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maps.R
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maps.R
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# WFP Climate Risk Project
# =----------------------
# Graphs.
# A. Esquivel - H. Achicanoy - C.Saavedra
# =----------------------
map_graphs <- function(iso3, country, seasons, Zone = 'all'){
# Reading the tables of future indices.
to_do <<- readxl::read_excel(glue::glue('{root}/1.Data/regions_ind.xlsx')) %>%
dplyr::filter(ISO3 == iso3) %>%
dplyr::rename('Livehood_z' = 'Livelihood zones', 'NT_X'= "NT-X")
# =----------------------------------------------
# Read all shp ...
# =----------------------------------------------
# Read GDAM's shp at administrative level 1.
shp <<- raster::shapefile(paste0(root , "/1.Data/shps/", tolower(country), "/",tolower(iso3),"_gadm/",country,"_GADM1.shp"))
shp_sf <- shp %>% sf::st_as_sf() %>%
dplyr::group_by(NAME_0) %>% dplyr::summarise()
shp_sf <<- shp_sf
# Regions shp
regions_all <- raster::shapefile(paste0(root , "/1.Data/shps/", tolower(country), "/",tolower(iso3),"_regions/",tolower(iso3),"_regions.shp"))
regions_all <- regions_all %>% sf::st_as_sf() %>%
dplyr::group_by(region) %>% dplyr::summarise() %>% arrange(region) %>%
dplyr::mutate(Short_Name = arrange(to_do, Regions)$Short_Name)
regions_all <<- regions_all
# =--- World boundaries.
map_world <- raster::shapefile(glue::glue('{root}/1.Data/shps/all_country/all_countries.shp')) %>%
sf::st_as_sf()
map_world <<- map_world
ctn <- map_world$CONTINENT[which(map_world$ISO3 == iso3)]
ctn <- map_world %>% dplyr::filter(CONTINENT == ctn)
ctn <<- ctn
# =---
# =--- water sources.
glwd1 <- raster::shapefile(glue::glue('{root}/1.Data/shps/GLWD/glwd_1_fixed.shp' ) )
crs(glwd1) <- crs(shp)
glwd2 <- raster::shapefile(glue::glue('{root}/1.Data/shps/GLWD/glwd_2_fixed.shp' ) )
crs(glwd2) <- crs(shp)
ext.sp <- raster::crop(glwd1, raster::extent(shp))
glwd1 <- rgeos::gSimplify(ext.sp, tol = 0.05, topologyPreserve = TRUE) %>%
sf::st_as_sf()
ext.sp2 <- raster::crop(glwd2, raster::extent(shp))
glwd2 <- rgeos::gSimplify(ext.sp2, tol = 0.05, topologyPreserve = TRUE) %>%
sf::st_as_sf()
glwd1 <<- glwd1; glwd2 <<- glwd2
# =--------------------------------------------
# =----------------------------------------------
# Read all data complete.
past <- tidyft::parse_fst(glue::glue('{root}/7.Results/{country}/past/{iso3}_indices.fst') )%>%
tibble::as_tibble() %>%
dplyr::mutate(time = 'Historic')
gcm <- c('INM-CM5-0', 'ACCESS-ESM1-5', 'EC-Earth3-Veg', 'MPI-ESM1-2-HR', 'MRI-ESM2-0')
ncores <- 5
future::plan(cluster, workers = ncores, gc = TRUE)
future <- tibble(file = list.files(glue::glue('{root}/7.Results/{country}/future/{gcm}/'), full.names = TRUE, recursive = TRUE, pattern = '_indices') ) %>%
dplyr::filter(!grepl('_monthly', file) & !grepl('_old', file) & !grepl('zone', file)) %>%
dplyr::mutate(shot_file = str_remove(file, pattern = glue::glue('{root}/7.Results/{country}/future/'))) %>%
dplyr::mutate(data = furrr::future_map(.x = file, .f = function(x){x <- tidyft::parse_fst(x) %>% tibble::as_tibble()})) %>%
dplyr::mutate(str_split(shot_file, '/') %>%
purrr::map(.f = function(x){tibble(gcm = x[1], time = x[3])}) %>%
dplyr::bind_rows()) %>%
dplyr::select(gcm, time, data) %>%
tidyr::unnest()
future:::ClusterRegistry("stop")
gc(reset = T)
if('CSDI' %in% names(future)) {
indices <- c('NDD', 'NT_X', 'NDWS', 'NWLD', 'NWLD50', 'NWLD90','SHI', 'gSeason', 'SLGP', 'LGP', 'CSDI')
} else {
indices <- c('NDD', 'NT_X', 'NDWS', 'NWLD', 'NWLD50', 'NWLD90','SHI', 'gSeason', 'SLGP', 'LGP')
}
future <- future %>% dplyr::select(-gcm) %>%
dplyr::group_by(time,id,x,y,season,year) %>%
dplyr::summarise_all(~mean(. , na.rm = TRUE)) %>%
dplyr::mutate_at(.vars = indices,
.funs = ~round(. , 0))
data_cons <- dplyr::bind_rows(past, future) %>%
dplyr::mutate(time1 = dplyr::case_when(time == 'Historic' ~ 1,
time == '2021-2040'~ 2,
time == '2041-2060'~ 3,
TRUE ~ NA_real_)) %>%
dplyr::select(time, time1 ,everything())
px_filter <- unique(data_cons$id)
# =---------------------------------------------------
spi_past <- tidyft::parse_fst(glue::glue('{root}/7.Results/{country}/past/{iso3}_spi_drought.fst') )%>%
tibble::as_tibble() %>%
dplyr::mutate(time = 'Historic')
spi_future <- tibble( file = list.files(glue::glue('{root}/7.Results/{country}/future/{gcm}/'), full.names = TRUE, recursive = TRUE, pattern = '_spi_drought') ) %>%
dplyr::filter(!grepl('_monthly', file) & !grepl('_old', file)) %>%
dplyr::mutate(shot_file = str_remove(file, pattern = glue::glue('{root}/7.Results/{country}/future/'))) %>%
dplyr::mutate(data = purrr::map(.x = file, .f = function(x){x <- tidyft::parse_fst(x) %>% tibble::as_tibble()})) %>%
dplyr::mutate(str_split(shot_file, '/') %>%
purrr::map(.f = function(x){tibble(gcm = x[1], time = x[3])}) %>%
dplyr::bind_rows()) %>%
dplyr::select(gcm, time, data) %>%
tidyr::unnest() %>% dplyr::select(-gcm) %>%
dplyr::group_by(time,id,x,y,season,year) %>%
dplyr::summarise_all(~mean(. , na.rm = TRUE))
spi_dat <- dplyr::bind_rows(spi_past, spi_future) %>%
dplyr::mutate(time1 = dplyr::case_when(time == 'Historic' ~ 1,
time == '2021-2040'~ 2,
time == '2041-2060'~ 3,
TRUE ~ NA_real_)) %>%
dplyr::mutate(year = as.numeric(year)) %>%
dplyr::mutate(SPI = spi * 100) %>%
dplyr::select(time, time1 , id, year, SPI, season, time) %>%
dplyr::filter(id %in% px_filter)
data_cons <- dplyr::full_join(data_cons, spi_dat) %>% tidyr::drop_na(time1)
# =----------------------------------------------------
# =- Climate ---
Climate <- tidyft::parse_fst(glue::glue('{root}/1.Data/observed_data/{iso3}/year/climate_1981_mod.fst')) %>% as_tibble()
