2017 Access Area 464 0
2017 Days at Sea 321 3
2018 Access Area 624 0
2018 Days at Sea 270 2
2019 Access Area 738 1
2019 Days at Sea 242 4


Zone Summary

FishSET’s `zone_summary’ function can summarize the data by zone (Ten Minute Square) using interactive maps to make exploratory data analysis easier.

Frequency

The number of observations by zone.

zone_out <- zone_summary(scallopMainDataTable, project = proj,
                         spat = scallopTenMNSQRSpatTable,
                         zone.dat = "ZONE_ID",
                         zone.spat = "TEN_ID",
                         output = "tab_plot",
                         count = TRUE,
                         breaks = NULL, n.breaks = 10,
                         na.rm = TRUE)
#> A line object has been specified, but lines is not in the mode
#> Adding lines to the mode...

zone_out$plot
zone_out$table %>%
  pretty_lab(cols = "n") %>%
  pretty_tab_sb(width = "40%")
ZONE_ID n
416965 265
387332 260
387322 211
387331 209
387314 206
406926 202
406932 194
387436 168
387446 158
406915 151
387323 147
406925 140
406916 137
387445 135
416966 135
387313 129
416862 122
387455 119
387465 114
397364 111
416861 111
387333 109
387426 98
406933 97
397315 96
397213 91
397232 91
406811 84
387341 82
397355 80
397365 80
406936 80
416662 80
397231 78
416944 78
397363 77
406942 76
416852 75
397241 74
406935 74
387456 68
416955 67
387435 66
397325 63
406931 62
406944 61
387324 59
397346 59
426764 58
407364 57
387342 56
407242 56
416843 55
397356 54
406715 54
416661 54
377424 53
407262 53
377433 52
397354 52
407365 52
407263 51
416653 51
387312 49
387315 49
387321 47
397212 46
406714 46
406943 46
416851 45
397223 44
387425 43
397335 43
406713 41
406723 41
407254 41
406945 40
397214 39
407243 39
407355 39
397366 38
407356 38
407366 38
397314 37
407253 37
377423 36
397345 36
406821 36
397326 35
397362 35
406611 35
407241 35
397211 34
416714 34
377442 33
377443 33
397336 33
406716 33
406722 33
416853 33
407244 32
407261 32
416643 32
387311 31
407363 31
416842 31
377414 30
397344 30
407251 30
377415 29
397242 29
407112 29
407121 29
397316 28
416954 28
407113 27
407131 27
416652 27
387466 25
407252 25
416713 25
397353 24
407354 23
416945 23
406724 22
416642 22
416844 22
416933 22
406712 21
407245 21
416934 21
377432 20
397222 20
407226 20
407346 20
426763 20
387464 19
407345 19
416651 19
416863 19
397251 18
406812 18
406831 18
416765 18
397221 17
397324 17
397343 17
416654 17
416956 17
416964 17
406733 16
406832 16
416663 16
377413 15
397313 15
406822 15
377452 14
397334 14
407234 14
407264 14
377434 13
406826 13
416711 13
416932 13
377425 12
387325 12
387334 12
397352 12
397361 12
406732 12
416816 12
406841 11
406965 11
407111 11
407122 11
416766 11
387416 10
406833 10
407225 10
407246 10
427044 10
397333 9
406721 9
407115 9
407255 9
407256 9
417031 9
427045 9
387351 8
397342 8
417164 8
427065 8
397224 7
397312 7
406711 7
407114 7
407235 7
407353 7
416756 7
387463 6
406813 6
407236 6
416826 6
416834 6
416845 6
416943 6
377323 5
377422 5
387335 5
387336 5
387434 5
406814 5
406815 5
406924 5
406954 5
407232 5
407343 5
407362 5
416641 5
416712 5
416825 5
426765 5
427056 5
377444 4
387032 4
387362 4
387432 4
406835 4
406914 4
407132 4
407141 4
407352 4
416721 4
416854 4
417061 4
377416 3
377453 3
387242 3
387352 3
387411 3
387452 3
397351 3
397426 3
397465 3
406612 3
406725 3
406861 3
406934 3
406946 3
406952 3
407055 3
407151 3
407266 3
407344 3
417042 3
417062 3
427066 3
367322 2
367614 2
377325 2
377335 2
377435 2
377464 2
387241 2
387246 2
387316 2
387326 2
387354 2
387355 2
387363 2
387365 2
387414 2
387415 2
387422 2
387444 2
387454 2
387462 2
396916 2
397233 2
397234 2
397311 2
397322 2
397323 2
397332 2
397456 2
397466 2
406613 2
406621 2
406735 2
406742 2
406862 2
406941 2
406955 2
407013 2
407021 2
407032 2
407041 2
407142 2
407216 2
407221 2
407265 2
407316 2
407335 2
407336 2
407361 2
416722 2
416744 2
416755 2
416824 2
416833 2
416841 2
416864 2
416912 2
416922 2
416935 2
416953 2
416963 2
417055 2
417262 2
426762 2
0 1
347231 1
347336 1
347415 1
347535 1
357232 1
357313 1
357322 1
357325 1
357346 1
357445 1
357516 1
367216 1
367444 1
367536 1
377021 1
377143 1
377214 1
377224 1
377231 1
377311 1
377312 1
377321 1
377322 1
377332 1
377346 1
377364 1
377366 1
377411 1
377426 1
377445 1
377465 1
386916 1
387025 1
387121 1
387122 1
387145 1
387212 1
387214 1
387225 1
387226 1
387231 1
387232 1
387233 1
387236 1
387244 1
387252 1
387343 1
387344 1
387345 1
387353 1
387364 1
387366 1
387424 1
387433 1
387461 1
387655 1
396713 1
396814 1
397225 1
397246 1
397261 1
397262 1
397263 1
397265 1
397321 1
397331 1
397446 1
397463 1
397464 1
406623 1
406626 1
406643 1
406652 1
406731 1
406744 1
406764 1
406765 1
406766 1
406816 1
406834 1
406852 1
406923 1
406966 1
407011 1
407012 1
407015 1
407016 1
407035 1
407045 1
407116 1
407123 1
407133 1
407134 1
407135 1
407163 1
407215 1
407223 1
407224 1
407231 1
407233 1
407325 1
407326 1
407333 1
407342 1
407466 1
416644 1
416664 1
416665 1
416715 1
416724 1
416742 1
416743 1
416746 1
416762 1
416764 1
416835 1
416856 1
416865 1
416866 1
416915 1
416916 1
416924 1
416931 1
416942 1
416952 1
416961 1
416962 1
417046 1
417051 1
417145 1
417146 1
417154 1
417161 1
417162 1
417163 1
417165 1
417166 1
417366 1
426752 1
426915 1
426964 1
427034 1
427043 1


Percent of observations by zone can be produced by using fun="percent".

zone_out <- 
  zone_summary(scallopMainDataTable,
               project = proj,
               spat = scallopTenMNSQRSpatTable,
               zone.dat = "ZONE_ID",
               zone.spat = "TEN_ID",
               output = "tab_plot",
               count = TRUE, fun = "percent",
               breaks = c(seq(.2, .5, .1), seq(1, 2, .5)), 
               na.rm = TRUE)
#> A line object has been specified, but lines is not in the mode
#> Adding lines to the mode...

zone_out$plot
zone_out$table %>% 
  pretty_lab(ignore = "ZONE_ID") %>% 
  pretty_tab_sb(width = "40%")
ZONE_ID n perc
416965 265 2.65
387332 260 2.6
387322 211 2.11
387331 209 2.09
387314 206 2.06
406926 202 2.02
406932 194 1.94
387436 168 1.68
387446 158 1.58
406915 151 1.51
387323 147 1.47
406925 140 1.4
406916 137 1.37
387445 135 1.35
416966 135 1.35
387313 129 1.29
416862 122 1.22
387455 119 1.19
387465 114 1.14
397364 111 1.11
416861 111 1.11
387333 109 1.09
387426 98 0.98
406933 97 0.97
397315 96 0.96
397213 91 0.91
397232 91 0.91
406811 84 0.84
387341 82 0.82
397355 80 0.8
397365 80 0.8
406936 80 0.8
416662 80 0.8
397231 78 0.78
416944 78 0.78
397363 77 0.77
406942 76 0.76
416852 75 0.75
397241 74 0.74
406935 74 0.74
387456 68 0.68
416955 67 0.67
387435 66 0.66
397325 63 0.63
406931 62 0.62
406944 61 0.61
387324 59 0.59
397346 59 0.59
426764 58 0.58
407364 57 0.57
387342 56 0.56
407242 56 0.56
416843 55 0.55
397356 54 0.54
406715 54 0.54
416661 54 0.54
377424 53 0.53
407262 53 0.53
377433 52 0.52
397354 52 0.52
407365 52 0.52
407263 51 0.51
416653 51 0.51
387312 49 0.49
387315 49 0.49
387321 47 0.47
397212 46 0.46
406714 46 0.46
406943 46 0.46
416851 45 0.45
397223 44 0.44
387425 43 0.43
397335 43 0.43
406713 41 0.41
406723 41 0.41
407254 41 0.41
406945 40 0.4
397214 39 0.39
407243 39 0.39
407355 39 0.39
397366 38 0.38
407356 38 0.38
407366 38 0.38
397314 37 0.37
407253 37 0.37
377423 36 0.36
397345 36 0.36
406821 36 0.36
397326 35 0.35
397362 35 0.35
406611 35 0.35
407241 35 0.35
397211 34 0.34
416714 34 0.34
377442 33 0.33
377443 33 0.33
397336 33 0.33
406716 33 0.33
406722 33 0.33
416853 33 0.33
407244 32 0.32
407261 32 0.32
416643 32 0.32
387311 31 0.31
407363 31 0.31
416842 31 0.31
377414 30 0.3
397344 30 0.3
407251 30 0.3
377415 29 0.29
397242 29 0.29
407112 29 0.29
407121 29 0.29
397316 28 0.28
416954 28 0.28
407113 27 0.27
407131 27 0.27
416652 27 0.27
387466 25 0.25
407252 25 0.25
416713 25 0.25
397353 24 0.24
407354 23 0.23
416945 23 0.23
406724 22 0.22
416642 22 0.22
416844 22 0.22
416933 22 0.22
406712 21 0.21
407245 21 0.21
416934 21 0.21
377432 20 0.2
397222 20 0.2
407226 20 0.2
407346 20 0.2
426763 20 0.2
387464 19 0.19
407345 19 0.19
416651 19 0.19
416863 19 0.19
397251 18 0.18
406812 18 0.18
406831 18 0.18
416765 18 0.18
397221 17 0.17
397324 17 0.17
397343 17 0.17
416654 17 0.17
416956 17 0.17
416964 17 0.17
406733 16 0.16
406832 16 0.16
416663 16 0.16
377413 15 0.15
397313 15 0.15
406822 15 0.15
377452 14 0.14
397334 14 0.14
407234 14 0.14
407264 14 0.14
377434 13 0.13
406826 13 0.13
416711 13 0.13
416932 13 0.13
377425 12 0.12
387325 12 0.12
387334 12 0.12
397352 12 0.12
397361 12 0.12
406732 12 0.12
416816 12 0.12
406841 11 0.11
406965 11 0.11
407111 11 0.11
407122 11 0.11
416766 11 0.11
387416 10 0.1
406833 10 0.1
407225 10 0.1
407246 10 0.1
427044 10 0.1
397333 9 0.09
406721 9 0.09
407115 9 0.09
407255 9 0.09
407256 9 0.09
417031 9 0.09
427045 9 0.09
387351 8 0.08
397342 8 0.08
417164 8 0.08
427065 8 0.08
397224 7 0.07
397312 7 0.07
406711 7 0.07
407114 7 0.07
407235 7 0.07
407353 7 0.07
416756 7 0.07
387463 6 0.06
406813 6 0.06
407236 6 0.06
416826 6 0.06
416834 6 0.06
416845 6 0.06
416943 6 0.06
377323 5 0.05
377422 5 0.05
387335 5 0.05
387336 5 0.05
387434 5 0.05
406814 5 0.05
406815 5 0.05
406924 5 0.05
406954 5 0.05
407232 5 0.05
407343 5 0.05
407362 5 0.05
416641 5 0.05
416712 5 0.05
416825 5 0.05
426765 5 0.05
427056 5 0.05
377444 4 0.04
387032 4 0.04
387362 4 0.04
387432 4 0.04
406835 4 0.04
406914 4 0.04
407132 4 0.04
407141 4 0.04
407352 4 0.04
416721 4 0.04
416854 4 0.04
417061 4 0.04
377416 3 0.03
377453 3 0.03
387242 3 0.03
387352 3 0.03
387411 3 0.03
387452 3 0.03
397351 3 0.03
397426 3 0.03
397465 3 0.03
406612 3 0.03
406725 3 0.03
406861 3 0.03
406934 3 0.03
406946 3 0.03
406952 3 0.03
407055 3 0.03
407151 3 0.03
407266 3 0.03
407344 3 0.03
417042 3 0.03
417062 3 0.03
427066 3 0.03
367322 2 0.02
367614 2 0.02
377325 2 0.02
377335 2 0.02
377435 2 0.02
377464 2 0.02
387241 2 0.02
387246 2 0.02
387316 2 0.02
387326 2 0.02
387354 2 0.02
387355 2 0.02
387363 2 0.02
387365 2 0.02
387414 2 0.02
387415 2 0.02
387422 2 0.02
387444 2 0.02
387454 2 0.02
387462 2 0.02
396916 2 0.02
397233 2 0.02
397234 2 0.02
397311 2 0.02
397322 2 0.02
397323 2 0.02
397332 2 0.02
397456 2 0.02
397466 2 0.02
406613 2 0.02
406621 2 0.02
406735 2 0.02
406742 2 0.02
406862 2 0.02
406941 2 0.02
406955 2 0.02
407013 2 0.02
407021 2 0.02
407032 2 0.02
407041 2 0.02
407142 2 0.02
407216 2 0.02
407221 2 0.02
407265 2 0.02
407316 2 0.02
407335 2 0.02
407336 2 0.02
407361 2 0.02
416722 2 0.02
416744 2 0.02
416755 2 0.02
416824 2 0.02
416833 2 0.02
416841 2 0.02
416864 2 0.02
416912 2 0.02
416922 2 0.02
416935 2 0.02
416953 2 0.02
416963 2 0.02
417055 2 0.02
417262 2 0.02
426762 2 0.02
0 1 0.01
347231 1 0.01
347336 1 0.01
347415 1 0.01
347535 1 0.01
357232 1 0.01
357313 1 0.01
357322 1 0.01
357325 1 0.01
357346 1 0.01
357445 1 0.01
357516 1 0.01
367216 1 0.01
367444 1 0.01
367536 1 0.01
377021 1 0.01
377143 1 0.01
377214 1 0.01
377224 1 0.01
377231 1 0.01
377311 1 0.01
377312 1 0.01
377321 1 0.01
377322 1 0.01
377332 1 0.01
377346 1 0.01
377364 1 0.01
377366 1 0.01
377411 1 0.01
377426 1 0.01
377445 1 0.01
377465 1 0.01
386916 1 0.01
387025 1 0.01
387121 1 0.01
387122 1 0.01
387145 1 0.01
387212 1 0.01
387214 1 0.01
387225 1 0.01
387226 1 0.01
387231 1 0.01
387232 1 0.01
387233 1 0.01
387236 1 0.01
387244 1 0.01
387252 1 0.01
387343 1 0.01
387344 1 0.01
387345 1 0.01
387353 1 0.01
387364 1 0.01
387366 1 0.01
387424 1 0.01
387433 1 0.01
387461 1 0.01
387655 1 0.01
396713 1 0.01
396814 1 0.01
397225 1 0.01
397246 1 0.01
397261 1 0.01
397262 1 0.01
397263 1 0.01
397265 1 0.01
397321 1 0.01
397331 1 0.01
397446 1 0.01
397463 1 0.01
397464 1 0.01
406623 1 0.01
406626 1 0.01
406643 1 0.01
406652 1 0.01
406731 1 0.01
406744 1 0.01
406764 1 0.01
406765 1 0.01
406766 1 0.01
406816 1 0.01
406834 1 0.01
406852 1 0.01
406923 1 0.01
406966 1 0.01
407011 1 0.01
407012 1 0.01
407015 1 0.01
407016 1 0.01
407035 1 0.01
407045 1 0.01
407116 1 0.01
407123 1 0.01
407133 1 0.01
407134 1 0.01
407135 1 0.01
407163 1 0.01
407215 1 0.01
407223 1 0.01
407224 1 0.01
407231 1 0.01
407233 1 0.01
407325 1 0.01
407326 1 0.01
407333 1 0.01
407342 1 0.01
407466 1 0.01
416644 1 0.01
416664 1 0.01
416665 1 0.01
416715 1 0.01
416724 1 0.01
416742 1 0.01
416743 1 0.01
416746 1 0.01
416762 1 0.01
416764 1 0.01
416835 1 0.01
416856 1 0.01
416865 1 0.01
416866 1 0.01
416915 1 0.01
416916 1 0.01
416924 1 0.01
416931 1 0.01
416942 1 0.01
416952 1 0.01
416961 1 0.01
416962 1 0.01
417046 1 0.01
417051 1 0.01
417145 1 0.01
417146 1 0.01
417154 1 0.01
417161 1 0.01
417162 1 0.01
417163 1 0.01
417165 1 0.01
417166 1 0.01
417366 1 0.01
426752 1 0.01
426915 1 0.01
426964 1 0.01
427034 1 0.01
427043 1 0.01


