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)
#> The legacy packages maptools, rgdal, and rgeos, underpinning the sp package,
#> which was just loaded, will retire in October 2023.
#> Please refer to R-spatial evolution reports for details, especially
#> https://r-spatial.org/r/2023/05/15/evolution4.html.
#> It may be desirable to make the sf package available;
#> package maintainers should consider adding sf to Suggests:.
#> The sp package is now running under evolution status 2
#> (status 2 uses the sf package in place of rgdal)
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)
#> Warning: package 'ggplot2' was built under R version 4.3.2
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 from the FishSET package.
load_maindata(dat = FishSET::scallop, project = proj)
#> Table saved to database
#>
#> ! Data saved to database as scallopMainDataTable20241113 (raw) and scallopMainDataTable (working).
#> Table is also in the working environment. !
This data contains 10000 rows and 19 variables.
View and upload the ten minute squares map and wind turbine closure areas from the FishSET package.
load_spatial(spat = FishSET::tenMNSQR, project = proj, name = "TenMNSQR")
#> Writing layer `scallopTenMNSQRSpatTable' to data source
#> `C:\Users\pcarvalho\AppData\Local\Temp\Rtmp6tK6yl/scallop/data/spat/scallopTenMNSQRSpatTable.geojson' using driver `GeoJSON'
#> Writing 5267 features with 9 fields and geometry type Polygon.
#> Writing layer `scallopTenMNSQRSpatTable20241113' to data source
#> `C:\Users\pcarvalho\AppData\Local\Temp\Rtmp6tK6yl/scallop/data/spat/scallopTenMNSQRSpatTable20241113.geojson' using driver `GeoJSON'
#> Writing 5267 features with 9 fields and geometry type Polygon.
#> Spatial table saved to project folder as scallopTenMNSQRSpatTable
load_spatial(spat = FishSET::windLease, project = proj, name = "WindClose")
#> Writing layer `scallopWindCloseSpatTable' to data source
#> `C:\Users\pcarvalho\AppData\Local\Temp\Rtmp6tK6yl/scallop/data/spat/scallopWindCloseSpatTable.geojson' using driver `GeoJSON'
#> Writing 32 features with 1 fields and geometry type Multi Polygon.
#> Writing layer `scallopWindCloseSpatTable20241113' to data source
#> `C:\Users\pcarvalho\AppData\Local\Temp\Rtmp6tK6yl/scallop/data/spat/scallopWindCloseSpatTable20241113.geojson' using driver `GeoJSON'
#> Writing 32 features with 1 fields and geometry type Multi Polygon.
#> Spatial table saved to project folder as scallopWindCloseSpatTable
Assign the regulatory zones (scallopTenMNSQRSpatTable
)
and closure areas (scallopWindCloseSpatTable
) to the
working environment.
scallopTenMNSQRSpatTable <- table_view("scallopTenMNSQRSpatTable", proj)
#> Reading layer `scallopTenMNSQRSpatTable' from data source
#> `C:\Users\pcarvalho\AppData\Local\Temp\Rtmp6tK6yl\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
#> `C:\Users\pcarvalho\AppData\Local\Temp\Rtmp6tK6yl\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
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 by subtracting 2020 trip costs from aggregated revenues in 2020 dollars.
scallopMainDataTable <-
scallopMainDataTable %>%
mutate(OPERATING_PROFIT_2020 = DOLLAR_ALL_SP_2020_OBSCURED - TRIP_COST_WINSOR_2020_DOL)
Summary Table
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. :357223 | 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 :400612 | 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: 5 | 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: 469 | Unique Obs: 10000 | Unique Obs: 10000 | Unique Obs: 10000 | Unique Obs: 10000 | Unique Obs: 1 | Unique Obs: 10000 | Unique Obs: 3539 | 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: NA | 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
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, ZoneID. Consider using na_filter to replace or remove NAs.
NaN Check
nan_filter(scallopMainDataTable,
project = proj,
replace = FALSE, remove = FALSE,
rep.value = NA, over_write = FALSE)
#> No NaNs found.
Unique Rows
unique_filter(scallopMainDataTable, project = proj, remove = FALSE)
#> Unique filter check for scallopMainDataTable dataset on 20241113
#> Each row is a unique choice occurrence. No further action required.
Empty Variables
“Empty” variables contain all NA
s.
empty_vars_filter(scallopMainDataTable, project = proj, remove = FALSE)
#> Empty vars check for scallopMainDataTable dataset on 20241113
#> No empty variables identified.
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
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.
#> Warning: 10 observations (0.1%) occur on land.
#> Warning: 35 observations (0.4%) occur on boundary line between regulatory zones.
#> 10 observations (0.1%) occur on land.
#> 35 observations (0.4%) 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 | 766 | 2 | 5 | 7.73 |
2008 | 822 | 821 | 0 | 1 | 8.22 |
2009 | 861 | 858 | 1 | 2 | 8.61 |
2010 | 864 | 862 | 1 | 1 | 8.64 |
2011 | 818 | 814 | 0 | 4 | 8.18 |
2012 | 784 | 783 | 0 | 1 | 7.84 |
2013 | 603 | 599 | 1 | 3 | 6.03 |
2014 | 523 | 520 | 1 | 2 | 5.23 |
2015 | 601 | 596 | 0 | 5 | 6.01 |
2016 | 682 | 680 | 0 | 2 | 6.82 |
2017 | 788 | 785 | 0 | 3 | 7.88 |
2018 | 896 | 891 | 0 | 5 | 8.96 |
2019 | 985 | 980 | 4 | 1 | 9.85 |
spat_qaqc_out[2:4]
#> $land_plot
#>
#> $boundary_plot
#>
#> $expected_plot
Data Creation
Landing Year
scallopMainDataTable <-
scallopMainDataTable %>%
mutate(DB_LANDING_YEAR = as.numeric(format(date_parser(DATE_TRIP), "%Y")))
Finagle LANDED_OBSCURED to Thousands of Pounds
scallopMainDataTable$LANDED_OBSCURED <- scallopMainDataTable$LANDED_OBSCURED / 1000
CPUE
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))
VPUE
Same as above but with revenue instead of meat pounds.
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 regulatory zone.
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.
#> Warning: At least one observation assigned to multiple regulatory zones.
#> Assigning observations to nearest polygon.
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)
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
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 |
387331 | 231 |
387322 | 211 |
406932 | 194 |
387314 | 193 |
406926 | 192 |
387446 | 164 |
406915 | 151 |
387323 | 148 |
387436 | 146 |
387313 | 141 |
406925 | 140 |
406916 | 137 |
387445 | 135 |
416966 | 131 |
416862 | 122 |
387455 | 119 |
416861 | 119 |
387465 | 114 |
387333 | 109 |
397364 | 107 |
387426 | 96 |
397315 | 96 |
406933 | 96 |
397213 | 94 |
397232 | 91 |
397363 | 82 |
397231 | 80 |
397355 | 80 |
397365 | 80 |
406811 | 80 |
406936 | 80 |
416662 | 80 |
416944 | 79 |
387341 | 76 |
406942 | 76 |
416852 | 75 |
406935 | 74 |
387456 | 68 |
397241 | 68 |
416955 | 67 |
387435 | 66 |
397346 | 65 |
397325 | 63 |
406931 | 62 |
416661 | 60 |
406944 | 59 |
407364 | 59 |
387324 | 58 |
387342 | 56 |
407242 | 56 |
426764 | 56 |
416843 | 55 |
377433 | 54 |
397356 | 54 |
406715 | 54 |
407262 | 53 |
407365 | 52 |
406714 | 51 |
377424 | 50 |
416653 | 50 |
387312 | 49 |
387315 | 49 |
387321 | 49 |
397354 | 49 |
407263 | 48 |
407254 | 47 |
397212 | 46 |
406821 | 46 |
406943 | 46 |
416851 | 45 |
397223 | 44 |
406723 | 44 |
387425 | 43 |
397335 | 43 |
406945 | 40 |
377423 | 39 |
407355 | 39 |
397314 | 38 |
397366 | 38 |
407356 | 38 |
407366 | 37 |
397214 | 36 |
397326 | 36 |
397345 | 36 |
406713 | 36 |
407244 | 36 |
397362 | 35 |
407241 | 35 |
377443 | 34 |
406716 | 34 |
407243 | 34 |
407261 | 34 |
377442 | 33 |
406722 | 33 |
407121 | 33 |
397316 | 32 |
416643 | 32 |
416714 | 32 |
377414 | 31 |
387311 | 31 |
397336 | 31 |
406611 | 31 |
407253 | 31 |
416842 | 31 |
416853 | 31 |
407251 | 30 |
377415 | 29 |
397211 | 29 |
397242 | 29 |
407112 | 29 |
407363 | 29 |
397344 | 28 |
416954 | 28 |
397353 | 27 |
407113 | 27 |
416652 | 27 |
416713 | 27 |
387466 | 25 |
407131 | 25 |
407252 | 25 |
407354 | 23 |
416933 | 23 |
416945 | 23 |
387464 | 22 |
416642 | 22 |
416934 | 22 |
426763 | 22 |
406712 | 21 |
407245 | 21 |
416844 | 21 |
377432 | 20 |
397222 | 20 |
407346 | 20 |
416651 | 20 |
406724 | 19 |
406831 | 19 |
407345 | 19 |
416863 | 19 |
397251 | 18 |
397343 | 18 |
406812 | 18 |
416654 | 18 |
416765 | 18 |
407264 | 17 |
416816 | 17 |
416956 | 17 |
416964 | 17 |
397221 | 16 |
397324 | 16 |
406832 | 16 |
407226 | 16 |
416663 | 16 |
397334 | 15 |
406822 | 15 |
377452 | 14 |
397313 | 14 |
406733 | 14 |
407234 | 14 |
387334 | 13 |
416932 | 13 |
377413 | 12 |
377425 | 12 |
377434 | 12 |
387325 | 12 |
397352 | 12 |
397361 | 12 |
406732 | 12 |
406826 | 12 |
406965 | 11 |
407111 | 11 |
407122 | 11 |
427044 | 11 |
387416 | 10 |
406721 | 10 |
406841 | 10 |
407225 | 10 |
397333 | 9 |
407115 | 9 |
407236 | 9 |
407255 | 9 |
407256 | 9 |
427045 | 9 |
387351 | 8 |
397342 | 8 |
406833 | 8 |
407246 | 8 |
416766 | 8 |
417031 | 8 |
417164 | 8 |
427065 | 8 |
397224 | 7 |
397312 | 7 |
407114 | 7 |
407235 | 7 |
407353 | 7 |
416711 | 7 |
416834 | 7 |
406813 | 6 |
406934 | 6 |
416756 | 6 |
416826 | 6 |
416845 | 6 |
416854 | 6 |
377323 | 5 |
377422 | 5 |
387335 | 5 |
387336 | 5 |
387434 | 5 |
387463 | 5 |
406814 | 5 |
406815 | 5 |
406924 | 5 |
406954 | 5 |
407141 | 5 |
407232 | 5 |
407343 | 5 |
407362 | 5 |
416641 | 5 |
416712 | 5 |
416825 | 5 |
426765 | 5 |
427056 | 5 |
387032 | 4 |
387362 | 4 |
387432 | 4 |
406835 | 4 |
406861 | 4 |
406914 | 4 |
407132 | 4 |
407352 | 4 |
416721 | 4 |
417061 | 4 |
377416 | 3 |
377453 | 3 |
387242 | 3 |
387352 | 3 |
387363 | 3 |