# Para los graphs... es importante tener en cuenta que se
# debe guardar las coordenadas, ya que sino no grafica correctamente.
coord <- Climate %>% dplyr::select(id, x, y) %>% unique()
rm(Climate)
# =----------------------------------------------
mapping_g <- function(R_zone, iso3, country, Period = seasons, data_cons = data_cons, coord = coord){
# R_zone <- to_do$Regions[1]
season <- names(Period)
st <- tail(Period[[1]], n=1); lt <- Period[[1]][1]
length_season <- length(Period[[1]])
days <- sum(lubridate::days_in_month(1:12)[Period[[1]]])
pet <- data_cons
data_cons <- data_cons %>% dplyr::filter(season == names(Period))
# =--------------
path <- glue::glue('{root}/7.Results/{country}/results/maps/{R_zone}_{season}')
dir.create(path,recursive = TRUE)
# =--------------
if(R_zone == 'all'){
if('CSDI' %in% names(future)){
zone <- regions_all # %>% sf::as_Spatial()
var_s <- to_do %>% dplyr::mutate( Regions = 'all', Livehood_z = 'all', Short_Name = 'all') %>%
dplyr::mutate_at(.vars = vars(ATR:CSDI) , .funs = function(x){x <- ifelse(x == '-', 0, x) %>% as.integer()}) %>%
dplyr::group_by(ISO3, Country, Regions, Livehood_z, Short_Name ) %>%
dplyr::summarise_all(. , sum, na.rm = TRUE) %>% dplyr::ungroup()
} else {
zone <- regions_all # %>% sf::as_Spatial()
var_s <- to_do %>% dplyr::mutate( Regions = 'all', Livehood_z = 'all', Short_Name = 'all') %>%
dplyr::mutate_at(.vars = vars(ATR:SHI) , .funs = function(x){x <- ifelse(x == '-', 0, x) %>% as.integer()}) %>%
dplyr::group_by(ISO3, Country, Regions, Livehood_z, Short_Name ) %>%
dplyr::summarise_all(. , sum, na.rm = TRUE) %>% dplyr::ungroup()
}
title = 'Country'
}else{
if('CSDI' %in% names(future)){
zone <- dplyr::filter(regions_all, region == R_zone) # %>% sf::as_Spatial()
var_s <- to_do %>% dplyr::filter(Regions == R_zone) %>%
dplyr::mutate_at(.vars = vars(ATR:CSDI) , .funs = function(x){x <- ifelse(x == '-', 0, x) %>% as.integer()})
} else{
zone <- dplyr::filter(regions_all, region == R_zone) # %>% sf::as_Spatial()
var_s <- to_do %>% dplyr::filter(Regions == R_zone) %>%
dplyr::mutate_at(.vars = vars(ATR:SHI) , .funs = function(x){x <- ifelse(x == '-', 0, x) %>% as.integer()})
}
title = dplyr::filter(to_do, Regions == R_zone)$Short_Name
}
# Aqui hacer el bufer --- menor tamano.
zone_bufer <- sf::st_buffer(zone, dist = 0.05) %>%
sf::st_union(.) %>% sf::as_Spatial()
# =--------------------------------------------
# Limits
xlims <<- sf::st_bbox(zone_bufer)[c(1, 3)]
ylims <<- sf::st_bbox(zone_bufer)[c(2, 4)]
vars <- dplyr::select(var_s, -ISO3, -Country, -Regions, -Livehood_z, -Short_Name) %>%
tidyr::gather(key = 'var',value = 'value') %>%
dplyr::filter(value > 0) %>% dplyr::pull(var)
if(sum(vars == 'NWLD') == 1){vars <- c(vars, "NWLD50", "NWLD90")}
if(sum(vars == 'THI') == 1){
vars <- c(vars[vars != 'THI'], glue::glue('THI_{0:3}'))
vars <- c(vars, 'THI_23')}
if(sum(vars == 'HSI') == 1){
vars <- c(vars[vars != 'HSI'], glue::glue('HSI_{0:3}'))
vars <- c(vars, 'HSI_23')}
# Temporal
basic_vars <- vars[!(vars %in% c("SLGP", "LGP"))]
# =-----------------
# Cortar por el crd.
crd <- data_cons %>%
dplyr::filter(year == 2019) %>% dplyr::mutate(ISO = iso3)
pnt <- crd %>% dplyr::select(x,y) %>% tidyr::drop_na() %>% sp::SpatialPoints(coords = .)
crs(pnt) <- crs(shp)
# dplyr::filter coordinates that are present in the county
pnt <- sp::over(pnt, zone_bufer) %>% data.frame %>% complete.cases() %>% which()
crd <- crd[pnt,]
id_f <- unique(crd$id)
coord_zone <- coord %>% dplyr::filter(id %in% id_f )
# =----------------------------------------------------
# Basic vars.
if('CSDI' %in% names(future)){
indices <- c('NDD', 'NT_X', 'NDWS', 'NWLD', 'NWLD50', 'NWLD90','SHI', 'CSDI')
} else {
indices <- c('NDD', 'NT_X', 'NDWS', 'NWLD', 'NWLD50', 'NWLD90','SHI')
}
to_graph <- data_cons %>% dplyr::filter(id %in% id_f) %>%
dplyr::mutate(THI_23 = THI_2 + THI_3, HSI_23 = HSI_2 + HSI_3) %>%
dplyr::select(time, time1, id, basic_vars) %>%
dplyr::group_by(time, time1, id) %>%
dplyr::summarise_all(~mean(. , na.rm = TRUE)) %>%
dplyr::mutate_at(.vars = basic_vars[basic_vars %in% indices],
.funs = ~round(. , 0)) %>%
dplyr::ungroup() %>% base::unique() %>%
dplyr::full_join(coord_zone, . )
#=-----------------------------------------------------------------------------------
# Lenght_season
to_graph <- dplyr::mutate_at(to_graph, .vars = basic_vars[basic_vars %in% c('NDD','NDWS', 'NWLD','NWLD50', 'NWLD90', 'NDD', 'NT_X')],
.funs = ~(./length_season) %>% round(. ,0) )
