inst/scripts/02.tables.r
In jae0/snowcrab: Snow crab assessment suite using aegis* and associated projects and data streams.
---
title: "Snow crab tables"
author:
- name: Snow crab group
# orcid: 0000-0003-3632-5723
# email: jae.choi@dfo-mpo.gc.ca
# email: choi.jae.seok@gmail.com
# corresponding: true
affiliation:
- name: Bedford Institute of Oceanography, Fisheries and Oceans Canada
city: Dartmouth
state: NS
url: www.bio.gc.ca
date: 2024-08-17
keywords:
- snow crab fishery assessment
- basic tables
abstract: |
Snow crab demographic structure and basic fishery performance tables.
toc: true
number-sections: true
highlight-style: pygments
bibliography: media/references.bib
# csl: media/canadian-journal-of-fisheries-and-aquatic-sciences.csl # see https://www.zotero.org/styles for more
license: "CC BY"
copyright:
holder: Jae S. Choi
year: 2024
citation:
container-title: https://github.com/jae0/bio.snowcrab/
doi: NA
funding: "The snow crab scientific survey was funded by the snow crab fishers of Maritimes Region of Atlantic Canada."
editor:
render-on-save: false
format:
html:
code-fold: true
html-math-method: katex
embed-resources: true
pdf:
pdf-engine: lualatex
docx: default
beamer:
pdf-engine: lualatex
---
<!-- Preamble
This is a Markdown/Quarto document ... To create HTML or PDF, etc, run:
make quarto FN=02.tables YR=2023 SOURCE=~/projects/bio.snowcrab/inst/markdown WK=~/bio.data/bio.snowcrab/assessments DOCEXTENSION=html # {via Quarto}
# make rmarkdown FN=02.tables YR=2023 SOURCE=~/projects/bio.snowcrab/inst/markdown WK=~/bio.data/bio.snowcrab/assessments DOCTYPE=pdf_document DOCEXTENSION=pdf {via Rmarkdown}
# make pdf FN=02.tables # {via pandoc}
Alter year and directories to reflect setup or copy Makefile and alter defaults to your needs.
YAML options:
jupyter: julia-1.10.4
https://quarto.org/docs/output-formats/all-formats.html
-->
````{=html}
<!-- NOTES: Make sure to have pulled observer data:
year.assessment = 2023
p = bio.snowcrab::load.environment( year.assessment=year.assessment )
yrs = 1996:year.assessment # redo all years
observer.db( DS="rawdata.redo", yrs=yrs )
observer.db( DS="bycatch.redo", yrs=yrs )
observer.db( DS="odb.redo", p=p ) # 3 minutes
observer.db( DS="bycatch_clean_data.redo", p=p, yrs=p$yrs ) # 3 minutes
quarto options:
The reserved prefixes are: fig, tbl, lst, tip, nte, wrn, imp, cau, thm, lem, cor, prp, cnj, def, exm, exr, sol, rem, eq, sec.
avoid using underscores (_) in labels and IDs as this can cause problems when rendering to PDF with LaTeX.
{#fig-elephant}
Default @fig-elephant Figure 1
Capitalized @Fig-elephant Figure 1
Custom Prefix [Fig @fig-elephant] Fig 1
No Prefix [-@fig-elephant] 1
::: {#fig-elephants layout-ncol=2}
{#fig-surus}
{#fig-hanno}
Famous Elephants
:::
See @fig-elephants for examples. In particular, @fig-hanno.
or:
#| label: fig-plots
#| fig-cap: "Plots"
#| fig-subcap:
#| - "Plot 1"
#| - "Plot 2"
#| layout-ncol: 2
| Col1 | Col2 | Col3 |
|------|------|------|
| A | B | C |
| E | F | G |
| A | G | G |
: My Caption {#tbl-letters}
See @tbl-letters.
or
::: {#tbl-letters}
| Col1 | Col2 | Col3 |
|------|------|------|
| A | B | C |
| E | F | G |
| A | G | G |
My Caption
:::
@fig-AAAA further explores the impact of temperature on ozone level.
```{r}
#| label: fig-AAAA
#| fig-cap: "Temperature and ozone level."
#| tbl-cap: "Fishery performance statistics."
#| eval: true
#| output: true
```
Black-Scholes (@eq-black-scholes) is a mathematical model:
$$
\frac{\partial \mathrm C}{ \partial \mathrm t } + \frac{1}{2}\sigma^{2} \mathrm S^{2}
\frac{\partial^{2} \mathrm C}{\partial \mathrm C^2}
+ \mathrm r \mathrm S \frac{\partial \mathrm C}{\partial \mathrm S}\ =
\mathrm r \mathrm C
$$ {#eq-black-scholes}
## Introduction {#sec-introduction}
See @sec-introduction
---
# citations: https://quarto.org/docs/authoring/citations.html; https://pandoc.org/MANUAL.html#citations
Blah Blah [see @knuth1984, pp. 33-35; also @wickham2015, chap. 1]
Blah Blah (see Knuth 1984, 33–35; also Wickham 2015, chap. 1)
Blah Blah [@knuth1984, pp. 33-35, 38-39 and passim]
Blah Blah (Knuth 1984, 33–35, 38–39 and passim)
Blah Blah [@wickham2015; @knuth1984].
Blah Blah (Wickham 2015; Knuth 1984).
Wickham says blah [-@wickham2015]
Wickham says blah (2015)
@knuth1984 says blah.
Knuth (1984) says blah.
@knuth1984 [p. 33] says blah.
Knuth (1984, 33) says blah.
#CSL: default is Chicago Manual of Style author-date format
https://github.com/citation-style-language/styles
https://www.zotero.org/styles
### References
::: {#refs}
:::
```
-->
````
# Set up environment
First set up environment. Data comes from:
- At seas observations of fishery (ISSDB)
- Dockside monitoring (Marfis)
- Snow crab survey
These are mostly imported and formatted in [01_snowcrab](https://github.com/jae0/bio.snowcrab/inst/scripts/01_snowcrab.R). The methods are mostly outlined in @Choi_et_al_2005b.
```{r}
#| label: setup
#| eval: true
#| output: false
require(aegis)
# Get data and format based upon parameters:
year.assessment = 2023
p = bio.snowcrab::load.environment( year.assessment=year.assessment )
# loadfunctions( "aegis")
# loadfunctions( "bio.snowcrab") # in case of local edits
# require(ggplot2)
# require(data.table)
require(gt) # table formatting
outtabledir = file.path( p$annual.results, "tables" )
years = as.character(1996: year.assessment)
regions = c("cfanorth", "cfasouth", "cfa4x")
nregions = length(regions)
FD = fishery_data() # mass in tonnes
fda = FD$summary_annual
dt = as.data.frame( fda[ which(fda$yr %in% c(year.assessment - c(0:10))),] )
dt = dt[,c("region", "yr", "Licenses", "TAC", "landings", "effort", "cpue")]
names(dt) = c("Region", "Year", "Licenses", "TAC", "Landings", "Effort", "CPUE")
rownames(dt) = NULL
odb0 = setDT(observer.db("odb"))
odb0$region = NA
for ( reg in regions) {
r = polygon_inside(x = odb0, region = aegis.polygons::polygon_internal_code(reg), planar=FALSE)
odb0$region[r] = reg
}
```
## Fishery statistics from at sea observations
NENS:
```{r}
#| label: table-fishery-nens-perf
#| tbl-cap: "Fishery performance statistics in NENS. Units are: TACs and Landings (tons, t), Effort ($\\times 10^3$ trap hauls, th) and CPUE (kg/th)."
#| eval: true
#| output: true
ii = which(dt$Region=="cfanorth")
oo = dt[ii, c("Year", "Licenses", "TAC", "Landings", "Effort", "CPUE")]
gt::gt(oo) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
SENS:
```{r}
#| label: table-fishery-sens-perf
#| tbl-cap: "Fishery performance statistics in SENS. Units are: TACs and Landings (tons, t), Effort ($\\times 10^3$ trap hauls, th) and CPUE (kg/th)."
#| eval: true
#| output: true
ii = which(dt$Region=="cfasouth")
oo = dt[ii, c("Year", "Licenses", "TAC", "Landings", "Effort", "CPUE")]
gt::gt(oo) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
4X:
```{r}
#| label: table-fishery-4x-perf
#| tbl-cap: "Fishery performance statistics in 4X. Units are: TACs and Landings (tons, t), Effort ($\\times 10^3$ trap hauls, th) and CPUE (kg/th). There were no landings or TACs in 2018/2019 due to indications of low abundance. The 2022 season is ongoing."