387411 | 3 |
387452 | 3 |
397323 | 3 |
397351 | 3 |
397426 | 3 |
397465 | 3 |
406612 | 3 |
406711 | 3 |
406725 | 3 |
406816 | 3 |
406834 | 3 |
406946 | 3 |
406952 | 3 |
407055 | 3 |
407151 | 3 |
407266 | 3 |
407344 | 3 |
416943 | 3 |
417042 | 3 |
417062 | 3 |
427066 | 3 |
367322 | 2 |
367614 | 2 |
377325 | 2 |
377335 | 2 |
377435 | 2 |
377444 | 2 |
377464 | 2 |
387226 | 2 |
387241 | 2 |
387246 | 2 |
387326 | 2 |
387354 | 2 |
387355 | 2 |
387365 | 2 |
387415 | 2 |
387422 | 2 |
387444 | 2 |
387454 | 2 |
387462 | 2 |
397233 | 2 |
397234 | 2 |
397261 | 2 |
397311 | 2 |
397322 | 2 |
397332 | 2 |
397456 | 2 |
397466 | 2 |
406613 | 2 |
406621 | 2 |
406734 | 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 |
407233 | 2 |
407265 | 2 |
407316 | 2 |
407335 | 2 |
407336 | 2 |
407361 | 2 |
416722 | 2 |
416744 | 2 |
416755 | 2 |
416761 | 2 |
416824 | 2 |
416833 | 2 |
416864 | 2 |
416912 | 2 |
416922 | 2 |
416935 | 2 |
416953 | 2 |
416961 | 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 |
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 |
387122 | 1 |
387145 | 1 |
387211 | 1 |
387212 | 1 |
387214 | 1 |
387225 | 1 |
387231 | 1 |
387232 | 1 |
387233 | 1 |
387236 | 1 |
387244 | 1 |
387252 | 1 |
387344 | 1 |
387345 | 1 |
387353 | 1 |
387361 | 1 |
387366 | 1 |
387413 | 1 |
387414 | 1 |
387424 | 1 |
387433 | 1 |
387461 | 1 |
387655 | 1 |
396714 | 1 |
396814 | 1 |
396916 | 1 |
397225 | 1 |
397246 | 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 |
406852 | 1 |
406923 | 1 |
406966 | 1 |
407011 | 1 |
407012 | 1 |
407015 | 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 |
407325 | 1 |
407326 | 1 |
407333 | 1 |
407342 | 1 |
407466 | 1 |
416644 | 1 |
416664 | 1 |
416665 | 1 |
416715 | 1 |
416724 | 1 |
416734 | 1 |
416742 | 1 |
416746 | 1 |
416762 | 1 |
416764 | 1 |
416831 | 1 |
416835 | 1 |
416841 | 1 |
416856 | 1 |
416865 | 1 |
416866 | 1 |
416915 | 1 |
416916 | 1 |
416924 | 1 |
416931 | 1 |
416942 | 1 |
416952 | 1 |
416962 | 1 |
417041 | 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 |
426761 | 1 |
426915 | 1 |
426964 | 1 |
427034 | 1 |
Percent 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, 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 |
387331 | 231 | 2.31 |
387322 | 211 | 2.11 |
406932 | 194 | 1.94 |
387314 | 193 | 1.93 |
406926 | 192 | 1.92 |
387446 | 164 | 1.64 |
406915 | 151 | 1.51 |
387323 | 148 | 1.48 |
387436 | 146 | 1.46 |
387313 | 141 | 1.41 |
406925 | 140 | 1.4 |
406916 | 137 | 1.37 |
387445 | 135 | 1.35 |
416966 | 131 | 1.31 |
416862 | 122 | 1.22 |
387455 | 119 | 1.19 |
416861 | 119 | 1.19 |
387465 | 114 | 1.14 |
387333 | 109 | 1.09 |
397364 | 107 | 1.07 |
387426 | 96 | 0.96 |
397315 | 96 | 0.96 |
406933 | 96 | 0.96 |
397213 | 94 | 0.94 |
397232 | 91 | 0.91 |
397363 | 82 | 0.82 |
397231 | 80 | 0.8 |
397355 | 80 | 0.8 |
397365 | 80 | 0.8 |
406811 | 80 | 0.8 |
406936 | 80 | 0.8 |
416662 | 80 | 0.8 |
416944 | 79 | 0.79 |
387341 | 76 | 0.76 |
406942 | 76 | 0.76 |
416852 | 75 | 0.75 |
406935 | 74 | 0.74 |
387456 | 68 | 0.68 |
397241 | 68 | 0.68 |
416955 | 67 | 0.67 |
387435 | 66 | 0.66 |
397346 | 65 | 0.65 |
397325 | 63 | 0.63 |
406931 | 62 | 0.62 |
416661 | 60 | 0.6 |
406944 | 59 | 0.59 |
407364 | 59 | 0.59 |
387324 | 58 | 0.58 |
387342 | 56 | 0.56 |
407242 | 56 | 0.56 |
426764 | 56 | 0.56 |
416843 | 55 | 0.55 |
377433 | 54 | 0.54 |
397356 | 54 | 0.54 |
406715 | 54 | 0.54 |
407262 | 53 | 0.53 |
407365 | 52 | 0.52 |
406714 | 51 | 0.51 |
377424 | 50 | 0.5 |
416653 | 50 | 0.5 |
387312 | 49 | 0.49 |
387315 | 49 | 0.49 |
387321 | 49 | 0.49 |
397354 | 49 | 0.49 |
407263 | 48 | 0.48 |
407254 | 47 | 0.47 |
397212 | 46 | 0.46 |
406821 | 46 | 0.46 |
406943 | 46 | 0.46 |
416851 | 45 | 0.45 |
397223 | 44 | 0.44 |
406723 | 44 | 0.44 |
387425 | 43 | 0.43 |
397335 | 43 | 0.43 |
406945 | 40 | 0.4 |
377423 | 39 | 0.39 |
407355 | 39 | 0.39 |
397314 | 38 | 0.38 |
397366 | 38 | 0.38 |
407356 | 38 | 0.38 |
407366 | 37 | 0.37 |
397214 | 36 | 0.36 |
397326 | 36 | 0.36 |
397345 | 36 | 0.36 |
406713 | 36 | 0.36 |
407244 | 36 | 0.36 |
397362 | 35 | 0.35 |
407241 | 35 | 0.35 |
377443 | 34 | 0.34 |
406716 | 34 | 0.34 |
407243 | 34 | 0.34 |
407261 | 34 | 0.34 |
377442 | 33 | 0.33 |
406722 | 33 | 0.33 |
407121 | 33 | 0.33 |
397316 | 32 | 0.32 |
416643 | 32 | 0.32 |
416714 | 32 | 0.32 |
377414 | 31 | 0.31 |
387311 | 31 | 0.31 |
397336 | 31 | 0.31 |
406611 | 31 | 0.31 |
407253 | 31 | 0.31 |
416842 | 31 | 0.31 |
416853 | 31 | 0.31 |
407251 | 30 | 0.3 |
377415 | 29 | 0.29 |
397211 | 29 | 0.29 |
397242 | 29 | 0.29 |
407112 | 29 | 0.29 |
407363 | 29 | 0.29 |
397344 | 28 | 0.28 |
416954 | 28 | 0.28 |
397353 | 27 | 0.27 |
407113 | 27 | 0.27 |
416652 | 27 | 0.27 |
416713 | 27 | 0.27 |
387466 | 25 | 0.25 |
407131 | 25 | 0.25 |
407252 | 25 | 0.25 |
407354 | 23 | 0.23 |
416933 | 23 | 0.23 |
416945 | 23 | 0.23 |
387464 | 22 | 0.22 |
416642 | 22 | 0.22 |
416934 | 22 | 0.22 |
426763 | 22 | 0.22 |
406712 | 21 | 0.21 |
407245 | 21 | 0.21 |
416844 | 21 | 0.21 |
377432 | 20 | 0.2 |
397222 | 20 | 0.2 |
407346 | 20 | 0.2 |
416651 | 20 | 0.2 |
406724 | 19 | 0.19 |
406831 | 19 | 0.19 |
407345 | 19 | 0.19 |
416863 | 19 | 0.19 |
397251 | 18 | 0.18 |
397343 | 18 | 0.18 |
406812 | 18 | 0.18 |
416654 | 18 | 0.18 |
416765 | 18 | 0.18 |
407264 | 17 | 0.17 |
416816 | 17 | 0.17 |
416956 | 17 | 0.17 |
416964 | 17 | 0.17 |
397221 | 16 | 0.16 |
397324 | 16 | 0.16 |
406832 | 16 | 0.16 |
407226 | 16 | 0.16 |
416663 | 16 | 0.16 |
397334 | 15 | 0.15 |
406822 | 15 | 0.15 |
377452 | 14 | 0.14 |
397313 | 14 | 0.14 |
406733 | 14 | 0.14 |
407234 | 14 | 0.14 |
387334 | 13 | 0.13 |
416932 | 13 | 0.13 |
377413 | 12 | 0.12 |
377425 | 12 | 0.12 |
377434 | 12 | 0.12 |
387325 | 12 | 0.12 |
397352 | 12 | 0.12 |
397361 | 12 | 0.12 |
406732 | 12 | 0.12 |
406826 | 12 | 0.12 |
406965 | 11 | 0.11 |
407111 | 11 | 0.11 |
407122 | 11 | 0.11 |
427044 | 11 | 0.11 |
387416 | 10 | 0.1 |
406721 | 10 | 0.1 |
406841 | 10 | 0.1 |
407225 | 10 | 0.1 |
397333 | 9 | 0.09 |
407115 | 9 | 0.09 |
407236 | 9 | 0.09 |
407255 | 9 | 0.09 |
407256 | 9 | 0.09 |
427045 | 9 | 0.09 |
387351 | 8 | 0.08 |
397342 | 8 | 0.08 |
406833 | 8 | 0.08 |
407246 | 8 | 0.08 |
416766 | 8 | 0.08 |
417031 | 8 | 0.08 |
417164 | 8 | 0.08 |
427065 | 8 | 0.08 |
397224 | 7 | 0.07 |
397312 | 7 | 0.07 |
407114 | 7 | 0.07 |
407235 | 7 | 0.07 |
407353 | 7 | 0.07 |
416711 | 7 | 0.07 |
416834 | 7 | 0.07 |
406813 | 6 | 0.06 |
406934 | 6 | 0.06 |
416756 | 6 | 0.06 |
416826 | 6 | 0.06 |
416845 | 6 | 0.06 |
416854 | 6 | 0.06 |
377323 | 5 | 0.05 |
377422 | 5 | 0.05 |
387335 | 5 | 0.05 |
387336 | 5 | 0.05 |
387434 | 5 | 0.05 |
387463 | 5 | 0.05 |
406814 | 5 | 0.05 |
406815 | 5 | 0.05 |
406924 | 5 | 0.05 |
406954 | 5 | 0.05 |
407141 | 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 |
387032 | 4 | 0.04 |
387362 | 4 | 0.04 |
387432 | 4 | 0.04 |
406835 | 4 | 0.04 |
406861 | 4 | 0.04 |
406914 | 4 | 0.04 |
407132 | 4 | 0.04 |
407352 | 4 | 0.04 |
416721 | 4 | 0.04 |
417061 | 4 | 0.04 |
377416 | 3 | 0.03 |
377453 | 3 | 0.03 |
387242 | 3 | 0.03 |
387352 | 3 | 0.03 |
387363 | 3 | 0.03 |
387411 | 3 | 0.03 |
387452 | 3 | 0.03 |
397323 | 3 | 0.03 |
397351 | 3 | 0.03 |
397426 | 3 | 0.03 |
397465 | 3 | 0.03 |
406612 | 3 | 0.03 |
406711 | 3 | 0.03 |
406725 | 3 | 0.03 |
406816 | 3 | 0.03 |
406834 | 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 |
416943 | 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 |
377444 | 2 | 0.02 |
377464 | 2 | 0.02 |
387226 | 2 | 0.02 |
387241 | 2 | 0.02 |
387246 | 2 | 0.02 |
387326 | 2 | 0.02 |
387354 | 2 | 0.02 |
387355 | 2 | 0.02 |
387365 | 2 | 0.02 |
387415 | 2 | 0.02 |
387422 | 2 | 0.02 |
387444 | 2 | 0.02 |
387454 | 2 | 0.02 |
387462 | 2 | 0.02 |
397233 | 2 | 0.02 |
397234 | 2 | 0.02 |
397261 | 2 | 0.02 |
397311 | 2 | 0.02 |
397322 | 2 | 0.02 |
397332 | 2 | 0.02 |
397456 | 2 | 0.02 |
397466 | 2 | 0.02 |
406613 | 2 | 0.02 |
406621 | 2 | 0.02 |
406734 | 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 |
407233 | 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 |
416761 | 2 | 0.02 |
416824 | 2 | 0.02 |
416833 | 2 | 0.02 |
416864 | 2 | 0.02 |
416912 | 2 | 0.02 |
416922 | 2 | 0.02 |
416935 | 2 | 0.02 |
416953 | 2 | 0.02 |
416961 | 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 |
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 |
387122 | 1 | 0.01 |
387145 | 1 | 0.01 |
387211 | 1 | 0.01 |
387212 | 1 | 0.01 |
387214 | 1 | 0.01 |
387225 | 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 |
387344 | 1 | 0.01 |
387345 | 1 | 0.01 |
387353 | 1 | 0.01 |
387361 | 1 | 0.01 |
387366 | 1 | 0.01 |
387413 | 1 | 0.01 |
387414 | 1 | 0.01 |
387424 | 1 | 0.01 |
387433 | 1 | 0.01 |
387461 | 1 | 0.01 |
387655 | 1 | 0.01 |
396714 | 1 | 0.01 |
396814 | 1 | 0.01 |
396916 | 1 | 0.01 |
397225 | 1 | 0.01 |
397246 | 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 |
406852 | 1 | 0.01 |
406923 | 1 | 0.01 |
406966 | 1 | 0.01 |
407011 | 1 | 0.01 |
407012 | 1 | 0.01 |
407015 | 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 |
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 |
416734 | 1 | 0.01 |
416742 | 1 | 0.01 |
416746 | 1 | 0.01 |
416762 | 1 | 0.01 |
416764 | 1 | 0.01 |
416831 | 1 | 0.01 |
416835 | 1 | 0.01 |
416841 | 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 |
416962 | 1 | 0.01 |
417041 | 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 |
426761 | 1 | 0.01 |
426915 | 1 | 0.01 |
426964 | 1 | 0.01 |
427034 | 1 | 0.01 |
Zone frequency (Access Area fleet).
