plot: ggplot versions of FLR class plot() methods

In flr/ggplotFL: Using ggplot2 in FLR

ggplot versions of FLR class plot() methods

Description

New basic plot methods for some FLR classes are defined in ggplotFL.

Usage

## S4 method for signature 'FLQuant,missing'
plot(x, probs = c(0.05, 0.25, 0.5, 0.75, 0.95), na.rm = FALSE, ...)

## S4 method for signature 'FLQuant,FLQuant'
plot(
  x,
  y,
  ...,
  probs = c(0.05, 0.25, 0.5, 0.75, 0.95),
  na.rm = FALSE,
  iter = NULL
)

## S4 method for signature 'FLQuants,missing'
plot(
  x,
  probs = c(0.05, 0.25, 0.5, 0.75, 0.95),
  na.rm = FALSE,
  worm = iter,
  iter = NULL
)

## S4 method for signature 'FLQuants,FLPar'
plot(x, y, ...)

## S4 method for signature 'FLQuants,FLPars'
plot(x, y, ...)

## S4 method for signature 'FLQuantPoint,missing'
plot(x, mean = TRUE, median = TRUE)

## S4 method for signature 'FLQuantPoint,FLQuant'
plot(x, y, na.rm = FALSE, ...)

## S4 method for signature 'FLQuantPoint,FLQuants'
plot(x, y, na.rm = FALSE, mean = TRUE, median = TRUE, ...)

## S4 method for signature 'FLPar,missing'
plot(x, names = NULL)

## S4 method for signature 'FLStock,missing'
plot(
  x,
  metrics = list(Rec = rec, SSB = ssb, Catch = catch, F = fbar),
  na.rm = TRUE,
  ...
)

## S4 method for signature 'FLStock,FLStock'
plot(
  x,
  y,
  metrics = list(Rec = rec, SSB = ssb, Catch = catch, F = fbar),
  probs = c(0.1, 0.33, 0.5, 0.66, 0.9),
  na.rm = TRUE,
  iter = NULL,
  ...
)

## S4 method for signature 'FLStock,FLPar'
plot(x, y, metrics = list(Rec = rec, SSB = ssb, Catch = catch, F = fbar), ...)

## S4 method for signature 'FLStocks,missing'
plot(
  x,
  metrics = list(Rec = function(x) unitSums(rec(x)), SB = function(x) unitSums(ssb(x)), C
    = function(x) unitSums(catch(x)), F = function(x) unitMeans(fbar(x))),
  probs = c(0.1, 0.33, 0.5, 0.66, 0.9),
  alpha = c(0.1, 0.4),
  worm = iter,
  iter = NULL,
  ...
)

## S4 method for signature 'FLStocks,missing'
plot(
  x,
  metrics = list(Rec = function(x) unitSums(rec(x)), SB = function(x) unitSums(ssb(x)), C
    = function(x) unitSums(catch(x)), F = function(x) unitMeans(fbar(x))),
  probs = c(0.1, 0.33, 0.5, 0.66, 0.9),
  alpha = c(0.1, 0.4),
  worm = iter,
  iter = NULL,
  ...
)

## S4 method for signature 'FLStocks,FLPar'
plot(
  x,
  y,
  na.rm = TRUE,
  metrics = function(x, y) FLQuants(SSB = ssb(x)/y[, "ssb", ], F = fbar(x)/y[, "harvest",
    ], Catch = catch(x))
)

## S4 method for signature 'FLStock,FLStocks'
plot(x, y, ...)

## S4 method for signature 'FLSR,missing'
plot(x, y, ...)

## S4 method for signature 'FLSRs,ANY'
plot(x, legend_label = names(x), facets = FALSE, ...)

## S4 method for signature 'FLBiol,missing'
plot(x, metrics = list(Rec = function(x) n(x)[1, ], B = tsb), ...)

## S4 method for signature 'FLBiols,missing'
plot(x, metrics = list(Rec = function(x) n(x)[1, ], B = tsb), ...)

## S4 method for signature 'FLIndexBiomass,missing'
plot(x, y, ...)

## S4 method for signature 'FLIndex,missing'
plot(x)

## S4 method for signature 'FLIndices,missing'
plot(x)

Arguments

x	FLR object to plot
probs	Quantiles to calculate along the iter dimension. A vector of length 5, for the lower outer, lower inner, central, upper inner and upper outer quantiles. Defaults to the 66 and 80 percent quantiles, plus median line.
na.rm	Should NAs be deleted in quantile calculations?, defaults to TRUE.
...	Other arguments to be passed to the corresponding ggplot call.
y	FLR object to plot
iter	Individual iterations to show as worm plots over the quantiles.
worm	Individual iterations to show as worm plots over the quantiles.
metrics	function returning an FLQuants for each FLStock
alpha	alpha values for the quantile ribbons, defaults to 0.10 and 0.40.
legend_label	function to create the legend labels

Details

The coertion to *data.frame* that is carried out in the plot methods sets the argument 'date=TRUE'. This generates a new column of class 'POSIXct' for the first day of the first month of each season. If the 'season' dimension of the object being plotted is of length greater than one, 'date' will be used as variable on the x axis of the plot. Otherwise, it will be 'year'. Keep this in mind when adding extra elements to the plot (see examples below).

A similar mechanism is used for the *y* axis, depending on the length of the 'iter' dimension. For objects with no *iters*, a single line is plotted for each *FLQuant*, and the *y* axis is mapped to the 'data' column of the *data.frame*. For objects with iterations, i.e. with length greater than 1 on the 'iter' dimension, the default plots show the quantiles of the distribution and the *y* axis is mapped to the middle quantile, by default '50 examples below on how to refer to these variables when adding elements to the plot.