#| eval: true
#| output: true
ii = which(dt$Region=="cfa4x")
oo = dt[ii, c("Year", "Licenses", "TAC", "Landings", "Effort", "CPUE")]
gt::gt(oo) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
## At sea observed data
### Carapace condition from observed data \< 95mm CW
```{r}
#| label: setup-observer-data
#| eval: true
#| output: false
odb = odb0[ cw < 95 & prodcd_id==0 & shell %in% c(1:5) & region %in% regions & sex==0, ] # male
```
NENS:
```{r}
#| label: table-fishery-nens-sublegal
#| tbl-cap: "Fishery performance statistics in NENS. Distribution of at sea observations of males less than 95 mm CW by year and shell condition."
#| eval: true
#| output: true
resN = dcast( odb0[ region=="cfanorth", .(N=.N), by=.(fishyr, shell) ], fishyr ~ shell, value.var="N", fill=0, drop=FALSE, na.rm=TRUE )
if ( "NA" %in% names(resN) ) resN$"NA" = NULL
names(resN) = c("Year", "CC1", "CC2", "CC3", "CC4", "CC5" )
resN$Total = rowSums( resN[, 2:6 ], na.rm=TRUE)
resN[, 2:6 ] = round(resN[, 2:6 ] / resN$Total * 100, digits=2)
gt::gt(resN) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
SENS:
```{r}
#| eval: true
#| output: true
#| label: table-fishery-sens-sublegal
#| tbl-cap: "Fishery performance statistics in SENS. Distribution of at sea observations of males less than 95 mm CW by year and shell condition."
resS = dcast( odb0[ region=="cfasouth", .(N=.N), by=.(fishyr, shell) ], fishyr ~ shell, value.var="N", fill=0, drop=FALSE, na.rm=TRUE )
if ( "NA" %in% names(resS)) resS$"NA" = NULL
names(resS) = c("Year", "CC1", "CC2", "CC3", "CC4", "CC5" )
resS$Total = rowSums( resS[, 2:6 ], na.rm=TRUE)
resS[, 2:6 ] = round(resS[, 2:6 ] / resS$Total * 100, digits=2)
gt::gt(resS) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
4X:
```{r}
#| eval: true
#| output: true
#| label: table-fishery-4x-sublegal
#| tbl-cap: "Fishery performance statistics in 4X. Distribution of at sea observations of males less than 95 mm CW by year and shell condition."
resX = dcast( odb0[ region=="cfa4x", .(N=.N), by=.(fishyr, shell) ], fishyr ~ shell, value.var="N", fill=0, drop=FALSE, na.rm=TRUE )
if ("NA" %in% names(resX)) resX$"NA" = NULL
names(resX) = c("Year", "CC1", "CC2", "CC3", "CC4", "CC5" )
resX$Total = rowSums( resX[, 2:6 ], na.rm=TRUE)
resX[, 2:6 ] = round(resX[, 2:6 ] / resX$Total * 100, digits=2)
gt::gt(resX) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
### Carapace condition from observed data \>= 95mm CW
```{r}
#| eval: true
#| output: false
odb = odb0[ cw >= 95 & cw < 170 & prodcd_id==0 & shell %in% c(1:5) & region %in% regions & sex==0, ] # male
```
NENS:
```{r}
#| eval: true
#| output: true
#| label: table-fishery-nens-comm
#| tbl-cap: "Fishery performance statistics in NENS. Distribution of at sea observations of males greater than 95 mm CW by year and shell condition."
resN = dcast( odb[ region=="cfanorth", .(N=.N), by=.(fishyr, shell) ], fishyr ~ shell, value.var="N", fill=0, drop=FALSE, na.rm=TRUE )
names(resN) = c("Year", "CC1", "CC2", "CC3", "CC4", "CC5" )
resN$Total = rowSums( resN[, 2:6 ], na.rm=TRUE)
resN[, 2:6 ] = round(resN[, 2:6 ] / resN$Total * 100, digits=2)
gt::gt(resN) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
SENS:
```{r}
#| eval: true
#| output: true
#| label: table-fishery-sens-comm
#| tbl-cap: "Fishery performance statistics in SENS. Distribution of at sea observations of males greater than 95 mm CW by year and shell condition."
resS = dcast( odb[ region=="cfasouth", .(N=.N), by=.(fishyr, shell) ], fishyr ~ shell, value.var="N", fill=0, drop=FALSE, na.rm=TRUE )
names(resS) = c("Year", "CC1", "CC2", "CC3", "CC4", "CC5" )
resS$Total = rowSums( resS[, 2:6 ], na.rm=TRUE)
resS[, 2:6 ] = round(resS[, 2:6 ] / resS$Total * 100, digits=2)
gt::gt(resS) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
4X:
```{r}
#| eval: true
#| output: true
#| label: table-fishery-4x-comm
#| tbl-cap: "Fishery performance statistics in 4X. Distribution of at sea observations of males greater than 95 mm CW by year and shell condition."
resX = dcast( odb[ region=="cfa4x", .(N=.N), by=.(fishyr, shell) ], fishyr ~ shell, value.var="N", fill=0, drop=FALSE, na.rm=TRUE )
names(resX) = c("Year", "CC1", "CC2", "CC3", "CC4", "CC5" )
resX$Total = rowSums( resX[, 2:6 ], na.rm=TRUE)
resX[, 2:6 ] = round(resX[, 2:6 ] / resX$Total * 100, digits=2)
gt::gt(resX) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
### Percent soft from observed data
There are two possible definitions:
- durometer \< 68 (Soft, Total)
- carapace conditions 1 and 2 (SoftSC, TotalSC)
```{r}
#| eval: true
#| output: true
odb = odb0[ cw >= 95 & cw < 170 & prodcd_id==0 & shell %in% c(1:5) & region %in% regions & sex==0, ] # male
shell_condition = odb[ !is.na(odb$region), .N, by=.(region, fishyr, shell) ]
shell_condition[, total:=sum(N, na.rm=TRUE), by=.(region, fishyr)]
shell_condition$percent = round(shell_condition$N / shell_condition$total, 3) * 100
shell_condition$Year = shell_condition$fishyr
```
NENS:
```{r}
#| eval: true
#| output: true
#| label: table-fishery-nens-soft-durometer
#| tbl-cap: "Fishery performance statistics in NENS. Distribution of at sea observations of males soft-shelled based on durometer (<68) and shell condition (1 and 2, SC)."
softN = odb[ region=="cfanorth" & durometer < 68, .(Soft=.N), by=.(fishyr ) ]
totalN = odb[ region=="cfanorth" & is.finite(durometer) , .(Total=.N), by=.(fishyr) ]
resN = softN[totalN, on="fishyr"]
resN = resN[, .(Year=fishyr, Soft=round(Soft/Total*100,2), Total=Total) ]
ssN = shell_condition[ region=="cfanorth" & shell %in% c(1,2), .(SoftSC=sum(percent), TotalSC=unique(total)[1]), by=.(Year)]
resN = resN[ssN, on="Year"]
gt::gt(resN) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
SENS:
```{r}
#| eval: true
#| output: true
#| label: table-fishery-sens-soft-durometer
#| tbl-cap: "Fishery performance statistics in SENS. Distribution of at sea observations of males soft-shelled based on durometer (<68) and shell condition (1 and 2, SC)."
softS = odb[ region=="cfasouth" & durometer < 68, .(Soft=.N), by=.(fishyr ) ]
totalS = odb[ region=="cfasouth" & is.finite(durometer) , .(Total=.N), by=.(fishyr) ]
resS = softS[totalS, on="fishyr"]
resS = resS[, .(Year=fishyr, Soft=round(Soft/Total*100,2), Total=Total) ]
ssS = shell_condition[ region=="cfasouth" & shell %in% c(1,2), .(SoftSC=sum(percent), TotalSC=unique(total)[1]), by=.(Year)]
resS = resS[ssS, on="Year"]
gt::gt(resS) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
4X:
```{r}
#| eval: true
#| output: true
#| label: table-fishery-4x-soft-durometer
#| tbl-cap: "Fishery performance statistics in 4X. Distribution of at sea observations of males soft-shelled based on durometer (<68) and shell condition (1 and 2, SC)."
softX = odb[ region=="cfa4x" & durometer < 68, .(Soft=.N), by=.(fishyr ) ]
totalX = odb[ region=="cfa4x" & is.finite(durometer) , .(Total=.N), by=.(fishyr) ]
resX = softX[totalX, on="fishyr"]
resX = resX[, .(Year=fishyr, Soft=round(Soft/Total*100,2), Total=Total) ]
ssX = shell_condition[ region=="cfa4x" & shell %in% c(1,2), .(SoftSC=sum(percent), TotalSC=unique(total)[1]), by=.(Year)]
resX = resX[ssX, on="Year"]
gt::gt(resX) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
<!--
# instars of interest: 11 and 12
# growth increment (assumming average weight in the midpoint of each increment)
growth.11.to.12 = predict.mass.g.from.CW.mm( mean(CW.interval.male(12)) ) - predict.mass.g.from.CW.mm (mean(CW.interval.male(11)) )
(growth.11.to.12)
# = 419 g
# 12to13 = ~450
-->
### Compare discard rates Maritimes:
NENS:discard
```{r}
#| eval: true
#| output: true
#| warning: false
#| error: false
#| label: table-fishery-nens-discard
#| tbl-cap: "Fishery performance statistics in NENS. Average by-catch discard rate by weight observed (kg/trap haul; and standard deviation, SD)."