zone_out <-
scallopMainDataTable %>%
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 |
387331 | 229 |
387322 | 208 |
406932 | 194 |
387314 | 192 |
406926 | 191 |
387446 | 163 |
387323 | 147 |
387436 | 145 |
406925 | 140 |
387445 | 133 |
416862 | 121 |
387455 | 117 |
387313 | 116 |
387465 | 109 |
387333 | 106 |
416861 | 106 |
387426 | 96 |
406933 | 96 |
397364 | 91 |
416662 | 80 |
406936 | 77 |
406942 | 76 |
387341 | 75 |
416852 | 75 |
397365 | 74 |
406935 | 73 |
387456 | 68 |
397355 | 68 |
387435 | 66 |
406931 | 62 |
397346 | 60 |
416661 | 60 |
406944 | 59 |
387324 | 57 |
397241 | 57 |
416843 | 55 |
377433 | 54 |
387342 | 54 |
397356 | 51 |
416653 | 49 |
387321 | 48 |
387315 | 47 |
377424 | 46 |
406943 | 46 |
416851 | 45 |
387425 | 42 |
377423 | 39 |
406945 | 39 |
416966 | 36 |
377442 | 33 |
377443 | 33 |
397366 | 32 |
416643 | 32 |
377414 | 31 |
416842 | 31 |
416853 | 31 |
416652 | 27 |
377415 | 26 |
387464 | 22 |
387466 | 22 |
406811 | 22 |
416642 | 22 |
397242 | 21 |
377432 | 20 |
416651 | 20 |
416863 | 19 |
406812 | 15 |
416654 | 15 |
416663 | 15 |
397251 | 14 |
406821 | 13 |
416765 | 13 |
377413 | 12 |
377434 | 12 |
387325 | 12 |
387334 | 12 |
377452 | 11 |
397363 | 11 |
377425 | 10 |
397345 | 9 |
416844 | 9 |
416956 | 9 |
397354 | 8 |
416766 | 8 |
387311 | 7 |
387351 | 7 |
406934 | 6 |
387312 | 5 |
387335 | 5 |
387463 | 5 |
406831 | 5 |
406954 | 5 |
416641 | 5 |
416854 | 5 |
417031 | 5 |
377422 | 4 |
387032 | 4 |
387336 | 4 |
387362 | 4 |
387432 | 4 |
397334 | 4 |
406861 | 4 |
406924 | 4 |
416756 | 4 |
377416 | 3 |
387242 | 3 |
387434 | 3 |
387452 | 3 |
397232 | 3 |
397333 | 3 |
397342 | 3 |
406813 | 3 |
406915 | 3 |
406946 | 3 |
406952 | 3 |
407246 | 3 |
407255 | 3 |
407343 | 3 |
416945 | 3 |
416965 | 3 |
417042 | 3 |
367322 | 2 |
367614 | 2 |
377323 | 2 |
377435 | 2 |
377444 | 2 |
377453 | 2 |
387226 | 2 |
387241 | 2 |
387246 | 2 |
387352 | 2 |
387365 | 2 |
387411 | 2 |
387415 | 2 |
387422 | 2 |
387454 | 2 |
387462 | 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 |
416961 | 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 |
377312 | 1 |
377332 | 1 |
377346 | 1 |
377364 | 1 |
377366 | 1 |
377411 | 1 |
377426 | 1 |
377445 | 1 |
377465 | 1 |
387122 | 1 |
387145 | 1 |
387211 | 1 |
387212 | 1 |
387214 | 1 |
387225 | 1 |
387232 | 1 |
387233 | 1 |
387236 | 1 |
387244 | 1 |
387326 | 1 |
387345 | 1 |
387353 | 1 |
387355 | 1 |
387361 | 1 |
387366 | 1 |
387413 | 1 |
387414 | 1 |
387416 | 1 |
387424 | 1 |
387433 | 1 |
387444 | 1 |
387461 | 1 |
387655 | 1 |
396916 | 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 |
397353 | 1 |
397446 | 1 |
397464 | 1 |
397465 | 1 |
397466 | 1 |
406611 | 1 |
406643 | 1 |
406652 | 1 |
406712 | 1 |
406714 | 1 |
406716 | 1 |
406724 | 1 |
406742 | 1 |
406816 | 1 |
406822 | 1 |
406835 | 1 |
406852 | 1 |
406916 | 1 |
406923 | 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 |
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 |
416963 | 1 |
417051 | 1 |
417146 | 1 |
426761 | 1 |
Zone frequency (Days at Sea fleet).
zone_out <-
scallopMainDataTable %>%
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 | 94 |
397232 | 88 |
397231 | 79 |
416944 | 79 |
397363 | 71 |
416955 | 66 |
397325 | 63 |
407364 | 59 |
406811 | 58 |
407242 | 56 |
426764 | 56 |
406715 | 54 |
407262 | 53 |
407365 | 52 |
406714 | 50 |
407263 | 48 |
407254 | 47 |
397212 | 45 |
387312 | 44 |
397223 | 44 |
406723 | 44 |
397335 | 43 |
397354 | 41 |
407356 | 38 |
397314 | 37 |
407355 | 37 |
407366 | 37 |
397214 | 36 |
406713 | 36 |
407244 | 36 |
397326 | 35 |
407241 | 35 |
407243 | 34 |
407261 | 34 |
397362 | 33 |
406716 | 33 |
406722 | 33 |
406821 | 33 |
397316 | 32 |
407121 | 32 |
416714 | 32 |
407253 | 31 |
397336 | 30 |
406611 | 30 |
407112 | 29 |
407251 | 29 |
397211 | 28 |
397344 | 28 |
407363 | 28 |
416954 | 28 |
397345 | 27 |
416713 | 27 |
397353 | 26 |
407113 | 26 |
387313 | 25 |
407131 | 25 |
407252 | 25 |
387311 | 24 |
407354 | 23 |
416933 | 22 |
416934 | 22 |
426763 | 22 |
407245 | 21 |
397222 | 20 |
406712 | 20 |
407346 | 20 |
416945 | 20 |
406724 | 18 |
407345 | 18 |
397343 | 17 |
407264 | 17 |
416816 | 17 |
416964 | 17 |
397221 | 16 |
397364 | 16 |
406832 | 16 |
407226 | 16 |
397324 | 15 |
406733 | 14 |
406822 | 14 |
406831 | 14 |
407234 | 14 |
416861 | 13 |
397313 | 12 |
397352 | 12 |
397355 | 12 |
397361 | 12 |
406732 | 12 |
406826 | 12 |
416844 | 12 |
416932 | 12 |
397241 | 11 |
397334 | 11 |
406965 | 11 |
407122 | 11 |
427044 | 11 |
406721 | 10 |
406841 | 10 |
407111 | 10 |
407225 | 10 |
387416 | 9 |
407115 | 9 |
407236 | 9 |
407256 | 9 |
427045 | 9 |
397242 | 8 |
406833 | 8 |
416956 | 8 |
417164 | 8 |
427065 | 8 |
397224 | 7 |
407114 | 7 |
407235 | 7 |
407353 | 7 |
416711 | 7 |
416834 | 7 |
397312 | 6 |
397333 | 6 |
397365 | 6 |
397366 | 6 |
407255 | 6 |
416826 | 6 |
416845 | 6 |
387465 | 5 |
397342 | 5 |
397346 | 5 |
406814 | 5 |
406815 | 5 |
407141 | 5 |
407232 | 5 |
407246 | 5 |
416712 | 5 |
416765 | 5 |
416825 | 5 |
426765 | 5 |
427056 | 5 |
377424 | 4 |
397251 | 4 |
406914 | 4 |
407132 | 4 |
407352 | 4 |
407362 | 4 |
416721 | 4 |
417061 | 4 |
377323 | 3 |
377415 | 3 |
377452 | 3 |
387322 | 3 |
387333 | 3 |
387363 | 3 |
387466 | 3 |
397323 | 3 |
397351 | 3 |
397356 | 3 |
406612 | 3 |
406711 | 3 |
406725 | 3 |
406812 | 3 |
406813 | 3 |
406834 | 3 |
406835 | 3 |
406936 | 3 |
407055 | 3 |
407151 | 3 |
407266 | 3 |
416654 | 3 |
417031 | 3 |
417062 | 3 |
427066 | 3 |
377325 | 2 |
377335 | 2 |
377425 | 2 |
377464 | 2 |
387315 | 2 |
387331 | 2 |
387332 | 2 |
387342 | 2 |
387354 | 2 |
387434 | 2 |
387445 | 2 |
387455 | 2 |
397233 | 2 |
397311 | 2 |
397465 | 2 |
406621 | 2 |
406734 | 2 |
406816 | 2 |
407013 | 2 |
407021 | 2 |
407041 | 2 |
407142 | 2 |
407216 | 2 |
407221 | 2 |
407233 | 2 |
407265 | 2 |
407316 | 2 |
407335 | 2 |
407343 | 2 |
407344 | 2 |
407361 | 2 |
416756 | 2 |
416761 | 2 |
416824 | 2 |
416833 | 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 |
387321 | 1 |
387323 | 1 |
387324 | 1 |
387326 | 1 |
387334 | 1 |
387336 | 1 |
387341 | 1 |
387344 | 1 |
387351 | 1 |
387352 | 1 |
387355 | 1 |
387411 | 1 |
387425 | 1 |
387436 | 1 |
387444 | 1 |
387446 | 1 |
396714 | 1 |
396814 | 1 |
397225 | 1 |
397234 | 1 |
397246 | 1 |
397261 | 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 |
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 |
407325 | 1 |
407333 | 1 |
407336 | 1 |
407466 | 1 |
416653 | 1 |
416663 | 1 |
416665 | 1 |
416722 | 1 |
416724 | 1 |
416734 | 1 |
416744 | 1 |
416762 | 1 |
416764 | 1 |
416831 | 1 |
416835 | 1 |
416841 | 1 |
416854 | 1 |
416862 | 1 |
416865 | 1 |
416915 | 1 |
416916 | 1 |
416924 | 1 |
416935 | 1 |
416942 | 1 |
416943 | 1 |
416962 | 1 |
416963 | 1 |
417041 | 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 |
Catch
Total catch (meats).