# Transform variables.
if(sum(basic_vars == 'SHI') == 1){
to_graph <- to_graph %>% dplyr::mutate(SHI = (SHI/days) )
}
# Voy a explicar esta parte...
if(sum(vars %in% c("SLGP", "LGP")) > 0 ){
if(sum(vars == 'SLGP') > 0){vars <- c(vars, 'SLGP_CV')}
vars_s <- vars[vars %in% c("SLGP", "LGP", 'SLGP_CV')]
peta <- data_cons %>%
dplyr::filter(id %in% id_f) %>%
dplyr::select(time, time1, id, year, gSeason, SLGP, LGP)
peta$date <- as.Date(peta$SLGP, origin = "2001-01-01") %>% lubridate::month()
peta <- peta %>%
dplyr::filter(date %in% Period[[1]]) %>%
dplyr::group_by(time, time1, id, gSeason) %>%
dplyr::summarise_all(list(mean, sd), na.rm = TRUE ) %>%
dplyr::ungroup() %>%
dplyr::mutate_at(.vars = c('SLGP_fn1', 'LGP_fn1'), .funs = ~round(. , 0)) %>%
dplyr::select(-LGP_fn2) %>%
dplyr::rename(SLGP = 'SLGP_fn1', LGP = 'LGP_fn1', SLGP_CV = 'SLGP_fn2') %>%
dplyr::mutate(SLGP_CV = (SLGP_CV/SLGP) * 100) %>%
dplyr::filter(gSeason == str_remove(season, 's') %>% as.numeric()) %>%
dplyr::select(id, time, time1, vars_s)
to_graph <- dplyr::full_join(to_graph, peta)
}
# =----------------------------------------------------
# MAX section ---- Explicar idea de Julian.
if(sum(vars == 'NWLD') > 0){
max_add <- data_cons %>% dplyr::filter(id %in% id_f) %>%
dplyr::select(time, time1, id, 'NWLD', 'NWLD50', 'NWLD90') %>%
dplyr::group_by(time, time1, id) %>% tidyr::drop_na() %>%
dplyr::summarise_all(~max(. , na.rm = TRUE)) %>%
dplyr::mutate_at(.vars = c('NWLD', 'NWLD50', 'NWLD90'),
.funs = ~round(. , 0)) %>%
dplyr::mutate_at(.vars = c('NWLD', 'NWLD50', 'NWLD90'),
.funs = ~(./length_season) %>% round(. ,0) ) %>% # Here
dplyr::ungroup() %>% unique() %>%
setNames(c('time','time1','id','NWLD_max', 'NWLD50_max', 'NWLD90_max'))
vars <- c(vars, 'NWLD_max', 'NWLD50_max', 'NWLD90_max')
to_graph <- to_graph %>% dplyr::full_join(. , max_add)
}
# =----------------------------------------------------
# Tabla completa... explicar esta parte.
special_base <- dplyr::select(pet, id, time, time1, season, ATR, AMT) %>%
dplyr::filter(id %in% id_f) %>%
dplyr::group_by(time, time1, season, id) %>%
dplyr::summarise_all(.funs = mean, na.rm = TRUE) %>%
dplyr::ungroup()
Special_limits <- special_base %>% dplyr::select(ATR, AMT) %>%
dplyr::summarise_all(.funs = c('min', 'max'), na.rm = TRUE)
# =----------------------------------------------------
# Aqui vuelve la base de datos por season.
max_lt <- to_graph %>% dplyr::select(contains('_max')) %>%
dplyr::summarise_all(.funs = c('min', 'max'), na.rm = TRUE)
limits <- to_graph %>% dplyr::select(-id, -x, -y, -time1 , -ATR, -AMT, -contains('_max')) %>%
dplyr::summarise_all(.funs = c('min', 'max'), na.rm = TRUE) %>%
bind_cols(max_lt) %>%
dplyr::bind_cols(Special_limits, . )
var_toG <- names(dplyr::select(to_graph, -id, -x, -y, -time, -time1))
# =---- Aqui se hacen los graphs continuos
for(i in 1:length(var_toG)){
j <- c('_min' , '_max')
my_limits <- dplyr::select(limits, glue::glue('{var_toG[i]}{j}')) %>% setNames(c('min', 'max')) %>% as.numeric()
my_breaks <- round(seq(my_limits[1], my_limits[2], length.out= 4), 0)
my_limits <- c(ifelse(my_limits[1] > my_breaks[1], my_breaks[1], my_limits[1]) ,ifelse(my_limits[2] < my_breaks[4], my_breaks[4], my_limits[2]))
if(var_toG[i] %in% c('SHI', glue::glue('THI_{0:3}'), glue::glue('HSI_{0:3}'), 'THI_23', 'HSI_23')){
my_limits <- c(0, 1)
my_breaks <- c(0, 0.3, 0.6, 1)
}
if(var_toG[i] %in% c('NDD','NDWS', 'NWLD','NWLD50', 'NWLD90', 'NDD', 'NT_X', 'NWLD_max','NWLD50_max', 'NWLD90_max')){
my_limits <- c(0, 31)
my_breaks <- c(0, 10, 20, 31)