region="cfanorth"
o = observer.db( DS="bycatch_summary", p=p, yrs=p$yrs, region=region )
resN = o$eff_summ[ order(fishyr), ]
names(resN) = c("Year", "Discards", "SD")
resN$Discards = round( resN$Discards*100, 2)
resN$SD = round( resN$SD*100, 2)
gt::gt(resN) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
SENS:
```{r}
#| eval: true
#| output: true
#| warning: false
#| error: false
#| label: table-fishery-sens-discard
#| tbl-cap: "Fishery performance statistics in SENS. Average by-catch discard rate by weight observed (kg/trap haul; and standard deviation, SD)."
region="cfasouth"
o = observer.db( DS="bycatch_summary", p=p, yrs=p$yrs, region=region )
resS = o$eff_summ[ order(fishyr), ]
names(resS) = c("Year", "Discards", "SD")
resS$Discards = round( resS$Discards*100, 2)
resS$SD = round( resS$SD*100, 2)
gt::gt(resS) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
4X:
```{r}
#| eval: true
#| output: true
#| warning: false
#| error: false
#| label: table-fishery-4x-discard
#| tbl-cap: "Fishery performance statistics in 4X. Average by-catch discard rate by weight observed (kg/trap haul; and standard deviation, SD)."
region="cfa4x"
o = observer.db( DS="bycatch_summary", p=p, yrs=p$yrs, region=region )
resX = o$eff_summ[ order(fishyr), ]
names(resX) = c("Year", "Discards", "SD")
resX$Discards = round( resX$Discards*100, 2)
resX$SD = round( resX$SD*100, 2)
gt::gt(resX) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
## Survey-based tables
### Carapace condition from trawl data \>= 95mm CW
```{r}
#| eval: true
#| output: false
det = snowcrab.db( p=p, DS="det.georeferenced" )
setDT(det)
det$fishyr = det$yr ## the counting routine expectes this variable
det = det[ cw >= 95 ,] # commerical sized crab only
years = sort( unique( det$yr ) )
det$region = NA
for ( reg in regions) {
r = polygon_inside(x = det, region = aegis.polygons::polygon_internal_code(reg), planar=FALSE)
det$region[r] = reg
}
```
NENS:
```{r}
#| eval: true
#| output: true
#| label: table-survey-nens-comm
#| tbl-cap: "Distribution of NENS survey: males less than 95 mm CW by year and shell condition."
resN = dcast( det[ region=="cfanorth" & !is.na(shell), .(N=.N), by=.(fishyr, shell) ], fishyr ~ shell, value.var="N", fill=0, drop=FALSE, na.rm=TRUE )
names(resN) = c("Year", "CC1", "CC2", "CC3", "CC4", "CC5" )
resN$Total = rowSums( resN[, 2:6 ], na.rm=TRUE)
resN[, 2:6 ] = round(resN[, 2:6 ] / resN$Total * 100, digits=2)
gt::gt(resN) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
SENS:
```{r}
#| eval: true
#| output: true
#| label: table-survey-sens-comm
#| tbl-cap: "Distribution of SENS survey: males less than 95 mm CW by year and shell condition."
resS = dcast( det[ region=="cfasouth" & !is.na(shell), .(N=.N), by=.(fishyr, shell) ], fishyr ~ shell, value.var="N", fill=0, drop=FALSE, na.rm=TRUE )
names(resS) = c("Year", "CC1", "CC2", "CC3", "CC4", "CC5" )
resS$Total = rowSums( resS[, 2:6 ], na.rm=TRUE)
resS[, 2:6 ] = round(resS[, 2:6 ] / resS$Total * 100, digits=2)
gt::gt(resS) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
4X:
```{r}
#| eval: true
#| output: true
#| label: table-survey-4X-comm
#| tbl-cap: "Distribution of 4X survey: males less than 95 mm CW by year and shell condition."
resX = dcast( det[ region=="cfa4x" & !is.na(shell), .(N=.N), by=.(fishyr, shell) ], fishyr ~ shell, value.var="N", fill=0, drop=FALSE, na.rm=TRUE )
names(resX) = c("Year", "CC1", "CC2", "CC3", "CC4", "CC5" )
resX$Total = rowSums( resX[, 2:6 ], na.rm=TRUE)
resX[, 2:6 ] = round(resX[, 2:6 ] / resX$Total * 100, digits=2)
gt::gt(resX) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
### Counts of stations in each area
```{r}
#| eval: true
#| output: true
#| label: table-survey-station-count
#| tbl-cap: "Survey station counts"
set = snowcrab.db(p=p, DS="set.clean")
setDT(set)
# check towquality .. this should always == 1
if (length( unique( set$towquality) ) != 1 ) print("error -- not good tows")
set$region = NA
for (reg in c( "cfanorth", "cfasouth", "cfa4x" ) ) {
d = polygon_inside(set[,c("lon","lat")], reg)
set$region[d] = reg
}
out = dcast( set[, .(N=.N), by=.(region, yr)], yr~region, value.var="N", fill=0, drop=FALSE, na.rm=TRUE )
out[,Total:=sum(cfanorth,cfasouth,cfa4x, na.rm=TRUE)]
out = out[, .(yr, cfanorth, cfasouth, cfa4x)]
names(out) = c("Year", "NENS", "SENS", "4X")
gt::gt(out) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
## References
<!--
deprecated = TRUE
if (deprecated) {
# the following are deprecated methods as of 2024 (JC), here only for reference .. most functionality is nowin the Quarto/Rmarkdown above.
# Tables obtained after completion of data assimilation and processing up to the end of "01.snowcrab.r"
year.assessment = 2023
p = bio.snowcrab::load.environment( year.assessment=year.assessment )
require(gridExtra)
library("xtable")
library("R2HTML")
odb0 = observer.db("odb")
regions = c("cfanorth", "cfasouth", "cfa4x")
nregions = length(regions)
#------------------------------------------------
#Fisheries statistics per region
tabledir = file.path(project.datadirectory("bio.snowcrab"), "data", "fisheries")
outtabledir= file.path(project.datadirectory("bio.snowcrab"), "assessments", p$year.assessment, "tables", "logbook")
if(!dir.exists(tabledir)) dir.create(tabledir, recursive =T)
if(!dir.exists(outtabledir)) dir.create(outtabledir, recursive =T)
setwd(tabledir)
NFS <- xtable(read.csv("NENS_FisherySummary.csv"))
SFS <- xtable(read.csv("SENS_FisherySummary.csv"))
Fx <- xtable(read.csv("4x_FisherySummary.csv"))
setwd(outtabledir)
print.xtable(NFS, type="latex", file="NENS_FisherySummary.tex")
print.xtable(NFS, type="html", file="NENS_FisherySummary.html")
print.xtable(SFS, type="latex", file="SENS_FisherySummary.tex")
print.xtable(SFS, type="html", file="SENS_FisherySummary.html")
print.xtable(Fx, type="latex", file="4x_FisherySummary.tex")
print.xtable(Fx, type="html", file="4x_FisherySummary.html")
#regions = c("cfaall")
#regions = c("cfanorth", "cfasouth", "cfa4x")
regions = c("cfanorth", "cfa23", "cfa24", "cfa4x")
l = NULL
for (r in regions) {
res = get.fishery.stats.by.region( Reg=r) #need to add the TACs per year and number of licences
#round the landings to ton
#round CPUE to no decimal places
#round the effort to per x1000 trap hauls
print(r)
print(res)
}
# ----------------------------------------
# Carapace condition from observed data < 95mm CW
outtabledir= file.path(project.datadirectory("bio.snowcrab"), "assessments", p$year.assessment, "tables", "observer")
dir.create(outtabledir, recursive=TRUE)
odb = odb0
odb = odb[ which( odb$cw < 95 & odb$prodcd_id=="0" ) ,]
regions = c("cfanorth", "cfasouth", "cfa4x")
nregions = length(regions)
years = sort( unique( odb$fishyr ) )
res = NULL
for (r in p$regions) {
for (y in years) {
out = proportion.cc (odb, region=r, year=y)
res = rbind( res, cbind( r, y, t(out)) )
}}
cnames = c("region", "fishyr", c(1:5), "ntot")
colnames(res) = cnames
print(res)
res = as.data.frame(res)
res[is.na(res)] <- NA
ct <- c("CC1", "CC2", "CC3", "CC4", "CC5", "Total")
setwd(outtabledir)
Rn= res[res$region=="cfanorth", 3:8]
print(Rn)