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 |
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 | 158.89 |
377414 | 466.52 |
377415 | 366.09 |
377416 | 37.6 |
377422 | 55.89 |
377423 | 478.14 |
377424 | 562.76 |
377425 | 125.49 |
377426 | 18.44 |
377432 | 287.28 |
377433 | 681.06 |
377434 | 166.51 |
377435 | 18.32 |
377442 | 470.09 |
377443 | 363.92 |
377444 | 36.05 |
377445 | 19.72 |
377452 | 197.48 |
377453 | 43.29 |
377464 | 14.9 |
377465 | 4.05 |
386916 | 16.4 |
387025 | 21.22 |
387032 | 68.92 |
387122 | 11.06 |
387145 | 5.41 |
387211 | 7.77 |
387212 | 15.87 |
387214 | 19.31 |
387225 | 9.97 |
387226 | 23.46 |
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 | 1,528.31 |
387314 | 2,031.72 |
387315 | 497.77 |
387321 | 702.64 |
387322 | 2,941.77 |
387323 | 1,897.66 |
387324 | 538.06 |
387325 | 121.75 |
387326 | 19.86 |
387331 | 3,365.83 |
387332 | 3,592.69 |
387333 | 1,399.13 |
387334 | 129.13 |
387335 | 44.94 |
387336 | 29.01 |
387341 | 913.52 |
387342 | 660.2 |
387344 | 11.41 |
387345 | 12.1 |
387351 | 116.39 |
387352 | 35.08 |
387353 | 15.26 |
387354 | 8.67 |
387355 | 42.13 |
387361 | 15.35 |
387362 | 59.19 |
387363 | 49.62 |
387365 | 22.69 |
387366 | 9 |
387411 | 36.98 |
387413 | 16.09 |
387414 | 16.39 |
387415 | 28.57 |
387416 | 46.93 |
387422 | 20.12 |
387424 | 1.05 |
387425 | 470.72 |
387426 | 1,214.53 |
387432 | 48.62 |
387433 | 9.9 |
387434 | 43.73 |
387435 | 918.96 |
387436 | 2,011.88 |
387444 | 3.98 |
387445 | 1,677.6 |
387446 | 2,275.37 |
387452 | 6.38 |
387454 | 15.87 |
387455 | 1,501.39 |
387456 | 684.99 |
387461 | 16.29 |
387462 | 19.86 |
387463 | 58.96 |
387464 | 327.72 |
387465 | 1,273.04 |
387466 | 218.03 |
387655 | 16.93 |
396714 | 32.79 |
396814 | 2.99 |
396916 | 18.21 |
397211 | 521.38 |
397212 | 789.34 |
397213 | 1,654.07 |
397214 | 471.64 |
397221 | 216.3 |
397222 | 436.89 |
397223 | 825.97 |
397224 | 123.28 |
397225 | 3.91 |
397231 | 1,213.34 |
397232 | 1,261.3 |
397233 | 30.27 |
397234 | 18.71 |
397241 | 853.25 |
397242 | 275.42 |
397246 | 6.24 |
397251 | 199.55 |
397261 | 11.31 |
397262 | 8.45 |
397263 | 16.67 |
397265 | 16.59 |
397311 | 28.06 |
397312 | 76.09 |
397313 | 154.12 |
397314 | 405.17 |
397315 | 1,572.3 |
397316 | 632.52 |
397321 | 0.51 |
397322 | 8.88 |
397323 | 53.08 |
397324 | 268.76 |
397325 | 1,163.86 |
397326 | 696.24 |
397331 | 11.13 |
397332 | 26.77 |
397333 | 56.98 |
397334 | 208.96 |
397335 | 829.41 |
397336 | 486.79 |
397342 | 108.93 |
397343 | 235.91 |
397344 | 359.81 |
397345 | 481.81 |
397346 | 832.96 |
397351 | 65.5 |
397352 | 132.16 |
397353 | 396.02 |
397354 | 738.15 |
397355 | 1,060.39 |
397356 | 505.38 |
397361 | 101.09 |
397362 | 371.53 |
397363 | 899.97 |
397364 | 1,210.82 |
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 | 768.97 |
406612 | 62.34 |
406613 | 13.51 |
406621 | 63.49 |
406623 | 14.86 |
406626 | 19.97 |
406643 | 19.41 |
406652 | 16.28 |
406711 | 95.79 |
406712 | 318.84 |
406713 | 848.35 |
406714 | 1,293.65 |
406715 | 1,460.23 |
406716 | 961.26 |
406721 | 266.38 |
406722 | 786.33 |
406723 | 1,114.7 |
406724 | 337.98 |
406725 | 58.35 |
406731 | 17.04 |
406732 | 211.19 |
406733 | 206.07 |
406734 | 34.28 |
406735 | 38.74 |
406742 | 17.94 |
406744 | 29.15 |
406764 | 19.99 |
406765 | 24.28 |
406766 | 41.51 |
406811 | 1,626.82 |
406812 | 315.57 |
406813 | 94.24 |
406814 | 127.86 |
406815 | 97.68 |
406816 | 41.23 |
406821 | 773.76 |
406822 | 325.11 |
406826 | 278.56 |
406831 | 308.22 |
406832 | 421.67 |
406833 | 199.14 |
406834 | 71.25 |
406835 | 64.23 |
406841 | 227.35 |
406852 | 12.19 |
406861 | 56.83 |
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 | 2,700.48 |
406931 | 815.89 |
406932 | 2,668.71 |
406933 | 1,344.46 |
406934 | 65.87 |
406935 | 946.1 |
406936 | 1,244.44 |
406941 | 11.59 |
406942 | 969.21 |
406943 | 690.91 |
406944 | 803.74 |
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 |
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 | 566.02 |
407122 | 200.86 |
407123 | 0.98 |
407131 | 444.16 |
407132 | 62.31 |
407133 | 19.74 |
407134 | 11.46 |
407135 | 9.26 |
407141 | 38.41 |
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 | 392.84 |
407231 | 21.21 |
407232 | 37.23 |
407233 | 32.67 |
407234 | 258.19 |
407235 | 120.52 |
407236 | 124.1 |
407241 | 641.75 |
407242 | 1,039.24 |
407243 | 646.64 |
407244 | 655.15 |
407245 | 298.17 |
407246 | 70.71 |
407251 | 588.31 |
407252 | 374.35 |
407253 | 545.99 |
407254 | 880.98 |
407255 | 108.38 |
407256 | 114.61 |
407261 | 574.39 |
407262 | 810.66 |
407263 | 1,065.46 |
407264 | 295.43 |
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 | 564.15 |
407364 | 1,008.24 |
407365 | 774.85 |
407366 | 738.62 |
407466 | 2.34 |
416641 | 87.62 |
416642 | 320.32 |
416643 | 447.06 |
416644 | 12.43 |
416651 | 255.86 |
416652 | 445.88 |
416653 | 710.55 |
416654 | 219.98 |
416661 | 896.82 |
416662 | 1,294.69 |
416663 | 253.66 |
416664 | 8.08 |
416665 | 31.08 |
416711 | 191.71 |
416712 | 126.24 |
416713 | 639.68 |
416714 | 704.2 |
416715 | 6.95 |
416721 | 56.7 |
416722 | 17.41 |
416724 | 6.52 |
416734 | 26.12 |
416742 | 19.18 |
416744 | 31.61 |
416746 | 5.36 |
416755 | 34.95 |
416756 | 90.37 |
416761 | 21.1 |
416762 | 30.14 |
416764 | 16.63 |
416765 | 255.13 |
416766 | 142.78 |
416816 | 377.3 |
416824 | 59.49 |
416825 | 90.94 |
416826 | 149.41 |
416831 | 0.94 |
416833 | 78.38 |
416834 | 110.99 |
416835 | 21.81 |
416841 | 20.89 |
416842 | 414.04 |
416843 | 777.62 |
416844 | 324.51 |
416845 | 124.43 |
416851 | 660.6 |
416852 | 1,092.63 |
416853 | 425.81 |
416854 | 113.32 |
416856 | 18.87 |
416861 | 1,552.84 |
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 | 402.59 |
416934 | 209.72 |
416935 | 29.96 |
416942 | 7.71 |
416943 | 73.72 |
416944 | 1,245.72 |
416945 | 366.91 |
416952 | 2.82 |
416953 | 34.7 |
416954 | 474.66 |
416955 | 967.83 |
416956 | 211.85 |
416961 | 32.71 |
416962 | 5.94 |
416963 | 35.36 |
416964 | 314.58 |
416965 | 3,756.98 |
416966 | 1,888.91 |
417031 | 100.26 |
417041 | 44.79 |
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 |
426761 | 16.55 |
426762 | 41.13 |
426763 | 489.92 |
426764 | 1,337.23 |
426765 | 135.53 |
426915 | 16.68 |
426964 | 52.44 |
427034 | 1.68 |
427044 | 173.06 |
427045 | 162.57 |
427056 | 50.11 |
427065 | 161.07 |
427066 | 50 |
Average catch (meats).
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 |
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 | 13.24 |
377414 | 15.05 |
377415 | 12.62 |
377416 | 12.53 |
377422 | 11.18 |
377423 | 12.26 |
377424 | 11.26 |
377425 | 10.46 |
377426 | 18.44 |
377432 | 14.36 |
377433 | 12.61 |
377434 | 13.88 |
377435 | 9.16 |
377442 | 14.25 |
377443 | 10.7 |
377444 | 18.02 |
377445 | 19.72 |
377452 | 14.11 |
377453 | 14.43 |
377464 | 7.45 |
377465 | 4.05 |
386916 | 16.4 |
387025 | 21.22 |
387032 | 17.23 |
387122 | 11.06 |
387145 | 5.41 |
387211 | 7.77 |
387212 | 15.87 |
387214 | 19.31 |
387225 | 9.97 |
387226 | 11.73 |
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 | 10.84 |
387314 | 10.53 |
387315 | 10.16 |
387321 | 14.34 |
387322 | 13.94 |
387323 | 12.82 |
387324 | 9.28 |
387325 | 10.15 |
387326 | 9.93 |
387331 | 14.57 |
387332 | 13.82 |
387333 | 12.84 |
387334 | 9.93 |
387335 | 8.99 |
387336 | 5.8 |
387341 | 12.02 |
387342 | 11.79 |
387344 | 11.41 |
387345 | 12.1 |
387351 | 14.55 |
387352 | 11.69 |
387353 | 15.26 |
387354 | 4.34 |
387355 | 21.07 |
387361 | 15.35 |
387362 | 14.8 |
387363 | 16.54 |
387365 | 11.35 |
387366 | 9 |
387411 | 12.33 |
387413 | 16.09 |
387414 | 16.39 |
387415 | 14.29 |
387416 | 4.69 |
387422 | 10.06 |
387424 | 1.05 |
387425 | 10.95 |
387426 | 12.65 |
387432 | 12.15 |
387433 | 9.9 |
387434 | 8.75 |
387435 | 13.92 |
387436 | 13.78 |
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 | 11.79 |
387464 | 14.9 |
387465 | 11.17 |
387466 | 8.72 |
387655 | 16.93 |
396714 | 32.79 |
396814 | 2.99 |
396916 | 18.21 |
397211 | 17.98 |
397212 | 17.16 |
397213 | 17.6 |
397214 | 13.1 |
397221 | 13.52 |
397222 | 21.84 |
397223 | 18.77 |
397224 | 17.61 |
397225 | 3.91 |
397231 | 15.17 |
397232 | 13.86 |
397233 | 15.13 |
397234 | 9.35 |
397241 | 12.55 |
397242 | 9.5 |
397246 | 6.24 |
397251 | 11.09 |
397261 | 5.66 |
397262 | 8.45 |
397263 | 16.67 |
397265 | 16.59 |
397311 | 14.03 |
397312 | 10.87 |
397313 | 11.01 |
397314 | 10.66 |
397315 | 16.38 |
397316 | 19.77 |
397321 | 0.51 |
397322 | 4.44 |
397323 | 17.69 |
397324 | 16.8 |
397325 | 18.47 |
397326 | 19.34 |
397331 | 11.13 |
397332 | 13.39 |
397333 | 6.33 |
397334 | 13.93 |
397335 | 19.29 |
397336 | 15.7 |
397342 | 13.62 |
397343 | 13.11 |
397344 | 12.85 |
397345 | 13.38 |
397346 | 12.81 |
397351 | 21.83 |
397352 | 11.01 |
397353 | 14.67 |
397354 | 15.06 |
397355 | 13.25 |
397356 | 9.36 |
397361 | 8.42 |
397362 | 10.62 |
397363 | 10.98 |
397364 | 11.32 |
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 | 24.81 |
