Package 'mixfishtools' reference manual

Title:	WGMIXFISH tools for reproducibility
Description:	Contains plot templates used in WGMIXFISH-ADVICE and Fisheries Overviews.
Authors:	Marc Taylor, Mikel Aristegui, Johnathan Ball, Harriet Cole, Paul Dolder
Maintainer:	Marc Taylor <[email protected]>
License:	GPL-3 + file LICENSE
Version:	0.4.3
Built:	2024-11-18 06:11:10 UTC
Source:	https://github.com/ices-tools-dev/mixfishtools

Geometric Mean

Description

Calculates the geometric mean of a vector

Usage

gm_mean(x, na.rm = TRUE, zero.propagate = FALSE)
gm_mean(x, na.rm = TRUE, zero.propagate = FALSE)

Arguments

`x`	numeric vector of positive numbers.
`na.rm`	Remove NAs befor calculation (as in `mean`)
`zero.propagate`	Logical. Should zeros be considered (resulting in output of zero)

References

From stackoverflow answer posted by Paul McMurdie

Examples

### simple usage
gm_mean(c(1:4))
gm_mean(c(-1:4)) # negative values not allowed
gm_mean(c(0:4)) # zeros do not propagate
gm_mean(c(0:4), zero.propagate=TRUE) #zeros allowed to propagate
gm_mean(c(1,2,3,4, NaN)) # na.rm=TRUE
gm_mean(c(1,2,3,4, NaN), na.rm=FALSE) # na.rm=FALSE

### example of proportional change
df <- data.frame(index1 = 5, index2 = 25) # two indices of differing magnitude
mult <- c(1.25, 1.5) # multiplier
df <- rbind(df, df*mult) # indices change by differing proportions
df # view dataframe
gm_mean(mult) # mean proportional increase
gm_mean(df[2,]) / gm_mean(df[1,]) # equal
gm_mean(df[2,] / df[1,]) # equal

### simple usage
gm_mean(c(1:4))
gm_mean(c(-1:4)) # negative values not allowed
gm_mean(c(0:4)) # zeros do not propagate
gm_mean(c(0:4), zero.propagate=TRUE) #zeros allowed to propagate
gm_mean(c(1,2,3,4, NaN)) # na.rm=TRUE
gm_mean(c(1,2,3,4, NaN), na.rm=FALSE) # na.rm=FALSE

### example of proportional change
df <- data.frame(index1 = 5, index2 = 25) # two indices of differing magnitude
mult <- c(1.25, 1.5) # multiplier
df <- rbind(df, df*mult) # indices change by differing proportions
df # view dataframe
gm_mean(mult) # mean proportional increase
gm_mean(df[2,]) / gm_mean(df[1,]) # equal
gm_mean(df[2,] / df[1,]) # equal

Plot fleet landings taken up relative to recent landings / quota

Description

Plot of a fleets catch difference from the recent catches or the quota. By fleet. Most- and least-limiting stocks are also denoted. Testing in response to WKMIXFISH2.

Usage

plot_catch_change(
  data = NULL,
  basis = "recent_catch",
  dataYrs = NULL,
  advYr = NULL,
  sc = "min",
  fleets_excl = NULL,
  refTable = NULL,
  xlab = "Stock",
  ylab = "catch change (tonnes)",
  fillLegendTitle = "Stock",
  colLegendTitle = "Limiting stock"
)
plot_catch_change(
  data = NULL,
  basis = "recent_catch",
  dataYrs = NULL,
  advYr = NULL,
  sc = "min",
  fleets_excl = NULL,
  refTable = NULL,
  xlab = "Stock",
  ylab = "catch change (tonnes)",
  fillLegendTitle = "Stock",
  colLegendTitle = "Limiting stock"
)

Arguments

`data`	data.frame Contains information on catch by fleet and stock
`basis`	is a character vector with the basis on which to compare the scenario landings, either 'recent_catch' or 'Quota'. When 'recent_catch' is used, the average landings from the defined years (argument 'dataYrs') is used as the reference instead of the advice year quota ('Quota')
`dataYrs`	is a vector of years on which to base recent catches. Used when ‘basis = ’recent_catch''.
`advYr`	is a vector of the year in which the scenario catches are generated.
`sc`	is a vector with the scenario to plot, e.g. "min"
`fleets_excl`	is a vector of fleet names not to plot, e.g. "OTH_OTH"
`refTable`	data.frame Contains stock look-up information for consistent plotting of stocks. 'Advice_name' defines the stock names corresponding to 'data' object. 'col' defines the color used to fill bars in plot. 'order' defines the order of stocks in the plot facets.
`xlab`	character X-axis label (Default: 'xlab = "Stock"')
`ylab`	character Y-axis label (Default: ‘ylab = "KW days (’000)"')
`fillLegendTitle`	character Fill legend title (Default: 'fillLegendTitle = "Effort stock"')
`colLegendTitle`	character Color legend title (Default: 'colLegendTitle = "Limiting stock"')

Details

Users will need to provide the data and reference table objects to produce the plot.

Value

plot output of class ggplot

Examples


# make example data
data(refTable) # reference table with stock advice names, colors, order, etc.
data(stfFltStkSum) # summary of fleet/stock-related catch variables
advYr <- 2022 # advice year

# replace short stock names with ICES stock codes
stfFltStkSum$stock <- refTable$stock[match(stfFltStkSum$stock,
  refTable$stock_short)]


p <- plot_catch_change(data = stfFltStkSum,
 basis = "Quota",
 dataYrs = 2020:2022,
 advYr = advYr,
 sc = "min",
 fleets_excl = "OTH_OTH",
 refTable = refTable,
 xlab = "Stock",
 ylab = "landings change (tonnes)",
 fillLegendTitle = "Stock",
 colLegendTitle = "Limiting stock")

print(p)

# export plot
# png("plot_change.png", width = 8, height = 10, units = "in", res = 400)
# print(p); dev.off()

# make example data
data(refTable) # reference table with stock advice names, colors, order, etc.
data(stfFltStkSum) # summary of fleet/stock-related catch variables
advYr <- 2022 # advice year

# replace short stock names with ICES stock codes
stfFltStkSum$stock <- refTable$stock[match(stfFltStkSum$stock,
  refTable$stock_short)]


p <- plot_catch_change(data = stfFltStkSum,
 basis = "Quota",
 dataYrs = 2020:2022,
 advYr = advYr,
 sc = "min",
 fleets_excl = "OTH_OTH",
 refTable = refTable,
 xlab = "Stock",
 ylab = "landings change (tonnes)",
 fillLegendTitle = "Stock",
 colLegendTitle = "Limiting stock")

print(p)

# export plot
# png("plot_change.png", width = 8, height = 10, units = "in", res = 400)
# print(p); dev.off()

Alluvial plot of catch

Description

Alluvial plot of catch

Usage

plot_catchAlluvial(
  data,
  refTable,
  text_repel = FALSE,
  text_size = 2,
  mult_x = c(0.1, 0.1),
  nudge_x = 1/3,
  stratum_width = 1/3,
  stratum_col = "white",
  xlab = NULL,
  ylab = "Catch [t]",
  fillLegendTitle = "Stock",
  addLegend = TRUE,
  plotTitle = NULL
)
plot_catchAlluvial(
  data,
  refTable,
  text_repel = FALSE,
  text_size = 2,
  mult_x = c(0.1, 0.1),
  nudge_x = 1/3,
  stratum_width = 1/3,
  stratum_col = "white",
  xlab = NULL,
  ylab = "Catch [t]",
  fillLegendTitle = "Stock",
  addLegend = TRUE,
  plotTitle = NULL
)