}
# Pattern.
if(var_toG[i] == 'AMT'){ pattern <- 'AMT\n(\u00B0C)' }
if(var_toG[i] == 'ATR'){ pattern <- 'ATR\n(mm/season)' }
if(var_toG[i] == 'TAI'){ pattern <- 'TAI\nAridity' }
if(var_toG[i] == 'SLGP'){ pattern <- 'SLGP\n(Day of\nthe year)' }
if(var_toG[i] == 'LGP'){ pattern <- 'LGP\n(days)' }
if(var_toG[i] %in% c('NDD', 'NDWS', 'NT_X', 'NWLD', 'NWLD50', 'NWLD90', 'NWLD_max', 'NWLD50_max', 'NWLD90_max')){ pattern <- glue::glue('{var_toG[i]}\n(days/month)') }
if(var_toG[i] == 'P5D'){ pattern <- 'P5D\n(mm/5 days)' }
if(var_toG[i] %in% c('SHI', 'HSI_0', 'HSI_1', 'HSI_2', 'HSI_3', 'HSI_23', 'THI_0', 'THI_1', 'THI_2', 'THI_3', 'THI_23')){ pattern <- glue::glue('{var_toG[i]}\n(prob)') }
if(var_toG[i] == 'P95'){ pattern <- 'P95\n(mm/day)' }
if(var_toG[i] == 'IRR'){ pattern <- 'IRR' }
if(var_toG[i] == 'SPI'){ pattern <- 'SPI\n(% area)' }
if(var_toG[i] == 'SLGP_CV'){ pattern <- 'SLGP\n(%)' }
if(var_toG[i] == 'CSDI'){ pattern <- 'CSDI\n(days)' }
if(var_toG[i] == 'ATR'){
mop <- scale_fill_gradientn(limits = my_limits,
breaks = unique(my_breaks),
labels = unique(my_breaks),
colours = blues9,
guide = guide_colourbar(barwidth = 25,
label.theme = element_text(angle = 25, size = 35)))
}else if(var_toG[i] == 'AMT'){
mop <- scale_fill_gradient(limits = my_limits,
breaks = unique(my_breaks),
labels = unique(my_breaks),
low = "yellow", high = "red",
guide = guide_colourbar(barwidth = 25,
label.theme = element_text(angle = 25, size = 35)))
}else{
mop <- scale_fill_viridis_c(limits = my_limits,
breaks = unique(my_breaks),
labels = unique(my_breaks),
guide = guide_colourbar(barwidth = 20,
label.theme = element_text(angle = 25, size = 35)))
}
gg <- ggplot() +
geom_tile(data = tidyr::drop_na(to_graph, !!rlang::sym(var_toG[i]) ), aes(x = x, y = y, fill = !!rlang::sym(var_toG[i]) )) +
geom_sf(data = glwd1, fill = 'lightblue', color = 'lightblue') +
geom_sf(data = glwd2, fill = 'lightblue', color = 'lightblue') +
geom_sf(data = ctn, fill = NA, color = gray(.5)) +
# geom_sf(data = shp_sf, fill = NA, color = gray(.1)) +
geom_sf(data = zone, fill = NA, color = 'black') +
mop +
labs(fill = pattern, x = NULL, y = NULL, title = title) +
coord_sf(xlim = xlims, ylim = ylims) +
facet_grid(~time1, labeller = as_labeller( c('1' = "Historic", '2' = "2021-2040", '3' = '2041-2060'))) +
theme_bw() +
theme(legend.position = 'bottom', text = element_text(size=35),
strip.background = element_rect(colour = "black", fill = "white"),
axis.text.x=element_blank(), axis.text.y=element_blank(),
legend.title=element_text(size=35),
legend.spacing = unit(5, units = 'cm'),
legend.spacing.x = unit(1.0, 'cm'), plot.title = element_text(hjust = 0.5))
ggplot2::ggsave(filename = glue::glue('{path}/C_{var_toG[i]}.png'), plot = gg, width = 15, height = 10, dpi = 300, device = 'jpeg', units = 'in')
}
# =-------------------------------------
# Anomaly Maps
if(length(unique(to_graph$time)) > 1){
to_process <- unique(to_graph$time)[unique(to_graph$time) != "Historic" ]
# This function organize
anom_table <- function(data_to, pro_time){
less_var <- names(data_to)[names(data_to) %in% c('x', 'y', 'time', 'time1')]
Fdata <- dplyr::filter(data_to, time == pro_time) %>%
dplyr::select(-less_var)
HT <- dplyr::filter(data_to,time == 'Historic') %>%
dplyr::select(-less_var)
px <- dplyr::intersect(unique(HT$id), unique(Fdata$id))
Fdata <- dplyr::filter(Fdata, id %in% px)
HT <- dplyr::filter(HT, id %in% px)
for(i in 2:ncol(HT)){Fdata[,i] <- Fdata[,i] - HT[,i]}
Fdata$ATR <- (Fdata$ATR/HT$ATR)*100
return(Fdata)}
# =----
anomalies <- tibble::tibble(time1 = to_process) %>%
dplyr::mutate(data = purrr::map(.x = time1, .f = anom_table, data_to = to_graph)) %>%
tidyr::unnest(data) %>%
dplyr::inner_join(dplyr::select(dplyr::filter(to_graph, time1 == 2), id, x, y), .)
# =-- This part is for run with the complete data base.
sp_anom_l <- tibble::tibble(time1 = to_process) %>%
dplyr::mutate(data = purrr::map(.x = time1, .f = anom_table, data_to = special_base)) %>%
tidyr::unnest(data) %>% dplyr::select(ATR, AMT) %>%
dplyr::summarise_all(.funs = c('min', 'max'), na.rm = TRUE)
# =---------
max_lt <- anomalies %>% dplyr::select(contains('_max')) %>%
dplyr::summarise_all(.funs = c('min', 'max'), na.rm = TRUE)
limits <- anomalies %>% dplyr::select(-id, -x, -y, -time1 , -ATR, -AMT, -contains('_max')) %>%
dplyr::summarise_all(.funs = c('min', 'max'), na.rm = TRUE) %>%
bind_cols(max_lt) %>%
bind_cols(sp_anom_l , . )
# Estos son los graphs de anomalias.
for(i in 1:length(var_toG)){
j <- c('_min' , '_max')
my_limits <- dplyr::select(limits, glue::glue('{var_toG[i]}{j}' )) %>% setNames(c('min', 'max')) %>% as.numeric()
my_breaks <- round(seq(my_limits[1], my_limits[2], length.out= 4), 0)
my_limits <- c(ifelse(my_limits[1] > my_breaks[1], my_breaks[1], my_limits[1]) ,ifelse(my_limits[2] < my_breaks[4], my_breaks[4], my_limits[2]))
if(var_toG[i] %in% c('SHI', glue::glue('THI_{0:3}'), glue::glue('HSI_{0:3}'), 'THI_23', 'HSI_23')){
my_limits <- c(-1, 1)
my_breaks <- c(-1, -0.5, 0, 0.5, 1)