406612 | 20.78 |
406613 | 6.75 |
406621 | 31.74 |
406623 | 14.86 |
406626 | 19.97 |
406643 | 19.41 |
406652 | 16.28 |
406711 | 31.93 |
406712 | 15.18 |
406713 | 23.57 |
406714 | 25.37 |
406715 | 27.04 |
406716 | 28.27 |
406721 | 26.64 |
406722 | 23.83 |
406723 | 25.33 |
406724 | 17.79 |
406725 | 19.45 |
406731 | 17.04 |
406732 | 17.6 |
406733 | 14.72 |
406734 | 17.14 |
406735 | 19.37 |
406742 | 8.97 |
406744 | 29.15 |
406764 | 19.99 |
406765 | 24.28 |
406766 | 41.51 |
406811 | 20.34 |
406812 | 17.53 |
406813 | 15.71 |
406814 | 25.57 |
406815 | 19.54 |
406816 | 13.74 |
406821 | 16.82 |
406822 | 21.67 |
406826 | 23.21 |
406831 | 16.22 |
406832 | 26.35 |
406833 | 24.89 |
406834 | 23.75 |
406835 | 16.06 |
406841 | 22.74 |
406852 | 12.19 |
406861 | 14.21 |
406862 | 15.05 |
406914 | 16.74 |
406915 | 17.84 |
406916 | 20.98 |
406923 | 18.9 |
406924 | 11.81 |
406925 | 13.25 |
406926 | 14.07 |
406931 | 13.16 |
406932 | 13.76 |
406933 | 14 |
406934 | 10.98 |
406935 | 12.79 |
406936 | 15.56 |
406941 | 5.8 |
406942 | 12.75 |
406943 | 15.02 |
406944 | 13.62 |
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 |
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 | 17.15 |
407122 | 18.26 |
407123 | 0.98 |
407131 | 17.77 |
407132 | 15.58 |
407133 | 19.74 |
407134 | 11.46 |
407135 | 9.26 |
407141 | 7.68 |
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 | 24.55 |
407231 | 21.21 |
407232 | 7.45 |
407233 | 16.34 |
407234 | 18.44 |
407235 | 17.22 |
407236 | 13.79 |
407241 | 18.34 |
407242 | 18.56 |
407243 | 19.02 |
407244 | 18.2 |
407245 | 14.2 |
407246 | 8.84 |
407251 | 19.61 |
407252 | 14.97 |
407253 | 17.61 |
407254 | 18.74 |
407255 | 12.04 |
407256 | 12.73 |
407261 | 16.89 |
407262 | 15.3 |
407263 | 22.2 |
407264 | 17.38 |
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 | 19.45 |
407364 | 17.09 |
407365 | 14.9 |
407366 | 19.96 |
407466 | 2.34 |
416641 | 17.52 |
416642 | 14.56 |
416643 | 13.97 |
416644 | 12.43 |
416651 | 12.79 |
416652 | 16.51 |
416653 | 14.21 |
416654 | 12.22 |
416661 | 14.95 |
416662 | 16.18 |
416663 | 15.85 |
416664 | 8.08 |
416665 | 31.08 |
416711 | 27.39 |
416712 | 25.25 |
416713 | 23.69 |
416714 | 22.01 |
416715 | 6.95 |
416721 | 14.18 |
416722 | 8.71 |
416724 | 6.52 |
416734 | 26.12 |
416742 | 19.18 |
416744 | 15.81 |
416746 | 5.36 |
416755 | 17.47 |
416756 | 15.06 |
416761 | 10.55 |
416762 | 30.14 |
416764 | 16.63 |
416765 | 14.17 |
416766 | 17.85 |
416816 | 22.19 |
416824 | 29.75 |
416825 | 18.19 |
416826 | 24.9 |
416831 | 0.94 |
416833 | 39.19 |
416834 | 15.86 |
416835 | 21.81 |
416841 | 20.89 |
416842 | 13.36 |
416843 | 14.14 |
416844 | 15.45 |
416845 | 20.74 |
416851 | 14.68 |
416852 | 14.57 |
416853 | 13.74 |
416854 | 18.89 |
416856 | 18.87 |
416861 | 13.05 |
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 | 17.5 |
416934 | 9.53 |
416935 | 14.98 |
416942 | 7.71 |
416943 | 24.57 |
416944 | 15.77 |
416945 | 15.95 |
416952 | 2.82 |
416953 | 17.35 |
416954 | 16.95 |
416955 | 14.45 |
416956 | 12.46 |
416961 | 16.35 |
416962 | 5.94 |
416963 | 17.68 |
416964 | 18.5 |
416965 | 14.18 |
416966 | 14.42 |
417031 | 12.53 |
417041 | 44.79 |
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 |
426761 | 16.55 |
426762 | 20.57 |
426763 | 22.27 |
426764 | 23.88 |
426765 | 27.11 |
426915 | 16.68 |
426964 | 52.44 |
427034 | 1.68 |
427044 | 15.73 |
427045 | 18.06 |
427056 | 10.02 |
427065 | 20.13 |
427066 | 16.67 |
Percent of total catch (meat).
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 |
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 | 1.59e+02 | 1.07e-01 |
377414 | 4.67e+02 | 3.15e-01 |
377415 | 3.66e+02 | 2.47e-01 |
377416 | 3.76e+01 | 2.54e-02 |
377422 | 5.59e+01 | 3.77e-02 |
377423 | 4.78e+02 | 3.23e-01 |
377424 | 5.63e+02 | 3.80e-01 |
377425 | 1.25e+02 | 8.47e-02 |
377426 | 1.84e+01 | 1.24e-02 |
377432 | 2.87e+02 | 1.94e-01 |
377433 | 6.81e+02 | 4.59e-01 |
377434 | 1.67e+02 | 1.12e-01 |
377435 | 1.83e+01 | 1.24e-02 |
377442 | 4.70e+02 | 3.17e-01 |
377443 | 3.64e+02 | 2.46e-01 |
377444 | 3.60e+01 | 2.43e-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 |
387122 | 1.11e+01 | 7.46e-03 |
387145 | 5.41e+00 | 3.65e-03 |
387211 | 7.77e+00 | 5.24e-03 |
387212 | 1.59e+01 | 1.07e-02 |
387214 | 1.93e+01 | 1.30e-02 |
387225 | 9.97e+00 | 6.73e-03 |
387226 | 2.35e+01 | 1.58e-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 | 1.53e+03 | 1.03e+00 |
387314 | 2.03e+03 | 1.37e+00 |
387315 | 4.98e+02 | 3.36e-01 |
387321 | 7.03e+02 | 4.74e-01 |
387322 | 2.94e+03 | 1.98e+00 |
387323 | 1.90e+03 | 1.28e+00 |
387324 | 5.38e+02 | 3.63e-01 |
387325 | 1.22e+02 | 8.21e-02 |
387326 | 1.99e+01 | 1.34e-02 |
387331 | 3.37e+03 | 2.27e+00 |
387332 | 3.59e+03 | 2.42e+00 |
387333 | 1.40e+03 | 9.44e-01 |
387334 | 1.29e+02 | 8.71e-02 |
387335 | 4.49e+01 | 3.03e-02 |
387336 | 2.90e+01 | 1.96e-02 |
387341 | 9.14e+02 | 6.16e-01 |
387342 | 6.60e+02 | 4.45e-01 |
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 |
387361 | 1.54e+01 | 1.04e-02 |
387362 | 5.92e+01 | 3.99e-02 |
387363 | 4.96e+01 | 3.35e-02 |
387365 | 2.27e+01 | 1.53e-02 |
387366 | 9.00e+00 | 6.07e-03 |
387411 | 3.70e+01 | 2.50e-02 |
387413 | 1.61e+01 | 1.09e-02 |
387414 | 1.64e+01 | 1.11e-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 | 1.21e+03 | 8.19e-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 | 2.01e+03 | 1.36e+00 |
387444 | 3.98e+00 | 2.68e-03 |
387445 | 1.68e+03 | 1.13e+00 |
387446 | 2.28e+03 | 1.54e+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 | 5.90e+01 | 3.98e-02 |
387464 | 3.28e+02 | 2.21e-01 |
387465 | 1.27e+03 | 8.59e-01 |
387466 | 2.18e+02 | 1.47e-01 |
387655 | 1.69e+01 | 1.14e-02 |
396714 | 3.28e+01 | 2.21e-02 |
396814 | 2.99e+00 | 2.02e-03 |
396916 | 1.82e+01 | 1.23e-02 |
397211 | 5.21e+02 | 3.52e-01 |
397212 | 7.89e+02 | 5.33e-01 |
397213 | 1.65e+03 | 1.12e+00 |
397214 | 4.72e+02 | 3.18e-01 |
397221 | 2.16e+02 | 1.46e-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 | 1.21e+03 | 8.19e-01 |
397232 | 1.26e+03 | 8.51e-01 |
397233 | 3.03e+01 | 2.04e-02 |
397234 | 1.87e+01 | 1.26e-02 |
397241 | 8.53e+02 | 5.76e-01 |
397242 | 2.75e+02 | 1.86e-01 |
397246 | 6.24e+00 | 4.21e-03 |
397251 | 2.00e+02 | 1.35e-01 |
397261 | 1.13e+01 | 7.63e-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 | 1.54e+02 | 1.04e-01 |
397314 | 4.05e+02 | 2.73e-01 |
397315 | 1.57e+03 | 1.06e+00 |
397316 | 6.33e+02 | 4.27e-01 |
397321 | 5.06e-01 | 3.41e-04 |
397322 | 8.88e+00 | 5.99e-03 |
397323 | 5.31e+01 | 3.58e-02 |
397324 | 2.69e+02 | 1.81e-01 |
397325 | 1.16e+03 | 7.85e-01 |
397326 | 6.96e+02 | 4.70e-01 |
397331 | 1.11e+01 | 7.51e-03 |
397332 | 2.68e+01 | 1.81e-02 |
397333 | 5.70e+01 | 3.84e-02 |
397334 | 2.09e+02 | 1.41e-01 |
397335 | 8.29e+02 | 5.60e-01 |
397336 | 4.87e+02 | 3.28e-01 |
397342 | 1.09e+02 | 7.35e-02 |
397343 | 2.36e+02 | 1.59e-01 |
397344 | 3.60e+02 | 2.43e-01 |
397345 | 4.82e+02 | 3.25e-01 |
397346 | 8.33e+02 | 5.62e-01 |
397351 | 6.55e+01 | 4.42e-02 |
397352 | 1.32e+02 | 8.92e-02 |
397353 | 3.96e+02 | 2.67e-01 |
397354 | 7.38e+02 | 4.98e-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 | 9.00e+02 | 6.07e-01 |
397364 | 1.21e+03 | 8.17e-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 | 7.69e+02 | 5.19e-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 | 9.58e+01 | 6.46e-02 |
406712 | 3.19e+02 | 2.15e-01 |
406713 | 8.48e+02 | 5.72e-01 |
406714 | 1.29e+03 | 8.73e-01 |
406715 | 1.46e+03 | 9.85e-01 |
406716 | 9.61e+02 | 6.49e-01 |
406721 | 2.66e+02 | 1.80e-01 |
406722 | 7.86e+02 | 5.31e-01 |
406723 | 1.11e+03 | 7.52e-01 |
406724 | 3.38e+02 | 2.28e-01 |
406725 | 5.84e+01 | 3.94e-02 |
406731 | 1.70e+01 | 1.15e-02 |
406732 | 2.11e+02 | 1.42e-01 |
406733 | 2.06e+02 | 1.39e-01 |
406734 | 3.43e+01 | 2.31e-02 |
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 | 1.63e+03 | 1.10e+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 | 4.12e+01 | 2.78e-02 |
406821 | 7.74e+02 | 5.22e-01 |
406822 | 3.25e+02 | 2.19e-01 |
406826 | 2.79e+02 | 1.88e-01 |
406831 | 3.08e+02 | 2.08e-01 |
406832 | 4.22e+02 | 2.84e-01 |
406833 | 1.99e+02 | 1.34e-01 |
406834 | 7.12e+01 | 4.81e-02 |
406835 | 6.42e+01 | 4.33e-02 |
406841 | 2.27e+02 | 1.53e-01 |
406852 | 1.22e+01 | 8.22e-03 |
406861 | 5.68e+01 | 3.83e-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 | 2.70e+03 | 1.82e+00 |
406931 | 8.16e+02 | 5.50e-01 |
406932 | 2.67e+03 | 1.80e+00 |
406933 | 1.34e+03 | 9.07e-01 |
406934 | 6.59e+01 | 4.44e-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 | 8.04e+02 | 5.42e-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 |
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 | 5.66e+02 | 3.82e-01 |
407122 | 2.01e+02 | 1.36e-01 |
407123 | 9.82e-01 | 6.63e-04 |
407131 | 4.44e+02 | 3.00e-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 | 3.84e+01 | 2.59e-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 | 3.93e+02 | 2.65e-01 |
