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Trends_Indices_Graphs_Maps.R
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Trends_Indices_Graphs_Maps.R
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### trends indices graphs and maps for real data
setwd("C:/GitHub/GAMYE_Elaboration")
library(tidyverse)
library(cmdstanr)
library(posterior)
library(sf)
library(geofacet)
source("functions/indices_cmdstan.R")
source("functions/posterior_summary_functions.R")
source("Functions/palettes.R")
MAs <- round(log(c(0.1,0.5,1,5,10,50)),2)# true mean abundances for different simulations
fls <- data.frame(species_f = c("Yellow-headed_Blackbird",
"Cinclus_mexicanus",
"Red_Knot",
"Dickcissel",
rep("Yellow-headed_Blackbird",length(MAs)*2),
"Yellow-headed_Blackbird",
"Cinclus_mexicanus",
"Red_Knot",
"Red_Knot"),
species = c("Yellow-headed Blackbird",
"Cinclus_mexicanus",
"Red Knot",
"Dickcissel",
rep("Yellow-headed Blackbird",length(MAs)*2),
"Yellow-headed Blackbird",
"Cinclus_mexicanus",
"Red Knot",
"Red Knot"),
data = c("BBS",
"CBC",
"Shorebird",
"BBS",
rep("BBS",length(MAs)*2),
"BBS",
"CBC",
"Shorebird",
"Shorebird"),
out_base = c(paste0("Yellow-headed_Blackbird","_real_","BBS"),
paste0("Cinclus_mexicanus","_CBC"),
paste0("Red_Knot","_Shorebird"),
paste0("Dickcissel","_real_","BBS"),
paste0("sim_breakpoint_cycle_",MAs,"_BBS"),
paste0("sim_nonSpatial_alt_breakpoint_cycle_",MAs,"_BBS"),
paste0("Yellow-headed_Blackbird","_real_Non_spatial","BBS"),
paste0("Cinclus_mexicanus","_CBC_Non_spatial"),
paste0("Red_Knot","_Shorebird_NonSpatial"),
paste0("Red_Knot","_Shorebird_iCAR_split")),
model = c(rep("Spatial",4),
rep("Spatial",length(MAs)),
rep("Non-Spatial",length(MAs)),
rep("Non-Spatial",3),
"Spatial-Split"),
y1 = c(1966,
1966,
1980,
1966,
rep(1966,length(MAs)*2),
1966,
1966,
1980,
1980),
strat_map_name = c("Stratum_Factored",
"strata_vec",
"stratn",
"Stratum_Factored",
rep("Stratum_Factored",length(MAs)*2),
"Stratum_Factored",
"strata_vec",
"stratn",
"stratn"),
real = c(TRUE,TRUE,TRUE,TRUE,
rep(FALSE,length(MAs)*2),
TRUE,TRUE,TRUE,TRUE))
# Species loop ------------------------------------------------------------
output_dir <- "output/"
stratum_trends <- NULL
all_trends <- NULL
indices_all_out <- NULL
Indices_all_out <- NULL
tt_map_list = vector(mode = "list",length = nrow(fls))
names(tt_map_list) <- fls$species_f
tt_map_data_list = tt_map_list
ind_plots_list = tt_map_list
Ind_plots_list = tt_map_list
conv_summaries <- NULL
for(i in c(1:nrow(fls))){
species = fls[i,"species"]
species_f <- fls[i,"species_f"]
dd <- fls[i,"data"]
out_base <- fls[i,"out_base"]
year_1 = fls[i,"y1"]
reald = fls[i,"real"]
st_n = fls[i,"strat_map_name"]
modl = fls[i,"model"]
if(reald){
load(paste0("Data/",species_f,dd,"_data.RData"))
ma <- -5
}else{
if(i < 11){
ma <- MAs[i-4]
load(paste0("Data/Simulated_data_",ma,"_breakpoint_cycle_BBS.RData"))
realized_strata_map = strata_map
}else{
ma <- MAs[i-(4+length(MAs))]
load(paste0("Data/Simulated_data_",ma,"_breakpoint_cycle_BBS.RData"))
realized_strata_map = strata_map
}
}
load(paste0(output_dir,"/",out_base,"_gamye_iCAR.RData"))
csv_files <- paste0(output_dir,out_base,"-",1:3,".csv")
stanfit <- as_cmdstan_fit(files = csv_files)
strat_df <- as.data.frame(realized_strata_map)
# convergence summary -----------------------------------------------------
stanf_df <- stanfit$draws(format = "df")
#optional removes the count-level parameters
drws <- stanf_df #%>%
# select(!contains(c("E[","noise_raw")))
conv_summ <- summarise_draws(drws) %>%
mutate(species = species,