Zone_summary can be used in conjunction with dplyr::filter to produce summaries for just one fleet (Access Area fleet).

zone_out <- 
  scallopMainDataTable %>% 
    dplyr::filter(fleet == "Access Area") %>% 
    zone_summary(project = proj,
                 spat = scallopTenMNSQRSpatTable, 
                 zone.dat = "ZONE_ID",
                 zone.spat = "TEN_ID",
                 output = "tab_plot",
                 count = TRUE,
                 breaks = NULL, n.breaks = 10, 
                 na.rm = TRUE)
#> A line object has been specified, but lines is not in the mode
#> Adding lines to the mode...

zone_out$plot
zone_out$table %>% 
  pretty_lab(cols = "n") %>% 
  pretty_tab_sb(width = "40%")
ZONE_ID n
387332 258
387322 208
387331 207
387314 205
406926 201
406932 194
387436 167
387446 157
387323 146
406925 140
387445 133
416862 121
387455 117
387465 109
387333 106
387313 104
416861 102
387426 98
406933 97
397364 94
387341 81
416662 80
406936 77
406942 76
416852 75
397365 74
406935 73
387456 68
397355 68
387435 66
397241 62
406931 62
406944 61
387324 58
397346 55
416843 55
387342 54
416661 54
377433 52
397356 51
416653 50
377424 49
387315 47
387321 46
406943 46
416851 45
387425 42
416966 40
406945 39
377423 36
377442 33
416853 33
377443 32
397366 32
416643 32
416842 31
377414 30
416652 27
377415 26
387466 22
406811 22
416642 22
397242 21
377432 20
387464 19
416651 19
416863 19
377413 15
406812 15
416663 15
397251 14
416654 14
377434 13
416765 13
387325 12
377452 11
387334 11
416766 11
377425 10
397345 9
416844 9
416956 9
397354 8
387311 7
387351 7
397363 7
387463 6
387312 5
387335 5
406954 5
416641 5
416756 5
417031 5
377422 4
377444 4
387032 4
387336 4
387362 4
387432 4
406611 4
406831 4
406924 4
377416 3
387242 3
387434 3
387452 3
397232 3
397333 3
397334 3
397342 3
406813 3
406821 3
406861 3
406915 3
406934 3
406946 3
406952 3
407246 3
407255 3
407343 3
416854 3
416945 3
416965 3
417042 3
367322 2
367614 2
377323 2
377435 2
377453 2
387241 2
387246 2
387352 2
387365 2
387411 2
387414 2
387415 2
387422 2
387454 2
387462 2
396916 2
397313 2
397322 2
397362 2
397426 2
397456 2
406613 2
406735 2
406862 2
406941 2
406955 2
407032 2
407355 2
416755 2
416864 2
416943 2
416953 2
417262 2
347415 1
347535 1
357313 1
357322 1
357346 1
357445 1
357516 1
367216 1
367444 1
367536 1
377021 1
377224 1
377311 1
377312 1
377332 1
377346 1
377364 1
377366 1
377411 1
377426 1
377445 1
377465 1
387121 1
387122 1
387145 1
387212 1
387214 1
387225 1
387226 1
387232 1
387233 1
387236 1
387244 1
387316 1
387326 1
387343 1
387345 1
387353 1
387355 1
387366 1
387416 1
387424 1
387433 1
387444 1
387461 1
387655 1
397211 1
397212 1
397231 1
397234 1
397261 1
397262 1
397263 1
397265 1
397312 1
397314 1
397315 1
397321 1
397324 1
397326 1
397331 1
397332 1
397336 1
397343 1
397344 1
397353 1
397446 1
397464 1
397465 1
397466 1
406643 1
406652 1
406712 1
406714 1
406716 1
406724 1
406742 1
406816 1
406822 1
406835 1
406841 1
406852 1
406916 1
406923 1
407016 1
407035 1
407045 1
407111 1
407113 1
407121 1
407251 1
407326 1
407336 1
407342 1
407344 1
407345 1
407362 1
407363 1
416644 1
416664 1
416711 1
416715 1
416722 1
416742 1
416744 1
416746 1
416856 1
416866 1
416931 1
416932 1
416933 1
416935 1
416952 1
416955 1
416961 1
416963 1
417051 1
417146 1


Zone frequency (Days at Sea fleet).

zone_out <- 
  scallopMainDataTable %>% 
    dplyr::filter(fleet == "Days at Sea") %>% 
    zone_summary(project = proj,
                 spat = scallopTenMNSQRSpatTable,
                 zone.dat = "ZONE_ID",
                 zone.spat = "TEN_ID",
                 output = "tab_plot",
                 count = TRUE,
                 breaks = NULL, n.breaks = 10, 
                 na.rm = TRUE)
#> A line object has been specified, but lines is not in the mode
#> Adding lines to the mode...

zone_out$plot
zone_out$table %>% 
  pretty_lab(cols = "n") %>% 
  pretty_tab_sb(width = "40%")
ZONE_ID n
416965 262
406915 148
406916 136
397315 95
416966 95
397213 91
397232 88
416944 78
397231 77
397363 70
416955 66
397325 63
406811 62
426764 58
407364 57
407242 56
406715 54
407262 53
407365 52
407263 51
397212 45
406714 45
387312 44
397223 44
397354 44
397335 43
406713 41
406723 41
407254 41
397214 39
407243 39
407356 38
407366 38
407253 37
407355 37
397314 36
407241 35
397326 34
416714 34
397211 33
397362 33
406722 33
406821 33
397336 32
406716 32
407244 32
407261 32
406611 31
407363 30
397344 29
407112 29
407251 29
397316 28
407121 28
416954 28
397345 27
407131 27
407113 26
387313 25
407252 25
416713 25
387311 24
397353 23
407354 23
406724 21
407245 21
416933 21
416934 21
397222 20
406712 20
407226 20
407346 20
416945 20
426763 20
407345 18
397221 17
397364 17
416964 17
397324 16
397343 16
406733 16
406832 16
406822 14
406831 14
407234 14
407264 14
397313 13
406826 13
416844 13
397241 12
397352 12
397355 12
397361 12
406732 12
416711 12
416816 12
416932 12
397334 11
406965 11
407122 11
406833 10
406841 10
407111 10
407225 10
427044 10
387416 9
406721 9
407115 9
407256 9
416861 9
427045 9
397242 8
416956 8
417164 8
427065 8
397224 7
406711 7
407114 7
407235 7
407246 7
407353 7
397312 6
397333 6
397365 6
397366 6
407236 6
407255 6
416826 6
416834 6
416845 6
387465 5
397342 5
406814 5
406815 5
407232 5
416712 5
416765 5
416825 5
426765 5
427056 5
377424 4
397251 4
397346 4
406914 4
407132 4
407141 4
407352 4
407362 4
416721 4
416943 4
417031 4
417061 4
377323 3
377415 3
377452 3
387322 3
387333 3
387466 3
397351 3
397356 3
406612 3
406725 3
406812 3
406813 3
406835 3
406936 3
407055 3
407151 3
407266 3
416654 3
417062 3
427066 3
377325 2
377335 2
377425 2
377464 2
387315 2
387331 2
387332 2
387342 2
387354 2
387363 2
387434 2
387445 2
387455 2
397233 2
397311 2
397323 2
397465 2
406621 2
407013 2
407021 2
407041 2
407142 2
407216 2
407221 2
407265 2
407316 2
407335 2
407343 2
407344 2
407361 2
416756 2
416824 2
416833 2
416841 2
416912 2
416922 2
417055 2
426762 2
0 1
347231 1
347336 1
357232 1
357325 1
377143 1
377214 1
377231 1
377321 1
377322 1
377422 1
377443 1
377453 1
386916 1
387025 1
387231 1
387252 1
387314 1
387316 1
387321 1
387323 1
387324 1
387326 1
387334 1
387336 1
387341 1
387344 1
387351 1
387352 1
387355 1
387364 1
387411 1
387425 1
387436 1
387444 1
387446 1
396713 1
396814 1
397225 1
397234 1
397246 1
397332 1
397426 1
397463 1
397466 1
406623 1
406626 1
406731 1
406742 1
406744 1
406764 1
406765 1
406766 1
406834 1
406924 1
406926 1
406935 1
406945 1
406966 1
407011 1
407012 1
407015 1
407116 1
407123 1
407133 1
407134 1
407135 1
407163 1
407215 1
407223 1
407224 1
407231 1
407233 1
407325 1
407333 1
407336 1
407466 1
416653 1
416663 1
416665 1
416722 1
416724 1
416743 1
416744 1
416762 1
416764 1
416835 1
416854 1
416862 1
416865 1
416915 1
416916 1
416924 1
416935 1
416942 1
416962 1
416963 1
417046 1
417145 1
417154 1
417161 1
417162 1
417163 1
417165 1
417166 1
417366 1
426752 1
426915 1
426964 1
427034 1
427043 1


Catch

FishSET’s `zone_summary’ function be used to aggregate total landings in each zone.

zone_out <- 
  zone_summary(scallopMainDataTable, 
               project = proj,
               spat = scallopTenMNSQRSpatTable, 
               zone.dat = "ZONE_ID",
               zone.spat = "TEN_ID",
               count = FALSE,
               var = "LANDED_OBSCURED", fun = "sum",
               breaks = c(1e3, 1e4, 5e4, 1e5, seq(1e6, 1.3e7, 2e6)),
               output = "tab_plot", na.rm = TRUE)
#> A line object has been specified, but lines is not in the mode
#> Adding lines to the mode...