407231 | 2.12e+01 | 1.43e-02 |
407232 | 3.72e+01 | 2.51e-02 |
407233 | 3.27e+01 | 2.20e-02 |
407234 | 2.58e+02 | 1.74e-01 |
407235 | 1.21e+02 | 8.13e-02 |
407236 | 1.24e+02 | 8.37e-02 |
407241 | 6.42e+02 | 4.33e-01 |
407242 | 1.04e+03 | 7.01e-01 |
407243 | 6.47e+02 | 4.36e-01 |
407244 | 6.55e+02 | 4.42e-01 |
407245 | 2.98e+02 | 2.01e-01 |
407246 | 7.07e+01 | 4.77e-02 |
407251 | 5.88e+02 | 3.97e-01 |
407252 | 3.74e+02 | 2.53e-01 |
407253 | 5.46e+02 | 3.68e-01 |
407254 | 8.81e+02 | 5.94e-01 |
407255 | 1.08e+02 | 7.31e-02 |
407256 | 1.15e+02 | 7.73e-02 |
407261 | 5.74e+02 | 3.88e-01 |
407262 | 8.11e+02 | 5.47e-01 |
407263 | 1.07e+03 | 7.19e-01 |
407264 | 2.95e+02 | 1.99e-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 | 5.64e+02 | 3.81e-01 |
407364 | 1.01e+03 | 6.80e-01 |
407365 | 7.75e+02 | 5.23e-01 |
407366 | 7.39e+02 | 4.98e-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 | 2.56e+02 | 1.73e-01 |
416652 | 4.46e+02 | 3.01e-01 |
416653 | 7.11e+02 | 4.79e-01 |
416654 | 2.20e+02 | 1.48e-01 |
416661 | 8.97e+02 | 6.05e-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 | 1.92e+02 | 1.29e-01 |
416712 | 1.26e+02 | 8.52e-02 |
416713 | 6.40e+02 | 4.32e-01 |
416714 | 7.04e+02 | 4.75e-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 |
416734 | 2.61e+01 | 1.76e-02 |
416742 | 1.92e+01 | 1.29e-02 |
416744 | 3.16e+01 | 2.13e-02 |
416746 | 5.36e+00 | 3.61e-03 |
416755 | 3.49e+01 | 2.36e-02 |
416756 | 9.04e+01 | 6.10e-02 |
416761 | 2.11e+01 | 1.42e-02 |
416762 | 3.01e+01 | 2.03e-02 |
416764 | 1.66e+01 | 1.12e-02 |
416765 | 2.55e+02 | 1.72e-01 |
416766 | 1.43e+02 | 9.63e-02 |
416816 | 3.77e+02 | 2.55e-01 |
416824 | 5.95e+01 | 4.01e-02 |
416825 | 9.09e+01 | 6.14e-02 |
416826 | 1.49e+02 | 1.01e-01 |
416831 | 9.43e-01 | 6.36e-04 |
416833 | 7.84e+01 | 5.29e-02 |
416834 | 1.11e+02 | 7.49e-02 |
416835 | 2.18e+01 | 1.47e-02 |
416841 | 2.09e+01 | 1.41e-02 |
416842 | 4.14e+02 | 2.79e-01 |
416843 | 7.78e+02 | 5.25e-01 |
416844 | 3.25e+02 | 2.19e-01 |
416845 | 1.24e+02 | 8.39e-02 |
416851 | 6.61e+02 | 4.46e-01 |
416852 | 1.09e+03 | 7.37e-01 |
416853 | 4.26e+02 | 2.87e-01 |
416854 | 1.13e+02 | 7.65e-02 |
416856 | 1.89e+01 | 1.27e-02 |
416861 | 1.55e+03 | 1.05e+00 |
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 | 4.03e+02 | 2.72e-01 |
416934 | 2.10e+02 | 1.41e-01 |
416935 | 3.00e+01 | 2.02e-02 |
416942 | 7.71e+00 | 5.20e-03 |
416943 | 7.37e+01 | 4.97e-02 |
416944 | 1.25e+03 | 8.40e-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 | 3.27e+01 | 2.21e-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 | 1.89e+03 | 1.27e+00 |
417031 | 1.00e+02 | 6.76e-02 |
417041 | 4.48e+01 | 3.02e-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 |
426761 | 1.66e+01 | 1.12e-02 |
426762 | 4.11e+01 | 2.78e-02 |
426763 | 4.90e+02 | 3.31e-01 |
426764 | 1.34e+03 | 9.02e-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 |
427044 | 1.73e+02 | 1.17e-01 |
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 fleet.
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 |
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 | 7.03 |
377414 | 7.86 |
377415 | 7.84 |
377416 | 9.86 |
377422 | 6.11 |
377423 | 6.84 |
377424 | 6.56 |
377425 | 7.03 |
377426 | 10.88 |
377432 | 7.46 |
377433 | 6.80 |
377434 | 9.16 |
377435 | 8.19 |
377442 | 6.94 |
377443 | 6.01 |
377444 | 9.93 |
377445 | 10.17 |
377452 | 8.94 |
377453 | 8.51 |
377464 | 6.06 |
377465 | 11.80 |
386916 | 9.25 |
387025 | 9.42 |
387032 | 8.35 |
387122 | 4.56 |
387145 | 2.66 |
387211 | 8.10 |
387212 | 8.47 |
387214 | 10.54 |
387225 | 6.15 |
387226 | 5.99 |
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 | 6.29 |
387314 | 6.61 |
387315 | 7.54 |
387321 | 7.08 |
387322 | 7.11 |
387323 | 6.57 |
387324 | 8.21 |
387325 | 8.81 |
387326 | 5.42 |
387331 | 6.45 |
387332 | 6.83 |
387333 | 7.33 |
387334 | 7.03 |
387335 | 5.16 |
387336 | 3.01 |
387341 | 6.87 |
387342 | 7.56 |
387344 | 8.95 |
387345 | 6.87 |
387351 | 8.21 |
387352 | 6.45 |
387353 | 13.61 |
387354 | 5.40 |
387355 | 10.71 |
387361 | 7.00 |
387362 | 7.64 |
387363 | 6.71 |
387365 | 6.53 |
387366 | 11.90 |
387411 | 6.98 |
387413 | 8.64 |
387414 | 7.60 |
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 | 5.69 |
387444 | 1.64 |
387445 | 6.68 |
387446 | 6.74 |
387452 | 4.87 |
387454 | 5.24 |
387455 | 7.02 |
387456 | 6.66 |
387461 | 13.20 |
387462 | 6.12 |
387463 | 9.45 |
387464 | 7.50 |
387465 | 6.72 |
387466 | 6.41 |
387655 | 6.46 |
396714 | 14.35 |
396814 | 7.58 |
396916 | 5.55 |
397211 | 8.78 |
397212 | 8.98 |
397213 | 8.83 |
397214 | 8.75 |
397221 | 6.56 |
397222 | 9.51 |
397223 | 9.31 |
397224 | 9.83 |
397225 | 5.02 |
397231 | 8.37 |
397232 | 8.02 |
397233 | 7.34 |
397234 | 7.16 |
397241 | 6.98 |
397242 | 6.22 |
397246 | 6.35 |
397251 | 8.65 |
397261 | 6.74 |
397262 | 7.27 |
397263 | 7.27 |
397265 | 6.49 |
397311 | 10.23 |
397312 | 6.98 |
397313 | 6.83 |
397314 | 6.53 |
397315 | 8.48 |
397316 | 9.09 |
397321 | 1.96 |
397322 | 9.44 |
397323 | 10.49 |
397324 | 8.20 |
397325 | 8.76 |
397326 | 8.90 |
397331 | 15.53 |
397332 | 9.08 |
397333 | 4.26 |
397334 | 7.89 |
397335 | 9.11 |
397336 | 8.00 |
397342 | 7.39 |
397343 | 7.21 |
397344 | 7.21 |
397345 | 7.60 |
397346 | 7.90 |
397351 | 12.31 |
397352 | 7.22 |
397353 | 8.27 |
397354 | 8.11 |
397355 | 8.02 |
397356 | 7.63 |
397361 | 5.17 |
397362 | 7.18 |
397363 | 7.07 |
397364 | 6.53 |
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 | 11.58 |
406612 | 11.53 |
406613 | 5.45 |
406621 | 13.34 |
406623 | 12.44 |
406626 | 11.23 |
406643 | 7.59 |
406652 | 9.04 |
406711 | 11.60 |
406712 | 9.78 |
406713 | 10.97 |
406714 | 11.13 |
406715 | 11.21 |
406716 | 10.88 |
406721 | 10.96 |
406722 | 11.07 |
406723 | 11.19 |
406724 | 10.30 |
406725 | 7.86 |
406731 | 14.58 |
406732 | 9.10 |
406733 | 9.59 |
406734 | 8.48 |
406735 | 5.77 |
406742 | 5.07 |
406744 | 11.08 |
406764 | 8.00 |
406765 | 7.49 |
406766 | 12.79 |
406811 | 8.85 |
406812 | 7.54 |
406813 | 9.28 |
406814 | 11.22 |
406815 | 11.85 |
406816 | 7.88 |
406821 | 8.46 |
406822 | 10.31 |
406826 | 10.99 |
406831 | 9.45 |
406832 | 12.37 |
406833 | 11.48 |
406834 | 12.95 |
406835 | 7.09 |
406841 | 8.94 |
406852 | 5.29 |
406861 | 5.73 |
406862 | 10.33 |
406914 | 8.27 |
406915 | 8.18 |
406916 | 9.42 |
406923 | 9.10 |
406924 | 7.47 |
406925 | 5.64 |
406926 | 5.36 |
406931 | 5.98 |
406932 | 6.66 |
406933 | 6.34 |
406934 | 5.29 |
406935 | 5.90 |
406936 | 5.85 |
406941 | 3.62 |
406942 | 6.82 |
406943 | 6.44 |
406944 | 6.13 |
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 |
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 | 8.25 |
407122 | 7.54 |
407123 | 2.43 |
407131 | 8.44 |
407132 | 7.16 |
407133 | 7.96 |
407134 | 9.84 |
407135 | 6.36 |
407141 | 5.07 |
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 | 9.33 |
407231 | 11.58 |
407232 | 7.87 |
407233 | 8.93 |
407234 | 8.23 |
407235 | 8.53 |
407236 | 8.54 |
407241 | 8.96 |
407242 | 8.46 |
407243 | 8.31 |
407244 | 8.28 |
407245 | 7.89 |
407246 | 6.65 |
407251 | 8.99 |
407252 | 7.92 |
407253 | 8.72 |
407254 | 8.91 |
407255 | 9.43 |
407256 | 7.91 |
407261 | 8.66 |
407262 | 8.37 |
407263 | 9.93 |
407264 | 9.05 |
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 | 8.59 |
407364 | 7.85 |
407365 | 7.67 |
407366 | 8.64 |
407466 | 3.33 |
416641 | 9.35 |
416642 | 7.51 |
416643 | 7.28 |
416644 | 7.29 |
416651 | 7.72 |
416652 | 8.44 |
416653 | 7.39 |
416654 | 8.02 |
416661 | 7.69 |
416662 | 7.76 |
416663 | 8.57 |
416664 | 5.61 |
416665 | 15.02 |
416711 | 9.98 |
416712 | 11.60 |
416713 | 10.63 |
416714 | 11.11 |
416715 | 5.83 |
416721 | 9.33 |
416722 | 7.99 |
416724 | 5.46 |
416734 | 10.08 |
416742 | 8.23 |
416744 | 8.44 |
416746 | 3.88 |
416755 | 5.43 |
416756 | 9.26 |
416761 | 8.16 |
416762 | 13.69 |
416764 | 14.26 |
416765 | 6.88 |
416766 | 7.95 |
416816 | 10.17 |
416824 | 12.46 |
416825 | 8.27 |
416826 | 9.99 |
416831 | 1.27 |
416833 | 12.95 |
416834 | 8.09 |
416835 | 10.08 |
416841 | 6.61 |
416842 | 5.77 |
416843 | 6.86 |
416844 | 8.77 |
416845 | 9.51 |
416851 | 6.27 |
416852 | 6.57 |
416853 | 7.26 |
416854 | 5.87 |
416856 | 8.08 |
416861 | 6.25 |
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 | 8.67 |
416934 | 5.88 |
416935 | 8.38 |
416942 | 5.17 |
416943 | 7.39 |
416944 | 6.40 |
416945 | 7.29 |
416952 | 2.13 |
416953 | 5.45 |
416954 | 8.01 |
416955 | 7.68 |
416956 | 5.53 |
416961 | 8.32 |
416962 | 4.06 |
416963 | 6.82 |
416964 | 8.21 |
416965 | 7.16 |
416966 | 6.75 |
417031 | 6.65 |
417041 | 14.49 |
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 |
426761 | 13.00 |
426762 | 13.01 |
426763 | 10.63 |
426764 | 10.08 |
426765 | 11.32 |
426915 | 8.18 |
426964 | 13.12 |
427034 | 2.48 |
427044 | 6.75 |
427045 | 6.16 |
427056 | 5.50 |
427065 | 10.49 |
427066 | 9.98 |
CPUE
Average CPUE for Access Area and Days at Sea fleets.
