Create animated maps to show changes in reporting rates across the year in Kenya

reporting_rate_distributions.R

# 0. set up ---------------------------------------------------------------

# libraries
library(rabm)
library(tidyverse)
library(geojsonio)
library(broom)
library(ggplot2)
library(gganimate)
library(transformr)

# atlas data
# needs to be split up because of API restrictions

kenya_pentads_split_1 <- rabm::extract_all(
  start_date = '2007-01-01', 
  end_date = '2018-04-15',
  region_type = "country",
  region_id = "kenya")

kenya_pentads_split_2 <- rabm::extract_all(
  start_date = '2018-04-15', 
  end_date = '2020-04-15',
  region_type = "country",
  region_id = "kenya")

kenya_pentad_data <- dplyr::bind_rows(
  
  kenya_pentads_split_1,
  kenya_pentads_split_2
  
) 

# species list
species_list <- rabm::get_species_list("kenya")

# spatial data
# download from: http://api.adu.org.za/sabap2/v2/coverage/project/kenya?format=geoJSON
# save it to your machine and replace the location below
spdf <- geojsonio::geojson_read("../Dropbox/BirdData/kenya_grid.geojson",  what = "sp")
spdf_fortified <- broom::tidy(spdf)
spdf_fortified$Pentad <- rep(spdf@data[["pentad"]], each = 5) # rep 5 times because there are 5 coordinates per pentad.s




# 1. function to make animation ----------------------------------------------

make_species_animation <- function(species_id,
                                   species_name,
                                   country_data,
                                   fortified_spatial_df){
  
  # get reporting rate per pentad-week
  rr_pentad_week <- country_data %>% 
    mutate(WeekOfYear = lubridate::week(StartDate)) %>% 
    group_by(Pentad, WeekOfYear) %>% 
    summarize(
      NumberOfLists = n_distinct(CardNo),
      CountOfSpecies = sum(if_else(Spp == species_id, 1, 0)),
      ReportingRate = (CountOfSpecies / NumberOfLists)
    ) %>% 
    arrange(Pentad, WeekOfYear)
  
  # make df to plot
  spdf_plot <- fortified_spatial_df %>% 
    # first make a base dataset with all weeks
    select(
      long,
      lat,
      group,
      id,
      Pentad
    ) %>% 
    slice(rep(1:n(), 
              53)) %>% 
    mutate(WeekOfYear = rep(1:53, each = n()/53)) %>% 
    # add in the reporting rates
    left_join(rr_pentad_week, by = c("Pentad", "WeekOfYear")) %>% 
    # cards that don't have a reporting rate are set to 0
    # this is weird but helps with the graph...
    # need to work on this a bit later
    replace_na(list(ReportingRate = 0)) %>% 
    select(
      
      long,
      lat,
      group,
      id,
      Pentad,
      WeekOfYear,
      ReportingRate
    ) 
  
  # base plot
  base_plot <- ggplot(data = spdf_plot, 
                      aes( x = long, 
                           y = lat, 
                           group = group,
                           fill= ReportingRate)) +
    theme_bw() +
    scale_fill_gradient(low = "#E8E8E8", high = "red") +
    geom_polygon(color="#E8E8E8") + 
    coord_map() +
    labs(title = glue::glue("Reporting Rates in Kenya: {species_name}"),
         subtitle = 'Week Number: {round(frame_time, 0)}') +
    transition_time(WeekOfYear) +
    ease_aes('linear')
  
  # animate base plot
  final_animation <- animate(base_plot,
                             duration = 30)
  
  return(final_animation)
  
}



# 2. build animation ---------------------------------------------------------

# some examples
red_capped_robin_chat <- make_species_animation(
  species_id = 579,
  species_name = "Red-capped Robin Chat",
  country_data = kenya_pentad_data,
  fortified_spatial_df = spdf_fortified)


hadada_ibis <- make_species_animation(
  species_id = 84,
  species_name = "Hadada Ibis",
  country_data = kenya_pentad_data,
  fortified_spatial_df = spdf_fortified)


spotted_flycatcher <- make_species_animation(
  species_id = 654,
  species_name = "Spotted Flycatcher",
  country_data = kenya_pentad_data,
  fortified_spatial_df = spdf_fortified)

barn_swallow <- make_species_animation(
  species_id = 493,
  species_name = "Barn Swallow",
  country_data = kenya_pentad_data,
  fortified_spatial_df = spdf_fortified)