zone_out$plot
zone_out$table %>% 
  pretty_lab(cols = "LANDED_OBSCURED") %>% 
  pretty_tab_sb(width = "40%")
ZONE_ID LANDED_OBSCURED
0 9.25
347231 4.09
347336 7.05
347415 5.1
347535 8
357232 0.38
357313 19.38
357322 16.28
357325 8.46
357346 8.57
357445 18.02
357516 13.54
367216 0.23
367322 34.26
367444 16.37
367536 17.75
367614 25.66
377021 19.06
377143 1.28
377214 6.91
377224 18.54
377231 18.82
377311 15.35
377312 11.21
377321 13.72
377322 5.82
377323 39.4
377325 42.44
377332 15.06
377335 28.53
377346 18.55
377364 18.32
377366 12.78
377411 1.35
377413 205.91
377414 450.2
377415 366.09
377416 37.6
377422 55.89
377423 433.28
377424 607.63
377425 125.49
377426 18.44
377432 287.28
377433 668.76
377434 166.57
377435 18.32
377442 470.09
377443 360.51
377444 51.7
377445 19.72
377452 197.48
377453 43.29
377464 14.9
377465 4.05
386916 16.4
387025 21.22
387032 68.92
387121 7.16
387122 11.06
387145 5.41
387212 15.87
387214 19.31
387225 9.97
387226 16.31
387231 31.66
387232 5.52
387233 7.28
387236 18.58
387241 31.76
387242 35.71
387244 16.54
387246 35.4
387252 8.57
387311 463.41
387312 608.28
387313 1,404.89
387314 2,166.96
387315 497.77
387316 17.44
387321 676.09
387322 2,941.77
387323 1,892.06
387324 543.66
387325 121.75
387326 19.86
387331 3,012.32
387332 3,592.69
387333 1,399.13
387334 113.45
387335 44.94
387336 29.01
387341 996.9
387342 660.2
387343 15.68
387344 11.41
387345 12.1
387351 116.39
387352 35.08
387353 15.26
387354 8.67
387355 42.13
387362 59.19
387363 41.97
387364 7.65
387365 22.69
387366 9
387411 36.98
387414 32.49
387415 28.57
387416 46.93
387422 20.12
387424 1.05
387425 470.72
387426 1,241.07
387432 48.62
387433 9.9
387434 43.73
387435 918.96
387436 2,365.39
387444 3.98
387445 1,677.6
387446 2,192
387452 6.38
387454 15.87
387455 1,501.39
387456 684.99
387461 16.29
387462 19.86
387463 77.89
387464 278.09
387465 1,273.04
387466 218.03
387655 16.93
396713 32.79
396814 2.99
396916 35.26
397211 600.24
397212 789.34
397213 1,603.78
397214 521.94
397221 231.84
397222 436.89
397223 825.97
397224 123.28
397225 3.91
397231 1,167.89
397232 1,261.3
397233 30.27
397234 18.71
397241 945.98
397242 275.42
397246 6.24
397251 199.55
397261 1.63
397262 8.45
397263 16.67
397265 16.59
397311 28.06
397312 76.09
397313 161.79
397314 397.49
397315 1,572.3
397316 562.6
397321 0.51
397322 8.88
397323 46.24
397324 275.6
397325 1,163.86
397326 680.71
397331 11.13
397332 26.77
397333 56.98
397334 195.16
397335 829.41
397336 532.25
397342 108.93
397343 214.7
397344 394.82
397345 481.81
397346 740.22
397351 65.5
397352 132.16
397353 364.49
397354 769.68
397355 1,060.39
397356 505.38
397361 101.09
397362 371.53
397363 838.3
397364 1,260.66
397365 845.8
397366 435.64
397426 47.87
397446 4.94
397456 14.52
397463 35.91
397464 6.05
397465 47.97
397466 33.32
406611 859.24
406612 62.34
406613 13.51
406621 63.49
406623 14.86
406626 19.97
406643 19.41
406652 16.28
406711 141.4
406712 318.84
406713 963.74
406714 1,178.26
406715 1,460.23
406716 926.24
406721 232.26
406722 786.33
406723 1,029
406724 423.68
406725 58.35
406731 17.04
406732 211.19
406733 240.34
406735 38.74
406742 17.94
406744 29.15
406764 19.99
406765 24.28
406766 41.51
406811 1,692.09
406812 315.57
406813 94.24
406814 127.86
406815 97.68
406816 16.72
406821 641.76
406822 325.11
406826 312.67
406831 303.19
406832 421.67
406833 242.52
406834 27.87
406835 64.23
406841 232.37
406852 12.19
406861 39.78
406862 30.11
406914 66.97
406915 2,693.93
406916 2,873.86
406923 18.9
406924 59.04
406925 1,855.35
406926 2,832.49
406931 815.89
406932 2,668.71
406933 1,350.58
406934 24.44
406935 946.1
406936 1,244.44
406941 11.59
406942 969.21
406943 690.91
406944 839.05
406945 588.62
406946 34.08
406952 43.46
406954 79.51
406955 12.28
406965 112.65
406966 2.73
407011 10.55
407012 10.54
407013 1.93
407015 0.56
407016 14.22
407021 41.65
407032 35.7
407035 17.44
407041 44.63
407045 8.37
407055 17.72
407111 171.5
407112 326.24
407113 259.39
407114 55.86
407115 35.59
407116 9.99
407121 513.41
407122 200.86
407123 0.98
407131 497.44
407132 62.31
407133 19.74
407134 11.46
407135 9.26
407141 29.87
407142 52.62
407151 75.45
407163 29.71
407215 3.84
407216 21.99
407221 29.96
407223 1.69
407224 28.65
407225 165.68
407226 445.45
407231 21.21
407232 37.23
407233 14.09
407234 258.64
407235 120.52
407236 60.2
407241 641.75
407242 1,039.24
407243 744.45
407244 575.49
407245 298.17
407246 89.87
407251 588.31
407252 374.35
407253 663.59
407254 763.38
407255 108.38
407256 114.61
407261 561.2
407262 810.66
407263 1,102.14
407264 258.76
407265 19.79
407266 46.11
407316 8.06
407325 14.97
407326 16.06
407333 24.62
407335 33.45
407336 32.48
407342 14.96
407343 53.54
407344 54.69
407345 307.7
407346 356.25
407352 73.62
407353 130.14
407354 424.77
407355 718.6
407356 640.95
407361 34.8
407362 82.18
407363 619.26
407364 953.13
407365 774.85
407366 742.88
407466 2.34
416641 87.62
416642 320.32
416643 447.06
416644 12.43
416651 244.7
416652 445.88
416653 727.75
416654 202.78
416661 801.7
416662 1,294.69
416663 253.66
416664 8.08
416665 31.08
416711 312.8
416712 126.24
416713 594.79
416714 749.09
416715 6.95
416721 56.7
416722 17.41
416724 6.52
416742 19.18
416743 26.12
416744 31.61
416746 5.36
416755 34.95
416756 101.52
416762 30.14
416764 16.63
416765 255.13
416766 182.65
416816 272.76
416824 59.49
416825 90.94
416826 149.41
416833 78.38
416834 84.77
416835 21.81
416841 21.83
416842 414.04
416843 777.62
416844 350.73
416845 124.43
416851 660.6
416852 1,092.63
416853 460.11
416854 79.03
416856 18.87
416861 1,428.56
416862 1,759.11
416863 254.12
416864 28.62
416865 46.85
416866 21.47
416912 26.55
416915 11.16
416916 17.97
416922 57.75
416924 9.16
416931 12.77
416932 263.93
416933 390.83
416934 204.99
416935 29.96
416942 7.71
416943 98.44
416944 1,237.49
416945 366.91
416952 2.82
416953 34.7
416954 474.66
416955 967.83
416956 211.85
416961 18.48
416962 5.94
416963 35.36
416964 314.58
416965 3,756.98
416966 1,947.93
417031 145.06
417042 18
417046 45.71
417051 19.49
417055 36.19
417061 80.1
417062 33.55
417145 0.8
417146 11.1
417154 4.09
417161 0.55
417162 1.13
417163 15.55
417164 83.43
417165 6.94
417166 0.59
417262 28.45
417366 5.19
426752 14.33
426762 41.13
426763 416.02
426764 1,411.12
426765 135.53
426915 16.68
426964 52.44
427034 1.68
427043 33.06
427044 140.01
427045 162.57
427056 50.11
427065 161.07
427066 50


FishSET’s `zone_summary’ function be used to construct average landings for trips in each zone.

zone_out <- 
  zone_summary(scallopMainDataTable,
               project = proj,
               spat = scallopTenMNSQRSpatTable, 
               zone.dat = "ZONE_ID",
               zone.spat = "TEN_ID",
               count = FALSE,
               var = "LANDED_OBSCURED", fun = "mean",
               breaks = c(1e3, 5e3, seq(1e4, 4e4, 5e3)),
               output = "tab_plot", na.rm = TRUE)
#> A line object has been specified, but lines is not in the mode
#> Adding lines to the mode...

zone_out$plot
zone_out$table %>% 
  pretty_lab(cols = "LANDED_OBSCURED") %>% 
  pretty_tab_sb(width = "40%")
ZONE_ID LANDED_OBSCURED
0 9.25
347231 4.09
347336 7.05
347415 5.1
347535 8
357232 0.38
357313 19.38
357322 16.28
357325 8.46
357346 8.57
357445 18.02
357516 13.54
367216 0.23
367322 17.13
367444 16.37
367536 17.75
367614 12.83
377021 19.06
377143 1.28
377214 6.91
377224 18.54
377231 18.82
377311 15.35
377312 11.21
377321 13.72
377322 5.82
377323 7.88
377325 21.22
377332 15.06
377335 14.27
377346 18.55
377364 18.32
377366 12.78
377411 1.35
377413 13.73
377414 15.01
377415 12.62
377416 12.53
377422 11.18
377423 12.04
377424 11.46
377425 10.46
377426 18.44
377432 14.36
377433 12.86
377434 12.81
377435 9.16
377442 14.25
377443 10.92
377444 12.92
377445 19.72
377452 14.11
377453 14.43
377464 7.45
377465 4.05
386916 16.4
387025 21.22
387032 17.23
387121 7.16
387122 11.06
387145 5.41
387212 15.87
387214 19.31
387225 9.97
387226 16.31
387231 31.66
387232 5.52
387233 7.28
387236 18.58
387241 15.88
387242 11.9
387244 16.54
387246 17.7
387252 8.57
387311 14.95
387312 12.41
387313 10.89
387314 10.52
387315 10.16
387316 8.72
387321 14.38
387322 13.94
387323 12.87
387324 9.21
387325 10.15
387326 9.93
387331 14.41
387332 13.82
387333 12.84
387334 9.45
387335 8.99
387336 5.8
387341 12.16
387342 11.79
387343 15.68
387344 11.41
387345 12.1
387351 14.55
387352 11.69
387353 15.26
387354 4.34
387355 21.07
387362 14.8
387363 20.99
387364 7.65
387365 11.35
387366 9
387411 12.33
387414 16.24
387415 14.29
387416 4.69
387422 10.06
387424 1.05
387425 10.95
387426 12.66
387432 12.15
387433 9.9
387434 8.75
387435 13.92
387436 14.08
387444 1.99
387445 12.43
387446 13.87
387452 2.13
387454 7.94
387455 12.62
387456 10.07
387461 16.29
387462 9.93
387463 12.98
387464 14.64
387465 11.17
387466 8.72
387655 16.93
396713 32.79
396814 2.99
396916 17.63
397211 17.65
397212 17.16
397213 17.62
397214 13.38
397221 13.64
397222 21.84
397223 18.77
397224 17.61
397225 3.91
397231 14.97
397232 13.86
397233 15.13
397234 9.35
397241 12.78
397242 9.5
397246 6.24
397251 11.09
397261 1.63
397262 8.45
397263 16.67
397265 16.59
397311 14.03
397312 10.87
397313 10.79
397314 10.74
397315 16.38
397316 20.09
397321 0.51
397322 4.44
397323 23.12
397324 16.21
397325 18.47
397326 19.45
397331 11.13
397332 13.39
397333 6.33
397334 13.94
397335 19.29
397336 16.13
397342 13.62
397343 12.63
397344 13.16
397345 13.38
397346 12.55
397351 21.83
397352 11.01
397353 15.19
397354 14.8
397355 13.25
397356 9.36
397361 8.42
397362 10.62
397363 10.89
397364 11.36
397365 10.57
397366 11.46
397426 15.96
397446 4.94
397456 7.26
397463 35.91
397464 6.05
397465 15.99
397466 16.66
406611 24.55
406612 20.78
406613 6.75
406621 31.74
406623 14.86
406626 19.97
406643 19.41
406652 16.28
406711 20.2
406712 15.18
406713 23.51
406714 25.61
406715 27.04
406716 28.07
406721 25.81
406722 23.83
406723 25.1
406724 19.26
406725 19.45
406731 17.04
406732 17.6
406733 15.02
406735 19.37
406742 8.97
406744 29.15
406764 19.99
406765 24.28
406766 41.51
406811 20.14
406812 17.53
406813 15.71
406814 25.57
406815 19.54
406816 16.72
406821 17.83
406822 21.67
406826 24.05
406831 16.84
406832 26.35
406833 24.25
406834 27.87
406835 16.06
406841 21.12
406852 12.19
406861 13.26
406862 15.05
406914 16.74
406915 17.84
406916 20.98
406923 18.9
406924 11.81
406925 13.25
406926 14.02
406931 13.16
406932 13.76
406933 13.92
406934 8.15
406935 12.79
406936 15.56
406941 5.8
406942 12.75
406943 15.02
406944 13.75
406945 14.72
406946 11.36
406952 14.49
406954 15.9
406955 6.14
406965 10.24
406966 2.73
407011 10.55
407012 10.54
407013 0.97
407015 0.56
407016 14.22
407021 20.82
407032 17.85
407035 17.44
407041 22.31
407045 8.37
407055 5.91
407111 15.59
407112 11.25
407113 9.61
407114 7.98
407115 3.95
407116 9.99
407121 17.7
407122 18.26
407123 0.98
407131 18.42
407132 15.58
407133 19.74
407134 11.46
407135 9.26
407141 7.47
407142 26.31
407151 25.15
407163 29.71
407215 3.84
407216 10.99
407221 14.98
407223 1.69
407224 28.65
407225 16.57
407226 22.27
407231 21.21
407232 7.45
407233 14.09
407234 18.47
407235 17.22
407236 10.03
407241 18.34
407242 18.56
407243 19.09
407244 17.98
407245 14.2
407246 8.99
407251 19.61
407252 14.97
407253 17.93
407254 18.62
407255 12.04
407256 12.73
407261 17.54
407262 15.3
407263 21.61
407264 18.48
407265 9.9
407266 15.37
407316 4.03
407325 14.97
407326 16.06
407333 24.62
407335 16.72
407336 16.24
407342 14.96
407343 10.71
407344 18.23
407345 16.19
407346 17.81
407352 18.4
407353 18.59
407354 18.47
407355 18.43
407356 16.87
407361 17.4
407362 16.44
407363 19.98
407364 16.72
407365 14.9
407366 19.55
407466 2.34
416641 17.52
416642 14.56
416643 13.97
416644 12.43
416651 12.88
416652 16.51
416653 14.27
416654 11.93
416661 14.85
416662 16.18
416663 15.85
416664 8.08
416665 31.08
416711 24.06
416712 25.25
416713 23.79
416714 22.03
416715 6.95
416721 14.18
416722 8.71
416724 6.52
416742 19.18
416743 26.12
416744 15.81
416746 5.36
416755 17.47
416756 14.5
416762 30.14
416764 16.63
416765 14.17
416766 16.6
416816 22.73
416824 29.75
416825 18.19
416826 24.9
416833 39.19
416834 14.13
416835 21.81
416841 10.92
416842 13.36
416843 14.14
416844 15.94
416845 20.74
416851 14.68
416852 14.57
416853 13.94
416854 19.76
416856 18.87
416861 12.87
416862 14.42
416863 13.37
416864 14.31
416865 46.85
416866 21.47
416912 13.28
416915 11.16
416916 17.97
416922 28.88
416924 9.16
416931 12.77
416932 20.3
416933 17.77
416934 9.76
416935 14.98
416942 7.71
416943 16.41
416944 15.87
416945 15.95
416952 2.82
416953 17.35
416954 16.95
416955 14.45
416956 12.46
416961 18.48
416962 5.94
416963 17.68
416964 18.5
416965 14.18
416966 14.43
417031 16.12
417042 6
417046 45.71
417051 19.49
417055 18.09
417061 20.03
417062 11.18
417145 0.8
417146 11.1
417154 4.09
417161 0.55
417162 1.13
417163 15.55
417164 10.43
417165 6.94
417166 0.59
417262 14.22
417366 5.19
426752 14.33
426762 20.57
426763 20.8
426764 24.33
426765 27.11
426915 16.68
426964 52.44
427034 1.68
427043 33.06
427044 14
427045 18.06
427056 10.02
427065 20.13
427066 16.67


Using the fun="percent" option computes the percent of total landings.

zone_out <- 
  zone_summary(scallopMainDataTable, project = proj,
               spat = scallopTenMNSQRSpatTable,
               zone.dat = "ZONE_ID",
               zone.spat = "TEN_ID",
               count = FALSE,
               var = "LANDED_OBSCURED", fun = "percent",
               breaks = seq(0, 2, .2),
               bin_colors = c("white", fishset_viridis(10)),
               output = "tab_plot", na.rm = TRUE)
#> A line object has been specified, but lines is not in the mode
#> Adding lines to the mode...