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 |
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 | 1.89 |
377414 | 1.99 |
377415 | 1.63 |
377416 | 1.24 |
377422 | 1.97 |
377423 | 1.84 |
377424 | 1.73 |
377425 | 1.51 |
377426 | 1.7 |
377432 | 2.1 |
377433 | 1.9 |
377434 | 1.54 |
377435 | 1.72 |
377442 | 2.06 |
377443 | 1.83 |
377444 | 1.9 |
377445 | 1.94 |
377452 | 1.83 |
377453 | 1.75 |
377464 | 1.38 |
377465 | 0.34 |
386916 | 1.77 |
387025 | 2.25 |
387032 | 2.1 |
387122 | 2.42 |
387145 | 2.04 |
387211 | 0.96 |
387212 | 1.87 |
387214 | 1.83 |
387225 | 1.62 |
387226 | 1.88 |
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 |
387321 | 2.07 |
387322 | 2.06 |
387323 | 1.96 |
387324 | 1.32 |
387325 | 1.47 |
387326 | 2.36 |
387331 | 2.23 |
387332 | 2.05 |
387333 | 1.78 |
387334 | 1.43 |
387335 | 1.48 |
387336 | 1.73 |
387341 | 1.84 |
387342 | 1.69 |
387344 | 1.28 |
387345 | 1.76 |
387351 | 1.66 |
387352 | 1.78 |
387353 | 1.12 |
387354 | 0.66 |
387355 | 1.92 |
387361 | 2.19 |
387362 | 1.95 |
387363 | 2.29 |
387365 | 1.66 |
387366 | 0.76 |
387411 | 1.7 |
387413 | 1.86 |
387414 | 2.16 |
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 | 2.4 |
387444 | 2.01 |
387445 | 2.03 |
387446 | 2.17 |
387452 | 0.51 |
387454 | 1.54 |
387455 | 1.99 |
387456 | 1.54 |
387461 | 1.23 |
387462 | 1.38 |
387463 | 1.36 |
387464 | 2.02 |
387465 | 1.68 |
387466 | 1.37 |
387655 | 2.62 |
396714 | 2.28 |
396814 | 0.39 |
396916 | 3.28 |
397211 | 1.81 |
397212 | 1.87 |
397213 | 1.87 |
397214 | 1.54 |
397221 | 1.99 |
397222 | 1.82 |
397223 | 2.06 |
397224 | 1.71 |
397225 | 0.78 |
397231 | 1.76 |
397232 | 1.62 |
397233 | 1.91 |
397234 | 1.36 |
397241 | 1.83 |
397242 | 1.49 |
397246 | 0.98 |
397251 | 1.35 |
397261 | 0.72 |
397262 | 1.16 |
397263 | 2.29 |
397265 | 2.56 |
397311 | 1.34 |
397312 | 1.44 |
397313 | 1.5 |
397314 | 1.55 |
397315 | 1.85 |
397316 | 2.05 |
397322 | 0.48 |
397323 | 1.63 |
397324 | 1.81 |
397325 | 1.95 |
397326 | 2.06 |
397331 | 0.72 |
397332 | 1.54 |
397333 | 1.18 |
397334 | 1.65 |
397335 | 1.92 |
397336 | 1.64 |
397342 | 1.8 |
397343 | 1.72 |
397344 | 1.59 |
397345 | 1.6 |
397346 | 1.61 |
397351 | 1.8 |
397352 | 1.44 |
397353 | 1.69 |
397354 | 1.86 |
397355 | 1.78 |
397356 | 1.27 |
397361 | 1.34 |
397362 | 1.39 |
397363 | 1.54 |
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 | 2.08 |
406612 | 1.81 |
406613 | 1.15 |
406621 | 2.4 |
406623 | 1.19 |
406626 | 1.78 |
406643 | 2.56 |
406652 | 1.8 |
406711 | 2.67 |
406712 | 1.54 |
406713 | 2.09 |
406714 | 2.22 |
406715 | 2.37 |
406716 | 2.52 |
406721 | 2.23 |
406722 | 2.13 |
406723 | 2.21 |
406724 | 1.68 |
406725 | 2.49 |
406731 | 1.17 |
406732 | 1.94 |
406733 | 1.59 |
406734 | 1.47 |
406735 | 3.41 |
406742 | 2.15 |
406744 | 2.63 |
406764 | 2.5 |
406765 | 3.24 |
406766 | 3.25 |
406811 | 2.22 |
406812 | 2.33 |
406813 | 1.7 |
406814 | 2.29 |
406815 | 1.85 |
406816 | 1.95 |
406821 | 1.97 |
406822 | 2.04 |
406826 | 2.09 |
406831 | 1.69 |
406832 | 2.12 |
406833 | 1.85 |
406834 | 1.86 |
406835 | 2.05 |
406841 | 2.45 |
406852 | 2.3 |
406861 | 2.57 |
406862 | 1.5 |
406914 | 1.84 |
406915 | 2 |
406916 | 2.15 |
406923 | 2.08 |
406924 | 1.65 |
406925 | 2.16 |
406926 | 2.38 |
406931 | 2.18 |
406932 | 2.09 |
406933 | 2.19 |
406934 | 2 |
406935 | 2.12 |
406936 | 2.56 |
406941 | 1.38 |
406942 | 1.85 |
406943 | 2.36 |
406944 | 2.15 |
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 |
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 | 1.94 |
407132 | 2.02 |
407133 | 2.48 |
407134 | 1.16 |
407135 | 1.46 |
407141 | 1.48 |
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 | 2.36 |
407231 | 1.83 |
407232 | 1.17 |
407233 | 1.82 |
407234 | 2.06 |
407235 | 1.88 |
407236 | 1.7 |
407241 | 2.1 |
407242 | 2.07 |
407243 | 2.13 |
407244 | 1.98 |
407245 | 1.74 |
407246 | 1.53 |
407251 | 2.14 |
407252 | 1.79 |
407253 | 1.88 |
407254 | 2.04 |
407255 | 1.29 |
407256 | 1.56 |
407261 | 1.86 |
407262 | 1.76 |
407263 | 2.13 |
407264 | 1.68 |
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 | 2.08 |
407364 | 2.08 |
407365 | 1.84 |
407366 | 2.16 |
407466 | 0.7 |
416641 | 2 |
416642 | 1.94 |
416643 | 1.99 |
416644 | 1.7 |
416651 | 1.52 |
416652 | 2.07 |
416653 | 1.95 |
416654 | 1.54 |
416661 | 1.93 |
416662 | 2.12 |
416663 | 1.9 |
416664 | 1.44 |
416665 | 2.07 |
416711 | 2.76 |
416712 | 2.21 |
416713 | 2.09 |
416714 | 1.93 |
416715 | 1.19 |
416721 | 1.36 |
416722 | 1.32 |
416724 | 1.19 |
416734 | 2.59 |
416742 | 2.33 |
416744 | 1.96 |
416746 | 1.38 |
416755 | 3.25 |
416756 | 1.8 |
416761 | 1.23 |
416762 | 2.2 |
416764 | 1.17 |
416765 | 2.29 |
416766 | 2.42 |
416816 | 1.86 |
416824 | 2.39 |
416825 | 2.13 |
416826 | 2.49 |
416831 | 0.74 |
416833 | 3.02 |
416834 | 1.74 |
416835 | 2.16 |
416841 | 3.16 |
416842 | 2.29 |
416843 | 2.3 |
416844 | 1.68 |
416845 | 2.07 |
416851 | 2.32 |
416852 | 2.2 |
416853 | 1.97 |
416854 | 3.27 |
416856 | 2.33 |
416861 | 2.11 |
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 | 1.95 |
416934 | 1.6 |
416935 | 1.79 |
416942 | 1.49 |
416943 | 2.96 |
416944 | 2.42 |
416945 | 2.09 |
416952 | 1.32 |
416953 | 3.18 |
416954 | 2.01 |
416955 | 1.92 |
416956 | 2.16 |
416961 | 2.41 |
416962 | 1.46 |
416963 | 2.62 |
416964 | 2.31 |
416965 | 1.86 |
416966 | 2.05 |
417031 | 1.55 |
417041 | 3.09 |
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 |
426761 | 1.27 |
426762 | 1.58 |
426763 | 2.04 |
426764 | 2.23 |
426765 | 2.4 |
426915 | 2.04 |
427034 | 0.68 |
427044 | 2.24 |
427045 | 2.67 |
427056 | 1.58 |
427065 | 1.58 |
427066 | 1.65 |
VPUE
Average VPUE for Access Area and Days at Sea fleets.