model = out_base,
data = dd)
conv_summaries <- bind_rows(conv_summaries,
conv_summ)
# exploring the spatial abundance dist ------------------------------------
#
# strat_samples <- posterior_samples(fit = stanfit,
# parm = "strata_raw",
# dims = "stratnumber")
# STRAT_samples <- posterior_samples(fit = stanfit,
# parm = "STRATA")
# sdstrata_samples <- posterior_samples(fit = stanfit,
# parm = "sdstrata")
# sd_tmp = sdstrata_samples %>% select(.draw,.value) %>%
# rename(sdstrata = .value)
# STRAT_tmp = STRAT_samples %>% select(.draw,.value) %>%
# rename(STRAT = .value)
# strata_sums <- strat_samples %>%
# left_join(sd_tmp) %>%
# left_join(STRAT_tmp) %>%
# mutate(strat = .value*sdstrata + STRAT) %>%
# group_by(stratnumber) %>%
# summarise(mean_est = mean(strat),
# lci_est = quantile(strat,0.025),
# uci_est = quantile(strat,0.975))
#
#
stan_df <- data.frame(stratnumber = stan_data$strat,
count = stan_data$count,
site = stan_data$site,
Year = stan_data$year)
if((dd %in% c("BBS","CBC")) & reald){
# obs_strat_means <- stan_df %>%
# group_by(stratnumber) %>%
# summarise(mean = mean(count),
# max = max(count),
# lmean = log(mean)) %>%
# left_join(.,strata_sums)
strat_z <- data.frame(stratnumber = 1:stan_data$nstrata,
zero = stan_data$nonzeroweight)
}else{
strat_z <- data.frame(stratnumber = 1:stan_data$nstrata,
zero = 1)
}
obs_means <- stan_df %>%
group_by(stratnumber,Year) %>%
summarise(n_surveys = n(),
mean_obs = mean(count),
max_obs = max(count)) %>%
left_join(.,strat_z) %>%
rename_with(.,~gsub(x = .x,pattern = "stratnumber",
replacement = st_n))
# plot_obs_m <- realized_strata_map %>%
# rename_with(~gsub(pattern = st_n,replacement = "stratnumber",x = .x)) %>%
# left_join(.,obs_strat_means,by = c("stratnumber"))
#
#
# obs_m_map <- ggplot()+
# geom_sf(data = plot_obs_m,aes(fill = lmean))+
# scale_fill_viridis_c()
# print(obs_m_map)
#
# est_m_map <- ggplot()+
# geom_sf(data = plot_obs_m,aes(fill = mean_est))+
# scale_fill_viridis_c()
# print(est_m_map)
#
# plot(obs_strat_means$lmean,obs_strat_means$mean_est)
# abline(0,1)
# indices and trends ------------------------------------------------------
strat_inds <- index_function(fit = stanfit,
parameter = "n",
year_1 = year_1,
strat = st_n)
strat_inds_smooth <- index_function(fit = stanfit,
parameter = "nsmooth",
year_1 = year_1,
strat = st_n)
if(!reald){
true_smooths <- log_true_traj %>%
ungroup() %>%
select(Stratum_Factored,Year,
True_scaled_smooth) %>%
rename(true_year = Year)
indices <- strat_inds$indices %>%
left_join(.,strat_df)%>%
mutate(version = "full") %>%
left_join(.,obs_means) %>%
left_join(.,true_smooths,by = c("true_year","Stratum_Factored"))
indices_smooth <- strat_inds_smooth$indices %>%
left_join(.,strat_df) %>%
mutate(version = "smooth")%>%
left_join(.,true_smooths,by = c("true_year","Stratum_Factored"))
}else{
indices <- strat_inds$indices %>%
left_join(.,strat_df)%>%
mutate(version = "full") %>%
left_join(.,obs_means)
indices_smooth <- strat_inds_smooth$indices %>%
left_join(.,strat_df) %>%
mutate(version = "smooth")
}
if(reald){rd = "real"}else{rd = "simulated"}
indices_all <- bind_rows(indices,
indices_smooth) %>%
rename_with(.,~gsub(st_n,replacement = "strat_plot",
.x)) %>%
mutate(original_strat_name = strat_plot,
species = species,
region_type = "Stratum",
simulated_data = rd,
mean_abundance = signif(exp(ma),2),
data = dd,
model = modl) %>%
rename_with(.,~gsub(replacement = st_n,pattern = "strat_plot",
.x))
indices_all_out <- bind_rows(indices_all_out,indices_all)
pd = ceiling(sqrt(length(unique(indices_all$original_strat_name))))