Arguments

`data`	data.frame Contains information on catch (or landings) corresponding to different fleet, metier, and stock combinations. Variable names should include 'stock', 'fleet', 'metier', and 'value'. Stock variable levels ('stock') should match those found in 'refTable' containing color and order information.
`refTable`	data.frame Contains stock look-up information for consistent plotting of stocks. 'stock' defines the stock names corresponding to 'data' object. 'col' defines the color used to fill bars in plot. 'order' defines the order of stocks in the plot facets.
`text_repel`	logical Should stratum labels ('stock', 'fleet', 'metier') be repelled to prevent overlapping (using 'ggrepel::geom_text_repel') (Default: 'text_repel = FALSE')
`text_size`	numeric Value for text label size (Default: 'text_size = 2')
`mult_x`	vector. Two values defining the multiplier to expand the x-axis (Default: 'mult_x = c(0.1, 0.1)'). Passes values to 'expansion(mult = mult_x)'.
`nudge_x`	numeric. Value to nudge direction of stratum labels when 'text_repel = TRUE'. (Default: 'nudge_x = 1/3')
`stratum_width`	numeric. Width of the stratum bar (Default: 'stratum_width = 1/3')
`stratum_col`	color definition. Color used to fll the non-stock strata (Default: 'stratum_col = "white"')
`xlab`	character X-axis label (Default: 'xlab = NULL' removes label)
`ylab`	character Y-axis label (Default: 'ylab = Catch [t]')
`fillLegendTitle`	character Fill legend title (Default: 'fillLegendTitle = "Stock"')
`addLegend`	logical Should legend of stock fill colors be added
`plotTitle`	character Plot title (Default: 'plotTitle = NULL' removes title)

Value

plot output of class ggplot

Examples


data("stfMtStkSum")
data("refTable")

## create catch data by fleet, metier, and stock
dataYr <- 2020
# add country for filter (if desired)
stfMtStkSum$country <- unlist(lapply(strsplit(stfMtStkSum$fleet, "_"),
  function(x){x[1]}))
# filter data
df <- subset(stfMtStkSum, year == 2020 & scenario == "min" &
  country == "BE")[,c("fleet", "metier", "stock", "landings")]
df$stock <- refTable$stock[match(df$stock, refTable$stock_short)]
names(df)[4] <- "value"

plot_catchAlluvial(data = df, refTable = refTable)

# repel labels
plot_catchAlluvial(data = df, refTable = refTable,
  text_repel = TRUE, text_size = 2)

# repel labels and suppress legend
plot_catchAlluvial(data = df, refTable = refTable,
  text_repel = TRUE, text_size = 2, addLegend = FALSE)

data("stfMtStkSum")
data("refTable")

## create catch data by fleet, metier, and stock
dataYr <- 2020
# add country for filter (if desired)
stfMtStkSum$country <- unlist(lapply(strsplit(stfMtStkSum$fleet, "_"),
  function(x){x[1]}))
# filter data
df <- subset(stfMtStkSum, year == 2020 & scenario == "min" &
  country == "BE")[,c("fleet", "metier", "stock", "landings")]
df$stock <- refTable$stock[match(df$stock, refTable$stock_short)]
names(df)[4] <- "value"

plot_catchAlluvial(data = df, refTable = refTable)

# repel labels
plot_catchAlluvial(data = df, refTable = refTable,
  text_repel = TRUE, text_size = 2)

# repel labels and suppress legend
plot_catchAlluvial(data = df, refTable = refTable,
  text_repel = TRUE, text_size = 2, addLegend = FALSE)

Plot landings or catch compositions

Description

Landings or catch compositions by stock for selected years, countries, fleets, metiers etc

Usage

plot_catchComp(
  data,
  refTable,
  filters = NULL,
  selectors = "metier",
  divider = NULL,
  yvar = "landings"
)
plot_catchComp(
  data,
  refTable,
  filters = NULL,
  selectors = "metier",
  divider = NULL,
  yvar = "landings"
)

Arguments

`data`	data.frame Contains information on fleet data to make catch (landings) compositions. Required variables are: 'year', 'area','country', 'fleet', 'metier','stock','landings', 'catch', and 'fleet_type' which indicates if the 'fleet' is a 'main' or 'residual' fleet.
`refTable`	data.frame A look-up reference table for stocks and associated attributes. The `refTable` data.frame lists stock names and corresponding colours for consistency across plots. To be used as a look-up table in converting between variable stock names and printed ones. 1) stock - ICES stock codes used in advice 2) order - stock order to be used in plots 3) col - stock colors for plots (e.g. pals::brewer.paired()) 4) stock_short - short stock name used in mixed fishery model
`filters`	list of character strings listing the 'year', 'area','country', 'fleet' and/or 'metier' to filter from `data`. Default value of `NULL` will produce catch compositions using all data in `data`.
`selectors`	character string of one of 'year', 'area','country', 'fleet' or 'metier'. The chosen selector will be plotted on the x-axis. Multiple variables can be listed as `selectors` and these will be concatenated into a "label" for plotting. The default value is `metier` and will produce catch compositions by 'metier'.
`divider`	character string of one of 'year', 'area','country', 'fleet' or 'metier'. Only one variable can be listed as a 'divider'. The chosen divider will be used to divide the catch compositions into subplots - e.g. one per 'fleet'. The default value of `NULL` will plot just one catch composition (i.e. no subplots).
`yvar`	character string of variable to be plotted on the y-axis (Default: yvar = "landings")

Details

Users will need to provide the data and refTable objects to produce the plot.

Value

plot output of class ggplot

Examples


# prepare example data
data(refTable)
data(stfMtStkSum)

# subset data to a single scenario (e.g. min)
data <- subset(stfMtStkSum, scenario == "min")

# add country and area identifiers (if desired)
tmp <- strsplit(data$metier, ".", fixed = TRUE)
data$area <- unlist(lapply(tmp, FUN = function(x){ifelse(length(x)==2, x[2], NA)}))
tmp <- strsplit(data$fleet, "_", fixed = TRUE)
data$country <- unlist(lapply(tmp, FUN = function(x){ifelse(length(x)==2, x[1], NA)}))


# replace stock with ICES stock code
data$stock <- refTable$stock[match(data$stock, refTable$stock_short)]


# Plot catch composition for each fleet over time
selectors <- c("year")
divider <- c("fleet")
p <- plot_catchComp(data, refTable, filters = NULL, selectors, divider, yvar = "catch")
print(p)

# ggplot format adjustments
p2 <- p + theme(text = element_text(size = 8),
  axis.text.x = element_text(angle = 90, vjust = 0, hjust=1)) +
  facet_wrap(divider,  scales = "fixed") # remove free axes
print(p2)

# export plot
# png("catchComp1.png", width = 7, height = 7, units = "in", res = 400)
#  print(p2); dev.off()


# Plot landings composition for each area by country-metier combinations
selectors <- c("country", "metier")
divider <- c("area")
p <- plot_catchComp(data,refTable,filters=NULL,selectors, divider)
print(p)

# Plot landings composition for each metier by country for 2022
filters <- list(year = 2022)
selectors <- c("metier")
divider <- c("country")
plot_catchComp(data, refTable, filters, selectors, divider)