}
# labels...
if(var_toG[i] == 'AMT'){ pattern <- 'AMT\n(\u00B0C)' }
if(var_toG[i] == 'ATR'){ pattern <- 'ATR\n(%)' }
if(var_toG[i] == 'TAI'){ pattern <- 'TAI\nAridity' }
if(var_toG[i] == 'SLGP'){ pattern <- 'SLGP\n(Day of\nthe year)' }
if(var_toG[i] == 'LGP'){ pattern <- 'LGP\n(days)' }
if(var_toG[i] %in% c('NDD', 'NDWS', 'NT_X', 'NWLD', 'NWLD50', 'NWLD90', 'NWLD_max', 'NWLD50_max', 'NWLD90_max')){ pattern <- glue::glue('{var_toG[i]}\n(days/month)') }
if(var_toG[i] == 'P5D'){ pattern <- 'P5D\n(mm/5 days)' }
if(var_toG[i] %in% c('SHI', 'HSI_0', 'HSI_1', 'HSI_2', 'HSI_3', 'HSI_23', 'THI_0', 'THI_1', 'THI_2', 'THI_3', 'THI_23')){ pattern <- glue::glue('{var_toG[i]}\n(prob)') }
if(var_toG[i] == 'P95'){ pattern <- 'P95\n(mm/day)' }
if(var_toG[i] == 'IRR'){ pattern <- 'IRR' }
if(var_toG[i] == 'SPI'){ pattern <- 'SPI\n(% area)' }
if(var_toG[i] == 'SLGP_CV'){ pattern <- 'SLGP_CV\n(%)' }
if(var_toG[i] == 'CSDI'){ pattern <- 'CSDI\n(days)' }
mop <- scale_fill_gradient2(
low = '#A50026', mid = 'white', high = '#000099',
limits = my_limits,
breaks = unique(my_breaks),
labels = unique(my_breaks),
guide = guide_colourbar(barwidth = 20, label.theme = element_text(angle = 25, size = 35)))
if(var_toG[i] == 'AMT'){
mop <- scale_fill_gradient2(
low = '#000099', mid = 'white', high = '#A50026',
limits = my_limits,
breaks = unique(my_breaks),
labels = unique(my_breaks),
guide = guide_colourbar(barwidth = 20, label.theme = element_text(angle = 25, size = 35)))
}
gg <- ggplot() +
geom_tile(data = tidyr::drop_na(anomalies, !!rlang::sym(var_toG[i]) ), aes(x = x, y = y, fill = !!rlang::sym(var_toG[i]) )) +
geom_sf(data = glwd1, fill = 'lightblue', color = 'lightblue') +
geom_sf(data = glwd2, fill = 'lightblue', color = 'lightblue') +
geom_sf(data = ctn, fill = NA, color = gray(.5)) +
# geom_sf(data = shp_sf, fill = NA, color = gray(.1)) +
geom_sf(data = zone, fill = NA, color = 'black') +
mop +
labs(fill = pattern, x = NULL, y = NULL, title = title) +
# scale_y_continuous(breaks = round(ylims, 2), n.breaks = 3) +
# scale_x_continuous(breaks = round(xlims, 2), n.breaks = 3) +
coord_sf(xlim = xlims, ylim = ylims) +
facet_grid(~time1) +
theme_bw() +
theme(legend.position = 'bottom', text = element_text(size=35),
axis.text.x=element_blank(), axis.text.y=element_blank(),
# axis.ticks.x=element_blank(), axis.ticks.y=element_blank(),
strip.background = element_rect(colour = "black", fill = "white"),
legend.title=element_text(size=35),
legend.spacing = unit(5, units = 'cm'),
legend.spacing.x = unit(1.0, 'cm'), plot.title = element_text(hjust = 0.5))
ggplot2::ggsave(filename = glue::glue('{path}/Anom_{var_toG[i]}.png'), plot = gg, width = 15, height = 10, dpi = 300, device = 'jpeg', units = 'in')
}
}
# =-------------------------------------
###### Categorization...
# Labels for graph
labels <- function(x){
Q_clas <- quantile(x, c(0.25, 0.5, 0.75), na.rm =TRUE)
return(Q_clas)}
# Clasification
transform_q <- function(x){
Q_clas <- quantile(x, c(0.25, 0.5, 0.75), na.rm =TRUE)
x <- case_when(x <= Q_clas[1] ~ 1,
x > Q_clas[1] & x <= Q_clas[2]~ 2,
x > Q_clas[2] & x <= Q_clas[3]~ 3,
x > Q_clas[3] ~ 4, TRUE ~ x)
return(x)}
# "SPI" "NDWS" "NWLD" "NT_X"
class_1 <- to_graph %>%
dplyr::mutate_at(.vars = vars(var_toG), .funs = transform_q)
# =--------------- Cambios de Julian.
ATR_class <- function(x){
Q_clas <- quantile(special_base$ATR, c(0.25, 0.5, 0.75), na.rm =TRUE)
x <- case_when(x <= Q_clas[1] ~ 1,
x > Q_clas[1] & x <= Q_clas[2]~ 2,
x > Q_clas[2] & x <= Q_clas[3]~ 3,
x > Q_clas[3] ~ 4, TRUE ~ x)
return(x)}
AMT_class <- function(x){
Q_clas <- quantile(special_base$AMT, c(0.25, 0.5, 0.75), na.rm =TRUE)
x <- case_when(x <= Q_clas[1] ~ 1,
x > Q_clas[1] & x <= Q_clas[2]~ 2,
x > Q_clas[2] & x <= Q_clas[3]~ 3,
x > Q_clas[3] ~ 4, TRUE ~ x)
return(x)}
class_1 <- to_graph %>% dplyr::select(id, time, time1, ATR, AMT) %>%
dplyr::mutate(ATR = ATR_class(ATR), AMT = AMT_class(AMT)) %>%
dplyr::left_join( . , dplyr::select(class_1, -AMT, -ATR)) %>%
dplyr::select(id,x,y,time,time1, everything(.))
# =---------------
# =--------------------------------------------------
if(sum(var_toG %in% c(glue::glue('THI_{0:3}'), glue::glue('HSI_{0:3}'), 'SHI', 'HSI_23', 'THI_23') ) > 0 ){
var_Q <- var_toG[-which(var_toG %in% c(glue::glue('THI_{0:3}'), glue::glue('HSI_{0:3}'), 'SHI', 'HSI_23', 'THI_23'))]
