condition_plots.R

condition_plots.R

  library(ggplot2)
  library(dplyr)
  library(patchwork)
  library(forcats)
  
  
  annualCondition <- read.csv("https://raw.githubusercontent.com/Laurels1/Condition/master/data/AnnualRelCond2018_GB.csv",
                              stringsAsFactors = FALSE)
  
  speciesNames <- annualCondition %>%
    dplyr::filter(sex == "F") %>%
    group_by(Species) %>% 
    mutate(scaleCond = scale(MeanCond, scale = TRUE, center = TRUE))
  
  xs = quantile(speciesNames$scaleCond, seq(0,1, length.out = 6), na.rm = TRUE)
  
  speciesNames <- speciesNames %>% 
    mutate(category = cut(scaleCond, breaks = xs, labels = c( "Poor Condition", 
                                                              "Bad",
                                                              "Neutral",
                                                              "Good",
                                                              "Good Condition")))
  
  
  sortNames <- speciesNames %>% 
    filter(YEAR <= 2014) %>%
    group_by(Species) %>% 
    summarize(total = sum(scaleCond)) %>% 
    arrange(total) %>% 
    mutate(Species = factor(Species, levels = unique(Species))) %>%
    pull(Species)

  speciesNames$Species <-   factor(speciesNames$Species, levels = sortNames)
  
  p1 <-   ggplot(speciesNames, aes(x = YEAR, y = forcats::fct_rev(Species), fill = category)) +
    geom_tile() +
    coord_equal() +
    scale_fill_brewer(palette = "RdBu", na.value = "black") +
    theme_bw() +
    theme(legend.position="bottom")
  
  p2 <- ggplot(speciesNames, aes(x = YEAR, y = forcats::fct_rev(Species), fill = scaleCond)) +
    geom_tile() +
    coord_equal() +
    scale_fill_viridis_c() +
    theme_bw() +
    theme(legend.position = "bottom")
  
  p1 + p2