# Plot landings compositions for each fleet by metier for Scottish fleets.
filters <- list(year=2022, country="SC")
selectors <- c("metier")
divider <- c("fleet")
plot_catchComp(data,refTable,filters,selectors, divider)


# prepare example data
data(refTable)
data(stfMtStkSum)

# subset data to a single scenario (e.g. min)
data <- subset(stfMtStkSum, scenario == "min")

# add country and area identifiers (if desired)
tmp <- strsplit(data$metier, ".", fixed = TRUE)
data$area <- unlist(lapply(tmp, FUN = function(x){ifelse(length(x)==2, x[2], NA)}))
tmp <- strsplit(data$fleet, "_", fixed = TRUE)
data$country <- unlist(lapply(tmp, FUN = function(x){ifelse(length(x)==2, x[1], NA)}))


# replace stock with ICES stock code
data$stock <- refTable$stock[match(data$stock, refTable$stock_short)]


# Plot catch composition for each fleet over time
selectors <- c("year")
divider <- c("fleet")
p <- plot_catchComp(data, refTable, filters = NULL, selectors, divider, yvar = "catch")
print(p)

# ggplot format adjustments
p2 <- p + theme(text = element_text(size = 8),
  axis.text.x = element_text(angle = 90, vjust = 0, hjust=1)) +
  facet_wrap(divider,  scales = "fixed") # remove free axes
print(p2)

# export plot
# png("catchComp1.png", width = 7, height = 7, units = "in", res = 400)
#  print(p2); dev.off()


# Plot landings composition for each area by country-metier combinations
selectors <- c("country", "metier")
divider <- c("area")
p <- plot_catchComp(data,refTable,filters=NULL,selectors, divider)
print(p)

# Plot landings composition for each metier by country for 2022
filters <- list(year = 2022)
selectors <- c("metier")
divider <- c("country")
plot_catchComp(data, refTable, filters, selectors, divider)

# Plot landings compositions for each fleet by metier for Scottish fleets.
filters <- list(year=2022, country="SC")
selectors <- c("metier")
divider <- c("fleet")
plot_catchComp(data,refTable,filters,selectors, divider)

Plot summarizing over- and under-quota catches by stock and scenario. Dashed line displays quota by stock. Colored background further emphasizes over- and under-quota catches. Used as the headline plot in WGMIXFISH-ADVICE.

Usage

plot_catchScenStk(
  data,
  adv,
  ofwhich = FALSE,
  xlab = "Scenario",
  ylab = "Catch [t]"
)
plot_catchScenStk(
  data,
  adv,
  ofwhich = FALSE,
  xlab = "Scenario",
  ylab = "Catch [t]"
)

Arguments

`data`	data.frame Contains catch ('catch') by scenario ('scenario') and stock ('stock').
`adv`	data.frame Contains advice ('advice') by stock ('stock'). Optional upper ('upper') and lower ('lower') advice limits can be included.
`ofwhich`	logical. If TRUE an of which limit will be plotted. Requires a 'catch_ofwhich' column in data and an 'advice_ofwhich' column in adv.
`xlab`	character X-axis label (Default: 'xlab = "Scenario"')
`ylab`	character Y-axis label (Default: 'ylab = "Catch [t]"')

Value

plot output of class ggplot

Examples


# make example data
data(stfFltStkSum)
head(stfFltStkSum)

# subset data to advice year and restrictive stocks
advYr <- 2022 # advice year
restr.stks <- c("COD-NS", "HAD", "PLE-EC", "PLE-NS", "POK", "SOL-EC",
  "SOL-NS", "TUR", "WHG-NS", "WIT")
stfFltStkSum <- subset(stfFltStkSum, year == advYr & stock %in% restr.stks)

# data for plotting (catch by scenario and stock)
catchScenStk <- aggregate(catch ~ scenario + stock, data = stfFltStkSum,
  FUN = sum)

# re-order scenarios (sq_E, max, min, ... )
catchScenStk$scenario <- factor(catchScenStk$scenario,
  levels = c("min", "max", "sq_E", "cod-ns"),
  labels = c("min", "max", "sq_E", "cod-ns"))
head(catchScenStk)

catchRange <- rbind(
  data.frame(stock = "COD-NS", advice = 14276, lower = 9701, upper = 14276),
  data.frame(stock = "HAD", advice = 128708, lower = 111702, upper = 128708),
  data.frame(stock = "PLE-EC", advice = 6365, lower = 4594, upper = 6365),
  data.frame(stock = "PLE-NS", advice = 142507, lower = 101854,
    upper = 195622),
  data.frame(stock = "POK", advice = 49614, lower = 30204, upper = 49614),
  data.frame(stock = "SOL-EC", advice = 1810, lower = 1068, upper = 2069),
  data.frame(stock = "SOL-NS", advice = 15330, lower = 9523, upper = 21805),
  data.frame(stock = "TUR", advice = 3609, lower = 2634, upper = 4564),
  data.frame(stock = "WHG-NS", advice = 88426, lower = 70169, upper = 91703),
  data.frame(stock = "WIT", advice = 1206, lower = 875, upper = 1206)
)

# use ICES stock codes
data(refTable)
head(refTable)
catchScenStk$stock <- refTable$stock[match(catchScenStk$stock,
  refTable$stock_short)]
catchRange$stock <- refTable$stock[match(catchRange$stock,
  refTable$stock_short)]


# plot without range
p <- plot_catchScenStk(data = catchScenStk, adv = catchRange[,1:2])
print(p)

# plot with range
p <- plot_catchScenStk(data = catchScenStk, adv = catchRange)
print(p)

# export plot
# png("catchScenStk1.png", width = 6, height = 5, units = "in", res = 400)
# print(p); dev.off()

# make example data
data(stfFltStkSum)
head(stfFltStkSum)

# subset data to advice year and restrictive stocks
advYr <- 2022 # advice year
restr.stks <- c("COD-NS", "HAD", "PLE-EC", "PLE-NS", "POK", "SOL-EC",
  "SOL-NS", "TUR", "WHG-NS", "WIT")
stfFltStkSum <- subset(stfFltStkSum, year == advYr & stock %in% restr.stks)

# data for plotting (catch by scenario and stock)
catchScenStk <- aggregate(catch ~ scenario + stock, data = stfFltStkSum,
  FUN = sum)

# re-order scenarios (sq_E, max, min, ... )
catchScenStk$scenario <- factor(catchScenStk$scenario,
  levels = c("min", "max", "sq_E", "cod-ns"),
  labels = c("min", "max", "sq_E", "cod-ns"))
head(catchScenStk)

catchRange <- rbind(
  data.frame(stock = "COD-NS", advice = 14276, lower = 9701, upper = 14276),
  data.frame(stock = "HAD", advice = 128708, lower = 111702, upper = 128708),
  data.frame(stock = "PLE-EC", advice = 6365, lower = 4594, upper = 6365),
  data.frame(stock = "PLE-NS", advice = 142507, lower = 101854,
    upper = 195622),
  data.frame(stock = "POK", advice = 49614, lower = 30204, upper = 49614),
  data.frame(stock = "SOL-EC", advice = 1810, lower = 1068, upper = 2069),
  data.frame(stock = "SOL-NS", advice = 15330, lower = 9523, upper = 21805),
  data.frame(stock = "TUR", advice = 3609, lower = 2634, upper = 4564),
  data.frame(stock = "WHG-NS", advice = 88426, lower = 70169, upper = 91703),
  data.frame(stock = "WIT", advice = 1206, lower = 875, upper = 1206)
)

# use ICES stock codes
data(refTable)
head(refTable)
catchScenStk$stock <- refTable$stock[match(catchScenStk$stock,
  refTable$stock_short)]
catchRange$stock <- refTable$stock[match(catchRange$stock,
  refTable$stock_short)]


# plot without range
p <- plot_catchScenStk(data = catchScenStk, adv = catchRange[,1:2])
print(p)

# plot with range
p <- plot_catchScenStk(data = catchScenStk, adv = catchRange)
print(p)

# export plot
# png("catchScenStk1.png", width = 6, height = 5, units = "in", res = 400)
# print(p); dev.off()

Plot fleet effort to uptake stock quotas

Description

Plot of effort required to uptake each stock's quota by fleet. Most- and least-limiting stocks are also denoted. Used in WGMIXFISH-ADVICE.

