Visualize the spatial distribution per species and stanza combination.

plot_spatial_ts(
  bio_spatial,
  bgm_as_df,
  vol,
  select_species = NULL,
  ncol = 7,
  polygon_overview = 0.2
)

Arguments

bio_spatial	Biomass per group and stanza in tonnes for each timestep, layer and polygon. This dataframe should be generated with `calculate_biomass_spatial`. The columns of the dataframe have to be 'species', 'species_stanza', 'polygon', 'layer', 'time' and 'atoutput'. Column 'atoutput' is the biomass in tonnes. Please use `combine_ages` to transform an age-based dataframe to a stanza based dataframe.
bgm_as_df	*.bgm file converted to a dataframe. Please use `convert_bgm` to convert your bgm-file to a dataframe with columns 'lat', 'long', 'inside_lat', 'inside_long' and 'polygon'.
vol	Volume per polygon and timestep. See model-preprocess.Rmd for details.
select_species	Character vector listing the species to plot. If no species are selected `NULL` (default) all available species are plotted.
ncol	Number of columns in final plot. Default is `7`.
polygon_overview	numeric value between 0 and 1 indicating the size used to plot the polygon overview in the upper right corner of the plot. Default is `0.2`.

Value

grob of 3 ggplot2 plots.

Examples

d <- system.file("extdata", "setas-model-new-trunk", package = "atlantistools")

bgm_as_df <- convert_bgm(file.path(d, "VMPA_setas.bgm"))
vol <- agg_data(ref_vol, groups = c("time", "polygon"), fun = sum, out = "volume")

# Spatial distribution in Atlantis is based on adu- and juv stanzas.
# Therefore, we need to aggregate the age-based biomass to
# stanzas with \code{\link{combine_ages}}.
bio_spatial <- combine_ages(ref_bio_sp, grp_col = "species", agemat = ref_agemat)
#> Joining, by = "species"

# Apply \code{\link{plot_spatial_ts}}
grobs <- plot_spatial_ts(bio_spatial, bgm_as_df, vol)
#> Joining, by = c("time", "polygon")
#> geom_path: Each group consists of only one observation. Do you need to adjust
#> the group aesthetic?
#> geom_path: Each group consists of only one observation. Do you need to adjust
#> the group aesthetic?
gridExtra::grid.arrange(grobs[[1]])
gridExtra::grid.arrange(grobs[[7]])