}else{var_Q <- var_toG}
# Quantile maps.
for(i in 1:length(var_Q)){
Q_q <- round(labels(dplyr::pull(to_graph[, var_Q[i]])), 0)
if(var_Q[i] == 'ATR'){
Q_q <- round(quantile(special_base$ATR, c(0.25, 0.5, 0.75), na.rm =TRUE), 0)
}
if(var_Q[i] == 'AMT'){
Q_q <- round(Q_clas <- quantile(special_base$AMT, c(0.25, 0.5, 0.75), na.rm =TRUE), 0)
}
labs_qq <- c('1' = glue::glue('{var_Q[i]} <= {Q_q[1]}'),
'2' = glue::glue('{Q_q[1]} < {var_Q[i]} <= {Q_q[2]}'),
'3' = glue::glue('{Q_q[2]} < {var_Q[i]} <= {Q_q[3]}'),
'4' = glue::glue('{var_Q[i]} > {Q_q[3]}'))
if(var_toG[i] == 'AMT'){ pattern <- 'AMT\n(\u00B0C)' }
if(var_toG[i] == 'ATR'){ pattern <- 'ATR\n(mm/season)' }
if(var_toG[i] == 'TAI'){ pattern <- 'TAI\nAridity' }
if(var_toG[i] == 'SLGP'){ pattern <- 'SLGP\n(Day of\nthe year)' }
if(var_toG[i] == 'LGP'){ pattern <- 'LGP\n(days)' }
if(var_toG[i] %in% c('NDD', 'NDWS', 'NT_X', 'NWLD', 'NWLD50', 'NWLD90', 'NWLD_max', 'NWLD50_max', 'NWLD90_max')){ pattern <- glue::glue('{var_toG[i]}\n(days/month)') }
if(var_toG[i] == 'P5D'){ pattern <- 'P5D\n(mm/5 days)' }
if(var_toG[i] %in% c('SHI', 'HSI_0', 'HSI_1', 'HSI_2', 'HSI_3', 'HSI_23', 'THI_0', 'THI_1', 'THI_2', 'THI_3', 'THI_23')){ pattern <- glue::glue('{var_toG[i]}\n(prob)') }
if(var_toG[i] == 'P95'){ pattern <- 'P95\n(mm/day)' }
if(var_toG[i] == 'IRR'){ pattern <- 'IRR' }
if(var_toG[i] == 'SPI'){ pattern <- 'SPI\n(% area)' }
if(var_toG[i] == 'SLGP_CV'){ pattern <- 'SLGP_CV\n(%)' }
if(var_toG[i] == 'CSDI'){ pattern <- 'CSDI\n(days)' }
gg <- ggplot() +
geom_tile(data = tidyr::drop_na(class_1, !!rlang::sym(var_Q[i]) ), aes(x = x, y = y, fill = !!rlang::sym(var_Q[i]) %>% as.factor(.) )) +
geom_sf(data = glwd1, fill = 'lightblue', color = 'lightblue') +
geom_sf(data = glwd2, fill = 'lightblue', color = 'lightblue') +
geom_sf(data = ctn, fill = NA, color = gray(.5)) +
# geom_sf(data = shp_sf, fill = NA, color = gray(.1)) +
geom_sf(data = zone, fill = NA, color = 'black') +
scale_fill_manual(values = c( '1' = "#A7D96A", '2' = "#FFFFC1", '3' = "#FDAE61", '4' = '#D7191B', '5' = "#533104"),
breaks = c('1', '2', '3', '4'), labels = labs_qq) +
labs(fill = pattern, x = NULL, y = NULL, title = title) +
scale_y_continuous(breaks = round(ylims, 2), n.breaks = 3) +
scale_x_continuous(breaks = round(xlims, 2), n.breaks = 3) +
coord_sf(xlim = xlims, ylim = ylims) +
facet_grid(~time1, labeller = as_labeller( c('1' = "Historic", '2' = "2021-2040", '3' = '2041-2060'))) +
theme_bw() +
guides(fill=guide_legend(nrow=2,byrow=TRUE)) +
theme(legend.position = 'bottom', text = element_text(size=35),
axis.text.x=element_blank(), axis.text.y=element_blank(),
strip.background = element_rect(colour = "black", fill = "white"),
legend.title=element_text(size=35),
legend.spacing = unit(5, units = 'cm'),
legend.spacing.x = unit(1.0, 'cm'), plot.title = element_text(hjust = 0.5))
ggplot2::ggsave(filename = glue::glue('{path}/Q_{var_Q[i]}.png'), plot = gg, width = 15, height = 10, dpi = 300, device = 'jpeg', units = 'in')
}
# =------------------------------------------
# Only graphs with special cat.
# 1. TAI
if(sum(names(to_graph) == 'TAI') > 0){
b <- to_graph %>%
dplyr::select(id, time1,TAI) %>%
dplyr::mutate(TAI =case_when(TAI <= 40 ~ 1,
TAI > 40 & TAI <= 60~ 2,
TAI > 60 & TAI <= 80~ 3,
TAI > 80 ~ 4,
TRUE ~ TAI) )
class_1 <- dplyr::full_join(class_1 %>% dplyr::select(-TAI), b)
}
# 2. 'NDWS', 'NDD'
if(sum(names(to_graph) %in% c('NDWS', 'NDD')) > 0){
var_b <- names(to_graph)[names(to_graph) %in% c('NDWS', 'NDD')]
b <- to_graph %>%
dplyr::select(id, time1, var_b) %>%
dplyr::mutate_at(.vars = var_b, .funs = function(x){
case_when(x <= 15 ~ 1,
x > 15 & x <= 20 ~ 2,
x > 20 & x <= 25 ~ 3,
x > 25 ~ 4,
TRUE ~ x)} )
class_1 <- dplyr::full_join(dplyr::select(class_1, -var_b), b)
}
# 3. NT_X
if(sum(names(to_graph) == 'NT_X') > 0){
b <- to_graph %>%
dplyr::select(id, time1, NT_X) %>%
dplyr::mutate(NT_X =case_when(NT_X <= 0 ~ 1,
NT_X > 0 & NT_X <= 5~ 2,
NT_X > 5 & NT_X <= 10~ 3,
NT_X > 10 ~ 4,
TRUE ~ NT_X) )
class_1 <- dplyr::full_join(class_1 %>% dplyr::select(-NT_X), b)
}
# 4. 'NWLD','NWLD50','NWLD90'
if(sum(names(to_graph) %in% c('NWLD','NWLD50','NWLD90', 'NWLD_max','NWLD50_max','NWLD90_max')) > 0){
var_b <- names(to_graph)[names(to_graph) %in% c('NWLD','NWLD50','NWLD90', 'NWLD_max','NWLD50_max','NWLD90_max')]
b <- to_graph %>%
dplyr::select(id, time1, var_b) %>%
dplyr::mutate_at(.vars = var_b, .funs = function(x){
case_when(x <= 2 ~ 1,
x > 2 & x <= 5 ~ 2,
x > 5 & x <= 8 ~ 3,
x > 8 ~ 4,
TRUE ~ x)} )
class_1 <- dplyr::full_join(dplyr::select(class_1, -var_b), b)
}
# 5. 'LGP'
if(sum(names(to_graph) == 'LGP') > 0){
b <- to_graph %>%
dplyr::select(id, time1, LGP) %>%
dplyr::mutate(LGP =case_when(LGP <= 40 ~ 1,
LGP > 40 & LGP <= 60~ 2,
LGP > 60 & LGP <= 90~ 3,
LGP > 90 ~ 4,
TRUE ~ LGP) )
class_1 <- dplyr::full_join(class_1 %>% dplyr::select(-LGP), b)
}
# 6. SLGP_CV
if(sum(names(to_graph) == 'SLGP_CV') > 0){
b <- to_graph %>%
dplyr::select(id, time1, SLGP_CV) %>%
dplyr::mutate(SLGP_CV =case_when(SLGP_CV <= 5 ~ 1,
SLGP_CV > 5 & SLGP_CV <= 15~ 2,
SLGP_CV > 15 & SLGP_CV <= 30~ 3,
SLGP_CV > 30 ~ 4,
TRUE ~ SLGP_CV) )
class_1 <- dplyr::full_join(class_1 %>% dplyr::select(-SLGP_CV), b)