Usage

plot_effortFltStk(
  data,
  refTable,
  xlab = "Stock",
  ylab = "Effort ['000 KW days]",
  fillLegendTitle = "Stock",
  colLegendTitle = "Limiting stock",
  linewidthDefault = 0.5,
  linewidthLimitation = 1
)
plot_effortFltStk(
  data,
  refTable,
  xlab = "Stock",
  ylab = "Effort ['000 KW days]",
  fillLegendTitle = "Stock",
  colLegendTitle = "Limiting stock",
  linewidthDefault = 0.5,
  linewidthLimitation = 1
)

Arguments

`data`	data.frame Contains information on effort required to uptake quotas by fleet and stock, plus designation of each stock's limitation status to the fleet's fishing effort. Stock variable names ('stock') should match those of `refTable`. Other required variables include: 'Limitation' - defines, by fleet, the most- ('most'), least- ('least'), and intermediate-limiting ('NA') stocks; 'quotaEffort' - the effort, by fleet, required to take up the quota share of each stock; 'sqEffort' - status quo effort corresponding to most recent data year before forecast.
`refTable`	data.frame Contains stock look-up information for consistent plotting of stocks. 'stock' defines the stock names corresponding to 'data' object. 'col' defines the color used to fill bars in plot. 'order' defines the order of stocks in the plot facets.
`xlab`	character X-axis label (Default: 'xlab = "Stock"')
`ylab`	character Y-axis label (Default: ‘ylab = "Effort [’000 KW days]"')
`fillLegendTitle`	character Fill legend title (Default: 'fillLegendTitle = "Effort stock"')
`colLegendTitle`	character Color legend title (Default: 'colLegendTitle = "Limiting stock"')
`linewidthDefault`	numeric Value for default line width of intermediate- limiting stocks (Default: 'linewidthDefault = 0.5')
`linewidthLimitation`	numeric Value for default line width of limiting stocks (Default: 'linewidthLimitation = 1')

Details

Users will need to provide the data and reference table objects to produce the plot. In the best case, effort associated with complete quota uptake by fleet ('data$quotaEffort') may be derived from scenarios restricting fleet catch one stock at a time. In the following example, however, effort levels are derived by linearly extrapolating the quota uptake levels by the effort of the "min" scenario. This is strictly linear when quotas are based on partial F, as in FCube. In FLBEIA, quotas are based on catch (or landings), which may deviate from a linear relationship when a stock is close full exploitation (should not result from an ICES harvest control rule).

Value

plot output of class ggplot

Examples


# example data for plot_effortFltStk --------------------------------------

data(refTable) # reference table with stock advice names, colors, order, etc.
data(stfFltSum) # summary of fleet-related variables (e.g. effort)
data(stfFltStkSum) # summary of fleet/stock-related catch variables

## get data from advice year

# catches by fleet and stock
advYr <- 2022 # advice year
df <- subset(stfFltStkSum, scenario == "min" & year == advYr)

## effort by fleet and scenario
eff <- subset(
 stfFltSum, scenario == "min" & year == advYr)[,c("fleet", "effort")]
sqEff <- subset(
 stfFltSum, scenario == "sq_E" & year == advYr)[,c("fleet", "effort")]
names(sqEff)[2] <- "sqEffort"
eff <- merge(x = eff, y = sqEff, all.x = TRUE)
df <- merge(x = df, y = eff, all.x = TRUE)
df$quotaEffort <- df$effort / df$quotaUpt

## Determine most- and least-limiting stock by fleet
# restrictive stocks
restr.stks <- c("COD-NS", "HAD", "PLE-EC", "PLE-NS", "POK", "SOL-EC",
 "SOL-NS", "TUR", "WHG-NS", "WIT", "NEP6", "NEP7", "NEP8", "NEP9")
fls <- unique(df$fleet)
df2 <- vector("list", length(fls))
names(df2) <- fls
for(i in seq(fls)){
 tmp <- subset(df, fleet == fls[i])
 tmp$Limitation <- NA # initial NA setting for all stocks

 # most-limiting (highest quota uptake in min scenario)
 mostLimStk <- subset(tmp, stock %in% restr.stks)
 mostLimStk <- mostLimStk$stock[which.max(mostLimStk$quotaUpt)]
 tmp$Limitation[which(tmp$stock == mostLimStk)] <- "most"

 # least-limiting (lowest quota uptake in max scenario)
 leastLimStk <- subset(stfFltStkSum, scenario == "max" & year == advYr &
   fleet == fls[i] & stock %in% restr.stks)
 leastLimStk <- leastLimStk$stock[which.min(leastLimStk$quotaUpt)]
 tmp$Limitation[which(tmp$stock == leastLimStk)] <- "least"

 # return result
 df2[[i]] <- tmp
}
df2 <- do.call("rbind", df2)

# replace short stock names with ICES stock codes
df2$stock <- refTable$stock[match(df2$stock, refTable$stock_short)]


# plot
p <- plot_effortFltStk(data = df2, refTable = refTable)
# png("effortFltStk1.png", width = 8, height = 10, units = "in", res = 400)
# print(p); dev.off()

# adjust ggplot2 settings
p <- p + theme(text = element_text(size = 12))
# png("effortFltStk2.png", width = 8, height = 10, units = "in", res = 400)
# print(p); dev.off()

# example data for plot_effortFltStk --------------------------------------

data(refTable) # reference table with stock advice names, colors, order, etc.
data(stfFltSum) # summary of fleet-related variables (e.g. effort)
data(stfFltStkSum) # summary of fleet/stock-related catch variables

## get data from advice year

# catches by fleet and stock
advYr <- 2022 # advice year
df <- subset(stfFltStkSum, scenario == "min" & year == advYr)

## effort by fleet and scenario
eff <- subset(
 stfFltSum, scenario == "min" & year == advYr)[,c("fleet", "effort")]
sqEff <- subset(
 stfFltSum, scenario == "sq_E" & year == advYr)[,c("fleet", "effort")]
names(sqEff)[2] <- "sqEffort"
eff <- merge(x = eff, y = sqEff, all.x = TRUE)
df <- merge(x = df, y = eff, all.x = TRUE)
df$quotaEffort <- df$effort / df$quotaUpt

## Determine most- and least-limiting stock by fleet
# restrictive stocks
restr.stks <- c("COD-NS", "HAD", "PLE-EC", "PLE-NS", "POK", "SOL-EC",
 "SOL-NS", "TUR", "WHG-NS", "WIT", "NEP6", "NEP7", "NEP8", "NEP9")
fls <- unique(df$fleet)
df2 <- vector("list", length(fls))
names(df2) <- fls
for(i in seq(fls)){
 tmp <- subset(df, fleet == fls[i])
 tmp$Limitation <- NA # initial NA setting for all stocks