rownames(Rn) = years
colnames(Rn) = ct
print.xtable(Rn, type="latex", file="table.CC.small.north.obs.tex")
HTML(Rn, file="table.CC.Small.north.obs.html")
Rs= res[res$region=="cfasouth", 3:8]
rownames(Rs) = years
colnames(Rs) = ct
print.xtable(Rs, type="latex", file="table.CC.small.south.obs.tex")
HTML(Rs, file="table.CC.small.south.obs.html")
Rx= res[res$region=="cfa4x", 3:8]
rownames(Rx) = years
colnames(Rx) = ct
print.xtable(Rs, type="latex", file="table.CC.small.4x.obs.tex")
HTML(Rx, file="table.CC.small.4x.obs.html")
# ----------------------------------------
# Carapace condition from observed data >=95mm CW
odb = odb0
odb = odb[ which( odb$cw >= 95 & odb$cw < 170 & odb$prodcd_id=="0" ) ,] # commerical sized crab only
years = sort( unique( odb$fishyr ) )
# get proportion by cc
regions = c("cfanorth", "cfasouth", "cfa4x")
years = sort( unique( odb$fishyr ) )
res = NULL
for (r in regions) {
for (y in years) {
out = proportion.cc (odb, region=r, year=y)
res = rbind( res, cbind( r, y, t(out)) )
}}
cnames = c("region", "fishyr", c(1:5), "ntot")
colnames(res) = cnames
print(res)
res = as.data.frame(res)
res[is.na(res)] <- NA
# for (i in cnames[-1]) res[,i] = as.numeric(as.character((res[,i])))
setwd(outtabledir)
ct <- c("CC1", "CC2", "CC3", "CC4", "CC5")
Rn = res[res$region=="cfanorth", 3:7]
#Rn = as.matrix( res[ which(res$region=="cfanorth") , as.character(c(1:5)) ] )
rownames(Rn) = years
colnames(Rn) = ct
print.xtable(Rn, type="latex", file="table.CC.large.north.obs.tex")
HTML(Rn, file="table.CC.large.north.obs.html")
#Rs = as.matrix( res[ which(res$region=="cfasouth") , as.character(c(1:5)) ] )
Rs = res[res$region=="cfasouth", 3:7]
rownames(Rs) = years
colnames(Rs) = ct
print.xtable(Rs, type="latex", file="table.CC.large.south.obs.tex")
HTML(Rs, file="table.CC.large.south.obs.html")
#Rx = as.matrix( res[ which(res$region=="cfa4x") , as.character(c(1:5)) ] )
Rx = res[res$region=="cfa4x", 3:7]
rownames(Rx) = years
colnames(Rx) = ct
print.xtable(Rx, type="latex", file="table.CC.large.4x.obs.tex")
HTML(Rx, file="table.CC.large.4x.obs.html")
# ----------------------------------------
# Percent soft from observed data
odb = odb0
odb = odb[ which( odb$cw > 95 & odb$cw < 170 & odb$prodcd_id=="0" ) ,] # commercial crab
years = sort( unique( odb$fishyr ) )
res = NULL
for (r in p$regions) {
for (y in years) {
out = proportion.soft (odb, region=r, year=y)
res = rbind( res, cbind( r, y, t(out)) )
}}
cnames = c("region", "fishyr", "pr.soft", "nsoft", "ntot")
colnames(res) = cnames
print(res)
res = as.data.frame(res)
for (i in cnames[-1]) res[,i] = as.numeric(as.character((res[,i])))
Rn = as.matrix( res[ which(res$region=="cfanorth") , ] )
rownames(Rn) = years
HTML(Rn, file="table.proportion.soft.north.obs.html")
Rs = as.matrix( res[ which(res$region=="cfasouth" ), ] )
rownames(Rs) = years
HTML(Rs, file="table.proportion.soft.south.obs.html")
Rx = as.matrix( res[ which(res$region=="cfa4x") , ] )
rownames(Rx) = years
HTML(Rx, file="table.proportion.soft.4x.obs.html")
# instars of interest: 11 and 12
# growth increment (assumming average weight in the midpoint of each increment)
growth.11.to.12 = predict.mass.g.from.CW.mm( mean(CW.interval.male(12)) ) - predict.mass.g.from.CW.mm (mean(CW.interval.male(11)) )
# = 419 g
# 12to13 = ~450
# Table of proportion discarded
odb = observer.db("odb")
regions = c("cfanorth", "cfasouth", "cfa4x")
years = sort( unique( odb$fishyr ) )
out = NULL
for (r in regions) {
for (y in years) {
res = proportion.legal (odb, region=r, year=y)
out = rbind(out, cbind( r, y, res[1], res[2], res[3] ) )
} }
out
HTML(out, file="table.proportion.discarded.html")
# ---------------------------------------- USED
# Carapace condition from trawl data >= 95mm CW ... not kriged .. simple proportions
det0 = snowcrab.db( p=p, DS="det.georeferenced" )
det0$fishyr = det0$yr ## the counting routine expectes this variable
det = det0[ which( det0$cw >= 95 ) ,] # commerical sized crab only
years = sort( unique( det$yr ) )
res = NULL
for (r in p$regions) {
for (y in years) {
out = proportion.cc (det, region=r, year=y)
res = rbind( res, cbind( r, y, t(out)) )
}}
cnames = c("region", "fishyr", c(1:5), "ntot")
colnames(res) = cnames
print(res)
res = as.data.frame(res)
for (i in cnames[-1]) res[,i] = as.numeric(as.character((res[,i])))
(res)
HTML(res, file="table.CC.large.survey.html")
# ------------------
# counts of stations in each area
# check towquality .. this should always == 1
set = snowcrab.db(p=p, DS="set.clean")
if (length( unique( set$towquality) ) != 1 ) print("error -- not good tows")
out = data.frame(yr=sort( unique(set$yr )) )
for (reg in c("cfaall", "cfanorth", "cfasouth","cfa4x" ) ) {
d = polygon_inside(set[,c("lon","lat")], reg)
e = as.data.frame( xtabs(~yr, data=set[d,]) )
names(e) = c("yr", reg)
e$yr = as.numeric(as.character(e$yr) )
out = merge(out, e, by="yr", all=T)
}
print(out)
HTML(out, file="table.tow_counts_survey.html")
# % mat calculations: deprecated ... size analysis is now in 01_snowcrab.R
# loc = file.path(sc.R, "size.data")
# dir.create(path=loc, recursive=T, showWarnings=F)
# outfilename = paste( c("mi", "mm", "fi", "fm"), "rdata", sep=".")
# outfile = file.path(loc, paste(outfilename))
# for (f in outfile) load(f)
# f.i = f.imm[which( rownames(f.imm)%in% sids ) ,]
# f.i.means = apply(X=f.i, MARGIN=2, FUN=mean)
# f.m = f.mat[which( rownames(f.mat)%in% sids ) ,]
# f.m.means = apply(X=f.m, MARGIN=2, FUN=mean)
# toplot = rbind(f.m.means, f.i.means)
# ii = as.data.frame(t(toplot))
# ii$cw = as.numeric(rownames(ii))
# ii$pmat = ii[,1]/ (ii[,1]+ii[,2]) * 100
# plot(ii$cw, ii$pmat)
# abline(h=50)
# str(ii)
#`data.frame': 70 obs. of 4 variables:
# $ f.m.means: num 0 0 0 0 0 ...
# $ f.i.means: num 2.80 6.19 20.05 24.29 74.11 ...
# $ cw : num 12 14 16 18 20 22 24 26 28 30 ...
# $ pmat : num 0 0 0 0 0 ...
# ---------------------------------------- NOT USED ____________
# Carapace condition from trawl data < 95mm CW ... not kriged .. simple proportions
det0 = snowcrab.db( p=p, DS="det.georeferenced" )
det0$fishyr = det0$yr ## the counting routine expectes this variable
det = det0[ which( det0$cw < 95 ) ,] # commerical sized crab only
years = sort( unique( det$yr ) )
res = NULL
for (r in p$regions) {
for (y in years) {
out = proportion.cc (det, region=r, year=y)
res = rbind( res, cbind( r, y, t(out)) )
}}
cnames = c("region", "fishyr", c(1:5), "ntot")
colnames(res) = cnames
print(res)
res = as.data.frame(res)
for (i in cnames[-1]) res[,i] = as.numeric(as.character((res[,i])))
(res)
}
-->
jae0/snowcrab documentation built on Nov. 6, 2024, 10:13 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
---
title: "Snow crab tables"
author:
- name: Snow crab group
# orcid: 0000-0003-3632-5723
# email: jae.choi@dfo-mpo.gc.ca
# email: choi.jae.seok@gmail.com
# corresponding: true
affiliation:
- name: Bedford Institute of Oceanography, Fisheries and Oceans Canada
city: Dartmouth
state: NS
url: www.bio.gc.ca
date: 2024-08-17
keywords:
- snow crab fishery assessment
- basic tables
abstract: |
Snow crab demographic structure and basic fishery performance tables.