zone_out$plot
zone_out$table %>% 
  pretty_lab(cols = c("LANDED_OBSCURED", "LANDED_OBSCURED_perc"), type = "scientific") %>%
  pretty_tab_sb(width = "60%")
ZONE_ID LANDED_OBSCURED LANDED_OBSCURED_perc
0 9.25e+00 6.24e-03
347231 4.09e+00 2.76e-03
347336 7.05e+00 4.76e-03
347415 5.10e+00 3.44e-03
347535 8.00e+00 5.39e-03
357232 3.83e-01 2.58e-04
357313 1.94e+01 1.31e-02
357322 1.63e+01 1.10e-02
357325 8.46e+00 5.71e-03
357346 8.57e+00 5.78e-03
357445 1.80e+01 1.22e-02
357516 1.35e+01 9.14e-03
367216 2.27e-01 1.53e-04
367322 3.43e+01 2.31e-02
367444 1.64e+01 1.10e-02
367536 1.77e+01 1.20e-02
367614 2.57e+01 1.73e-02
377021 1.91e+01 1.29e-02
377143 1.28e+00 8.61e-04
377214 6.91e+00 4.66e-03
377224 1.85e+01 1.25e-02
377231 1.88e+01 1.27e-02
377311 1.54e+01 1.04e-02
377312 1.12e+01 7.56e-03
377321 1.37e+01 9.25e-03
377322 5.82e+00 3.93e-03
377323 3.94e+01 2.66e-02
377325 4.24e+01 2.86e-02
377332 1.51e+01 1.02e-02
377335 2.85e+01 1.93e-02
377346 1.85e+01 1.25e-02
377364 1.83e+01 1.24e-02
377366 1.28e+01 8.62e-03
377411 1.35e+00 9.11e-04
377413 2.06e+02 1.39e-01
377414 4.50e+02 3.04e-01
377415 3.66e+02 2.47e-01
377416 3.76e+01 2.54e-02
377422 5.59e+01 3.77e-02
377423 4.33e+02 2.92e-01
377424 6.08e+02 4.10e-01
377425 1.25e+02 8.47e-02
377426 1.84e+01 1.24e-02
377432 2.87e+02 1.94e-01
377433 6.69e+02 4.51e-01
377434 1.67e+02 1.12e-01
377435 1.83e+01 1.24e-02
377442 4.70e+02 3.17e-01
377443 3.61e+02 2.43e-01
377444 5.17e+01 3.49e-02
377445 1.97e+01 1.33e-02
377452 1.97e+02 1.33e-01
377453 4.33e+01 2.92e-02
377464 1.49e+01 1.01e-02
377465 4.05e+00 2.73e-03
386916 1.64e+01 1.11e-02
387025 2.12e+01 1.43e-02
387032 6.89e+01 4.65e-02
387121 7.16e+00 4.83e-03
387122 1.11e+01 7.46e-03
387145 5.41e+00 3.65e-03
387212 1.59e+01 1.07e-02
387214 1.93e+01 1.30e-02
387225 9.97e+00 6.73e-03
387226 1.63e+01 1.10e-02
387231 3.17e+01 2.14e-02
387232 5.52e+00 3.72e-03
387233 7.28e+00 4.91e-03
387236 1.86e+01 1.25e-02
387241 3.18e+01 2.14e-02
387242 3.57e+01 2.41e-02
387244 1.65e+01 1.12e-02
387246 3.54e+01 2.39e-02
387252 8.57e+00 5.78e-03
387311 4.63e+02 3.13e-01
387312 6.08e+02 4.10e-01
387313 1.40e+03 9.48e-01
387314 2.17e+03 1.46e+00
387315 4.98e+02 3.36e-01
387316 1.74e+01 1.18e-02
387321 6.76e+02 4.56e-01
387322 2.94e+03 1.98e+00
387323 1.89e+03 1.28e+00
387324 5.44e+02 3.67e-01
387325 1.22e+02 8.21e-02
387326 1.99e+01 1.34e-02
387331 3.01e+03 2.03e+00
387332 3.59e+03 2.42e+00
387333 1.40e+03 9.44e-01
387334 1.13e+02 7.65e-02
387335 4.49e+01 3.03e-02
387336 2.90e+01 1.96e-02
387341 9.97e+02 6.73e-01
387342 6.60e+02 4.45e-01
387343 1.57e+01 1.06e-02
387344 1.14e+01 7.70e-03
387345 1.21e+01 8.16e-03
387351 1.16e+02 7.85e-02
387352 3.51e+01 2.37e-02
387353 1.53e+01 1.03e-02
387354 8.67e+00 5.85e-03
387355 4.21e+01 2.84e-02
387362 5.92e+01 3.99e-02
387363 4.20e+01 2.83e-02
387364 7.65e+00 5.16e-03
387365 2.27e+01 1.53e-02
387366 9.00e+00 6.07e-03
387411 3.70e+01 2.50e-02
387414 3.25e+01 2.19e-02
387415 2.86e+01 1.93e-02
387416 4.69e+01 3.17e-02
387422 2.01e+01 1.36e-02
387424 1.05e+00 7.08e-04
387425 4.71e+02 3.18e-01
387426 1.24e+03 8.37e-01
387432 4.86e+01 3.28e-02
387433 9.90e+00 6.68e-03
387434 4.37e+01 2.95e-02
387435 9.19e+02 6.20e-01
387436 2.37e+03 1.60e+00
387444 3.98e+00 2.68e-03
387445 1.68e+03 1.13e+00
387446 2.19e+03 1.48e+00
387452 6.38e+00 4.30e-03
387454 1.59e+01 1.07e-02
387455 1.50e+03 1.01e+00
387456 6.85e+02 4.62e-01
387461 1.63e+01 1.10e-02
387462 1.99e+01 1.34e-02
387463 7.79e+01 5.25e-02
387464 2.78e+02 1.88e-01
387465 1.27e+03 8.59e-01
387466 2.18e+02 1.47e-01
387655 1.69e+01 1.14e-02
396713 3.28e+01 2.21e-02
396814 2.99e+00 2.02e-03
396916 3.53e+01 2.38e-02
397211 6.00e+02 4.05e-01
397212 7.89e+02 5.33e-01
397213 1.60e+03 1.08e+00
397214 5.22e+02 3.52e-01
397221 2.32e+02 1.56e-01
397222 4.37e+02 2.95e-01
397223 8.26e+02 5.57e-01
397224 1.23e+02 8.32e-02
397225 3.91e+00 2.64e-03
397231 1.17e+03 7.88e-01
397232 1.26e+03 8.51e-01
397233 3.03e+01 2.04e-02
397234 1.87e+01 1.26e-02
397241 9.46e+02 6.38e-01
397242 2.75e+02 1.86e-01
397246 6.24e+00 4.21e-03
397251 2.00e+02 1.35e-01
397261 1.63e+00 1.10e-03
397262 8.45e+00 5.70e-03
397263 1.67e+01 1.12e-02
397265 1.66e+01 1.12e-02
397311 2.81e+01 1.89e-02
397312 7.61e+01 5.13e-02
397313 1.62e+02 1.09e-01
397314 3.97e+02 2.68e-01
397315 1.57e+03 1.06e+00
397316 5.63e+02 3.80e-01
397321 5.06e-01 3.41e-04
397322 8.88e+00 5.99e-03
397323 4.62e+01 3.12e-02
397324 2.76e+02 1.86e-01
397325 1.16e+03 7.85e-01
397326 6.81e+02 4.59e-01
397331 1.11e+01 7.51e-03
397332 2.68e+01 1.81e-02
397333 5.70e+01 3.84e-02
397334 1.95e+02 1.32e-01
397335 8.29e+02 5.60e-01
397336 5.32e+02 3.59e-01
397342 1.09e+02 7.35e-02
397343 2.15e+02 1.45e-01
397344 3.95e+02 2.66e-01
397345 4.82e+02 3.25e-01
397346 7.40e+02 4.99e-01
397351 6.55e+01 4.42e-02
397352 1.32e+02 8.92e-02
397353 3.64e+02 2.46e-01
397354 7.70e+02 5.19e-01
397355 1.06e+03 7.15e-01
397356 5.05e+02 3.41e-01
397361 1.01e+02 6.82e-02
397362 3.72e+02 2.51e-01
397363 8.38e+02 5.66e-01
397364 1.26e+03 8.51e-01
397365 8.46e+02 5.71e-01
397366 4.36e+02 2.94e-01
397426 4.79e+01 3.23e-02
397446 4.94e+00 3.34e-03
397456 1.45e+01 9.80e-03
397463 3.59e+01 2.42e-02
397464 6.05e+00 4.08e-03
397465 4.80e+01 3.24e-02
397466 3.33e+01 2.25e-02
406611 8.59e+02 5.80e-01
406612 6.23e+01 4.21e-02
406613 1.35e+01 9.11e-03
406621 6.35e+01 4.28e-02
406623 1.49e+01 1.00e-02
406626 2.00e+01 1.35e-02
406643 1.94e+01 1.31e-02
406652 1.63e+01 1.10e-02
406711 1.41e+02 9.54e-02
406712 3.19e+02 2.15e-01
406713 9.64e+02 6.50e-01
406714 1.18e+03 7.95e-01
406715 1.46e+03 9.85e-01
406716 9.26e+02 6.25e-01
406721 2.32e+02 1.57e-01
406722 7.86e+02 5.31e-01
406723 1.03e+03 6.94e-01
406724 4.24e+02 2.86e-01
406725 5.84e+01 3.94e-02
406731 1.70e+01 1.15e-02
406732 2.11e+02 1.42e-01
406733 2.40e+02 1.62e-01
406735 3.87e+01 2.61e-02
406742 1.79e+01 1.21e-02
406744 2.91e+01 1.97e-02
406764 2.00e+01 1.35e-02
406765 2.43e+01 1.64e-02
406766 4.15e+01 2.80e-02
406811 1.69e+03 1.14e+00
406812 3.16e+02 2.13e-01
406813 9.42e+01 6.36e-02
406814 1.28e+02 8.63e-02
406815 9.77e+01 6.59e-02
406816 1.67e+01 1.13e-02
406821 6.42e+02 4.33e-01
406822 3.25e+02 2.19e-01
406826 3.13e+02 2.11e-01
406831 3.03e+02 2.05e-01
406832 4.22e+02 2.84e-01
406833 2.43e+02 1.64e-01
406834 2.79e+01 1.88e-02
406835 6.42e+01 4.33e-02
406841 2.32e+02 1.57e-01
406852 1.22e+01 8.22e-03
406861 3.98e+01 2.68e-02
406862 3.01e+01 2.03e-02
406914 6.70e+01 4.52e-02
406915 2.69e+03 1.82e+00
406916 2.87e+03 1.94e+00
406923 1.89e+01 1.27e-02
406924 5.90e+01 3.98e-02
406925 1.86e+03 1.25e+00
406926 2.83e+03 1.91e+00
406931 8.16e+02 5.50e-01
406932 2.67e+03 1.80e+00
406933 1.35e+03 9.11e-01
406934 2.44e+01 1.65e-02
406935 9.46e+02 6.38e-01
406936 1.24e+03 8.40e-01
406941 1.16e+01 7.82e-03
406942 9.69e+02 6.54e-01
406943 6.91e+02 4.66e-01
406944 8.39e+02 5.66e-01
406945 5.89e+02 3.97e-01
406946 3.41e+01 2.30e-02
406952 4.35e+01 2.93e-02
406954 7.95e+01 5.36e-02
406955 1.23e+01 8.29e-03
406965 1.13e+02 7.60e-02
406966 2.73e+00 1.84e-03
407011 1.06e+01 7.12e-03
407012 1.05e+01 7.11e-03
407013 1.93e+00 1.30e-03
407015 5.64e-01 3.81e-04
407016 1.42e+01 9.60e-03
407021 4.16e+01 2.81e-02
407032 3.57e+01 2.41e-02
407035 1.74e+01 1.18e-02
407041 4.46e+01 3.01e-02
407045 8.37e+00 5.65e-03
407055 1.77e+01 1.20e-02
407111 1.71e+02 1.16e-01
407112 3.26e+02 2.20e-01
407113 2.59e+02 1.75e-01
407114 5.59e+01 3.77e-02
407115 3.56e+01 2.40e-02
407116 9.99e+00 6.74e-03
407121 5.13e+02 3.46e-01
407122 2.01e+02 1.36e-01
407123 9.82e-01 6.63e-04
407131 4.97e+02 3.36e-01
407132 6.23e+01 4.20e-02
407133 1.97e+01 1.33e-02
407134 1.15e+01 7.74e-03
407135 9.26e+00 6.25e-03
407141 2.99e+01 2.02e-02
407142 5.26e+01 3.55e-02
407151 7.55e+01 5.09e-02
407163 2.97e+01 2.00e-02
407215 3.84e+00 2.59e-03
407216 2.20e+01 1.48e-02
407221 3.00e+01 2.02e-02
407223 1.69e+00 1.14e-03
407224 2.87e+01 1.93e-02
407225 1.66e+02 1.12e-01
407226 4.45e+02 3.01e-01
407231 2.12e+01 1.43e-02
407232 3.72e+01 2.51e-02
407233 1.41e+01 9.50e-03
407234 2.59e+02 1.74e-01
407235 1.21e+02 8.13e-02
407236 6.02e+01 4.06e-02
407241 6.42e+02 4.33e-01
407242 1.04e+03 7.01e-01
407243 7.44e+02 5.02e-01
407244 5.75e+02 3.88e-01
407245 2.98e+02 2.01e-01
407246 8.99e+01 6.06e-02
407251 5.88e+02 3.97e-01
407252 3.74e+02 2.53e-01
407253 6.64e+02 4.48e-01
407254 7.63e+02 5.15e-01
407255 1.08e+02 7.31e-02
407256 1.15e+02 7.73e-02
407261 5.61e+02 3.79e-01
407262 8.11e+02 5.47e-01
407263 1.10e+03 7.44e-01
407264 2.59e+02 1.75e-01
407265 1.98e+01 1.34e-02
407266 4.61e+01 3.11e-02
407316 8.06e+00 5.44e-03
407325 1.50e+01 1.01e-02
407326 1.61e+01 1.08e-02
407333 2.46e+01 1.66e-02
407335 3.34e+01 2.26e-02
407336 3.25e+01 2.19e-02
407342 1.50e+01 1.01e-02
407343 5.35e+01 3.61e-02
407344 5.47e+01 3.69e-02
407345 3.08e+02 2.08e-01
407346 3.56e+02 2.40e-01
407352 7.36e+01 4.97e-02
407353 1.30e+02 8.78e-02
407354 4.25e+02 2.87e-01
407355 7.19e+02 4.85e-01
407356 6.41e+02 4.32e-01
407361 3.48e+01 2.35e-02
407362 8.22e+01 5.54e-02
407363 6.19e+02 4.18e-01
407364 9.53e+02 6.43e-01
407365 7.75e+02 5.23e-01
407366 7.43e+02 5.01e-01
407466 2.34e+00 1.58e-03
416641 8.76e+01 5.91e-02
416642 3.20e+02 2.16e-01
416643 4.47e+02 3.02e-01
416644 1.24e+01 8.38e-03
416651 2.45e+02 1.65e-01
416652 4.46e+02 3.01e-01
416653 7.28e+02 4.91e-01
416654 2.03e+02 1.37e-01
416661 8.02e+02 5.41e-01
416662 1.29e+03 8.73e-01
416663 2.54e+02 1.71e-01
416664 8.08e+00 5.45e-03
416665 3.11e+01 2.10e-02
416711 3.13e+02 2.11e-01
416712 1.26e+02 8.52e-02
416713 5.95e+02 4.01e-01
416714 7.49e+02 5.05e-01
416715 6.95e+00 4.69e-03
416721 5.67e+01 3.83e-02
416722 1.74e+01 1.17e-02
416724 6.52e+00 4.40e-03
416742 1.92e+01 1.29e-02
416743 2.61e+01 1.76e-02
416744 3.16e+01 2.13e-02
416746 5.36e+00 3.61e-03
416755 3.49e+01 2.36e-02
416756 1.02e+02 6.85e-02
416762 3.01e+01 2.03e-02
416764 1.66e+01 1.12e-02
416765 2.55e+02 1.72e-01
416766 1.83e+02 1.23e-01
416816 2.73e+02 1.84e-01
416824 5.95e+01 4.01e-02
416825 9.09e+01 6.14e-02
416826 1.49e+02 1.01e-01
416833 7.84e+01 5.29e-02
416834 8.48e+01 5.72e-02
416835 2.18e+01 1.47e-02
416841 2.18e+01 1.47e-02
416842 4.14e+02 2.79e-01
416843 7.78e+02 5.25e-01
416844 3.51e+02 2.37e-01
416845 1.24e+02 8.39e-02
416851 6.61e+02 4.46e-01
416852 1.09e+03 7.37e-01
416853 4.60e+02 3.10e-01
416854 7.90e+01 5.33e-02
416856 1.89e+01 1.27e-02
416861 1.43e+03 9.64e-01
416862 1.76e+03 1.19e+00
416863 2.54e+02 1.71e-01
416864 2.86e+01 1.93e-02
416865 4.68e+01 3.16e-02
416866 2.15e+01 1.45e-02
416912 2.66e+01 1.79e-02
416915 1.12e+01 7.53e-03
416916 1.80e+01 1.21e-02
416922 5.78e+01 3.90e-02
416924 9.16e+00 6.18e-03
416931 1.28e+01 8.62e-03
416932 2.64e+02 1.78e-01
416933 3.91e+02 2.64e-01
416934 2.05e+02 1.38e-01
416935 3.00e+01 2.02e-02
416942 7.71e+00 5.20e-03
416943 9.84e+01 6.64e-02
416944 1.24e+03 8.35e-01
416945 3.67e+02 2.48e-01
416952 2.82e+00 1.90e-03
416953 3.47e+01 2.34e-02
416954 4.75e+02 3.20e-01
416955 9.68e+02 6.53e-01
416956 2.12e+02 1.43e-01
416961 1.85e+01 1.25e-02
416962 5.94e+00 4.01e-03
416963 3.54e+01 2.39e-02
416964 3.15e+02 2.12e-01
416965 3.76e+03 2.53e+00
416966 1.95e+03 1.31e+00
417031 1.45e+02 9.79e-02
417042 1.80e+01 1.21e-02
417046 4.57e+01 3.08e-02
417051 1.95e+01 1.31e-02
417055 3.62e+01 2.44e-02
417061 8.01e+01 5.40e-02
417062 3.36e+01 2.26e-02
417145 8.00e-01 5.40e-04
417146 1.11e+01 7.49e-03
417154 4.09e+00 2.76e-03
417161 5.51e-01 3.72e-04
417162 1.13e+00 7.65e-04
417163 1.56e+01 1.05e-02
417164 8.34e+01 5.63e-02
417165 6.94e+00 4.68e-03
417166 5.86e-01 3.95e-04
417262 2.84e+01 1.92e-02
417366 5.19e+00 3.50e-03
426752 1.43e+01 9.66e-03
426762 4.11e+01 2.78e-02
426763 4.16e+02 2.81e-01
426764 1.41e+03 9.52e-01
426765 1.36e+02 9.14e-02
426915 1.67e+01 1.13e-02
426964 5.24e+01 3.54e-02
427034 1.68e+00 1.13e-03
427043 3.31e+01 2.23e-02
427044 1.40e+02 9.45e-02
427045 1.63e+02 1.10e-01
427056 5.01e+01 3.38e-02
427065 1.61e+02 1.09e-01
427066 5.00e+01 3.37e-02