 # most-limiting (highest quota uptake in min scenario)
 mostLimStk <- subset(tmp, stock %in% restr.stks)
 mostLimStk <- mostLimStk$stock[which.max(mostLimStk$quotaUpt)]
 tmp$Limitation[which(tmp$stock == mostLimStk)] <- "most"

 # least-limiting (lowest quota uptake in max scenario)
 leastLimStk <- subset(stfFltStkSum, scenario == "max" & year == advYr &
   fleet == fls[i] & stock %in% restr.stks)
 leastLimStk <- leastLimStk$stock[which.min(leastLimStk$quotaUpt)]
 tmp$Limitation[which(tmp$stock == leastLimStk)] <- "least"

 # return result
 df2[[i]] <- tmp
}
df2 <- do.call("rbind", df2)

# replace short stock names with ICES stock codes
df2$stock <- refTable$stock[match(df2$stock, refTable$stock_short)]


# plot
p <- plot_effortFltStk(data = df2, refTable = refTable)
# png("effortFltStk1.png", width = 8, height = 10, units = "in", res = 400)
# print(p); dev.off()

# adjust ggplot2 settings
p <- p + theme(text = element_text(size = 12))
# png("effortFltStk2.png", width = 8, height = 10, units = "in", res = 400)
# print(p); dev.off()

Bar chart of landings by stock and metier

Description

Bar chart of landings by stock and by metier/gear groupings. Used in WGMIXFISH-ADVICE

Usage

plot_landByMetStock(
  data,
  refTable,
  xlab = "",
  ylab = "Landings ['000 t]",
  fillLegendTitle = "Stock"
)
plot_landByMetStock(
  data,
  refTable,
  xlab = "",
  ylab = "Landings ['000 t]",
  fillLegendTitle = "Stock"
)

Arguments

`data`	data.frame Contains information on the landings (or catch) in tonnes by stock and metier/gear grouping from the fleet data used at WGMIXFISH-ADVICE. Stock variable names ('stock') should match those of `refTable`.
`refTable`	data.frame A look-up reference table for stocks and associated attributes. The `refTable` data.frame lists stock names and corresponding colours for consistency across plots. To be used as a look-up table in converting between variable stock names and printed ones. 1) stock - ICES stock codes used in advice 2) order - stock order to be used in plots 3) col - stock colors for plots (e.g. pals::brewer.paired()) 4) stock_short - short stock name used in mixed fishery model
`xlab`	character X-axis label (Default (blank): 'xlab = ""')
`ylab`	character Y-axis label (Default: 'ylab = "Landings [t]"')
`fillLegendTitle`	character Fill legend title Other required variables include: 'metier' which defines the metier code or gear grouping code; 'value' the value of landings (or catch) for each 'stock' and 'metier'

Details

Users will need to provide the data object to produce the plot.

Value

plot output of class ggplot

Examples


# make example data
data(stfMtStkSum)
head(stfMtStkSum)
data(refTable)
head(refTable)

data <- stfMtStkSum

# add metier_cat
tmp <- strsplit(data$metier, ".", fixed = TRUE)
data$metier_cat <- unlist(lapply(tmp, FUN = function(x){x[1]}))

# select final data year and a single scenario, and aggregated total landings
# by stock and metier
datYr <- 2020
data <- subset(data, year == datYr & scenario == "min")
agg <- aggregate(landings ~ metier_cat + stock, data, FUN = sum, na.rm = TRUE)

# In the North Sea model, all Nephrops FUs area aggregated together
agg$isNEP <- seq(nrow(agg)) %in% grep("NEP", agg$stock)
agg1 <- subset(agg, !isNEP)[,c(1:3)]
agg2 <- aggregate(landings ~ metier_cat, data = subset(agg, isNEP),
  FUN = sum, na.rm = TRUE)
agg2$stock <- "Nephrops"
agg <- merge(agg1, agg2, all = TRUE)
agg <- agg[,c("stock", "metier_cat", "landings")]

names(agg) <- c("stock", "metier","value")
agg

# subset included metiers
metIncl <- c("TR1", "TR2", "BT1", "BT2", "GN1", "GT1", "LL1", "beam_oth",
  "pots", "OTH", "MIS")
agg <- subset(agg, metier %in% metIncl)

# replace stock with ICES stock code
agg$stock <- refTable$stock[match(agg$stock, refTable$stock_short)]

plot_landByMetStock(data = agg, refTable)

# make example data
data(stfMtStkSum)
head(stfMtStkSum)
data(refTable)
head(refTable)

data <- stfMtStkSum

# add metier_cat
tmp <- strsplit(data$metier, ".", fixed = TRUE)
data$metier_cat <- unlist(lapply(tmp, FUN = function(x){x[1]}))

# select final data year and a single scenario, and aggregated total landings
# by stock and metier
datYr <- 2020
data <- subset(data, year == datYr & scenario == "min")
agg <- aggregate(landings ~ metier_cat + stock, data, FUN = sum, na.rm = TRUE)

# In the North Sea model, all Nephrops FUs area aggregated together
agg$isNEP <- seq(nrow(agg)) %in% grep("NEP", agg$stock)
agg1 <- subset(agg, !isNEP)[,c(1:3)]
agg2 <- aggregate(landings ~ metier_cat, data = subset(agg, isNEP),
  FUN = sum, na.rm = TRUE)
agg2$stock <- "Nephrops"
agg <- merge(agg1, agg2, all = TRUE)
agg <- agg[,c("stock", "metier_cat", "landings")]

names(agg) <- c("stock", "metier","value")
agg

# subset included metiers
metIncl <- c("TR1", "TR2", "BT1", "BT2", "GN1", "GT1", "LL1", "beam_oth",
  "pots", "OTH", "MIS")
agg <- subset(agg, metier %in% metIncl)

# replace stock with ICES stock code
agg$stock <- refTable$stock[match(agg$stock, refTable$stock_short)]

plot_landByMetStock(data = agg, refTable)

Pie chart of landings by stock

Description

Pie chart of landings by stock. Used in WGMIXFISH-ADVICE

Usage

plot_landByStock(
  data,
  refTable,
  ylab = "Landings [t]",
  fillLegendTitle = "Stock"
)
plot_landByStock(
  data,
  refTable,
  ylab = "Landings [t]",
  fillLegendTitle = "Stock"
)

Arguments

`data`	data.frame Contains information on the stocks to include and their landings (or catch) to plot. Stock variable names ('stock') should match those of `refTable`. Other required variables include: 'value' the value of landings (or catch) for each stock; and 'col' which defines the fill colour as a hex colour code, by stock, to be used.
`refTable`	data.frame A look-up reference table for stocks and associated attributes. The `refTable` data.frame lists stock names and corresponding colours for consistency across plots. To be used as a look-up table in converting between variable stock names and printed ones. 1) stock - ICES stock codes used in advice 2) order - stock order to be used in plots 3) col - stock colors for plots (e.g. pals::brewer.paired()) 4) stock_short - short stock name used in mixed fishery model
`ylab`	character Y-axis label (Default: 'ylab = "Landings [t]"')
`fillLegendTitle`	character Fill legend title

Details

Users will need to provide the data object to produce the plot.