toc: true
number-sections: true
highlight-style: pygments
bibliography: media/references.bib
# csl: media/canadian-journal-of-fisheries-and-aquatic-sciences.csl # see https://www.zotero.org/styles for more
license: "CC BY"
copyright:
holder: Jae S. Choi
year: 2024
citation:
container-title: https://github.com/jae0/bio.snowcrab/
doi: NA
funding: "The snow crab scientific survey was funded by the snow crab fishers of Maritimes Region of Atlantic Canada."
editor:
render-on-save: false
format:
html:
code-fold: true
html-math-method: katex
embed-resources: true
pdf:
pdf-engine: lualatex
docx: default
beamer:
pdf-engine: lualatex
---
<!-- Preamble
This is a Markdown/Quarto document ... To create HTML or PDF, etc, run:
make quarto FN=02.tables YR=2023 SOURCE=~/projects/bio.snowcrab/inst/markdown WK=~/bio.data/bio.snowcrab/assessments DOCEXTENSION=html # {via Quarto}
# make rmarkdown FN=02.tables YR=2023 SOURCE=~/projects/bio.snowcrab/inst/markdown WK=~/bio.data/bio.snowcrab/assessments DOCTYPE=pdf_document DOCEXTENSION=pdf {via Rmarkdown}
# make pdf FN=02.tables # {via pandoc}
Alter year and directories to reflect setup or copy Makefile and alter defaults to your needs.
YAML options:
jupyter: julia-1.10.4
https://quarto.org/docs/output-formats/all-formats.html
-->
````{=html}
<!-- NOTES: Make sure to have pulled observer data:
year.assessment = 2023
p = bio.snowcrab::load.environment( year.assessment=year.assessment )
yrs = 1996:year.assessment # redo all years
observer.db( DS="rawdata.redo", yrs=yrs )
observer.db( DS="bycatch.redo", yrs=yrs )
observer.db( DS="odb.redo", p=p ) # 3 minutes
observer.db( DS="bycatch_clean_data.redo", p=p, yrs=p$yrs ) # 3 minutes
quarto options:
The reserved prefixes are: fig, tbl, lst, tip, nte, wrn, imp, cau, thm, lem, cor, prp, cnj, def, exm, exr, sol, rem, eq, sec.
avoid using underscores (_) in labels and IDs as this can cause problems when rendering to PDF with LaTeX.
{#fig-elephant}
Default @fig-elephant Figure 1
Capitalized @Fig-elephant Figure 1
Custom Prefix [Fig @fig-elephant] Fig 1
No Prefix [-@fig-elephant] 1
::: {#fig-elephants layout-ncol=2}
{#fig-surus}
{#fig-hanno}
Famous Elephants
:::
See @fig-elephants for examples. In particular, @fig-hanno.
or:
#| label: fig-plots
#| fig-cap: "Plots"
#| fig-subcap:
#| - "Plot 1"
#| - "Plot 2"
#| layout-ncol: 2
| Col1 | Col2 | Col3 |
|------|------|------|
| A | B | C |
| E | F | G |
| A | G | G |
: My Caption {#tbl-letters}
See @tbl-letters.
or
::: {#tbl-letters}
| Col1 | Col2 | Col3 |
|------|------|------|
| A | B | C |
| E | F | G |
| A | G | G |
My Caption
:::
@fig-AAAA further explores the impact of temperature on ozone level.
```{r}
#| label: fig-AAAA
#| fig-cap: "Temperature and ozone level."
#| tbl-cap: "Fishery performance statistics."
#| eval: true
#| output: true
```
Black-Scholes (@eq-black-scholes) is a mathematical model:
$$
\frac{\partial \mathrm C}{ \partial \mathrm t } + \frac{1}{2}\sigma^{2} \mathrm S^{2}
\frac{\partial^{2} \mathrm C}{\partial \mathrm C^2}
+ \mathrm r \mathrm S \frac{\partial \mathrm C}{\partial \mathrm S}\ =
\mathrm r \mathrm C
$$ {#eq-black-scholes}
## Introduction {#sec-introduction}
See @sec-introduction
---
# citations: https://quarto.org/docs/authoring/citations.html; https://pandoc.org/MANUAL.html#citations
Blah Blah [see @knuth1984, pp. 33-35; also @wickham2015, chap. 1]
Blah Blah (see Knuth 1984, 33–35; also Wickham 2015, chap. 1)
Blah Blah [@knuth1984, pp. 33-35, 38-39 and passim]
Blah Blah (Knuth 1984, 33–35, 38–39 and passim)
Blah Blah [@wickham2015; @knuth1984].
Blah Blah (Wickham 2015; Knuth 1984).
Wickham says blah [-@wickham2015]
Wickham says blah (2015)
@knuth1984 says blah.
Knuth (1984) says blah.
@knuth1984 [p. 33] says blah.
Knuth (1984, 33) says blah.
#CSL: default is Chicago Manual of Style author-date format
https://github.com/citation-style-language/styles
https://www.zotero.org/styles
### References
::: {#refs}
:::
```
-->
````
# Set up environment
First set up environment. Data comes from:
- At seas observations of fishery (ISSDB)
- Dockside monitoring (Marfis)
- Snow crab survey
These are mostly imported and formatted in [01_snowcrab](https://github.com/jae0/bio.snowcrab/inst/scripts/01_snowcrab.R). The methods are mostly outlined in @Choi_et_al_2005b.
```{r}
#| label: setup
#| eval: true
#| output: false
require(aegis)
# Get data and format based upon parameters:
year.assessment = 2023
p = bio.snowcrab::load.environment( year.assessment=year.assessment )
# loadfunctions( "aegis")
# loadfunctions( "bio.snowcrab") # in case of local edits
# require(ggplot2)
# require(data.table)
require(gt) # table formatting
outtabledir = file.path( p$annual.results, "tables" )
years = as.character(1996: year.assessment)
regions = c("cfanorth", "cfasouth", "cfa4x")
nregions = length(regions)
FD = fishery_data() # mass in tonnes
fda = FD$summary_annual
dt = as.data.frame( fda[ which(fda$yr %in% c(year.assessment - c(0:10))),] )
dt = dt[,c("region", "yr", "Licenses", "TAC", "landings", "effort", "cpue")]
names(dt) = c("Region", "Year", "Licenses", "TAC", "Landings", "Effort", "CPUE")
rownames(dt) = NULL
odb0 = setDT(observer.db("odb"))
odb0$region = NA
for ( reg in regions) {
r = polygon_inside(x = odb0, region = aegis.polygons::polygon_internal_code(reg), planar=FALSE)
odb0$region[r] = reg
}
```
## Fishery statistics from at sea observations
NENS:
```{r}
#| label: table-fishery-nens-perf
#| tbl-cap: "Fishery performance statistics in NENS. Units are: TACs and Landings (tons, t), Effort ($\\times 10^3$ trap hauls, th) and CPUE (kg/th)."
#| eval: true
#| output: true
ii = which(dt$Region=="cfanorth")
oo = dt[ii, c("Year", "Licenses", "TAC", "Landings", "Effort", "CPUE")]
gt::gt(oo) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
SENS:
```{r}
#| label: table-fishery-sens-perf
#| tbl-cap: "Fishery performance statistics in SENS. Units are: TACs and Landings (tons, t), Effort ($\\times 10^3$ trap hauls, th) and CPUE (kg/th)."
#| eval: true
#| output: true
ii = which(dt$Region=="cfasouth")
oo = dt[ii, c("Year", "Licenses", "TAC", "Landings", "Effort", "CPUE")]
gt::gt(oo) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
4X:
```{r}
#| label: table-fishery-4x-perf
#| tbl-cap: "Fishery performance statistics in 4X. Units are: TACs and Landings (tons, t), Effort ($\\times 10^3$ trap hauls, th) and CPUE (kg/th). There were no landings or TACs in 2018/2019 due to indications of low abundance. The 2022 season is ongoing."
