In diazrenata/sadspace: Exploring SAD space

knitr::opts_chunk$set(echo = TRUE)
library(dplyr)
library(ggplot2)

di_dfs <- read.csv(here::here("analysis", "di_dfs.csv"), stringsAsFactors = F)

di_dfs <- di_dfs %>%
  mutate(community_name = paste0(s0, "_", n0),
         lognparts = log(as.numeric(nparts))) %>%
  mutate(more_than_10 = lognparts > 10)

Ambitious heatmaps of ROV:

di_dfs_comparable <- di_dfs %>%
  filter(nunique >= 500)

subsampled_dfs <- list()
for(i in 1:length(unique(di_dfs_comparable$community_name))) {
  subsampled_dfs[[i]] <- filter(di_dfs, community_name == unique(di_dfs_comparable$community_name)[i])

  sim_options <- unique(subsampled_dfs[[i]]$sim)

  sim_options <- sim_options[1:min(length(sim_options), 500)]

  subsampled_dfs[[i]] <- filter(subsampled_dfs[[i]], sim %in% sim_options)

}

subsampled_dfs <- bind_rows(subsampled_dfs)


ggplot(subsampled_dfs, aes(x = simpson, group = community_name, color = lognparts, linetype = more_than_10)) +
  geom_density(alpha = 0, size = .5) +
  theme_bw() +
  #facet_wrap(vars(more_than_10)) +
  scale_color_viridis_c(end = .95, direction = -1)


ggplot(subsampled_dfs, aes(x = skew, group = community_name, color = lognparts, linetype = more_than_10)) +
  geom_density(alpha = 0, size = .5) +
  theme_bw() +
  #facet_wrap(vars(more_than_10)) +
  scale_color_viridis_c(end = .95, direction = -1)