Value

plot output of class ggplot

Examples


# make example data
data(stfFltStkSum)
head(stfFltStkSum)

data(refTable)
head(refTable)

# select final data year and a single scenario, and aggregated total landings
datYr <- 2020
dat <- subset(stfFltStkSum, year == datYr & scenario == "min")
agg <- aggregate(landings ~ stock, dat, sum, na.rm = TRUE)

# In the North Sea model, all Nephrops FUs area aggregated together
agg$isNEP <- seq(nrow(agg)) %in% grep("NEP", agg$stock)

agg <- rbind(subset(agg, !isNEP)[,c(1:2)],
  data.frame(stock = "Nephrops", landings = sum(subset(agg, isNEP)$landings)))

# replace stock with ICES stock code
agg$stock <- refTable$stock[match(agg$stock, refTable$stock_short)]

names(agg) <- c("stock", "value")
agg

plot_landByStock(data = agg, refTable)

# make example data
data(stfFltStkSum)
head(stfFltStkSum)

data(refTable)
head(refTable)

# select final data year and a single scenario, and aggregated total landings
datYr <- 2020
dat <- subset(stfFltStkSum, year == datYr & scenario == "min")
agg <- aggregate(landings ~ stock, dat, sum, na.rm = TRUE)

# In the North Sea model, all Nephrops FUs area aggregated together
agg$isNEP <- seq(nrow(agg)) %in% grep("NEP", agg$stock)

agg <- rbind(subset(agg, !isNEP)[,c(1:2)],
  data.frame(stock = "Nephrops", landings = sum(subset(agg, isNEP)$landings)))

# replace stock with ICES stock code
agg$stock <- refTable$stock[match(agg$stock, refTable$stock_short)]

names(agg) <- c("stock", "value")
agg

plot_landByStock(data = agg, refTable)

Metier to Metier to Fleet Sankey plot

Description

function to plot metier to mixedfish metier and fleet flow to provide a description and visualization of how metiers are constructed

Usage

plot_MetMetFleet(MetMetData, MetFleetData = NULL, Col_2_Link = NA)
plot_MetMetFleet(MetMetData, MetFleetData = NULL, Col_2_Link = NA)

Arguments

`MetMetData`	data.frame containing the original metier from the accession file and the output metier and a Link value (default assumption is Landings)
`MetFleetData`	data.frame containing the the output metier and the fleet to be used in the model and a Link value (default assumption is Landings)
`Col_2_Link`	column name (character) for the linking "value" variable. Default assumption is NA and the function defaults to Landings column

Details

Users will need to provide a data frame with three columns, two for metiers and one for the value used to link them.if a second dataframe is provided to link through to fleets you will need a metier column matching the output metier of the first, a fleet column and a value to link them. The data must be surmised to the metier columns using a group_by statement or similar. Where a metier goes to itself for example SDN_DEF to SDN_DEF you will experiences a doughnut

Value

a sanky plot, see the example for how to save a static sankey plot.

Examples

mtcars$Name <- rownames(mtcars)
dat <- mtcars %>% select(Name,gear,hp)
dat$gear <- as.character(dat$gear )
names(dat) <- c("Original_Metier","Metier","hp")

P <- plot_MetMetFleet(dat,Col_2_Link = "hp")

# Sankey plots are interactive by nature and are saved as an html, to get a static image they
# are captured using webshot from the htmlwidgets

P <- htmlwidgets::prependContent(P, htmltools::tags$h1("Title"))
P <- htmlwidgets::appendContent(P, htmltools::tags$p("Caption"))

# save plot
# saveNetwork(P, file =file.path("Plot_path" ,paste("A_Name","_sn.html",sep="")))
# save as png
# webshot::webshot(
# url = file.path("Plot_path",
#   paste("A_Name","_sn.html",sep="")),
# file.path("Metier_Sankey", paste(i,"_sn.png",sep="")),
# vwidth = 640,
# vheight=840)


mtcars$Name <- rownames(mtcars)
dat <- mtcars %>% select(Name,gear,hp)
dat$gear <- as.character(dat$gear )
names(dat) <- c("Original_Metier","Metier","hp")

P <- plot_MetMetFleet(dat,Col_2_Link = "hp")

# Sankey plots are interactive by nature and are saved as an html, to get a static image they
# are captured using webshot from the htmlwidgets

P <- htmlwidgets::prependContent(P, htmltools::tags$h1("Title"))
P <- htmlwidgets::appendContent(P, htmltools::tags$p("Caption"))

# save plot
# saveNetwork(P, file =file.path("Plot_path" ,paste("A_Name","_sn.html",sep="")))
# save as png
# webshot::webshot(
# url = file.path("Plot_path",
#   paste("A_Name","_sn.html",sep="")),
# file.path("Metier_Sankey", paste(i,"_sn.png",sep="")),
# vwidth = 640,
# vheight=840)

Plot over- and undershoot of stock quotas by fleet

Description

Plot of over- and undershoot of each stock's quota by fleet. Most- and least-limiting stocks are also denoted.

Usage

plot_overUnderFltStk(
  data,
  refTable,
  yExt = 0.3,
  xlab = "Stock",
  ylab = "Predicted catch [t] with advice undershoot (negative extent)",
  borderSize = 0.5,
  fillLegendTitle = "Stock",
  colLegendTitle = "Limiting stock"
)
plot_overUnderFltStk(
  data,
  refTable,
  yExt = 0.3,
  xlab = "Stock",
  ylab = "Predicted catch [t] with advice undershoot (negative extent)",
  borderSize = 0.5,
  fillLegendTitle = "Stock",
  colLegendTitle = "Limiting stock"
)

Arguments

`data`	data.frame Contains information on effort required to uptake quotas by fleet and stock, plus designation of each stock's limitation status to the fleet's fishing effort. Stock variable names ('Advice_name') should match those of `refTable`. Other required variables include: 'Limitation' - defines, by fleet, the most- ('most'), least- ('least'), and intermediate-limiting ('NA') stocks; 'quotaEffort' - the effort, by fleet, required to take up the quota share of each stock; 'sqEffort' - status quo effort corresponding to most recent data year before forecast.
`refTable`	data.frame Contains stock look-up information for consistent plotting of stocks. 'Advice_name' defines the stock names corresponding to 'data' object. 'col' defines the color used to fill bars in plot. 'order' defines the order of stocks in the plot facets.
`yExt`	Fraction of absolute range to extend y-axis for each fleet facet (Default: yExt = 0.3).
`xlab`	character X-axis label (Default: xlab = "Stock")
`ylab`	character Y-axis label (Default: ylab = "Predicted catch [t] with advice undershoot (negative extent)")
`borderSize`	line width of border around bars (Default: borderSize=0.5)
`fillLegendTitle`	character Fill legend title (Default: 'fillLegendTitle = "Stock"')
`colLegendTitle`	character Color legend title (Default: 'colLegendTitle = "Limiting stock"')

Details

Users will need to provide the data and reference table objects to produce the plot. In the best case, effort associated with complete quota uptake by fleet ('data$effortQuota') may be derived from scenarios restricting fleet catch one stock at a time. In the following example, however, effort levels are derived by linearly extrapolating the quota uptake levels by the effort of the "min" scenario. This is strictly linear when quotas are based on partial F, as in FCube. In FLBEIA, quotas are based on catch (or landings), which may deviate from a linear relationship when a stock is close full exploitation (should not result from an ICES harvest control rule).