Trip Length

Average trip length for Access Area and Days at Sea fleets combined

zone_out <- 
  zone_summary(scallopMainDataTable, project = proj,
               spat = scallopTenMNSQRSpatTable, 
               zone.dat = "ZONE_ID",
               zone.spat = "TEN_ID",
               count = FALSE,
               var = "TRIP_LENGTH", fun = "mean",
               breaks = seq(2, 16, 2),
               output = "tab_plot", na.rm = TRUE)
#> A line object has been specified, but lines is not in the mode
#> Adding lines to the mode...

zone_out$plot
zone_out$table %>% 
  pretty_lab(cols = "TRIP_LENGTH", type = "decimal") %>% 
  pretty_tab_sb(width = "40%")
ZONE_ID TRIP_LENGTH
0 5.99
347231 10.17
347336 4.88
347415 7.44
347535 3.58
357232 3.28
357313 7.17
357322 8.70
357325 3.82
357346 5.02
357445 6.08
357516 8.54
367216 1.88
367322 8.80
367444 16.08
367536 9.27
367614 6.91
377021 7.25
377143 3.00
377214 7.79
377224 11.04
377231 13.71
377311 7.00
377312 6.60
377321 7.71
377322 4.92
377323 5.31
377325 9.87
377332 10.84
377335 9.20
377346 7.03
377364 6.95
377366 12.97
377411 4.33
377413 6.91
377414 7.88
377415 7.84
377416 9.86
377422 6.11
377423 6.58
377424 6.75
377425 7.03
377426 10.88
377432 7.46
377433 6.86
377434 8.20
377435 8.19
377442 6.94
377443 6.04
377444 9.77
377445 10.17
377452 8.94
377453 8.51
377464 6.06
377465 11.80
386916 9.25
387025 9.42
387032 8.35
387121 4.59
387122 4.56
387145 2.66
387212 8.47
387214 10.54
387225 6.15
387226 7.39
387231 12.48
387232 5.61
387233 2.71
387236 6.17
387241 6.31
387242 4.33
387244 9.62
387246 7.31
387252 4.15
387311 7.33
387312 7.47
387313 6.31
387314 6.59
387315 7.54
387316 8.54
387321 7.15
387322 7.11
387323 6.58
387324 8.16
387325 8.81
387326 5.42
387331 6.48
387332 6.83
387333 7.33
387334 6.94
387335 5.16
387336 3.01
387341 6.91
387342 7.56
387343 8.20
387344 8.95
387345 6.87
387351 8.21
387352 6.45
387353 13.61
387354 5.40
387355 10.71
387362 7.64
387363 8.64
387364 2.84
387365 6.53
387366 11.90
387411 6.98
387414 8.12
387415 11.38
387416 3.03
387422 4.11
387424 0.77
387425 5.32
387426 5.68
387432 6.47
387433 4.71
387434 5.51
387435 7.02
387436 5.76
387444 1.64
387445 6.68
387446 6.71
387452 4.87
387454 5.24
387455 7.02
387456 6.66
387461 13.20
387462 6.12
387463 8.86
387464 7.73
387465 6.72
387466 6.41
387655 6.46
396713 14.35
396814 7.58
396916 5.02
397211 8.62
397212 8.98
397213 8.74
397214 8.97
397221 6.79
397222 9.51
397223 9.31
397224 9.83
397225 5.02
397231 8.29
397232 8.02
397233 7.34
397234 7.16
397241 7.02
397242 6.22
397246 6.35
397251 8.65
397261 4.50
397262 7.27
397263 7.27
397265 6.49
397311 10.23
397312 6.98
397313 7.19
397314 6.37
397315 8.48
397316 9.19
397321 1.96
397322 9.44
397323 13.38
397324 7.99
397325 8.76
397326 8.86
397331 15.53
397332 9.08
397333 4.26
397334 7.87
397335 9.11
397336 8.22
397342 7.39
397343 7.16
397344 7.27
397345 7.60
397346 7.95
397351 12.31
397352 7.22
397353 8.51
397354 8.01
397355 8.02
397356 7.63
397361 5.17
397362 7.18
397363 7.07
397364 6.52
397365 7.13
397366 9.01
397426 8.26
397446 9.22
397456 6.35
397463 12.66
397464 4.62
397465 6.79
397466 9.14
406611 11.41
406612 11.53
406613 5.45
406621 13.34
406623 12.44
406626 11.23
406643 7.59
406652 9.04
406711 9.94
406712 9.78
406713 10.72
406714 11.37
406715 11.21
406716 10.86
406721 10.68
406722 11.07
406723 11.05
406724 10.70
406725 7.86
406731 14.58
406732 9.10
406733 9.45
406735 5.77
406742 5.07
406744 11.08
406764 8.00
406765 7.49
406766 12.79
406811 8.84
406812 7.54
406813 9.28
406814 11.22
406815 11.85
406816 5.19
406821 8.96
406822 10.31
406826 11.18
406831 9.65
406832 12.37
406833 11.51
406834 15.58
406835 7.09
406841 8.65
406852 5.29
406861 6.15
406862 10.33
406914 8.27
406915 8.18
406916 9.42
406923 9.10
406924 7.47
406925 5.64
406926 5.42
406931 5.98
406932 6.66
406933 6.33
406934 4.08
406935 5.90
406936 5.85
406941 3.62
406942 6.82
406943 6.44
406944 6.16
406945 5.94
406946 6.78
406952 7.92
406954 6.31
406955 5.15
406965 6.38
406966 3.62
407011 3.92
407012 4.42
407013 2.00
407015 2.01
407016 11.49
407021 7.83
407032 5.74
407035 7.88
407041 8.92
407045 7.42
407055 4.83
407111 6.25
407112 6.99
407113 6.01
407114 5.31
407115 4.10
407116 11.13
407121 8.42
407122 7.54
407123 2.43
407131 8.57
407132 7.16
407133 7.96
407134 9.84
407135 6.36
407141 5.22
407142 14.05
407151 9.69
407163 9.27
407215 3.21
407216 5.01
407221 7.91
407223 1.90
407224 10.96
407225 7.73
407226 8.87
407231 11.58
407232 7.87
407233 8.54
407234 7.99
407235 8.53
407236 8.38
407241 8.96
407242 8.46
407243 8.45
407244 8.23
407245 7.89
407246 6.39
407251 8.99
407252 7.92
407253 8.77
407254 8.90
407255 9.43
407256 7.91
407261 8.78
407262 8.37
407263 9.74
407264 9.55
407265 9.69
407266 7.17
407316 2.92
407325 13.62
407326 10.60
407333 11.70
407335 7.40
407336 7.55
407342 6.33
407343 6.71
407344 9.20
407345 7.37
407346 8.16
407352 6.81
407353 8.52
407354 8.13
407355 8.61
407356 8.41
407361 8.19
407362 7.82
407363 8.73
407364 7.75
407365 7.67
407366 8.65
407466 3.33
416641 9.35
416642 7.51
416643 7.28
416644 7.29
416651 7.67
416652 8.44
416653 7.45
416654 7.91
416661 7.62
416662 7.76
416663 8.57
416664 5.61
416665 15.02
416711 10.41
416712 11.60
416713 10.96
416714 10.84
416715 5.83
416721 9.33
416722 7.99
416724 5.46
416742 8.23
416743 10.08
416744 8.44
416746 3.88
416755 5.43
416756 9.16
416762 13.69
416764 14.26
416765 6.88
416766 7.68
416816 10.03
416824 12.46
416825 8.27
416826 9.99
416833 12.95
416834 7.49
416835 10.08
416841 3.94
416842 5.77
416843 6.86
416844 8.91
416845 9.51
416851 6.27
416852 6.57
416853 7.10
416854 6.52
416856 8.08
416861 6.22
416862 6.97
416863 7.11
416864 3.62
416865 11.89
416866 10.41
416912 8.05
416915 5.44
416916 8.54
416922 10.04
416924 5.71
416931 4.54
416932 10.24
416933 8.72
416934 6.00
416935 8.38
416942 5.17
416943 6.85
416944 6.38
416945 7.29
416952 2.13
416953 5.45
416954 8.01
416955 7.68
416956 5.53
416961 5.16
416962 4.06
416963 6.82
416964 8.21
416965 7.16
416966 6.69
417031 7.52
417042 3.35
417046 13.08
417051 10.53
417055 12.42
417061 8.24
417062 6.70
417145 1.71
417146 5.57
417154 4.40
417161 2.20
417162 5.42
417163 7.96
417164 6.37
417165 4.93
417166 2.97
417262 9.28
417366 3.75
426752 8.85
426762 13.01
426763 10.55
426764 10.13
426765 11.32
426915 8.18
426964 13.12
427034 2.48
427043 10.88
427044 6.34
427045 6.16
427056 5.50
427065 10.49
427066 9.98