}
# Aqui hay que hacer algunos cambios. Ya que la variable
# de tipo 23 se construyo anteriormente.
# 7. 'THI_0', 'THI_1', 'THI_2', 'THI_3', 'HSI_0', 'HSI_1', 'HSI_2', 'HSI_3', 'SHI'
if(sum(names(to_graph) %in% c('THI_0', 'THI_1', 'THI_2', 'THI_3', 'THI_23', 'HSI_0', 'HSI_1', 'HSI_2', 'HSI_3', 'HSI_23', 'SHI')) > 0){
var_b <- names(to_graph)[names(to_graph) %in% c('THI_0', 'THI_1', 'THI_2', 'THI_3', 'THI_23', 'HSI_0', 'HSI_1', 'HSI_2', 'HSI_3', 'HSI_23', 'SHI')]
b <- to_graph %>%
dplyr::select(id, time1, var_b) %>%
dplyr::mutate_at(.vars = var_b, .funs = function(x){
case_when(x <= 0.25 ~ 1,
x > 0.25 & x <= 0.5 ~ 2,
x > 0.5 & x <= 0.75 ~ 3,
x > 0.75 ~ 4,
TRUE ~ x)} )
class_1 <- dplyr::full_join(dplyr::select(class_1, -var_b), b)
}
# 8. SPI
if(sum(names(to_graph) == 'SPI') > 0){
b <- to_graph %>%
dplyr::select(id, time1, SPI) %>%
dplyr::mutate(SPI =case_when(SPI <= 25 ~ 1,
SPI > 25 & SPI <= 50~ 2,
SPI > 50 & SPI <= 75~ 3,
SPI > 75 ~ 4,
TRUE ~ SPI) )
class_1 <- dplyr::full_join(class_1 %>% dplyr::select(-SPI), b)
}
# =-----
fix_Vars <- var_toG[var_toG %in% c('SPI','TAI', 'NDWS', 'NDD', 'NT_X', 'NWLD','NWLD50','NWLD90', 'LGP', 'THI_0', 'THI_1', 'THI_2', 'THI_3', 'HSI_0', 'HSI_1', 'HSI_2', 'HSI_3', 'SHI', 'SLGP_CV', 'THI_23', 'HSI_23', 'NWLD_max','NWLD50_max','NWLD90_max')]
# fixed categorical class maps.
for(i in 1:length(fix_Vars)){
labs_qq <- case_when('1' = fix_Vars[i] %in% c('TAI', 'NDWS', 'NDD', 'NT_X', 'NWLD','NWLD50','NWLD90', 'NWLD_max','NWLD50_max','NWLD90_max') ~ c('No significant stress', 'Moderate stress', 'Severe','Extreme'),
'2' = fix_Vars[i] %in% c('LGP') ~ c('Very low','Low','Moderate','High'),
'3' = fix_Vars[i] %in% c('SLGP_CV') ~ c('Low','Moderate','High','Extreme'),
'4' = fix_Vars[i] %in% c('THI_0', 'THI_1', 'THI_2', 'THI_3', 'THI_23', 'HSI_0', 'HSI_1', 'HSI_2', 'HSI_3', 'HSI_23', 'SHI') ~ c('Low','Moderate','High','Very high'),
'5' = fix_Vars[i] == 'SPI' ~ c('Limited','Significant','Substantial','Very large'))
if(fix_Vars[i] == 'TAI'){ pattern <- 'TAI\nAridity' }
if(fix_Vars[i] == 'LGP'){ pattern <- 'LGP\n(days)' }
if(fix_Vars[i] %in% c('NDD', 'NDWS', 'NT_X', 'NWLD', 'NWLD50', 'NWLD90', 'NWLD_max', 'NWLD50_max', 'NWLD90_max')){ pattern <- glue::glue('{var_toG[i]}\n(days/month)') }
if(fix_Vars[i] == 'P5D'){ pattern <- 'P5D\n(mm/5 days)' }
if(fix_Vars[i] %in% c('SHI', 'HSI_0', 'HSI_1', 'HSI_2', 'HSI_3', 'THI_0', 'THI_1', 'THI_2', 'THI_3')){pattern <- glue::glue('{var_toG[i]}\n(prob)') }
if(fix_Vars[i] == 'SPI'){ pattern <- 'SPI\n(% area)' }
if(fix_Vars[i] == 'SLGP_CV'){ pattern <- 'SLGP_CV\n(%)' }
if(fix_Vars[i] == 'THI_23'){ pattern <- 'THI_2 + THI_3\n(prob)' }
if(fix_Vars[i] == 'HSI_23'){ pattern <- 'HSI_2 + HSI_3\n(prob)' }
gg <- ggplot() +
geom_tile(data = tidyr::drop_na(class_1, !!rlang::sym(fix_Vars[i]) ), aes(x = x, y = y, fill = !!rlang::sym(fix_Vars[i]) %>% as.factor(.) )) +
geom_sf(data = glwd1, fill = 'lightblue', color = 'lightblue') +
geom_sf(data = glwd2, fill = 'lightblue', color = 'lightblue') +
geom_sf(data = ctn, fill = NA, color = gray(.5)) +
# geom_sf(data = shp_sf, fill = NA, color = gray(.1)) +
geom_sf(data = zone, fill = NA, color = 'black') +
scale_fill_manual(values = c( '1' = "#A7D96A", '2' = "#FFFFC1", '3' = "#FDAE61", '4' = '#D7191B', '5' = "#533104"),
breaks = c('1', '2', '3', '4'), labels = labs_qq) +
labs(fill = pattern, x = NULL, y = NULL, title = title) +
scale_y_continuous(breaks = round(ylims, 2), n.breaks = 3) +
scale_x_continuous(breaks = round(xlims, 2), n.breaks = 3) +
coord_sf(xlim = xlims, ylim = ylims) +
facet_grid(~time1, labeller = as_labeller( c('1' = "Historic", '2' = "2021-2040", '3' = '2041-2060'))) +
theme_bw() +
guides(fill=guide_legend(nrow=2,byrow=TRUE)) +
theme(legend.position = 'bottom', text = element_text(size=35),
axis.text.x=element_blank(), axis.text.y=element_blank(),
strip.background = element_rect(colour = "black", fill = "white"),
legend.title=element_text(size=35),
legend.spacing = unit(5, units = 'cm'),
legend.spacing.x = unit(1.0, 'cm'), plot.title = element_text(hjust = 0.5))
ggplot2::ggsave(filename = glue::glue('{path}/Fix_{fix_Vars[i]}.png'), plot = gg, width = 15, height = 10, dpi = 300, device = 'jpeg', units = 'in')