#| eval: true
#| output: true
ii = which(dt$Region=="cfa4x")
oo = dt[ii, c("Year", "Licenses", "TAC", "Landings", "Effort", "CPUE")]
gt::gt(oo) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
## At sea observed data
### Carapace condition from observed data \< 95mm CW
```{r}
#| label: setup-observer-data
#| eval: true
#| output: false
odb = odb0[ cw < 95 & prodcd_id==0 & shell %in% c(1:5) & region %in% regions & sex==0, ] # male
```
NENS:
```{r}
#| label: table-fishery-nens-sublegal
#| tbl-cap: "Fishery performance statistics in NENS. Distribution of at sea observations of males less than 95 mm CW by year and shell condition."
#| eval: true
#| output: true
resN = dcast( odb0[ region=="cfanorth", .(N=.N), by=.(fishyr, shell) ], fishyr ~ shell, value.var="N", fill=0, drop=FALSE, na.rm=TRUE )
if ( "NA" %in% names(resN) ) resN$"NA" = NULL
names(resN) = c("Year", "CC1", "CC2", "CC3", "CC4", "CC5" )
resN$Total = rowSums( resN[, 2:6 ], na.rm=TRUE)
resN[, 2:6 ] = round(resN[, 2:6 ] / resN$Total * 100, digits=2)
gt::gt(resN) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
SENS:
```{r}
#| eval: true
#| output: true
#| label: table-fishery-sens-sublegal
#| tbl-cap: "Fishery performance statistics in SENS. Distribution of at sea observations of males less than 95 mm CW by year and shell condition."
resS = dcast( odb0[ region=="cfasouth", .(N=.N), by=.(fishyr, shell) ], fishyr ~ shell, value.var="N", fill=0, drop=FALSE, na.rm=TRUE )
if ( "NA" %in% names(resS)) resS$"NA" = NULL
names(resS) = c("Year", "CC1", "CC2", "CC3", "CC4", "CC5" )
resS$Total = rowSums( resS[, 2:6 ], na.rm=TRUE)
resS[, 2:6 ] = round(resS[, 2:6 ] / resS$Total * 100, digits=2)
gt::gt(resS) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
4X:
```{r}
#| eval: true
#| output: true
#| label: table-fishery-4x-sublegal
#| tbl-cap: "Fishery performance statistics in 4X. Distribution of at sea observations of males less than 95 mm CW by year and shell condition."
resX = dcast( odb0[ region=="cfa4x", .(N=.N), by=.(fishyr, shell) ], fishyr ~ shell, value.var="N", fill=0, drop=FALSE, na.rm=TRUE )
if ("NA" %in% names(resX)) resX$"NA" = NULL
names(resX) = c("Year", "CC1", "CC2", "CC3", "CC4", "CC5" )
resX$Total = rowSums( resX[, 2:6 ], na.rm=TRUE)
resX[, 2:6 ] = round(resX[, 2:6 ] / resX$Total * 100, digits=2)
gt::gt(resX) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
### Carapace condition from observed data \>= 95mm CW
```{r}
#| eval: true
#| output: false
odb = odb0[ cw >= 95 & cw < 170 & prodcd_id==0 & shell %in% c(1:5) & region %in% regions & sex==0, ] # male
```
NENS:
```{r}
#| eval: true
#| output: true
#| label: table-fishery-nens-comm
#| tbl-cap: "Fishery performance statistics in NENS. Distribution of at sea observations of males greater than 95 mm CW by year and shell condition."
resN = dcast( odb[ region=="cfanorth", .(N=.N), by=.(fishyr, shell) ], fishyr ~ shell, value.var="N", fill=0, drop=FALSE, na.rm=TRUE )
names(resN) = c("Year", "CC1", "CC2", "CC3", "CC4", "CC5" )
resN$Total = rowSums( resN[, 2:6 ], na.rm=TRUE)
resN[, 2:6 ] = round(resN[, 2:6 ] / resN$Total * 100, digits=2)
gt::gt(resN) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
SENS:
```{r}
#| eval: true
#| output: true
#| label: table-fishery-sens-comm
#| tbl-cap: "Fishery performance statistics in SENS. Distribution of at sea observations of males greater than 95 mm CW by year and shell condition."
resS = dcast( odb[ region=="cfasouth", .(N=.N), by=.(fishyr, shell) ], fishyr ~ shell, value.var="N", fill=0, drop=FALSE, na.rm=TRUE )
names(resS) = c("Year", "CC1", "CC2", "CC3", "CC4", "CC5" )
resS$Total = rowSums( resS[, 2:6 ], na.rm=TRUE)
resS[, 2:6 ] = round(resS[, 2:6 ] / resS$Total * 100, digits=2)
gt::gt(resS) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
4X:
```{r}
#| eval: true
#| output: true
#| label: table-fishery-4x-comm
#| tbl-cap: "Fishery performance statistics in 4X. Distribution of at sea observations of males greater than 95 mm CW by year and shell condition."
resX = dcast( odb[ region=="cfa4x", .(N=.N), by=.(fishyr, shell) ], fishyr ~ shell, value.var="N", fill=0, drop=FALSE, na.rm=TRUE )
names(resX) = c("Year", "CC1", "CC2", "CC3", "CC4", "CC5" )
resX$Total = rowSums( resX[, 2:6 ], na.rm=TRUE)
resX[, 2:6 ] = round(resX[, 2:6 ] / resX$Total * 100, digits=2)
gt::gt(resX) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
### Percent soft from observed data
There are two possible definitions:
- durometer \< 68 (Soft, Total)
- carapace conditions 1 and 2 (SoftSC, TotalSC)
```{r}
#| eval: true
#| output: true
odb = odb0[ cw >= 95 & cw < 170 & prodcd_id==0 & shell %in% c(1:5) & region %in% regions & sex==0, ] # male
shell_condition = odb[ !is.na(odb$region), .N, by=.(region, fishyr, shell) ]
shell_condition[, total:=sum(N, na.rm=TRUE), by=.(region, fishyr)]
shell_condition$percent = round(shell_condition$N / shell_condition$total, 3) * 100
shell_condition$Year = shell_condition$fishyr
```
NENS:
```{r}
#| eval: true
#| output: true
#| label: table-fishery-nens-soft-durometer
#| tbl-cap: "Fishery performance statistics in NENS. Distribution of at sea observations of males soft-shelled based on durometer (<68) and shell condition (1 and 2, SC)."
softN = odb[ region=="cfanorth" & durometer < 68, .(Soft=.N), by=.(fishyr ) ]
totalN = odb[ region=="cfanorth" & is.finite(durometer) , .(Total=.N), by=.(fishyr) ]
resN = softN[totalN, on="fishyr"]
resN = resN[, .(Year=fishyr, Soft=round(Soft/Total*100,2), Total=Total) ]
ssN = shell_condition[ region=="cfanorth" & shell %in% c(1,2), .(SoftSC=sum(percent), TotalSC=unique(total)[1]), by=.(Year)]
resN = resN[ssN, on="Year"]
gt::gt(resN) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
SENS:
```{r}
#| eval: true
#| output: true
#| label: table-fishery-sens-soft-durometer
#| tbl-cap: "Fishery performance statistics in SENS. Distribution of at sea observations of males soft-shelled based on durometer (<68) and shell condition (1 and 2, SC)."
softS = odb[ region=="cfasouth" & durometer < 68, .(Soft=.N), by=.(fishyr ) ]
totalS = odb[ region=="cfasouth" & is.finite(durometer) , .(Total=.N), by=.(fishyr) ]
resS = softS[totalS, on="fishyr"]
resS = resS[, .(Year=fishyr, Soft=round(Soft/Total*100,2), Total=Total) ]
ssS = shell_condition[ region=="cfasouth" & shell %in% c(1,2), .(SoftSC=sum(percent), TotalSC=unique(total)[1]), by=.(Year)]
resS = resS[ssS, on="Year"]
gt::gt(resS) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
4X:
```{r}
#| eval: true
#| output: true
#| label: table-fishery-4x-soft-durometer
#| tbl-cap: "Fishery performance statistics in 4X. Distribution of at sea observations of males soft-shelled based on durometer (<68) and shell condition (1 and 2, SC)."
softX = odb[ region=="cfa4x" & durometer < 68, .(Soft=.N), by=.(fishyr ) ]
totalX = odb[ region=="cfa4x" & is.finite(durometer) , .(Total=.N), by=.(fishyr) ]
resX = softX[totalX, on="fishyr"]
resX = resX[, .(Year=fishyr, Soft=round(Soft/Total*100,2), Total=Total) ]
ssX = shell_condition[ region=="cfa4x" & shell %in% c(1,2), .(SoftSC=sum(percent), TotalSC=unique(total)[1]), by=.(Year)]
resX = resX[ssX, on="Year"]
gt::gt(resX) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
<!--
# instars of interest: 11 and 12
# growth increment (assumming average weight in the midpoint of each increment)
growth.11.to.12 = predict.mass.g.from.CW.mm( mean(CW.interval.male(12)) ) - predict.mass.g.from.CW.mm (mean(CW.interval.male(11)) )
(growth.11.to.12)
# = 419 g
# 12to13 = ~450
-->
### Compare discard rates Maritimes:
NENS:discard
```{r}
#| eval: true
#| output: true
#| warning: false
#| error: false
#| label: table-fishery-nens-discard
#| tbl-cap: "Fishery performance statistics in NENS. Average by-catch discard rate by weight observed (kg/trap haul; and standard deviation, SD)."