CPUE

Average CPUE for Access Area and Days at Sea fleets combined

zone_out <- 
  scallopMainDataTable %>% 
    filter(CPUE_p >= .025 & CPUE_p <= .975) %>%
    zone_summary(project = proj,
                 spat = scallopTenMNSQRSpatTable, 
                 zone.dat = "ZONE_ID",
                 zone.spat = "TEN_ID",
                 count = FALSE,
                 var = "CPUE", fun = "mean",
                 na.rm = TRUE, output = "tab_plot")
#> A line object has been specified, but lines is not in the mode
#> Adding lines to the mode...
zone_out$plot
zone_out$table %>% 
  pretty_lab(cols = "CPUE") %>%
  pretty_tab_sb(width = "50%")
ZONE_ID CPUE
0 1.54
347231 0.4
347336 1.45
347415 0.69
347535 2.23
357313 2.7
357322 1.87
357325 2.21
357346 1.71
357445 2.96
357516 1.59
367322 1.98
367444 1.02
367536 1.91
367614 1.9
377021 2.63
377143 0.42
377214 0.89
377224 1.68
377231 1.37
377311 2.19
377312 1.7
377321 1.78
377322 1.18
377323 1.31
377325 2.06
377332 1.39
377335 1.67
377346 2.64
377364 2.64
377366 0.98
377413 2.01
377414 1.98
377415 1.63
377416 1.24
377422 1.97
377423 1.87
377424 1.72
377425 1.51
377426 1.7
377432 2.1
377433 1.93
377434 1.53
377435 1.72
377442 2.06
377443 1.87
377444 1.33
377445 1.94
377452 1.83
377453 1.75
377464 1.38
377465 0.34
386916 1.77
387025 2.25
387032 2.1
387121 1.56
387122 2.42
387145 2.04
387212 1.87
387214 1.83
387225 1.62
387226 2.21
387231 2.54
387232 0.98
387233 2.69
387236 3.01
387241 2.52
387242 2.7
387244 1.72
387246 2.42
387252 2.07
387311 1.68
387312 1.65
387313 1.76
387314 1.63
387315 1.43
387316 1.02
387321 2.06
387322 2.06
387323 1.97
387324 1.32
387325 1.47
387326 2.36
387331 2.25
387332 2.05
387333 1.78
387334 1.39
387335 1.48
387336 1.73
387341 1.85
387342 1.69
387343 1.91
387344 1.28
387345 1.76
387351 1.66
387352 1.78
387353 1.12
387354 0.66
387355 1.92
387362 1.95
387363 2.09
387364 2.7
387365 1.66
387366 0.76
387411 1.7
387414 2.01
387415 1.38
387416 0.63
387422 2.2
387424 1.36
387425 2.01
387426 2.11
387432 2.1
387433 2.1
387434 1.42
387435 2.01
387436 2.36
387444 2.01
387445 2.03
387446 2.18
387452 0.51
387454 1.54
387455 1.99
387456 1.54
387461 1.23
387462 1.38
387463 1.67
387464 1.91
387465 1.68
387466 1.37
387655 2.62
396713 2.28
396814 0.39
396916 3.54
397211 1.83
397212 1.87
397213 1.89
397214 1.53
397221 1.96
397222 1.82
397223 2.06
397224 1.71
397225 0.78
397231 1.75
397232 1.62
397233 1.91
397234 1.36
397241 1.86
397242 1.49
397246 0.98
397251 1.35
397261 0.36
397262 1.16
397263 2.29
397265 2.56
397311 1.34
397312 1.44
397313 1.44
397314 1.58
397315 1.85
397316 2.06
397322 0.48
397323 1.72
397324 1.79
397325 1.95
397326 2.08
397331 0.72
397332 1.54
397333 1.18
397334 1.64
397335 1.92
397336 1.66
397342 1.8
397343 1.67
397344 1.63
397345 1.6
397346 1.56
397351 1.8
397352 1.44
397353 1.7
397354 1.84
397355 1.78
397356 1.27
397361 1.34
397362 1.39
397363 1.53
397364 1.67
397365 1.47
397366 1.33
397426 2.6
397446 0.54
397456 1.43
397463 2.84
397464 1.31
397465 1.84
397466 1.83
406611 2.09
406612 1.81
406613 1.15
406621 2.4
406623 1.19
406626 1.78
406643 2.56
406652 1.8
406711 1.87
406712 1.54
406713 2.13
406714 2.2
406715 2.37
406716 2.5
406721 2.2
406722 2.13
406723 2.21
406724 1.75
406725 2.49
406731 1.17
406732 1.94
406733 1.57
406735 3.41
406742 2.15
406744 2.63
406764 2.5
406765 3.24
406766 3.25
406811 2.21
406812 2.33
406813 1.7
406814 2.29
406815 1.85
406816 3.22
406821 2
406822 2.04
406826 2.12
406831 1.74
406832 2.12
406833 1.86
406834 1.79
406835 2.05
406841 2.31
406852 2.3
406861 2.15
406862 1.5
406914 1.84
406915 2
406916 2.15
406923 2.08
406924 1.65
406925 2.16
406926 2.35
406931 2.18
406932 2.09
406933 2.18
406934 1.89
406935 2.12
406936 2.56
406941 1.38
406942 1.85
406943 2.36
406944 2.17
406945 2.45
406946 1.9
406952 1.87
406954 2.54
406955 1.14
406965 1.57
406966 0.75
407011 2.69
407012 2.39
407013 0.97
407016 1.24
407021 2.66
407032 3.13
407035 2.21
407041 2.45
407045 1.13
407055 1
407111 2.23
407112 1.51
407113 1.44
407114 1.17
407115 0.9
407116 0.9
407121 1.97
407122 1.96
407123 0.4
407131 2
407132 2.02
407133 2.48
407134 1.16
407135 1.46
407141 1.37
407142 1.87
407151 2.46
407163 3.2
407215 1.2
407216 1.68
407221 1.71
407223 0.89
407224 2.61
407225 2.01
407226 2.29
407231 1.83
407232 1.17
407233 1.65
407234 2.1
407235 1.88
407236 1.28
407241 2.1
407242 2.07
407243 2.12
407244 1.95
407245 1.74
407246 1.6
407251 2.14
407252 1.79
407253 1.9
407254 2.05
407255 1.29
407256 1.56
407261 1.9
407262 1.76
407263 2.09
407264 1.72
407265 0.93
407266 2.13
407316 1.46
407325 1.1
407326 1.51
407333 2.1
407335 2.21
407336 2.14
407342 2.36
407343 1.91
407344 1.93
407345 2.1
407346 2.13
407352 2.39
407353 2.26
407354 2.1
407355 1.99
407356 2.05
407361 2.45
407362 1.99
407363 2.1
407364 2.07
407365 1.84
407366 2.12
407466 0.7
416641 2
416642 1.94
416643 1.99
416644 1.7
416651 1.53
416652 2.07
416653 1.95
416654 1.53
416661 1.93
416662 2.12
416663 1.9
416664 1.44
416665 2.07
416711 2.32
416712 2.21
416713 2.05
416714 1.96
416715 1.19
416721 1.36
416722 1.32
416724 1.19
416742 2.33
416743 2.59
416744 1.96
416746 1.38
416755 3.25
416756 1.73
416762 2.2
416764 1.17
416765 2.29
416766 2.31
416816 1.84
416824 2.39
416825 2.13
416826 2.49
416833 3.02
416834 1.65
416835 2.16
416841 1.95
416842 2.29
416843 2.3
416844 1.71
416845 2.07
416851 2.32
416852 2.2
416853 2.09
416854 3.02
416856 2.33
416861 2.09
416862 2.12
416863 1.92
416864 1.95
416866 2.06
416912 1.54
416915 2.05
416916 2.1
416922 2.83
416924 1.61
416931 2.81
416932 1.95
416933 1.97
416934 1.63
416935 1.79
416942 1.49
416943 1.89
416944 2.44
416945 2.09
416952 1.32
416953 3.18
416954 2.01
416955 1.92
416956 2.16
416961 3.58
416962 1.46
416963 2.62
416964 2.31
416965 1.86
416966 2.07
417031 1.72
417042 1.68
417046 3.49
417051 1.85
417055 1.47
417061 2.51
417062 1.63
417145 0.47
417146 1.99
417154 0.93
417163 1.95
417164 1.68
417165 1.41
417262 1.69
417366 1.38
426752 1.62
426762 1.58
426763 2.01
426764 2.24
426765 2.4
426915 2.04
427034 0.68
427043 3.04
427044 2.16
427045 2.67
427056 1.58
427065 1.58
427066 1.65


VPUE

Average VPUE for Access Area and Days at Sea fleets combined

zone_out <- 
scallopMainDataTable %>% 
  filter(VPUE_p >= .025 & VPUE_p <= .975) %>% 
  zone_summary(project = proj,
               spat = scallopTenMNSQRSpatTable, 
               zone.dat = "ZONE_ID",
               zone.spat = "TEN_ID",
               count = FALSE,
               var = "VPUE", fun = "mean",
               breaks = seq(5e3, 3.5e4, 5e3),
               na.rm = TRUE, output = "tab_plot")
#> A line object has been specified, but lines is not in the mode
#> Adding lines to the mode...
zone_out$plot
zone_out$table %>% 
  pretty_lab(cols = "VPUE") %>%
  pretty_tab_sb(width = "50%")
ZONE_ID VPUE
0 20,066.88
347231 5,233.3
347336 9,536.37
347415 6,245
347535 13,387.7
357313 29,074.82
357322 18,211
357325 21,177.49
357346 17,164.08
357445 20,436.98
357516 9,512.45
367322 17,407.07
367444 9,199.07
367536 14,360.14
367614 16,568.37
377021 15,220.15
377143 2,972.54
377214 5,317.38
377224 16,476.79
377231 8,170.41
377311 26,822.81
377312 13,127.43
377321 13,434.37
377322 7,041.88
377323 11,581.27
377325 12,130.84
377332 16,060.76
377335 11,417.53
377346 24,630.52
377364 25,996.45
377366 6,541.51
377411 3,223.53
377413 15,582.86
377414 18,207.06
377415 14,184.33
377416 10,497.06
377422 21,187.48
377423 18,814.16
377424 16,907.38
377425 12,962.48
377426 14,369.52
377432 17,715.69
377433 16,459.05
377434 14,528.69
377435 12,618
377442 17,520.48
377443 17,868
377444 9,820.63
377445 18,938.53
377452 15,840.37
377453 15,715.07
377464 8,308.95
377465 3,258.77
386916 19,522.87
387025 11,269.26
387032 15,592.71
387121 12,751.43
387122 19,119.7
387145 13,241.61
387212 14,723.55
387214 17,120.83
387225 17,248.08
387226 16,198.11
387231 16,100.56
387232 7,865.37
387233 17,077.27
387236 25,007.26
387241 32,325.13
387242 27,450.17
387244 14,405.85
387246 29,200.35
387252 17,578.02
387311 14,767.65
387312 13,616.01
387313 16,801.8
387314 15,197.02
387315 15,279.03
387316 8,920.23
387321 16,653.18
387322 17,106.76
387323 16,931.58
387324 12,421.16
387325 12,167.14
387326 21,491.37
387331 18,597.51
387332 18,086.73
387333 14,989.19
387334 13,271.7
387335 11,897.8
387336 14,605.61
387341 17,116.38
387342 16,552.53
387343 24,829.31
387344 7,908.01
387345 9,678.65
387351 10,759.52
387352 11,899.78
387353 5,862.56
387354 4,740.2
387355 17,876.98
387362 22,655.34
387363 14,020.27
387364 24,345.14
387365 12,012.21
387366 4,159.28
387411 17,535.9
387414 14,919.84
387415 10,997.41
387416 9,156.18
387422 27,716.33
387424 9,321.52
387425 15,339.67
387426 16,918.81
387432 18,434.58
387433 16,301.81
387434 11,544.51
387435 16,566.05
387436 20,564.78
387444 15,588.32
387445 16,584.24
387446 19,032.19
387452 4,023.37
387454 11,893.68
387455 16,885.25
387456 14,103.45
387461 7,519.72
387462 12,251.89
387463 11,237.24
387464 15,098.61
387465 15,267.54
387466 13,701.88
387655 34,145.28
396713 15,809.38
396916 29,795.75
397211 17,478.77
397212 17,917.6
397213 17,020.92
397214 14,587.45
397221 16,913.37
397222 15,046.59
397223 18,926.78
397224 14,012.34
397225 4,492.43
397231 15,836.4
397232 15,044.28
397233 16,910.95
397234 10,408.03
397241 19,711.21
397242 16,049.47
397246 6,700.17
397251 12,438.1
397261 3,747.42
397262 6,973.96
397263 24,589.25
397265 21,418.28
397311 10,479.96
397312 12,565.45
397313 12,763.1
397314 11,836.89
397315 17,415.85
397316 18,835.31
397321 2,659.27
397322 4,852.85
397323 14,665.03
397324 14,056.15
397325 16,306.41
397326 19,413.8
397331 6,397.24
397332 14,650.51
397333 10,719.23
397334 13,286.09
397335 16,130.4
397336 14,363.18
397342 16,767.06
397343 12,122.04
397344 12,245.57
397345 14,027.06
397346 14,156.59
397351 13,124.93
397352 11,299.38
397353 13,968.76
397354 15,552.68
397355 16,918.93
397356 11,629.44
397361 10,499.68
397362 10,186.87
397363 12,925.88
397364 16,113.9
397365 13,619.27
397366 11,535.26
397426 17,220.02
397446 4,509.4
397456 15,481.3
397463 21,312.16
397464 13,336.86
397465 16,157.31
397466 15,982.59
406611 17,834.35
406612 17,713.63
406613 13,435.28
406621 21,360.15
406623 7,757.35
406626 26,779.3
406643 26,987.59
406652 23,785.72
406711 17,240.17
406712 15,392.82
406713 22,208.37
406714 18,575.46
406715 20,725.75
406716 23,140.27
406721 21,876.89
406722 21,673.77
406723 21,319.75
406724 15,588.06
406725 23,094.56
406731 20,526.99
406732 20,644.99
406733 13,836.09
406735 30,360.99
406742 21,694.58
406744 22,579.05
406764 26,732.27
406765 34,208.01
406766 27,856.93
406811 21,767.87
406812 22,288.88
406813 12,987.03
406814 14,417.75
406815 17,797.24
406816 34,069.54
406821 17,973.87
406822 18,257.39
406826 19,855.89
406831 17,399.98
406832 19,063.13
406833 21,090.92
406834 12,892.94
406835 22,930.51
406841 19,347.14
406852 22,307.23
406861 18,832.54
406862 14,363.63
406914 19,919.01
406915 19,902.03
406916 21,387.52
406923 20,916.52
406924 18,837.25
406925 21,888.73
406926 23,608.78
406931 19,125.17
406932 18,553.28
406933 20,464.82
406934 23,694.07
406935 21,334.61
406936 23,444.56
406941 11,520.69
406942 15,838.64
406943 21,262.03
406944 20,367.85
406945 23,329.49
406946 12,421.53
406952 18,592.22
406954 21,730.93
406955 11,827.36
406965 18,602.93
406966 10,101.45
407011 32,914.52
407012 21,397.99
407013 8,495.69
407015 3,397.01
407016 10,726.37
407021 21,263.92
407032 33,670.01
407035 15,792.95
407041 22,835.79
407045 13,396.23
407055 13,149.76
407111 19,630.04
407112 15,793.91
407113 12,656.23
407114 10,582.02
407115 7,267.04
407116 9,169.37
407121 19,923.91
407122 15,993.83
407123 4,655.15
407131 17,779.79
407132 17,444.06
407133 27,730.46
407134 10,695.32
407135 12,808.62
407141 15,369.48
407142 24,273.41
407151 28,616.11
407163 37,491.78
407215 15,635.66
407216 15,669.92
407221 10,243.03
407223 9,822.67
407224 21,570.23
407225 23,673.57
407226 22,399.32
407231 13,376.62
407232 12,613.95
407233 19,862.15
407234 23,690.57
407235 18,461.23
407236 9,607.95
407241 22,507.93
407242 21,841.71
407243 20,159.13
407244 19,507.55
407245 15,971.83
407246 13,928.7
407251 21,040.27
407252 18,562.52
407253 17,846.31
407254 19,667.57
407255 12,958.01
407256 13,837.37
407261 18,331.4
407262 16,600.49
407263 18,142.1
407264 14,782.42
407265 6,451.31
407266 24,248.83
407316 9,524.72
407325 6,866.98
407326 9,721.77
407333 12,212.72
407335 20,038.13
407336 16,959.22
407342 31,151.72
407343 13,149.85
407344 13,931.31
407345 19,110.63
407346 21,863.35
407352 22,898.61
407353 19,857.05
407354 18,298.5
407355 17,771.4
407356 20,766.15
407361 20,761.54
407362 15,707.07
407363 16,799.37
407364 18,576.63
407365 15,769.39
407366 20,624.35
407466 8,101.06
416641 21,239.38
416642 22,408.23
416643 21,635.48
416644 20,456.54
416651 16,575.33
416652 21,301.24
416653 21,128.94
416654 17,231.8
416661 19,604.7
416662 21,412.99
416663 20,194.41
416664 14,389.01
416665 14,012.74
416711 23,361.93
416712 27,403.16
416713 18,357.89
416714 14,977.74
416715 10,631.92
416721 9,194.16
416722 8,982.02
416724 7,914.93
416742 23,168.71
416743 22,729.77
416744 18,264.33
416746 14,585.26
416755 23,296.45
416756 14,065.5
416762 13,784.28
416764 8,797.07
416765 18,310.69
416766 21,399.6
416816 19,763.75
416824 31,818.89
416825 20,547.8
416826 25,320.97
416833 32,257.58
416834 16,285.47
416835 14,192.33
416841 21,348.17
416842 23,491.23
416843 20,366.22
416844 18,250.78
416845 15,830.39
416851 24,513.21
416852 20,728.48
416853 19,117.05
416854 24,811.5
416856 25,329.63
416861 21,336.35
416862 19,843.13
416863 17,279.87
416864 12,844.64
416866 21,624.18
416912 20,435.41
416915 23,881.14
416916 26,263.29
416922 36,853.47
416924 16,693.17
416931 28,680.88
416932 22,311.11
416933 23,852.79
416934 20,408.51
416935 21,555.01
416942 24,590.32
416943 22,723.02
416944 24,574.25
416945 23,177.86
416952 8,741.99
416953 30,718.26
416954 18,703.97
416955 21,145.19
416956 22,416.44
416961 34,240.2
416962 16,041.16
416963 26,014.77
416964 17,495.85
416965 19,174.35
416966 21,297.83
417031 17,464.38
417042 18,096.42
417046 30,722.89
417051 21,575.57
417055 19,912.46
417061 16,927.48
417062 16,311.68
417145 5,652.97
417146 27,221.02
417154 9,655.15
417161 2,751.97
417162 2,669.66
417163 19,330.85
417164 16,245.26
417165 12,797.17
417262 13,434.36
417366 7,902.65
426752 19,077.19
426762 14,553.95
426763 16,025.21
426764 22,180.35
426765 23,306.26
426915 25,682.59
427034 9,367.42
427043 36,171.17
427044 26,533.56
427045 28,173.63
427056 17,770.43
427065 22,131.82
427066 23,369.7


Other Spatial Units

The zone_summary function can be used on any spatial unit (spat) . By setting spat = scallopWindCloseSpatTable, the exploratory data analysis of trips inside the Wind polygons can be performed.