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 |
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 | 14,645.75 |
377414 | 18,158.24 |
377415 | 14,184.33 |
377416 | 10,497.06 |
377422 | 21,187.48 |
377423 | 18,609.31 |
377424 | 16,945.32 |
377425 | 12,962.48 |
377426 | 14,369.52 |
377432 | 17,715.69 |
377433 | 16,224.45 |
377434 | 14,699.1 |
377435 | 12,618 |
377442 | 17,520.48 |
377443 | 17,559.37 |
377444 | 11,410.51 |
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 |
387122 | 19,119.7 |
387145 | 13,241.61 |
387211 | 10,880.47 |
387212 | 14,723.55 |
387214 | 17,120.83 |
387225 | 17,248.08 |
387226 | 14,474.77 |
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 | 16,900.28 |
387314 | 15,044.21 |
387315 | 15,279.03 |
387321 | 16,593.31 |
387322 | 17,106.76 |
387323 | 16,887.27 |
387324 | 12,459.08 |
387325 | 12,167.14 |
387326 | 21,491.37 |
387331 | 18,493.65 |
387332 | 18,086.73 |
387333 | 14,989.19 |
387334 | 14,234.84 |
387335 | 11,897.8 |
387336 | 14,605.61 |
387341 | 17,301.61 |
387342 | 16,552.53 |
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 |
387361 | 26,822.81 |
387362 | 22,655.34 |
387363 | 17,461.9 |
387365 | 12,012.21 |
387366 | 4,159.28 |
387411 | 17,535.9 |
387413 | 15,216.64 |
387414 | 14,623.04 |
387415 | 10,997.41 |
387416 | 9,156.18 |
387422 | 27,716.33 |
387424 | 9,321.52 |
387425 | 15,339.67 |
387426 | 16,956.22 |
387432 | 18,434.58 |
387433 | 16,301.81 |
387434 | 11,544.51 |
387435 | 16,566.05 |
387436 | 21,015.89 |
387444 | 15,588.32 |
387445 | 16,584.24 |
387446 | 18,866.26 |
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 | 9,789.45 |
387464 | 15,756.83 |
387465 | 15,267.54 |
387466 | 13,701.88 |
387655 | 34,145.28 |
396714 | 15,809.38 |
396916 | 34,694.21 |
397211 | 17,340.17 |
397212 | 17,917.6 |
397213 | 16,842.22 |
397214 | 14,856.27 |
397221 | 17,048.13 |
397222 | 15,046.59 |
397223 | 18,926.78 |
397224 | 14,012.34 |
397225 | 4,492.43 |
397231 | 15,826.5 |
397232 | 15,044.28 |
397233 | 16,910.95 |
397234 | 10,408.03 |
397241 | 19,391.73 |
397242 | 16,049.47 |
397246 | 6,700.17 |
397251 | 12,438.1 |
397261 | 5,353.7 |
397262 | 6,973.96 |
397263 | 24,589.25 |
397265 | 21,418.28 |
397311 | 10,479.96 |
397312 | 12,565.45 |
397313 | 13,452.49 |
397314 | 11,638.34 |
397315 | 17,415.85 |
397316 | 18,799.04 |
397321 | 2,659.27 |
397322 | 4,852.85 |
397323 | 14,606.04 |
397324 | 14,029.16 |
397325 | 16,306.41 |
397326 | 19,284.6 |
397331 | 6,397.24 |
397332 | 14,650.51 |
397333 | 10,719.23 |
397334 | 13,426.2 |
397335 | 16,130.4 |
397336 | 14,290.7 |
397342 | 16,767.06 |
397343 | 12,637.39 |
397344 | 11,792.51 |
397345 | 14,027.06 |
397346 | 14,974.06 |
397351 | 13,124.93 |
397352 | 11,299.38 |
397353 | 14,242.79 |
397354 | 15,501.09 |
397355 | 16,918.93 |
397356 | 11,629.44 |
397361 | 10,499.68 |
397362 | 10,186.87 |
397363 | 13,280.33 |
397364 | 15,972.62 |
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 | 17,419.51 |
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 | 17,510.03 |
406712 | 15,392.82 |
406713 | 22,290.16 |
406714 | 18,875.5 |
406715 | 20,725.75 |
406716 | 23,242.5 |
406721 | 21,276.42 |
406722 | 21,673.77 |
406723 | 21,207.56 |
406724 | 14,942.85 |
406725 | 23,094.56 |
406731 | 20,526.99 |
406732 | 20,644.99 |
406733 | 13,924.24 |
406734 | 13,219 |
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 | 21,664.21 |
406812 | 22,288.88 |
406813 | 12,987.03 |
406814 | 14,417.75 |
406815 | 17,797.24 |
406816 | 23,630.6 |
406821 | 18,871.91 |
406822 | 18,257.39 |
406826 | 20,218.05 |
406831 | 16,796.98 |
406832 | 19,063.13 |
406833 | 20,044.47 |
406834 | 21,148.79 |
406835 | 22,930.51 |
406841 | 20,566.95 |
406852 | 22,307.23 |
406861 | 20,348.73 |
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 | 23,691.87 |
406931 | 19,125.17 |
406932 | 18,553.28 |
406933 | 20,538.51 |
406934 | 21,947.38 |
406935 | 21,334.61 |
406936 | 23,444.56 |
406941 | 11,520.69 |
406942 | 15,838.64 |
406943 | 21,262.03 |
406944 | 20,250.1 |
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 |
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 | 20,216.06 |
407122 | 15,993.83 |
407123 | 4,655.15 |
407131 | 16,654.88 |
407132 | 17,444.06 |
407133 | 27,730.46 |
407134 | 10,695.32 |
407135 | 12,808.62 |
407141 | 16,239.29 |
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 | 22,475.12 |
407231 | 13,376.62 |
407232 | 12,613.95 |
407233 | 22,132 |
407234 | 22,710.02 |
407235 | 18,461.23 |
407236 | 15,555.06 |
407241 | 22,507.93 |
407242 | 21,841.71 |
407243 | 20,021.23 |
407244 | 19,997.02 |
407245 | 15,971.83 |
407246 | 12,953.09 |
407251 | 21,040.27 |
407252 | 18,562.52 |
407253 | 17,994.47 |
407254 | 19,321.57 |
407255 | 12,958.01 |
407256 | 13,837.37 |
407261 | 17,855.18 |
407262 | 16,600.49 |
407263 | 18,226.74 |
407264 | 15,169.03 |
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 | 16,551.31 |
407364 | 18,635.1 |
407365 | 15,769.39 |
407366 | 21,121.07 |
407466 | 8,101.06 |
416641 | 21,239.38 |
416642 | 22,408.23 |
416643 | 21,635.48 |
416644 | 20,456.54 |
416651 | 16,397.36 |
416652 | 21,301.24 |
416653 | 21,139.36 |
416654 | 17,419.34 |
416661 | 19,658.95 |
416662 | 21,412.99 |
416663 | 20,194.41 |
416664 | 14,389.01 |
416665 | 14,012.74 |
416711 | 28,605.16 |
416712 | 27,403.16 |
416713 | 18,643.73 |
416714 | 14,604.91 |
416715 | 10,631.92 |
416721 | 9,194.16 |
416722 | 8,982.02 |
416724 | 7,914.93 |
416734 | 22,729.77 |
416742 | 23,168.71 |
416744 | 18,264.33 |
416746 | 14,585.26 |
416755 | 23,296.45 |
416756 | 14,240.41 |
416761 | 15,664.43 |
416762 | 13,784.28 |
416764 | 8,797.07 |
416765 | 18,310.69 |
416766 | 21,525.36 |
416816 | 19,765.48 |
416824 | 31,818.89 |
416825 | 20,547.8 |
416826 | 25,320.97 |
416831 | 9,518.58 |
416833 | 32,257.58 |
416834 | 16,080.05 |
416835 | 14,192.33 |
416841 | 33,177.77 |
416842 | 23,491.23 |
416843 | 20,366.22 |
416844 | 18,429.9 |
416845 | 15,830.39 |
416851 | 24,513.21 |
416852 | 20,728.48 |
416853 | 18,273.17 |
416854 | 27,132.76 |
416856 | 25,329.63 |
416861 | 21,748.31 |
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 | 23,627.12 |
416934 | 20,008.21 |
416935 | 21,555.01 |
416942 | 24,590.32 |
416943 | 32,629.4 |
416944 | 24,291.62 |
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 | 22,483.29 |
416962 | 16,041.16 |
416963 | 26,014.77 |
416964 | 17,495.85 |
416965 | 19,174.35 |
416966 | 20,973.55 |
417031 | 16,926.99 |
417041 | 21,763.47 |
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 |
426761 | 9,867.44 |
426762 | 14,553.95 |
426763 | 16,298.11 |
426764 | 22,193.75 |
426765 | 23,306.26 |
426915 | 25,682.59 |
427034 | 9,367.42 |
427044 | 27,409.71 |
427045 | 28,173.63 |
427056 | 17,770.43 |
427065 | 22,131.82 |
427066 | 23,369.7 |
Closure Summary
Number of observations in closure 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$plot
Percent of observations in closure areas.
zone_out <-
zone_summary(scallopMainDataTable, project = proj,
spat = scallopWindCloseSpatTable,
zone.dat = "closeID",
zone.spat = "NAME",
fun = "percent",
count = TRUE,
na.rm = TRUE, dat.center = FALSE,
output = "tab_plot")
#> A line object has been specified, but lines is not in the mode
#> Adding lines to the mode...
zone_out$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$plot
Percent of total revenue by closure area.
zone_out <-
zone_summary(scallopMainDataTable, project = proj,
spat = scallopWindCloseSpatTable,
zone.dat = "closeID",
zone.spat = "NAME",
var = "DOLLAR_OBSCURED",
fun = "percent",
count = FALSE,
na.rm = TRUE, dat.center = FALSE,
output = "tab_plot")
#> A line object has been specified, but lines is not in the mode
#> Adding lines to the mode...
zone_out$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$plot
Percent of total revenue by fleet.
zone_out <-
zone_summary(scallopMainDataTable, project = proj,
spat = scallopWindCloseSpatTable,
zone.dat = "closeID",
zone.spat = "NAME",
var = "DOLLAR_OBSCURED", group = "fleet",
fun = "percent",
count = FALSE,
na.rm = TRUE, dat.center = FALSE,
output = "tab_plot")
#> A line object has been specified, but lines is not in the mode
#> Adding lines to the mode...
#> A line object has been specified, but lines is not in the mode
#> Adding lines to the mode...
zone_out$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
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.379]
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.488]
Temporal Plots
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
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
#>
#> $agg_plot
Scatter Plots
Trip length by meat catch.
xy_plot(scallopMainDataTable, proj,
var1 = "TRIP_LENGTH", var2 = "LANDED_OBSCURED",
regress = FALSE, alpha = .3)
Trip length by revenue.
xy_plot(scallopMainDataTable, proj,
var1 = "TRIP_LENGTH", var2 = "DOLLAR_OBSCURED",
regress = FALSE, alpha = .3)
Trip length by trip cost (Winsor).
xy_plot(scallopMainDataTable, proj,
var1 = "TRIP_LENGTH", var2 = "TRIP_COST_WINSOR_2020_DOL",
regress = FALSE, alpha = .3)
Correlation Matrix
corr_outs <-
corr_out(scallopMainDataTable, proj,
variables = "all",
method = "pearson",
show_coef = FALSE)
#> Warning: No variance found in fleetAssignPlaceholder. Removed from correlation
#> test
corr_outs$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 | 0.14 | -0.03 | 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 | 0.04 | 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 | 0.47 | 0.19 | 0.24 | 0.23 | 0.22 | 0.22 | 0.17 | 0.07 | 0.17 | 0.14 | 0.24 | 0.44 |
port-lon | 0.16 | 0.10 | 0.13 | 0.99 | 1.00 | 0.68 | 0.72 | 0.17 | 0.51 | 0.53 | 0.48 | 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 | 0.42 | 0.43 | 0.39 | 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 | 0.41 | 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 | 0.15 | 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 | 0.42 | 0.44 | 0.16 | 1.00 | 0.87 | 0.97 | 0.20 | 0.27 | 0.26 | 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 | 0.84 | 0.19 | 0.25 | 0.24 | 0.23 | 0.23 | 0.16 | 0.06 | 0.14 | 0.13 | 0.23 | 0.79 |
ZoneID | 0.14 | 0.04 | 0.10 | 0.47 | 0.48 | 0.39 | 0.41 | 0.15 | 0.97 | 0.84 | 1.00 | 0.17 | 0.25 | 0.24 | 0.23 | 0.23 | 0.13 | 0.04 | 0.11 | 0.12 | 0.21 | 0.95 |
LANDED-OBSCURED | -0.03 | 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 | 0.16 |
DOLLAR-OBSCURED | 0.17 | 0.00 | 0.58 | 0.24 | 0.26 | 0.20 | 0.22 | 0.59 | 0.27 | 0.25 | 0.25 | 0.91 | 1.00 | 1.00 | 0.91 | 0.91 | 0.17 | 0.30 | 0.65 | 0.45 | 0.74 | 0.24 |
DOLLAR-2020-OBSCURED | 0.08 | 0.01 | 0.60 | 0.23 | 0.25 | 0.19 | 0.21 | 0.62 | 0.26 | 0.24 | 0.24 | 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 | 0.23 | 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 | 0.23 | 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 | 0.10 |
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 | 0.45 | 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 | 0.24 | 0.25 | 0.22 | 0.22 | 0.13 | 0.22 | 0.23 | 0.21 | 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 | 0.95 | 0.16 | 0.24 | 0.23 | 0.22 | 0.22 | 0.13 | 0.04 | 0.10 | 0.12 | 0.20 | 1.00 |
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 | 1.54 |
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 | 1.55 |
2017 | Access Area | 2 |
2017 | Days at Sea | 2.07 |
2018 | Access Area | 2.31 |
2018 | Days at Sea | 2.08 |
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 | 18,180.2 |
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 | 13,539.66 |
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 | 18,421.18 |
2017 | Access Area | 21,327.81 |
2017 | Days at Sea | 18,722.65 |
2018 | Access Area | 21,660.23 |
2018 | Days at Sea | 19,218.95 |
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
LANDED_OBSCURED
KDE, ECDF, and CDF of meat catch.
density_plot(scallopMainDataTable, proj,
var = "LANDED_OBSCURED",
type = "all", tran = "log")
KDE, ECDF, and CDF of meat catch by fleet.
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()