Value

plot output of class ggplot

Examples


# example data for plot_effortFltStk --------------------------------------

data(refTable) # reference table with stock advice names, colors, order, etc.
data(stfFltSum) # summary of fleet-related variables (e.g. effort)
data(stfFltStkSum) # summary of fleet/stock-related catch variables

## get data from advice year

# catches by fleet and stock
advYr <- 2022 # advice year
df <- subset(stfFltStkSum, scenario == "min" & year == advYr)

## effort by fleet and scenario
eff <- subset(
 stfFltSum, scenario == "min" & year == advYr)[,c("fleet", "effort")]
sqEff <- subset(
 stfFltSum, scenario == "sq_E" & year == advYr)[,c("fleet", "effort")]
names(sqEff)[2] <- "sqEffort"
eff <- merge(x = eff, y = sqEff, all.x = TRUE)
df <- merge(x = df, y = eff, all.x = TRUE)
df$quotaEffort <- df$effort / df$quotaUpt


## Determine most- and least-limiting stock by fleet
# restrictive stocks
restr.stks <- c("COD-NS", "HAD", "PLE-EC", "PLE-NS", "POK", "SOL-EC",
 "SOL-NS", "TUR", "WHG-NS", "WIT", "NEP6", "NEP7", "NEP8", "NEP9")
fls <- unique(df$fleet)
df2 <- vector("list", length(fls))
names(df2) <- fls
for(i in seq(fls)){
 tmp <- subset(df, fleet == fls[i])
 tmp$Limitation <- NA # initial NA setting for all stocks

 # most-limiting (highest quota uptake in min scenario)
 mostLimStk <- subset(tmp, stock %in% restr.stks)
 mostLimStk <- mostLimStk$stock[which.max(mostLimStk$quotaUpt)]
 tmp$Limitation[which(tmp$stock == mostLimStk)] <- "most"

 # least-limiting (lowest quota uptake in max scenario)
 leastLimStk <- subset(stfFltStkSum, scenario == "max" & year == advYr &
   fleet == fls[i] & stock %in% restr.stks)
 leastLimStk <- leastLimStk$stock[which.min(leastLimStk$quotaUpt)]
 tmp$Limitation[which(tmp$stock == leastLimStk)] <- "least"

 # return result
 df2[[i]] <- tmp
}
df2 <- do.call("rbind", df2)

# replace short stock names with ICES stock codes
df2$stock <- refTable$stock[match(df2$stock, refTable$stock_short)]


# plot
p <- plot_overUnderFltStk(data = df2, refTable = refTable)
p
# png("overUnderFltStk1.png", width = 8, height = 10, units = "in", res = 400)
# print(p); dev.off()

# adjust ggplot2 settings
p <- p + theme(text = element_text(size = 12))
p
# png("overUnderFltStk2.png", width = 8, height = 10, units = "in", res = 400)
# print(p); dev.off()


# example data for plot_effortFltStk --------------------------------------

data(refTable) # reference table with stock advice names, colors, order, etc.
data(stfFltSum) # summary of fleet-related variables (e.g. effort)
data(stfFltStkSum) # summary of fleet/stock-related catch variables

## get data from advice year

# catches by fleet and stock
advYr <- 2022 # advice year
df <- subset(stfFltStkSum, scenario == "min" & year == advYr)

## effort by fleet and scenario
eff <- subset(
 stfFltSum, scenario == "min" & year == advYr)[,c("fleet", "effort")]
sqEff <- subset(
 stfFltSum, scenario == "sq_E" & year == advYr)[,c("fleet", "effort")]
names(sqEff)[2] <- "sqEffort"
eff <- merge(x = eff, y = sqEff, all.x = TRUE)
df <- merge(x = df, y = eff, all.x = TRUE)
df$quotaEffort <- df$effort / df$quotaUpt


## Determine most- and least-limiting stock by fleet
# restrictive stocks
restr.stks <- c("COD-NS", "HAD", "PLE-EC", "PLE-NS", "POK", "SOL-EC",
 "SOL-NS", "TUR", "WHG-NS", "WIT", "NEP6", "NEP7", "NEP8", "NEP9")
fls <- unique(df$fleet)
df2 <- vector("list", length(fls))
names(df2) <- fls
for(i in seq(fls)){
 tmp <- subset(df, fleet == fls[i])
 tmp$Limitation <- NA # initial NA setting for all stocks

 # most-limiting (highest quota uptake in min scenario)
 mostLimStk <- subset(tmp, stock %in% restr.stks)
 mostLimStk <- mostLimStk$stock[which.max(mostLimStk$quotaUpt)]
 tmp$Limitation[which(tmp$stock == mostLimStk)] <- "most"

 # least-limiting (lowest quota uptake in max scenario)
 leastLimStk <- subset(stfFltStkSum, scenario == "max" & year == advYr &
   fleet == fls[i] & stock %in% restr.stks)
 leastLimStk <- leastLimStk$stock[which.min(leastLimStk$quotaUpt)]
 tmp$Limitation[which(tmp$stock == leastLimStk)] <- "least"

 # return result
 df2[[i]] <- tmp
}
df2 <- do.call("rbind", df2)

# replace short stock names with ICES stock codes
df2$stock <- refTable$stock[match(df2$stock, refTable$stock_short)]


# plot
p <- plot_overUnderFltStk(data = df2, refTable = refTable)
p
# png("overUnderFltStk1.png", width = 8, height = 10, units = "in", res = 400)
# print(p); dev.off()

# adjust ggplot2 settings
p <- p + theme(text = element_text(size = 12))
p
# png("overUnderFltStk2.png", width = 8, height = 10, units = "in", res = 400)
# print(p); dev.off()

Relative fleet effort to uptake stock quotas

Description

Plot of relative effort required to uptake each stock's quota by fleet. To be used in fishery overviews.

Usage

plot_relEffortFltStk(
  data,
  limits = c(-100, 100),
  xlab = "Stock",
  ylab = "Fleet",
  fillLegendTitle = "Variation\n in effort"
)
plot_relEffortFltStk(
  data,
  limits = c(-100, 100),
  xlab = "Stock",
  ylab = "Fleet",
  fillLegendTitle = "Variation\n in effort"
)

Arguments

`data`	data.frame Contains information on relative effort (to status quo effort, 'var') required to uptake quotas by fleet ('fleet') and stock ('scenario').
`limits`	vector Two value vector with lower and upper limits for fill colors (Default: 'limits = c(-100,100)')
`xlab`	character X-axis label (Default: 'xlab = "Stock"')
`ylab`	character Y-axis label (Default: 'ylab = "Fleet"')
`fillLegendTitle`	character Fill legend title (Default: 'fillLegendTitle = "Variation in effort"')

Details

Users will need to provide the data and reference table objects to produce the plot. In the best case, effort associated with complete quota uptake by fleet may be derived from scenarios restricting fleet catch one stock at a time. In the following example, however, effort levels are derived by linearly extrapolating the quota uptake levels by the effort of the "min" scenario. This is strictly linear when quotas are based on partial F, as in FCube. In FLBEIA, quotas are based on catch (or landings), which may deviate from a linear relationship when a stock is close full exploitation (should not result from an ICES harvest control rule).

