In pratikunterwegs/process-pattern: What the Package Does (One Line, Title Case)

Agent weight evolution

# load libraries
library(data.table)
library(glue)
library(stringr)

# source funs
source("R/fun_dist_disp.R")
source("R/fun_read_pos.R")
source("R/fun_steplength_timescale.R")
source("R/fun_read_config.R")
source("R/fun_wt_vals.R")

List files and read data

needs to be made recursive over dirs.

paths = list.dirs(path = "data", recursive = F)
paths = paths[grep("sim", paths)]

# get weight data
data = lapply(
  paths, get_wt_data, 
  gen_start = 250, gen_end = 250, gen_add = NULL,
  n_agents = 5000
)

Read and link parameters

params = lapply(paths, read_config)

data = Map(function(df, p) {
  df[, names(p) := list(p["growth"], p["type"], p["rep_"])]
}, data, params)

data = rbindlist(data)

fwrite(data, file = "data/data_lt_wt_gens.csv")

Scale weights

# read the data in if needed
data = fread("data/data_lt_wt_gens.csv")

# select movement weights
data = data[, !(sprintf("wt_%i", c(1, seq(6, 8))))]

# scale by sum of absolute values
data[, wt_abs_sum := apply(
  data[, c("wt_2", "wt_3", "wt_4")], 
  1, 
  FUN = function(x) {
    sum(abs(x))
  })
]

data[, c("wt_2", "wt_3", "wt_4") := lapply(
  .SD, `/`, wt_abs_sum
), .SDcols = c("wt_2", "wt_3", "wt_4")]

# assign foraging strategy
data[, comp_strat := dplyr::case_when(
  type == "obligate" & wt_5 < 0 ~ "klept",
  type == "interf" ~ "mixed",
  T ~ "forager"
)]

# remove wt_5
data$wt_5 = NULL

Save scaled weights.

fwrite(data, file = "data/data_lt_wts_scaled.csv")

pratikunterwegs/process-pattern documentation built on Dec. 22, 2021, 9:50 a.m.