Number of observations in the Wind areas.

zone_out <- 
  zone_summary(scallopMainDataTable, project = proj,
               spat = scallopWindCloseSpatTable, 
               zone.dat = "closeID",
               zone.spat = "NAME",
               count = TRUE,
               na.rm = TRUE, dat.center = FALSE, 
               output = "tab_plot")
#> A line object has been specified, but lines is not in the mode
#> Adding lines to the mode...

zone_out$table %>% 
  pretty_lab() %>% 
  pretty_tab_sb(width = "40%")
closeID n
NA 9,938
OCS-A 0512 Remainder 30
Empire Wind 8
beach_lane_cable_routes 4
Ocean Wind 3
Bay State Wind 2
OCS-A 0487 Remainder 2
OCS-A 0501 Remainder 2
OCS-A 0519 Remainder 2
US Wind 2
OCS-A 0482 1
OCS-A 0498 Remainder 1
OCS-A 0499 1
OCS-A 0520 1
Revolution Wind 1
Sunrise Wind 1
mayflower_cable_routes 1 

zone_out$plot


Percent of observations in Wind areas.

zone_out <- 
  zone_summary(scallopMainDataTable, project = proj,
               spat = scallopWindCloseSpatTable, 
               zone.dat = "closeID",
               zone.spat = "NAME",
               fun = "percent",
               count = TRUE,
               na.rm = TRUE, dat.center = FALSE, 
               output = "tab_plot")
#> A line object has been specified, but lines is not in the mode
#> Adding lines to the mode...

zone_out$table %>% 
  pretty_lab() %>% 
  pretty_tab_sb(width = "40%")
closeID n perc
NA 9,938 99.38
OCS-A 0512 Remainder 30 0.3
Empire Wind 8 0.08
beach_lane_cable_routes 4 0.04
Ocean Wind 3 0.03
Bay State Wind 2 0.02
OCS-A 0487 Remainder 2 0.02
OCS-A 0501 Remainder 2 0.02
OCS-A 0519 Remainder 2 0.02
US Wind 2 0.02
OCS-A 0482 1 0.01
OCS-A 0498 Remainder 1 0.01
OCS-A 0499 1 0.01
OCS-A 0520 1 0.01
Revolution Wind 1 0.01
Sunrise Wind 1 0.01
mayflower_cable_routes 1 0.01 

zone_out$plot


Percent of total revenue by Wind area.

zone_out <- 
  zone_summary(scallopMainDataTable, project = proj,
               spat = scallopWindCloseSpatTable,
               zone.dat = "closeID",
               zone.spat = "NAME",
               var = "DOLLAR_OBSCURED",
               fun = "percent",
               count = FALSE, 
               na.rm = TRUE, dat.center = FALSE,
               output = "tab_plot")
#> A line object has been specified, but lines is not in the mode
#> Adding lines to the mode...

zone_out$table %>% 
  pretty_lab() %>% 
  pretty_tab_sb(width = "70%")
closeID DOLLAR_OBSCURED DOLLAR_OBSCURED_perc
Bay State Wind 268,052.8 0.02
Empire Wind 989,665.8 0.07
OCS-A 0482 76,754.35 0.01
OCS-A 0487 Remainder 142,405.4 0.01
OCS-A 0498 Remainder 61,682.96 0
OCS-A 0499 41,571 0
OCS-A 0501 Remainder 108,313.3 0.01
OCS-A 0512 Remainder 4,638,950 0.34
OCS-A 0519 Remainder 189,762.3 0.01
OCS-A 0520 4,094.6 0
Ocean Wind 429,331.8 0.03
Revolution Wind 7,498.53 0
Sunrise Wind 102,066.5 0.01
US Wind 33,066.77 0
beach_lane_cable_routes 376,909.3 0.03
mayflower_cable_routes 209,699.3 0.02
NA 1,366,902,773 99.44 

zone_out$plot


Percent of total revenue by fleet can be constructed using the group="fleet" argument.

zone_out <- 
  zone_summary(scallopMainDataTable, project = proj,
               spat = scallopWindCloseSpatTable,
               zone.dat = "closeID",
               zone.spat = "NAME",
               var = "DOLLAR_OBSCURED", group = "fleet",
               fun = "percent",
               count = FALSE, 
               na.rm = TRUE, dat.center = FALSE,
               output = "tab_plot")
#> A line object has been specified, but lines is not in the mode
#> Adding lines to the mode...
#> A line object has been specified, but lines is not in the mode
#> Adding lines to the mode...

zone_out$table %>% 
  pretty_lab() %>% 
  pretty_tab_sb(width = "70%")
closeID fleet DOLLAR_OBSCURED DOLLAR_OBSCURED_perc
Bay State Wind Days at Sea 268,052.8 0.02
Empire Wind Access Area 78,328.81 0.01
Empire Wind Days at Sea 911,337 0.07
OCS-A 0482 Access Area 76,754.35 0.01
OCS-A 0487 Remainder Days at Sea 142,405.4 0.01
OCS-A 0498 Remainder Access Area 61,682.96 0
OCS-A 0499 Access Area 41,571 0
OCS-A 0501 Remainder Days at Sea 108,313.3 0.01
OCS-A 0512 Remainder Access Area 34,369.16 0
OCS-A 0512 Remainder Days at Sea 4,604,581 0.33
OCS-A 0519 Remainder Access Area 189,762.3 0.01
OCS-A 0520 Days at Sea 4,094.6 0
Ocean Wind Access Area 141,135.1 0.01
Ocean Wind Days at Sea 288,196.8 0.02
Revolution Wind Days at Sea 7,498.53 0
Sunrise Wind Days at Sea 102,066.5 0.01
US Wind Access Area 33,066.77 0
beach_lane_cable_routes Days at Sea 376,909.3 0.03
mayflower_cable_routes Days at Sea 209,699.3 0.02
NA Access Area 676,272,015 49.2
NA Days at Sea 690,630,758 50.24 
zone_out$plot
#> [[1]]
#> 
#> [[2]]


Average meat catch per closure area.

zone_out <- 
  zone_summary(scallopMainDataTable, project = proj,
               spat = scallopWindCloseSpatTable, 
               zone.dat = "closeID",
               zone.spat = "NAME",
               var = "LANDED_OBSCURED",
               fun = "mean",
               count = FALSE,
               na.rm = TRUE, dat.center = FALSE, 
               output = "tab_plot")
#> A line object has been specified, but lines is not in the mode
#> Adding lines to the mode...

zone_out$table %>% 
  pretty_lab() %>% 
  pretty_tab_sb(width = "50%")
closeID LANDED_OBSCURED
Bay State Wind 12.88
Empire Wind 16.1
OCS-A 0482 9.9
OCS-A 0487 Remainder 9.16
OCS-A 0498 Remainder 6.05
OCS-A 0499 4.94
OCS-A 0501 Remainder 6.05
OCS-A 0512 Remainder 16.62
OCS-A 0519 Remainder 11.28
OCS-A 0520 0.36
Ocean Wind 15.99
Revolution Wind 0.59
Sunrise Wind 9.99
US Wind 1.67
beach_lane_cable_routes 9.95
mayflower_cable_routes 15.43
NA 14.83 

zone_out$plot


Average meat catch by fleet.

zone_out <- 
  zone_summary(scallopMainDataTable, project = proj,
               spat = scallopWindCloseSpatTable, 
               zone.dat = "closeID",
               zone.spat = "NAME",
               var = "LANDED_OBSCURED", group = "fleet",
               fun = "mean",
               count = FALSE,
               na.rm = TRUE, dat.center = FALSE, 
               output = "tab_plot")
#> A line object has been specified, but lines is not in the mode
#> Adding lines to the mode...
#> A line object has been specified, but lines is not in the mode
#> Adding lines to the mode...

zone_out$table %>% 
  pretty_lab() %>% 
  pretty_tab_sb(width = "60%")
closeID fleet LANDED_OBSCURED
Bay State Wind Days at Sea 12.88
Empire Wind Access Area 10.55
Empire Wind Days at Sea 16.89
OCS-A 0482 Access Area 9.9
OCS-A 0487 Remainder Days at Sea 9.16
OCS-A 0498 Remainder Access Area 6.05
OCS-A 0499 Access Area 4.94
OCS-A 0501 Remainder Days at Sea 6.05
OCS-A 0512 Remainder Access Area 5.5
OCS-A 0512 Remainder Days at Sea 17
OCS-A 0519 Remainder Access Area 11.28
OCS-A 0520 Days at Sea 0.36
Ocean Wind Access Area 17.51
Ocean Wind Days at Sea 15.23
Revolution Wind Days at Sea 0.59
Sunrise Wind Days at Sea 9.99
US Wind Access Area 1.67
beach_lane_cable_routes Days at Sea 9.95
mayflower_cable_routes Days at Sea 15.43
NA Access Area 13.01
NA Days at Sea 17.24 

zone_out$plot
#> [[1]]
#> 
#> [[2]]


Outliers

FishSET has some functionality to detect outliers, and remove if necessary.

POUNDS

outlier_table(scallopMainDataTable, proj,
              x = "LANDED_OBSCURED") %>% 
  pretty_lab() %>% 
  pretty_tab()
Vector outlier_check N mean median SD min max NAs skew
LANDED_OBSCURED None 10,000 14.82 15.64 8.86 0.02 76.51 0 0.87
LANDED_OBSCURED 5_95_quant 9,000 14.31 15.64 6.64 1.6 31.45 0 0.06
LANDED_OBSCURED 25_75_quant 5,000 14.65 15.64 3.09 8.27 18.8 0 -0.49
LANDED_OBSCURED mean_2SD 9,567 13.73 15.07 7.27 0.02 32.51 0 0.05
LANDED_OBSCURED mean_3SD 9,892 14.46 15.47 8.18 0.02 41.28 0 0.44
LANDED_OBSCURED median_2SD 9,615 13.83 15.13 7.37 0.02 33.34 0 0.1
LANDED_OBSCURED median_3SD 9,908 14.51 15.49 8.25 0.02 42.05 0 0.47 
outlier_plot(scallopMainDataTable, proj,
             x = "LANDED_OBSCURED", 
             dat.remove = "none",
             x.dist = "normal",
             output.screen = TRUE)

#> TableGrob (3 x 2) "arrange": 4 grobs
#>   z     cells    name                grob
#> 1 1 (2-2,1-1) arrange      gtable[layout]
#> 2 2 (2-2,2-2) arrange      gtable[layout]
#> 3 3 (3-3,1-1) arrange      gtable[layout]
#> 4 4 (1-1,1-2) arrange text[GRID.text.434]
outlier_plot(scallopMainDataTable, proj,
             x = "LANDED_OBSCURED", 
             dat.remove = "mean_3SD",
             x.dist = "normal",
             output.screen = TRUE)

#> TableGrob (3 x 2) "arrange": 4 grobs
#>   z     cells    name                grob
#> 1 1 (2-2,1-1) arrange      gtable[layout]
#> 2 2 (2-2,2-2) arrange      gtable[layout]
#> 3 3 (3-3,1-1) arrange      gtable[layout]
#> 4 4 (1-1,1-2) arrange text[GRID.text.540]


Temporal Plots

FishSET can plot variables over time.

temp_plot(scallopMainDataTable, proj,
          var.select = "LANDED_OBSCURED",
          len.fun = "percent",
          agg.fun = "sum",
          date.var = "DATE_TRIP",
          pages = "multi")
#> $scatter_plot

#> 
#> $unique_plot

#> 
#> $agg_plot

This plot reveals some seasonality in the Landed pounds.


temp_plot(scallopMainDataTable, proj,
          var.select = "TRIP_LENGTH",
          len.fun = "percent",
          agg.fun = "sum",
          date.var = "DATE_TRIP",
          pages = "multi")
#> $scatter_plot

#> 
#> $unique_plot

#> 
#> $agg_plot


temp_plot(scallopMainDataTable, proj,
          var.select = "DOLLAR_OBSCURED",
          len.fun = "percent",
          agg.fun = "sum",
          date.var = "DATE_TRIP",
          pages = "multi")
#> $scatter_plot

#> 
#> $unique_plot

#> 
#> $agg_plot


Scatter Plots

Trip length by meat catch.

xy_plot(scallopMainDataTable, proj,
        var1 = "TRIP_LENGTH", var2 = "LANDED_OBSCURED",
        regress = FALSE, alpha = .3)


Trip length by revenue.

xy_plot(scallopMainDataTable, proj,
        var1 = "TRIP_LENGTH", var2 = "DOLLAR_OBSCURED",
        regress = FALSE, alpha = .3)


Trip length by trip cost (Winsor).

xy_plot(scallopMainDataTable, proj,
        var1 = "TRIP_LENGTH", var2 = "TRIP_COST_WINSOR_2020_DOL",
        regress = FALSE, alpha = .3)

The winsorization of trip costs is apparent in the horizontal line of points just over $30,000.


Correlation Matrix

A simple table of two-way correlations can be illuminative.

corr_outs <-
  corr_out(scallopMainDataTable, proj,
           variables = "all",
           method = "pearson", 
           show_coef = FALSE)
#> Warning: No variance found in fleetAssignPlaceholder. Removed from correlation
#> test