Value

plot output of class ggplot

Examples

# make data
data(refTable) # reference table with stock advice names, colors, order, etc.
data(stfFltSum) # summary of fleet-related variables (e.g. effort)
data(stfFltStkSum) # summary of fleet/stock-related catch variables

## get data from advice year
advYr <- 2022 # advice year
df <- subset(stfFltStkSum, scenario == "min" & year == advYr)

eff <- subset(
  stfFltSum, scenario == "min" & year == advYr)[,c("fleet", "effort")]
sqEff <- subset(
  stfFltSum, scenario == "sq_E" & year == advYr)[,c("fleet", "effort")]
names(sqEff)[2] <- "sqEffort"
eff <- merge(x = eff, y = sqEff, all.x = TRUE)
df <- merge(x = df, y = eff, all.x = TRUE)
df$quotaEffort <- df$effort / df$quotaUpt
df$relEffort <- df$quotaEffort / df$sqEffort

# df$scenario <- df$stock

restr.stks <- c("COD-NS", "HAD", "PLE-EC", "PLE-NS", "POK", "SOL-EC",
  "SOL-NS", "TUR", "WHG-NS", "WIT", "NEP6", "NEP7", "NEP8", "NEP9")
df <- subset(df, stock %in% restr.stks)

# replace short stock names with ICES stock codes
df$stock <- refTable$stock[match(df$stock, refTable$stock_short)]

# adjust stock order for the plot
df$stock <- factor(df$stock, levels = refTable$stock)


# convert to percentage change
df$var <- 100*(df$relEffort-1)

# optional upper limit (e.g. 100)
df$var <- ifelse(df$var > 100, 100, df$var)

# plot
p <- plot_relEffortFltStk(data = df)
print(p)

# export plot
# png("relEffortFltStk1.png", width = 4, height = 6, units = "in", res = 400)
# print(p); dev.off()


# make data
data(refTable) # reference table with stock advice names, colors, order, etc.
data(stfFltSum) # summary of fleet-related variables (e.g. effort)
data(stfFltStkSum) # summary of fleet/stock-related catch variables

## get data from advice year
advYr <- 2022 # advice year
df <- subset(stfFltStkSum, scenario == "min" & year == advYr)

eff <- subset(
  stfFltSum, scenario == "min" & year == advYr)[,c("fleet", "effort")]
sqEff <- subset(
  stfFltSum, scenario == "sq_E" & year == advYr)[,c("fleet", "effort")]
names(sqEff)[2] <- "sqEffort"
eff <- merge(x = eff, y = sqEff, all.x = TRUE)
df <- merge(x = df, y = eff, all.x = TRUE)
df$quotaEffort <- df$effort / df$quotaUpt
df$relEffort <- df$quotaEffort / df$sqEffort

# df$scenario <- df$stock

restr.stks <- c("COD-NS", "HAD", "PLE-EC", "PLE-NS", "POK", "SOL-EC",
  "SOL-NS", "TUR", "WHG-NS", "WIT", "NEP6", "NEP7", "NEP8", "NEP9")
df <- subset(df, stock %in% restr.stks)

# replace short stock names with ICES stock codes
df$stock <- refTable$stock[match(df$stock, refTable$stock_short)]

# adjust stock order for the plot
df$stock <- factor(df$stock, levels = refTable$stock)


# convert to percentage change
df$var <- 100*(df$relEffort-1)

# optional upper limit (e.g. 100)
df$var <- ifelse(df$var > 100, 100, df$var)

# plot
p <- plot_relEffortFltStk(data = df)
print(p)

# export plot
# png("relEffortFltStk1.png", width = 4, height = 6, units = "in", res = 400)
# print(p); dev.off()

Look-up reference table for stocks and associated attributes

Description

The refTable data.frame lists stock names and corresponding colors for consistency across plots. To be used as a look-up table in converting between variable stock names and printed ones.

1) stock - ICES stock codes used in advice
2) order - stock order to be used in plots
3) col - stock colors for plots (e.g. pals::brewer.paired())
4) stock_short - short stock name used in mixed fishery model

Usage

data(refTable)
data(refTable)

Format

bla bla

Source

WGMIXFISH-Advice 2021, North Sea case study. (https://github.com/ices-taf/2021_NrS_MixedFisheriesAdvice)

Examples


data(refTable)
refTable


data(refTable)
refTable

Data.frame containing short-term forecast summary of catch-related variables per stock and fleet combination

Description

The stfFltStkSum data.frame is an output of 'FLBEIA::fltStkSum()'. Provides example data for use in 'plot_effortFltStk'.

scenario - advice scenario
year - advice year
fleet - fleet names
stock - stock names used in mixed fishery model
iter - iteration number
catch -
landings -
discards -
discRat -
price -
tacshare - fraction of the total stock quota for a given fleet
quota - advised catch quota
quotaUptake - effort required to take up quota
choke - (logical) is stock the limiting one for the fleet

Usage

data(stfFltStkSum)
data(stfFltStkSum)

Format

data.frame

Source

WGMIXFISH-Advice 2021, North Sea case study (https://github.com/ices-taf/2021_NrS_MixedFisheriesAdvice)

Examples


data(stfFltStkSum)
head(stfFltStkSum)


data(stfFltStkSum)
head(stfFltStkSum)

Data.frame containing short-term forecast summary of catch-related variables per fleet

Description

The stfFltSum data.frame is an output of 'FLBEIA::fltSum()'. Provides example data for use in 'plot_effortFltStk'.

scenario - scenario name
year - year
fleet - fleet names
iter - iteration number
catch -
landings -
discards -
capacity -
effort -
fcosts -
vcosts -
costs -
grossValue -
nVessels -
discRat -
grossSurplus -
price -
salaries -
gva -
profitability -
fep -
netProfit -
quotaUptake - effort required to take up quota

Usage

data(stfFltSum)
data(stfFltSum)

Format

data.frame

Source

WGMIXFISH-Advice 2021, North Sea case study. (https://github.com/ices-taf/2021_NrS_MixedFisheriesAdvice)

Examples


data(stfFltSum)
head(stfFltSum)


data(stfFltSum)
head(stfFltSum)

Data.frame containing short-term forecast summary of catch-related variables per stock, fleet, and metier combination

Description

The stfMtStkSum data.frame is an output of 'FLBEIA::mtStkSum()'. Provides example data for use in 'plot_catchComp'.

scenario - advice scenario
year - advice year
fleet - fleet names
metier - metier names
stock - stock names used in mixed fishery model
iter - iteration number
catch -
landings -
discards -
discRat -
price -

Usage

data(stfMtStkSum)
data(stfMtStkSum)

Format

data.frame

Source

WGMIXFISH-Advice 2021, North Sea case study (https://github.com/ices-taf/2021_NrS_MixedFisheriesAdvice)

Examples


data(stfMtStkSum)
head(stfMtStkSum)


data(stfMtStkSum)
head(stfMtStkSum)

Package 'mixfishtools'

Help Index

Geometric Mean

Description

Usage

Arguments

References

Examples

Plot fleet landings taken up relative to recent landings / quota

Description

Usage

Arguments

Details

Value

Examples

Alluvial plot of catch

Description

Usage

Arguments

Value

Examples

Plot landings or catch compositions

Description

Usage

Arguments

Details

Value

Examples

Headline advice plot

Description

Usage

Arguments

Value

Examples

Plot fleet effort to uptake stock quotas

Description

Usage

Arguments

Details

Value

Examples

Bar chart of landings by stock and metier

Description

Usage

Arguments

Details

Value

Examples

Pie chart of landings by stock

Description

Usage

Arguments

Details

Value

Examples

Metier to Metier to Fleet Sankey plot

Description

Usage

Arguments

Details

Value

Examples

Plot over- and undershoot of stock quotas by fleet

Description

Usage

Arguments

Details

Value

Examples

Relative fleet effort to uptake stock quotas

Description

Usage

Arguments

Details

Value

Examples

Look-up reference table for stocks and associated attributes

Description

Usage

Format