pl_inds <- ggplot(data = indices_all,aes(x = true_year,y = median))+
geom_point(aes(x = true_year,y = mean_obs*zero,alpha = n_surveys),
inherit.aes = FALSE)+
geom_ribbon(aes(ymin = lci,ymax = uci,fill = version),alpha = 0.1)+
geom_line(aes(colour = version))+
labs(title = species)+
scale_y_continuous(limits = c(0,NA))+
facet_wrap(vars(original_strat_name),
nrow = pd,
ncol = pd,
scales = "free_y")
if(!reald){
pl_inds <- pl_inds +
geom_line(data = indices_all,
aes(x = true_year,
y = True_scaled_smooth),
colour = "black",
alpha = 0.3,
inherit.aes = FALSE)
}
# print(pl_inds)
# geofacet ----------------------------------------------------------------
# row_col_cat <- function(x,n){
# cat_b = c(0,1/n*(1:n))
# qq = quantile(x,cat_b)
# qq[1] <- qq[1]-1
# qq[n+1] <- qq[n+1]+1
# cc <- as.integer(cut(x,
# qq,
# ordered_result = TRUE))
# return(cc)
# }
#
#
# centres <- suppressWarnings(st_centroid(realized_strata_map)) %>%
# rename_with(.,~gsub(pattern = st_n,
# replacement = "strat_tmp",
# x = .x)) %>%
# arrange(strat_tmp) %>%
# rename_with(.,~gsub(replacement = st_n,
# pattern = "strat_tmp",
# x = .x))
#
# coords <- st_coordinates(centres)%>%
# as.data.frame()
# coords[,st_n] <- 1:nrow(coords)
# coords <- coords %>%
# mutate(y_cat = row_col_cat(-Y,pd)) %>%
# group_by(y_cat) %>%
# mutate(x_cat = rank(X))
#
# inds_geo <- indices_all %>%
# left_join(.,coords)
#
# lbls = inds_geo %>%
# ungroup() %>%
# select(x_cat,y_cat,strat) %>%
# distinct()
if(dd == "Shorebird"){
strat_grid <- geofacet::grid_auto(realized_strata_map,
codes = st_n,
names = "hex_name",
seed = 2019)
}
if(dd == "BBS"){
strat_grid <- geofacet::grid_auto(realized_strata_map,
codes = st_n,
names = "Stratum",
seed = 2019)
}
if(dd == "CBC"){
strat_grid <- geofacet::grid_auto(realized_strata_map,
codes = st_n,
names = "stratum",
seed = 2019)
}
indices_geo <- indices_all %>%
rename_with(~gsub(x = .x,
pattern = st_n,
replacement = "strat_labs"))
g_inds <- suppressMessages(ggplot(data = indices_geo,aes(x = true_year,y = median))+
geom_point(aes(x = true_year,y = mean_obs*zero,alpha = n_surveys),
inherit.aes = FALSE)+
geom_ribbon(aes(ymin = lci,ymax = uci,fill = version),alpha = 0.1)+
geom_line(aes(colour = version))+
labs(title = species)+
scale_y_continuous(limits = c(0,NA))+
geofacet::facet_geo(~strat_labs,grid = strat_grid,
scales = "free")+
theme(strip.text = element_text(size = 6),
strip.background = element_blank(),
panel.spacing = unit(0.1,"mm"),
axis.text.x = element_text(size = 5)))
if(!reald){
g_inds <- g_inds +
geom_line(aes(x = true_year,
y = True_scaled_smooth),
colour = "black",
alpha = 0.3,
inherit.aes = FALSE)
}
pdf(paste0("figures/",out_base,"_geofacet.pdf"),
height = 14,
width = 14)
print(g_inds)
dev.off()
ind_plots_list[[species_f]] <- pl_inds
tyrs = unique(c(2009,1999,1990,1980,1970,year_1))
tyrs = tyrs[which(tyrs >= year_1)]
tyrs2 <- rep(2019,length(tyrs))
tyrs2 <- c(tyrs2,tyrs+10)
tyrs <- c(tyrs,tyrs)
tt_map_data <- vector(mode = "list",length = length(tyrs))
names(tt_map_data) <- paste0("TY",tyrs,"-",tyrs2)
tt_map = tt_map_data
for(j in 1:length(tyrs)){
yy = tyrs[j]
yy2 = tyrs2[j]
tt <- trends_function(ind_list = strat_inds_smooth,
start_year = yy,
end_year = yy2) %>%
mutate(species = species,
first_year = yy,
last_year = yy2,
region_type = "Stratum",
simulated_data = rd,
mean_abundance = signif(exp(ma),2),
data = dd,
model = modl)
ttmd <- realized_strata_map %>%
left_join(.,tt,by = st_n) %>%
mutate(trend_plot = trend_plot_cats(trend))
tt_map_data[[paste0("TY",yy,"-",yy2)]] <- ttmd
xb = range(st_coordinates(realized_strata_map)[,"X"])