region="cfanorth"
o = observer.db( DS="bycatch_summary", p=p, yrs=p$yrs, region=region )
resN = o$eff_summ[ order(fishyr), ]
names(resN) = c("Year", "Discards", "SD")
resN$Discards = round( resN$Discards*100, 2)
resN$SD = round( resN$SD*100, 2)
gt::gt(resN) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
SENS:
```{r}
#| eval: true
#| output: true
#| warning: false
#| error: false
#| label: table-fishery-sens-discard
#| tbl-cap: "Fishery performance statistics in SENS. Average by-catch discard rate by weight observed (kg/trap haul; and standard deviation, SD)."
region="cfasouth"
o = observer.db( DS="bycatch_summary", p=p, yrs=p$yrs, region=region )
resS = o$eff_summ[ order(fishyr), ]
names(resS) = c("Year", "Discards", "SD")
resS$Discards = round( resS$Discards*100, 2)
resS$SD = round( resS$SD*100, 2)
gt::gt(resS) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
4X:
```{r}
#| eval: true
#| output: true
#| warning: false
#| error: false
#| label: table-fishery-4x-discard
#| tbl-cap: "Fishery performance statistics in 4X. Average by-catch discard rate by weight observed (kg/trap haul; and standard deviation, SD)."
region="cfa4x"
o = observer.db( DS="bycatch_summary", p=p, yrs=p$yrs, region=region )
resX = o$eff_summ[ order(fishyr), ]
names(resX) = c("Year", "Discards", "SD")
resX$Discards = round( resX$Discards*100, 2)
resX$SD = round( resX$SD*100, 2)
gt::gt(resX) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
## Survey-based tables
### Carapace condition from trawl data \>= 95mm CW
```{r}
#| eval: true
#| output: false
det = snowcrab.db( p=p, DS="det.georeferenced" )
setDT(det)
det$fishyr = det$yr ## the counting routine expectes this variable
det = det[ cw >= 95 ,] # commerical sized crab only
years = sort( unique( det$yr ) )
det$region = NA
for ( reg in regions) {
r = polygon_inside(x = det, region = aegis.polygons::polygon_internal_code(reg), planar=FALSE)
det$region[r] = reg
}
```
NENS:
```{r}
#| eval: true
#| output: true
#| label: table-survey-nens-comm
#| tbl-cap: "Distribution of NENS survey: males less than 95 mm CW by year and shell condition."
resN = dcast( det[ region=="cfanorth" & !is.na(shell), .(N=.N), by=.(fishyr, shell) ], fishyr ~ shell, value.var="N", fill=0, drop=FALSE, na.rm=TRUE )
names(resN) = c("Year", "CC1", "CC2", "CC3", "CC4", "CC5" )
resN$Total = rowSums( resN[, 2:6 ], na.rm=TRUE)
resN[, 2:6 ] = round(resN[, 2:6 ] / resN$Total * 100, digits=2)
gt::gt(resN) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
SENS:
```{r}
#| eval: true
#| output: true
#| label: table-survey-sens-comm
#| tbl-cap: "Distribution of SENS survey: males less than 95 mm CW by year and shell condition."
resS = dcast( det[ region=="cfasouth" & !is.na(shell), .(N=.N), by=.(fishyr, shell) ], fishyr ~ shell, value.var="N", fill=0, drop=FALSE, na.rm=TRUE )
names(resS) = c("Year", "CC1", "CC2", "CC3", "CC4", "CC5" )
resS$Total = rowSums( resS[, 2:6 ], na.rm=TRUE)
resS[, 2:6 ] = round(resS[, 2:6 ] / resS$Total * 100, digits=2)
gt::gt(resS) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
4X:
```{r}
#| eval: true
#| output: true
#| label: table-survey-4X-comm
#| tbl-cap: "Distribution of 4X survey: males less than 95 mm CW by year and shell condition."
resX = dcast( det[ region=="cfa4x" & !is.na(shell), .(N=.N), by=.(fishyr, shell) ], fishyr ~ shell, value.var="N", fill=0, drop=FALSE, na.rm=TRUE )
names(resX) = c("Year", "CC1", "CC2", "CC3", "CC4", "CC5" )
resX$Total = rowSums( resX[, 2:6 ], na.rm=TRUE)
resX[, 2:6 ] = round(resX[, 2:6 ] / resX$Total * 100, digits=2)
gt::gt(resX) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
### Counts of stations in each area
```{r}
#| eval: true
#| output: true
#| label: table-survey-station-count
#| tbl-cap: "Survey station counts"
set = snowcrab.db(p=p, DS="set.clean")
setDT(set)
# check towquality .. this should always == 1
if (length( unique( set$towquality) ) != 1 ) print("error -- not good tows")
set$region = NA
for (reg in c( "cfanorth", "cfasouth", "cfa4x" ) ) {
d = polygon_inside(set[,c("lon","lat")], reg)
set$region[d] = reg
}
out = dcast( set[, .(N=.N), by=.(region, yr)], yr~region, value.var="N", fill=0, drop=FALSE, na.rm=TRUE )
out[,Total:=sum(cfanorth,cfasouth,cfa4x, na.rm=TRUE)]
out = out[, .(yr, cfanorth, cfasouth, cfa4x)]
names(out) = c("Year", "NENS", "SENS", "4X")
gt::gt(out) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
```
## References
<!--
deprecated = TRUE
if (deprecated) {
# the following are deprecated methods as of 2024 (JC), here only for reference .. most functionality is nowin the Quarto/Rmarkdown above.
# Tables obtained after completion of data assimilation and processing up to the end of "01.snowcrab.r"
year.assessment = 2023
p = bio.snowcrab::load.environment( year.assessment=year.assessment )
require(gridExtra)
library("xtable")
library("R2HTML")
odb0 = observer.db("odb")
regions = c("cfanorth", "cfasouth", "cfa4x")
nregions = length(regions)
#------------------------------------------------
#Fisheries statistics per region
tabledir = file.path(project.datadirectory("bio.snowcrab"), "data", "fisheries")
outtabledir= file.path(project.datadirectory("bio.snowcrab"), "assessments", p$year.assessment, "tables", "logbook")
if(!dir.exists(tabledir)) dir.create(tabledir, recursive =T)
if(!dir.exists(outtabledir)) dir.create(outtabledir, recursive =T)
setwd(tabledir)
NFS <- xtable(read.csv("NENS_FisherySummary.csv"))
SFS <- xtable(read.csv("SENS_FisherySummary.csv"))
Fx <- xtable(read.csv("4x_FisherySummary.csv"))
setwd(outtabledir)
print.xtable(NFS, type="latex", file="NENS_FisherySummary.tex")
print.xtable(NFS, type="html", file="NENS_FisherySummary.html")
print.xtable(SFS, type="latex", file="SENS_FisherySummary.tex")
print.xtable(SFS, type="html", file="SENS_FisherySummary.html")
print.xtable(Fx, type="latex", file="4x_FisherySummary.tex")
print.xtable(Fx, type="html", file="4x_FisherySummary.html")
#regions = c("cfaall")
#regions = c("cfanorth", "cfasouth", "cfa4x")
regions = c("cfanorth", "cfa23", "cfa24", "cfa4x")
l = NULL
for (r in regions) {
res = get.fishery.stats.by.region( Reg=r) #need to add the TACs per year and number of licences
#round the landings to ton
#round CPUE to no decimal places
#round the effort to per x1000 trap hauls
print(r)
print(res)
}
# ----------------------------------------
# Carapace condition from observed data < 95mm CW
outtabledir= file.path(project.datadirectory("bio.snowcrab"), "assessments", p$year.assessment, "tables", "observer")
dir.create(outtabledir, recursive=TRUE)
odb = odb0
odb = odb[ which( odb$cw < 95 & odb$prodcd_id=="0" ) ,]
regions = c("cfanorth", "cfasouth", "cfa4x")
nregions = length(regions)
years = sort( unique( odb$fishyr ) )
res = NULL
for (r in p$regions) {
for (y in years) {
out = proportion.cc (odb, region=r, year=y)
res = rbind( res, cbind( r, y, t(out)) )