See Also

ISOdate ggplot

Examples

 # Plot a single FLQuant
 data(ple4)
 plot(catch.n(ple4))

 # Plot an FLQuant with iters, shows quantiles
 flq <- rnorm(100, catch(ple4), 60000)
 plot(flq)

 # Specify quantiles, default is c(0.10, 0.33, 0.50, 0.66, 0.90)
 plot(flq, probs=c(0.05, 0.25, 0.50, 0.75, 0.95))

 # Adding extra elements to an FLQuant plot, with seasons
 flq <- FLQuant(runif(200), dim=c(1,15,1,4))
 plot(flq) + geom_point(aes(x=date, y=data, colour=season))

# or without them
 flq <- FLQuant(runif(200), dim=c(1,15))
 plot(flq) + geom_point(aes(x=year, y=data))

# For an object with iter
 flq <- rlnorm(100, flq, 0.4)
 plot(flq) + geom_point(aes(x=year, y=data))

# To plot(FLQuant) as in previous versions of ggplotFL
plot(rnorm(300, catch(ple4), catch(ple4)/2), probs=c(0.10, 0.5, 0.90)) +
  geom_flquantiles(probs=c(0.01), linetype=3, colour="red", alpha=0.1) +
  geom_flquantiles(probs=c(0.99), linetype=3, colour="red", alpha=0.1)
# plot(FLQuant, FLQuant, ...) to place in one facet
plot(catch(ple4), landings(ple4))
# Add legend by hand
plot(rnorm(200, landings(ple4), 8000), discards(ple4)) +
  scale_colour_discrete(name="Yield (t)", labels=c("Landings", "Discards")) +
  theme(legend.position="bottom")
 # Plot an FLQuants created from ple4 FLStock
 data(ple4)
 plot(FLQuants(SSB=ssb(ple4), rec=rec(ple4)))
 plot(FLQuants(SSB=ssb(ple4), rec=rec(ple4)), probs = NULL)
 # plot for FLQuants, FLPar
 data(ple4)
 rps <- FLPar(F=0.14, Catch=1.29e5, Rec=9.38e5, SSB=1.8e5)
 fqs <- metrics(ple4)
 plot(fqs, rps)
 # Works also if reptsa are given for some panels
 rps <- FLPar(F=0.14, Catch=1.29e5, SSB=1.8e5)
 plot(fqs, rps)
 # plot for FLQuants, FLPars
 data(ple4)
 rps <- FLPars(F=FLPar(Fmsy=0.14, Fpa=0.35), SSB=FLPar(SBmsy=1.8e5, SBlim=1.1e5))
 fqs <- metrics(ple4, list(SSB=ssb, F=fbar))
 plot(fqs, rps) + ylim(c(0, NA))
# plot for FLQuantPoint
fqp <- FLQuantPoint(rlnorm(300, log(catch(ple4)), 0.20))
plot(fqp)
# plot for FLQuantPoint, FLQuant
plot(fqp, rlnorm(3, log(catch(ple4)), 0.20))
# plot for FLQuantPoint, FLQuants
fqp <- FLQuantPoint(rlnorm(300, log(catch(ple4)), 0.20))
fqs <- divide(rlnorm(3, log(catch(ple4)), 0.20))
plot(fqp, fqs)
par <- FLPar(alpha=rnorm(200, 0.6, 0.2), beta=rlnorm(200, 0.8, 0.3))
plot(par)
# plot of an FLStock
 data(ple4)
 plot(ple4)
# plot for FLStock, FLPar
data(ple4)
rps <- FLPar(F=0.14, Catch=1.29e5, Rec=9.38e5, SSB=1.8e5)
plot(ple4, rps)
# plot for FLStocks
data(ple4)
pls <- FLStocks(runA=ple4, runB=qapply(ple4, function(x) x*1.10))
plot(pls)
# geom_flpar can be used draw refpts lines and labels
plot(pls, metrics=list(SSB=ssb, F=fbar)) +
  facet_grid(qname~stock, scales='free') +
  geom_flpar(data=FLPars(SSB=FLPar(Blim=300000, Bpa=230000),
  F=FLPar(FMSY=0.21)), x=c(1960), stock='runA', fill=alpha('white', 0.4))
# plot for FLStocks
data(ple4)
pls <- FLStocks(runA=ple4, runB=qapply(ple4, function(x) x*1.10))
plot(pls)
# geom_flpar can then be used draw refpts lines and labels
plot(pls, metrics=list(SSB=ssb, F=fbar)) +
  facet_grid(qname~stock, scales='free') +
  geom_flpar(data=FLPars(SSB=FLPar(Blim=300000, Bpa=230000),
  F=FLPar(FMSY=0.21)), x=c(1960), stock='runA', fill=alpha('white', 0.4))
# plot for FLSR
 data(nsher)
 plot(nsher)
 # plot for FLSRs
 data(nsher)
 srs <- FLSRs(sapply(c('segreg', 'bevholt'), function(x) {
   y <- nsher
   model(y) <- x
   return(fmle(y))
 }))
 plot(srs, facets=TRUE)
 plot(srs, legend_label=eqlabel)
 plot(srs, legend_label=modlabel)
 # Plot a FLIndex object
 data(ple4.index)
 plot(ple4.index)
 # Plot a FLIndices object
 data(ple4.indices)
 plot(ple4.indices)
 plot(ple4.indices) +
   geom_smooth(formula=y ~ x, se=FALSE, method="loess", linewidth=0.2)

flr/ggplotFL documentation built on Feb. 10, 2024, 3:57 p.m.