yb = range(st_coordinates(realized_strata_map)[,"Y"])
prov_state <- bbsBayes::load_map(stratify_by = "state")
ttm <- ggplot(data = ttmd)+
geom_sf(data = prov_state,alpha = 0,
colour = grey(0.8),inherit.aes = FALSE)+
geom_sf(aes(fill = trend_plot))+
coord_sf(xlim = xb,
ylim = yb)+
theme_bw()+
scale_colour_manual(values = map_palette_s,
aesthetics = c("fill"),
guide = guide_legend(reverse=TRUE),
name = paste0("Trend"))
# print(ttm)
tt_map[[paste0("TY",yy,"-",yy2)]] <- ttm
stratum_trends <- bind_rows(stratum_trends,tt)
}
if(reald){
pdf(file = paste0("Figures/",out_base,"trend_maps.pdf"))
}else{
pdf(file = paste0("Figures/",out_base,"trend_maps.pdf"))
}
for(j in 1:length(tt_map)){
print(tt_map[[j]])
}
dev.off()
# Overall Annual indices
if(dd == "Shorebird"){
sw_inds <- index_function(fit = stanfit,
parameter = "N",
year_1 = year_1,
strat = NULL,
first_dim = "y")
Inds <- sw_inds$indices %>%
mutate(version = "full",
simulated_data = rd,
mean_abundance = signif(exp(ma),2),
data = dd,
model = modl)
sw_smooth <- index_function(fit = stanfit,
parameter = "NSmooth",
year_1 = year_1,
strat = NULL,
first_dim = "y")
### one-off save of overall smooth indices for spaghetti plot
if(out_base == "Red_Knot_Shorebird"){
save(list = "sw_smooth",
file = "output/Red_Knot_SW_indices.RData")
}
Inds_smooth <- sw_smooth$indices %>%
mutate(version = "smooth",
simulated_data = rd,
mean_abundance = signif(exp(ma),2),
data = dd,
model = modl)
Indices_all <- bind_rows(Inds,
Inds_smooth) %>%
mutate(species = species,
region_type = "Survey_wide")
Indices_all_out <- bind_rows(Indices_all_out,Indices_all)
pl_Inds <- ggplot(data = Indices_all,aes(x = Year,y = median))+
geom_ribbon(aes(ymin = lci,ymax = uci,fill = version),alpha = 0.1)+
geom_line(aes(colour = version))+
labs(title = species)
ind_plots_list[[species_f]] <- pl_Inds
}else{
sw_inds <- index_function(fit = stanfit,
parameter = "n",
year_1 = year_1,
strat = st_n,
weights_df = strat_df,
area = "AREA_1",#"Area",
summary_regions = NULL)
Inds <- sw_inds$indices %>%
mutate(version = "full",
simulated_data = rd,
mean_abundance = signif(exp(ma),2),
data = dd,
model = modl)
sw_smooth <- index_function(fit = stanfit,
parameter = "nsmooth",
year_1 = year_1,
strat = st_n,
weights_df = strat_df,
area = "AREA_1",#"Area",
summary_regions = NULL)
Inds_smooth <- sw_smooth$indices %>%
mutate(version = "smooth",
simulated_data = rd,
mean_abundance = signif(exp(ma),2),
data = dd,
model = modl)
Indices_all <- bind_rows(Inds,
Inds_smooth) %>%
mutate(species = species,
region_type = "Survey_wide")
Indices_all_out <- bind_rows(Indices_all_out,Indices_all)
pl_Inds <- ggplot(data = Indices_all,aes(x = Year,y = median))+
geom_ribbon(aes(ymin = lci,ymax = uci,fill = version),alpha = 0.1)+
geom_line(aes(colour = version))+
labs(title = species)
#print(pl_Inds)
ind_plots_list[[species_f]] <- pl_Inds
}
pdf(file = paste0("Figures/",out_base,"_Indices.pdf"))
print(pl_Inds)
print(pl_inds)
dev.off()
tt_map_data_list[[species_f]] <- tt_map_data
tt_map_list[[species_f]] <- tt_map
for(j in 1:length(tyrs)){
yy = tyrs[j]
yy2 = tyrs2[j]
TT <- trends_function(ind_list = sw_smooth,
start_year = yy,
end_year = yy2) %>%
mutate(species = species,
first_year = yy,
last_year = yy2,
region_type = "Survey_wide")
all_trends <- bind_rows(all_trends,TT)
}
print(i)
}#end species loop
save(list = c("all_trends",
"tt_map_data_list",
"tt_map_list",
"indices_all_out",
"stratum_trends",
"Indices_all_out",
"ind_plots_list",
"Ind_plots_list",
"conv_summaries"),
file = "output/real_data_summaries.RData")