}}
cnames = c("region", "fishyr", c(1:5), "ntot")
colnames(res) = cnames
print(res)
res = as.data.frame(res)
res[is.na(res)] <- NA
ct <- c("CC1", "CC2", "CC3", "CC4", "CC5", "Total")
setwd(outtabledir)
Rn= res[res$region=="cfanorth", 3:8]
print(Rn)
rownames(Rn) = years
colnames(Rn) = ct
print.xtable(Rn, type="latex", file="table.CC.small.north.obs.tex")
HTML(Rn, file="table.CC.Small.north.obs.html")
Rs= res[res$region=="cfasouth", 3:8]
rownames(Rs) = years
colnames(Rs) = ct
print.xtable(Rs, type="latex", file="table.CC.small.south.obs.tex")
HTML(Rs, file="table.CC.small.south.obs.html")
Rx= res[res$region=="cfa4x", 3:8]
rownames(Rx) = years
colnames(Rx) = ct
print.xtable(Rs, type="latex", file="table.CC.small.4x.obs.tex")
HTML(Rx, file="table.CC.small.4x.obs.html")
# ----------------------------------------
# Carapace condition from observed data >=95mm CW
odb = odb0
odb = odb[ which( odb$cw >= 95 & odb$cw < 170 & odb$prodcd_id=="0" ) ,] # commerical sized crab only
years = sort( unique( odb$fishyr ) )
# get proportion by cc
regions = c("cfanorth", "cfasouth", "cfa4x")
years = sort( unique( odb$fishyr ) )
res = NULL
for (r in regions) {
for (y in years) {
out = proportion.cc (odb, region=r, year=y)
res = rbind( res, cbind( r, y, t(out)) )
}}
cnames = c("region", "fishyr", c(1:5), "ntot")
colnames(res) = cnames
print(res)
res = as.data.frame(res)
res[is.na(res)] <- NA
# for (i in cnames[-1]) res[,i] = as.numeric(as.character((res[,i])))
setwd(outtabledir)
ct <- c("CC1", "CC2", "CC3", "CC4", "CC5")
Rn = res[res$region=="cfanorth", 3:7]
#Rn = as.matrix( res[ which(res$region=="cfanorth") , as.character(c(1:5)) ] )
rownames(Rn) = years
colnames(Rn) = ct
print.xtable(Rn, type="latex", file="table.CC.large.north.obs.tex")
HTML(Rn, file="table.CC.large.north.obs.html")
#Rs = as.matrix( res[ which(res$region=="cfasouth") , as.character(c(1:5)) ] )
Rs = res[res$region=="cfasouth", 3:7]
rownames(Rs) = years
colnames(Rs) = ct
print.xtable(Rs, type="latex", file="table.CC.large.south.obs.tex")
HTML(Rs, file="table.CC.large.south.obs.html")
#Rx = as.matrix( res[ which(res$region=="cfa4x") , as.character(c(1:5)) ] )
Rx = res[res$region=="cfa4x", 3:7]
rownames(Rx) = years
colnames(Rx) = ct
print.xtable(Rx, type="latex", file="table.CC.large.4x.obs.tex")
HTML(Rx, file="table.CC.large.4x.obs.html")
# ----------------------------------------
# Percent soft from observed data
odb = odb0
odb = odb[ which( odb$cw > 95 & odb$cw < 170 & odb$prodcd_id=="0" ) ,] # commercial crab
years = sort( unique( odb$fishyr ) )
res = NULL
for (r in p$regions) {
for (y in years) {
out = proportion.soft (odb, region=r, year=y)
res = rbind( res, cbind( r, y, t(out)) )
}}
cnames = c("region", "fishyr", "pr.soft", "nsoft", "ntot")
colnames(res) = cnames
print(res)
res = as.data.frame(res)
for (i in cnames[-1]) res[,i] = as.numeric(as.character((res[,i])))
Rn = as.matrix( res[ which(res$region=="cfanorth") , ] )
rownames(Rn) = years
HTML(Rn, file="table.proportion.soft.north.obs.html")
Rs = as.matrix( res[ which(res$region=="cfasouth" ), ] )
rownames(Rs) = years
HTML(Rs, file="table.proportion.soft.south.obs.html")
Rx = as.matrix( res[ which(res$region=="cfa4x") , ] )
rownames(Rx) = years
HTML(Rx, file="table.proportion.soft.4x.obs.html")
# instars of interest: 11 and 12
# growth increment (assumming average weight in the midpoint of each increment)
growth.11.to.12 = predict.mass.g.from.CW.mm( mean(CW.interval.male(12)) ) - predict.mass.g.from.CW.mm (mean(CW.interval.male(11)) )
# = 419 g
# 12to13 = ~450
# Table of proportion discarded
odb = observer.db("odb")
regions = c("cfanorth", "cfasouth", "cfa4x")
years = sort( unique( odb$fishyr ) )
out = NULL
for (r in regions) {
for (y in years) {
res = proportion.legal (odb, region=r, year=y)
out = rbind(out, cbind( r, y, res[1], res[2], res[3] ) )
} }
out
HTML(out, file="table.proportion.discarded.html")
# ---------------------------------------- USED
# Carapace condition from trawl data >= 95mm CW ... not kriged .. simple proportions
det0 = snowcrab.db( p=p, DS="det.georeferenced" )
det0$fishyr = det0$yr ## the counting routine expectes this variable
det = det0[ which( det0$cw >= 95 ) ,] # commerical sized crab only
years = sort( unique( det$yr ) )
res = NULL
for (r in p$regions) {
for (y in years) {
out = proportion.cc (det, region=r, year=y)
res = rbind( res, cbind( r, y, t(out)) )
}}
cnames = c("region", "fishyr", c(1:5), "ntot")
colnames(res) = cnames
print(res)
res = as.data.frame(res)
for (i in cnames[-1]) res[,i] = as.numeric(as.character((res[,i])))
(res)
HTML(res, file="table.CC.large.survey.html")
# ------------------
# counts of stations in each area
# check towquality .. this should always == 1
set = snowcrab.db(p=p, DS="set.clean")
if (length( unique( set$towquality) ) != 1 ) print("error -- not good tows")
out = data.frame(yr=sort( unique(set$yr )) )
for (reg in c("cfaall", "cfanorth", "cfasouth","cfa4x" ) ) {
d = polygon_inside(set[,c("lon","lat")], reg)
e = as.data.frame( xtabs(~yr, data=set[d,]) )
names(e) = c("yr", reg)
e$yr = as.numeric(as.character(e$yr) )
out = merge(out, e, by="yr", all=T)
}
print(out)
HTML(out, file="table.tow_counts_survey.html")
# % mat calculations: deprecated ... size analysis is now in 01_snowcrab.R
# loc = file.path(sc.R, "size.data")
# dir.create(path=loc, recursive=T, showWarnings=F)
# outfilename = paste( c("mi", "mm", "fi", "fm"), "rdata", sep=".")
# outfile = file.path(loc, paste(outfilename))
# for (f in outfile) load(f)
# f.i = f.imm[which( rownames(f.imm)%in% sids ) ,]
# f.i.means = apply(X=f.i, MARGIN=2, FUN=mean)
# f.m = f.mat[which( rownames(f.mat)%in% sids ) ,]
# f.m.means = apply(X=f.m, MARGIN=2, FUN=mean)
# toplot = rbind(f.m.means, f.i.means)
# ii = as.data.frame(t(toplot))
# ii$cw = as.numeric(rownames(ii))
# ii$pmat = ii[,1]/ (ii[,1]+ii[,2]) * 100
# plot(ii$cw, ii$pmat)
# abline(h=50)
# str(ii)
#`data.frame': 70 obs. of 4 variables:
# $ f.m.means: num 0 0 0 0 0 ...
# $ f.i.means: num 2.80 6.19 20.05 24.29 74.11 ...
# $ cw : num 12 14 16 18 20 22 24 26 28 30 ...
# $ pmat : num 0 0 0 0 0 ...
# ---------------------------------------- NOT USED ____________
# Carapace condition from trawl data < 95mm CW ... not kriged .. simple proportions
det0 = snowcrab.db( p=p, DS="det.georeferenced" )
det0$fishyr = det0$yr ## the counting routine expectes this variable
det = det0[ which( det0$cw < 95 ) ,] # commerical sized crab only
years = sort( unique( det$yr ) )
res = NULL
for (r in p$regions) {
for (y in years) {
out = proportion.cc (det, region=r, year=y)
res = rbind( res, cbind( r, y, t(out)) )
}}
cnames = c("region", "fishyr", c(1:5), "ntot")
colnames(res) = cnames
print(res)
res = as.data.frame(res)
for (i in cnames[-1]) res[,i] = as.numeric(as.character((res[,i])))
(res)
}
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