corr_outs$plot

corr_outs$table %>% 
  pretty_tab_sb(width = "100%")
TRIPID PERMIT.y TRIP-LENGTH port-lat port-lon previous-port-lat previous-port-lon TRIP-COST-WINSOR-2020-DOL DDLAT DDLON ZoneID LANDED-OBSCURED DOLLAR-OBSCURED DOLLAR-2020-OBSCURED DOLLAR-ALL-SP-2020-OBSCURED OPERATING-PROFIT-2020 DB-LANDING-YEAR CPUE CPUE-p VPUE VPUE-p ZONE-ID
TRIPID 1.00 -0.03 -0.16 0.17 0.16 0.15 0.15 -0.27 0.15 0.16 0.13 -0.02 0.17 0.08 0.07 0.09 0.99 0.02 0.10 0.17 0.32 0.13
PERMIT.y -0.03 1.00 0.03 0.10 0.10 0.09 0.10 0.00 0.05 0.05 0.03 0.00 0.00 0.01 0.00 0.00 -0.03 0.01 -0.02 0.01 -0.01 0.03
TRIP-LENGTH -0.16 0.03 1.00 0.09 0.13 0.04 0.08 0.83 0.12 0.11 0.10 0.65 0.58 0.60 0.55 0.51 -0.15 -0.03 0.04 -0.04 0.01 0.10
port-lat 0.17 0.10 0.09 1.00 0.99 0.69 0.70 0.13 0.50 0.51 0.44 0.19 0.24 0.23 0.22 0.22 0.17 0.07 0.17 0.14 0.25 0.44
port-lon 0.16 0.10 0.13 0.99 1.00 0.68 0.72 0.17 0.51 0.53 0.45 0.22 0.26 0.25 0.24 0.23 0.17 0.07 0.18 0.14 0.25 0.45
previous-port-lat 0.15 0.09 0.04 0.69 0.68 1.00 0.98 0.09 0.41 0.43 0.36 0.15 0.20 0.19 0.18 0.18 0.15 0.06 0.15 0.12 0.22 0.36
previous-port-lon 0.15 0.10 0.08 0.70 0.72 0.98 1.00 0.13 0.44 0.47 0.38 0.17 0.22 0.21 0.20 0.20 0.16 0.06 0.15 0.12 0.22 0.38
TRIP-COST-WINSOR-2020-DOL -0.27 0.00 0.83 0.13 0.17 0.09 0.13 1.00 0.16 0.15 0.14 0.62 0.59 0.62 0.57 0.53 -0.29 0.02 0.13 0.04 0.13 0.14
DDLAT 0.15 0.05 0.12 0.50 0.51 0.41 0.44 0.16 1.00 0.87 0.92 0.20 0.27 0.27 0.25 0.25 0.14 0.05 0.12 0.13 0.22 0.92
DDLON 0.16 0.05 0.11 0.51 0.53 0.43 0.47 0.15 0.87 1.00 0.79 0.19 0.25 0.24 0.23 0.23 0.16 0.06 0.14 0.13 0.23 0.79
ZoneID 0.13 0.03 0.10 0.44 0.45 0.36 0.38 0.14 0.92 0.79 1.00 0.17 0.24 0.23 0.22 0.22 0.13 0.04 0.11 0.12 0.20 1.00
LANDED-OBSCURED -0.02 0.00 0.65 0.19 0.22 0.15 0.17 0.62 0.20 0.19 0.17 1.00 0.91 0.92 0.85 0.84 -0.02 0.33 0.70 0.37 0.60 0.17
DOLLAR-OBSCURED 0.17 0.00 0.58 0.24 0.26 0.20 0.22 0.59 0.27 0.25 0.24 0.91 1.00 1.00 0.91 0.91 0.17 0.30 0.65 0.46 0.74 0.24
DOLLAR-2020-OBSCURED 0.08 0.01 0.60 0.23 0.25 0.19 0.21 0.62 0.27 0.24 0.23 0.92 1.00 1.00 0.92 0.91 0.08 0.30 0.65 0.45 0.72 0.23
DOLLAR-ALL-SP-2020-OBSCURED 0.07 0.00 0.55 0.22 0.24 0.18 0.20 0.57 0.25 0.23 0.22 0.85 0.91 0.92 1.00 1.00 0.07 0.28 0.60 0.41 0.66 0.22
OPERATING-PROFIT-2020 0.09 0.00 0.51 0.22 0.23 0.18 0.20 0.53 0.25 0.23 0.22 0.84 0.91 0.91 1.00 1.00 0.09 0.28 0.61 0.42 0.67 0.22
DB-LANDING-YEAR 0.99 -0.03 -0.15 0.17 0.17 0.15 0.16 -0.29 0.14 0.16 0.13 -0.02 0.17 0.08 0.07 0.09 1.00 0.02 0.10 0.17 0.32 0.13
CPUE 0.02 0.01 -0.03 0.07 0.07 0.06 0.06 0.02 0.05 0.06 0.04 0.33 0.30 0.30 0.28 0.28 0.02 1.00 0.48 0.93 0.43 0.04
CPUE-p 0.10 -0.02 0.04 0.17 0.18 0.15 0.15 0.13 0.12 0.14 0.11 0.70 0.65 0.65 0.60 0.61 0.10 0.48 1.00 0.57 0.88 0.11
VPUE 0.17 0.01 -0.04 0.14 0.14 0.12 0.12 0.04 0.13 0.13 0.12 0.37 0.46 0.45 0.41 0.42 0.17 0.93 0.57 1.00 0.63 0.12
VPUE-p 0.32 -0.01 0.01 0.25 0.25 0.22 0.22 0.13 0.22 0.23 0.20 0.60 0.74 0.72 0.66 0.67 0.32 0.43 0.88 0.63 1.00 0.20
ZONE-ID 0.13 0.03 0.10 0.44 0.45 0.36 0.38 0.14 0.92 0.79 1.00 0.17 0.24 0.23 0.22 0.22 0.13 0.04 0.11 0.12 0.20 1.00

Note the strong correlation between TRIP_LENGTH and the TRIP_COST_WINSOR variables. This is because Trip costs are predicted from a statistical relationship between Trip length and costs on a subset of observed trips. There also is a moderate correlation between port_lat and previous_port_lat. This occurs because trips usually use 1 port.


Active Vessels

Vessel count by year.

ves_out <- 
  vessel_count(scallopMainDataTable, proj,
               v_id = "PERMIT.y",
               date = "DATE_TRIP",
               period = "year", type = "line")
#> Joining with `by = join_by(DATE_TRIP)`
#> Warning: Setting row names on a tibble is deprecated.

ves_out$table %>% pretty_tab()
year PERMIT.y
2007 98
2008 96
2009 101
2010 100
2011 100
2012 98
2013 95
2014 96
2015 90
2016 92
2017 97
2018 98
2019 98 
ves_out$plot


Vessel count by fleet.

ves_out <- 
  vessel_count(scallopMainDataTable, proj,
               v_id = "PERMIT.y",
               group = "fleet",
               output = "table")
#> Joining with `by = join_by(fleet)`
#> Warning: Setting row names on a tibble is deprecated.

ves_out$table %>% pretty_tab()
#> Warning: Unknown or uninitialised column: `table`.
 
ves_out$plot 
#> Warning: Unknown or uninitialised column: `plot`.
#> NULL


Vessel count by year and fleet.

ves_out <- 
  vessel_count(scallopMainDataTable, proj,
               v_id = "PERMIT.y",
               group = "fleet",
               date = "DATE_TRIP",
               period = "year", type = "line")
#> Joining with `by = join_by(fleet, DATE_TRIP)`
#> Warning: Setting row names on a tibble is deprecated.

ves_out$table %>% 
  pretty_lab(cols = "PERMIT.y") %>% 
  pretty_tab_sb(width = "40%")
year fleet PERMIT.y
2007 Access Area 94
2007 Days at Sea 91
2008 Access Area 93
2008 Days at Sea 89
2009 Access Area 100
2009 Days at Sea 99
2010 Access Area 97
2010 Days at Sea 95
2011 Access Area 98
2011 Days at Sea 92
2012 Access Area 97
2012 Days at Sea 95
2013 Access Area 91
2013 Days at Sea 89
2014 Access Area 89
2014 Days at Sea 91
2015 Access Area 87
2015 Days at Sea 85
2016 Access Area 91
2016 Days at Sea 86
2017 Access Area 97
2017 Days at Sea 86
2018 Access Area 98
2018 Days at Sea 84
2019 Access Area 98
2019 Days at Sea 80 

ves_out$plot


vessel count by gearcode.

ves_out <- 
  vessel_count(scallopMainDataTable, proj,
               v_id = "PERMIT.y",
               group = "GEARCODE", tran = "log")
#> Joining with `by = join_by(GEARCODE)`
#> Warning: Setting row names on a tibble is deprecated.

ves_out$table %>% 
  pretty_lab() %>% 
  pretty_tab()
GEARCODE PERMIT.y
DREDGE 130
OTHER 1
TRAWL-BOTTOM 5 

ves_out$plot


Catch

Total meat catch by year.

catch_out <- 
species_catch(scallopMainDataTable, proj,
              species = "LANDED_OBSCURED",
              date = "DATE_TRIP", 
              period = "year",
              fun = "sum",
              type = "line", format_lab = "decimal")
#> Joining with `by = join_by(DATE_TRIP)`

catch_out$table %>% 
  pretty_lab(cols = "LANDED_OBSCURED") %>%
  pretty_tab()
year LANDED_OBSCURED
2007 9,931.51
2008 11,702.12
2009 13,420.05
2010 13,637.72
2011 13,996.77
2012 13,476.78
2013 8,982.47
2014 7,316.64
2015 7,706.52
2016 8,464.29
2017 11,756.92
2018 13,500.97
2019 14,328.6 

catch_out$plot


Total meat catch by fleet.

catch_out <- 
  species_catch(scallopMainDataTable, proj,
                species = "LANDED_OBSCURED",
                group = "fleet",
                fun = "sum",
                type = "bar", format_lab = "decimal")
#> Joining with `by = join_by(fleet)`

catch_out$table %>% 
  pretty_lab(cols = "LANDED_OBSCURED") %>%
  pretty_tab()
fleet LANDED_OBSCURED
Access Area 73,842.46
Days at Sea 74,378.91 

catch_out$plot


Total meat catch by year and fleet.

catch_out <- 
  species_catch(scallopMainDataTable, proj,
                species = "LANDED_OBSCURED",
                date = "DATE_TRIP", 
                period = "year",
                group = "fleet",
                fun = "sum",
                type = "line", format_lab = "decimal")
#> Joining with `by = join_by(fleet, DATE_TRIP)`

catch_out$table %>% 
  pretty_lab(cols = "LANDED_OBSCURED") %>%
  pretty_tab_sb(width = "40%")
year fleet LANDED_OBSCURED
2007 Access Area 5,706.6
2007 Days at Sea 4,224.91
2008 Access Area 7,216.21
2008 Days at Sea 4,485.92
2009 Access Area 6,843.84
2009 Days at Sea 6,576.2
2010 Access Area 5,520.37
2010 Days at Sea 8,117.35
2011 Access Area 6,366.48
2011 Days at Sea 7,630.29
2012 Access Area 5,680.09
2012 Days at Sea 7,796.69
2013 Access Area 2,141.4
2013 Days at Sea 6,841.07
2014 Access Area 1,693.68
2014 Days at Sea 5,622.95
2015 Access Area 3,921.89
2015 Days at Sea 3,784.62
2016 Access Area 3,928.59
2016 Days at Sea 4,535.7
2017 Access Area 6,112.54
2017 Days at Sea 5,644.38
2018 Access Area 8,712.77
2018 Days at Sea 4,788.2
2019 Access Area 9,998
2019 Days at Sea 4,330.6 

catch_out$plot


CPUE

Average CPUE by year.

cpue_out <- 
  species_catch(scallopMainDataTable, proj,
                species = "CPUE",
                date = "DATE_TRIP", 
                period = "year",
                fun = "mean", type = "line")
#> Joining with `by = join_by(DATE_TRIP)`

cpue_out$table %>% 
  pretty_lab(cols = "CPUE") %>%
  pretty_tab()
year CPUE
2007 1.71
2008 2.14
2009 2.11
2010 1.98
2011 2.16
2012 2.06
2013 1.96
2014 1.78
2015 1.84
2016 1.53
2017 2.03
2018 2.25
2019 2.24 

cpue_out$plot


Average CPUE by year and fleet.

cpue_out <- 
  species_catch(scallopMainDataTable, proj,
                species = "CPUE",
                date = "DATE_TRIP", 
                period = "year",
                group = "fleet",
                fun = "mean", type = "line")
#> Joining with `by = join_by(fleet, DATE_TRIP)`

cpue_out$table %>% 
  pretty_lab(cols = "CPUE") %>%
  pretty_tab_sb(width = "40%")
year fleet CPUE
2007 Access Area 2.13
2007 Days at Sea 1.19
2008 Access Area 2.76
2008 Days at Sea 1.29
2009 Access Area 2.05
2009 Days at Sea 2.18
2010 Access Area 1.71
2010 Days at Sea 2.28
2011 Access Area 1.89
2011 Days at Sea 2.6
2012 Access Area 1.72
2012 Days at Sea 2.49
2013 Access Area 1.24
2013 Days at Sea 2.45
2014 Access Area 1.64
2014 Days at Sea 1.85
2015 Access Area 2.31
2015 Days at Sea 1.36
2016 Access Area 1.53
2016 Days at Sea 1.54
2017 Access Area 2
2017 Days at Sea 2.07
2018 Access Area 2.31
2018 Days at Sea 2.09
2019 Access Area 2.28
2019 Days at Sea 2.12 

cpue_out$plot


VPUE

Average VPUE by year.

vpue_out <- 
  species_catch(scallopMainDataTable, proj,
                species = "VPUE",
                date = "DATE_TRIP", 
                period = "year",
                fun = "mean", type = "line")
#> Joining with `by = join_by(DATE_TRIP)`

vpue_out$table %>% 
  pretty_lab(cols = "VPUE") %>%
  pretty_tab()
year VPUE
2007 11,216.91
2008 14,807.41
2009 13,694.33
2010 16,039.3
2011 21,511.01
2012 20,405.14
2013 22,201.91
2014 22,354.77
2015 22,822.66
2016 18,153.58
2017 20,256.65
2018 20,940.58
2019 21,062.97 

vpue_out$plot


Average VPUE by year and fleet.

vpue_out <- 
  species_catch(scallopMainDataTable, proj,
                species = "VPUE",
                date = "DATE_TRIP", 
                period = "year",
                group = "fleet",
                fun = "mean", type = "line")
#> Joining with `by = join_by(fleet, DATE_TRIP)`

vpue_out$table %>% 
  pretty_lab(cols = "VPUE") %>%
  pretty_tab_sb(width = "40%")
year fleet VPUE
2007 Access Area 14,054.84
2007 Days at Sea 7,787.08
2008 Access Area 19,176.6
2008 Days at Sea 8,944.48
2009 Access Area 13,567.8
2009 Days at Sea 13,854.48
2010 Access Area 14,678.53
2010 Days at Sea 17,574.35
2011 Access Area 18,967.12
2011 Days at Sea 25,531.48
2012 Access Area 17,096.89
2012 Days at Sea 24,522.17
2013 Access Area 14,437.7
2013 Days at Sea 27,552.04
2014 Access Area 20,732.08
2014 Days at Sea 23,097.61
2015 Access Area 28,535.36
2015 Days at Sea 16,879.25
2016 Access Area 17,909.21
2016 Days at Sea 18,364.85
2017 Access Area 21,327.81
2017 Days at Sea 18,722.65
2018 Access Area 21,625.58
2018 Days at Sea 19,289.61
2019 Access Area 20,920.47
2019 Days at Sea 21,404.03 

vpue_out$plot


Average VPUE by gearcode.

vpue_out <- 
  species_catch(scallopMainDataTable, proj,
                species = "VPUE",
                group = "GEARCODE",
                fun = "mean", type = "line")
#> Joining with `by = join_by(GEARCODE)`

vpue_out$table %>% 
pretty_lab() %>%
  pretty_tab()
GEARCODE VPUE
DREDGE 18,745.93
OTHER 14,069.03
TRAWL-BOTTOM 10,273.8 

vpue_out$plot + angled_theme()


Distributions

FishSET can make various distributional plots

LANDED_OBSCURED

KDE, ECDF, and CDF of meat catch, note the tran= "log" option to do a log transformation of LANDED_OBSCURED.

density_plot(scallopMainDataTable, proj,
             var = "LANDED_OBSCURED", 
             type = "all", tran = "log")



KDE, ECDF, and CDF of meat catch by fleet can be easily produced using the group="fleet" option.

density_plot(scallopMainDataTable, proj,
             var = "LANDED_OBSCURED",
             group = "fleet", position = "stack", 
             type = "all", tran = "log", pages = "multi")


KDE of meat catch by year.

density_plot(scallopMainDataTable, proj,
             var = "LANDED_OBSCURED", 
             facet_by = "DB_LANDING_YEAR",
             filter_by = "DB_LANDING_YEAR",
             filter_value = 2007:2013,
             type = "kde", bw = 1.5, 
             tran = "log", scale = "free_y") + 
  angled_theme()


density_plot(scallopMainDataTable, proj,
             var = "LANDED_OBSCURED", 
             facet_by = "DB_LANDING_YEAR",
             filter_by = "DB_LANDING_YEAR",
             filter_value = 2014:2019,
             type = "kde", bw = 1.5, 
             tran = "log", scale = "free_y") + 
  angled_theme()