In pratikunterwegs/klepto-move-evol: R code for the Kleptomove Simulation

library(data.table)
# plotting
library(ggplot2)
library(patchwork)
library(colorspace)

# read in data
data <- fread("data/results/data_pop_activity_budget.csv")

# remove random data
data = data[sim_type != "random",]

# rearrange
data$sim_type <- factor(data$sim_type,
  levels = c("foragers", "obligate", "facultative")
)

# remove excess growth
data <- data[regrowth <= 0.1, ]

Fitness in relation to regrowth and scenario

# get last 10 generations
data_equi <- data[gen > max(gen) - 10 & regrowth <= 0.05, ]

# look at handling and fitness
cols_we_want <- setdiff(
  colnames(data_equi),
  c("variable", "value")
)
data_fitness <- data_equi[, ..cols_we_want]

# get unique
data_fitness <- unique(data_fitness)

# boxplot handling time
data_fitness_summary <-
  data_fitness[, unlist(lapply(.SD, function(x) {
    list(
      median = median(x),
      sd = sd(x)
    )
  }), recursive = F),
  .SDcols = c("pop_fitness"),
  by = c("sim_type", "regrowth")
  ]

# make a manual colorscale
manual_colours = colorspace::qualitative_hcl(
  n = 3, 
  palette = "Harmonic", h = 150, 
  c = 50, l = 60
)
names(manual_colours) = levels(data_fitness_summary$sim_type)

# boxplot fitness
fig_strategy_fitness <-
  ggplot()+
  geom_line(
    data = data_fitness_summary,
    aes((regrowth),
      y = pop_fitness.median,
      col = sim_type,
      group = sim_type
    ),
    size = 1
  ) +
  geom_pointrange(
    data = data_fitness_summary,
    aes(
      regrowth,
      y = pop_fitness.median,
      ymin = pop_fitness.median -
        pop_fitness.sd,
      ymax = pop_fitness.median +
        pop_fitness.sd,
      group = sim_type,
      fill = sim_type
    ),
    size = 0.5,
    shape = 21,
    col = "black"
  ) +
  scale_fill_manual(
    values = manual_colours,
    labels = c(
      foragers = "Scenario 1",
      obligate = "Scenario 2",
      facultative = "Scenario 3"
    )
  ) +
  scale_colour_manual(
    values = manual_colours,
    labels = c(
      foragers = "Scenario 1",
      obligate = "Scenario 2",
      facultative = "Scenario 3"
    )
  ) +
  scale_x_continuous(
    breaks = c(0.001, 0.01, 0.025, 0.05)
  ) +
  scale_y_continuous(
    breaks = seq(0, 60, 20)
  ) +
  coord_cartesian(
    ylim = c(0, 60),
    xlim = c(0, 0.051),
    expand = T
  ) +
  theme_test(
    base_size = 8,
    base_family = "Arial"
  )+
  theme(
    legend.position = "none",
    axis.title.x = ggtext::element_markdown()
  ) +
  labs(
    x = "Regrowth rate (r<sub>max</max>)",
    y = "Mean per capita intake",
    colour = NULL,
    fill = NULL,
    shape = NULL
  )

Foraging and stealing across regrowth and scenario

# get the stealing and foraging over regrowth rates
data_strategy_summary <- data_equi[variable %in% c(
  "stealing",
  "foraging"
) &
  regrowth <= 0.05, ]

# set sim type as factor
data_strategy_summary$sim_type = factor(data_strategy_summary$sim_type)

# remove stealing in foragers
data_strategy_summary = data_strategy_summary[
  !(variable == "stealing" & sim_type == "foragers"),
]

# summarise proportion per sim type and growth rate
data_strategy_summary <-
  data_strategy_summary[, unlist(lapply(.SD, function(x) {
    list(
      median = median(x),
      sd = sd(x)
    )
  }), recursive = F),
  .SDcols = c("value"),
  by = c("sim_type", "regrowth", "variable")
  ]

# split by variable
data_strategy_summary <- split(data_strategy_summary,
  by = "variable"
)

subfigures_strategy_growth <- Map(function(df, name) {
  yaxis_name <- sprintf("Prop. %s", stringr::str_to_sentence(name))
  # plot figure
  ggplot(df) +
    geom_path(
      aes(regrowth, value.median,
        group = interaction(sim_type, variable),
        colour = sim_type
      ),
      size = 1,
      show.legend = F
    ) +
    geom_pointrange(
      aes(regrowth,
        y = value.median,
        ymin = value.median - value.sd,
        ymax = value.median + value.sd,
        fill = sim_type
      ),
      shape = 21,
      colour = "black",
      size = 0.5,
      show.legend = F
    ) +
    scale_fill_manual(
      values = manual_colours,
      labels = c(
        foragers = "Scenario 1",
        obligate = "Scenario 2",
        facultative = "Scenario 3"
      )
    ) +
    scale_colour_manual(
      values = manual_colours,
      labels = c(
        foragers = "Scenario 1",
        obligate = "Scenario 2",
        facultative = "Scenario 3"
      )
    ) +
    scale_y_continuous(
      breaks = seq(0, 1, 0.25)
    ) +
    scale_x_continuous(breaks = c(
      0.001,
      0.01,
      0.025,
      0.05
    )) +
    coord_cartesian(
      ylim = c(0, 1),
      expand = T
    ) +
    theme_test(
      base_size = 8,
      base_family = "Arial"
    )+
    theme(
      legend.position = "none",
      axis.title.x = ggtext::element_markdown()
    ) +
    labs(
      x = "Regrowth rate (r<sub>max</max>)",
      y = yaxis_name,
      colour = NULL,
      fill = NULL,
      shape = NULL
    )
}, data_strategy_summary, c("Searching for prey", "Searching for handlers"))

Make Figure model comparison

# point plots
point_plots <- append(
  subfigures_strategy_growth,
  list(fig_strategy_fitness)
)

# wrap all figures
figure_06 <-
  wrap_plots(point_plots) +
    plot_annotation(
      tag_levels = "A"
    ) +
    plot_layout(guides = "collect") &
    theme(
      legend.position = "bottom",
      plot.tag = element_text(
        face = "bold",
        size = 12
      )
    )

ggsave(
  figure_06,
  filename = "figures/fig_06.png",
  height = 75, width = 160, units = "mm"
)