}
# =--------------------------------------------------
# Overlays variables.
group_H <- tibble(vars = c('AMT', 'ATR', 'SPI', 'TAI', 'NDWS', 'NDD', 'NT_X', 'THI_0', 'THI_1', 'THI_2', 'THI_3', 'HSI_0', 'HSI_1', 'HSI_2','HSI_3','SHI', 'NWLD','NWLD50', 'NWLD90', 'IRR', 'LGP', 'SLGP', 'P5D', 'P95', 'SLGP_CV'),
group = c('No', 'No', 'Drought', 'Drought', 'Drought', 'Drought', 'Heat', 'No','No', 'Heat', 'Heat', 'No', 'No', 'Heat', 'Heat', 'Heat','Waterlogging & flooding', 'Waterlogging & flooding', 'Waterlogging & flooding','Drought', 'Agricultural productivity', 'No', 'Waterlogging & flooding', 'Waterlogging & flooding','Agricultural productivity' ))
high_lvl <- c('SPI', 'TAI','NDWS', 'NDD','NT_X','THI_2','THI_3','HSI_2','HSI_3','SHI','NWLD','NWLD50', 'NWLD90','IRR','P5D','P95', 'SLGP_CV')
low_lvl <- 'LGP'
final_groups <- group_H %>% dplyr::filter(group != 'No') %>% dplyr::filter(vars %in% var_toG)
if(sum(dplyr::pull(final_groups, vars) == 'HSI_2') > 0){
final_groups <- dplyr::filter(final_groups, vars %in% c('HSI_2', 'HSI_3')) %>%
dplyr::slice(1) %>% dplyr::mutate(vars = 'HSI_23') %>%
dplyr::bind_rows(dplyr::filter(final_groups, !(vars %in% c('HSI_2', 'HSI_3'))),.)
high_lvl <- c(high_lvl[-which(high_lvl %in% c('HSI_2', 'HSI_3'))], 'HSI_23')
}
if(sum(dplyr::pull(final_groups, vars) == 'THI_2') > 0){
final_groups <- dplyr::filter(final_groups, vars %in% c('THI_2', 'THI_3')) %>%
dplyr::slice(1) %>% dplyr::mutate(vars = 'THI_23') %>%
dplyr::bind_rows(dplyr::filter(final_groups, !(vars %in% c('THI_2', 'THI_3'))),.)
high_lvl <- c(high_lvl[-which(high_lvl %in% c('THI_2', 'THI_3'))], 'THI_23')
}
if(sum(dplyr::pull(final_groups, vars) == 'NWLD')){
final_groups <- dplyr::filter(final_groups, vars %in% c('NWLD', 'NWLD50', 'NWLD90')) %>%
dplyr::mutate(vars = c('NWLD_max', 'NWLD50_max', 'NWLD90_max')) %>%
dplyr::bind_rows(final_groups, .)
high_lvl <- c(high_lvl, 'NWLD_max', 'NWLD50_max', 'NWLD90_max')
}
# =-----
# =- Low level var reclassify
vars_lvl <- names(class_1)[names(class_1) %in% low_lvl]
if(length(low_lvl) > 0){
class_1 <- class_1 %>%
dplyr::mutate_at(.vars = vars_lvl, .funs = function(x){ifelse(x %in% 1:2, 1, 0)})
}
# =- High level var reclassify
vars_lvl <- names(class_1)[names(class_1) %in% high_lvl]
if(length(vars_lvl) > 0){
class_1 <- class_1 %>%
dplyr::mutate_at(.vars = vars_lvl, .funs = function(x){ifelse(x %in% 3:4, 1, 0)})
}
class_1 <- class_1 %>% unique() %>%
dplyr::select(id,x,y,time,time1, final_groups$vars)
# =-----------------------------------------------
groups <- dplyr::count(final_groups,group)
all_Hz <- groups %>% dplyr::select(group) %>%
dplyr::group_split(group) %>%
purrr::map(.f = function(x){ x <- x$group
z <- str_split( x, ' ')[[1]][1]
dat_mod <- class_1 %>%
dplyr::select(id,time1, dplyr::filter(final_groups, group == x)$vars) %>%
dplyr::rowwise() %>%
dplyr::mutate(x = sum(c_across(dplyr::filter(final_groups, group == x)$vars), na.rm = T) %>% round(. , 0)) %>%
dplyr::select( id, time1, x ) %>% dplyr::ungroup() %>%
unique() %>% setNames(c('id', 'time1', z))
return(dat_mod)})
all_Hz <- all_Hz %>% purrr::reduce(.f = dplyr::inner_join) %>%
dplyr::inner_join( coord, .)
# Drought - Heat - Waterlogging - Agricultural
all_Hz <- all_Hz %>%
dplyr::mutate(Dr_Ha = glue::glue('{Drought}-{Heat}'),
Dr_Wa = glue::glue('{Drought}-{Waterlogging }'),
Ha_Wa = glue::glue('{Heat}-{Waterlogging}'))
# if(sum(names(all_Hz) == 'Agricultural') == 1){
# all_Hz <- all_Hz %>%
# dplyr::mutate(Dr_Ag = glue::glue('{Drought}-{Agricultural }'),
# Ag_Wa = glue::glue('{Agricultural }-{Waterlogging }'),
# Ha_Ag = glue::glue('{Heat}-{Agricultural }'))}
path_raster <- glue::glue('{root}/7.Results/{country}/results/maps/{R_zone}_{season}/tif')
dir.create(path_raster,recursive = TRUE)
write_fst(all_Hz , glue::glue('{path_raster}/Final.fst'))
rasterize_mod <- function(ind, time_t, all_Hz = all_Hz){
df <- all_Hz %>% dplyr::filter(time1 == time_t) %>% dplyr::select(x, y, ind)
dfr <- raster::rasterFromXYZ(df)
raster::writeRaster(dfr, filename = glue::glue('{path_raster}/{ind}_{time_t}.tif'))
}
# If you need Agricultural, add in ind, and change all 3 to 4.
tibble::tibble(ind = rep(c('Drought','Heat', 'Waterlogging'), each = 3),
time_t = rep(1:3, times = 3) ) %>%
dplyr::mutate(raster = purrr::map2(.x = ind, .y = time_t, .f = rasterize_mod, all_Hz = all_Hz))
# =-----------------
# Univariate maps.
uni_vars <- names(all_Hz)[which(names(all_Hz) %in% c('Agricultural', 'Drought', 'Heat', 'Waterlogging' ))]
for(i in 1:length(uni_vars)){
pattern <- case_when(
uni_vars[i] == "Agricultural" ~ 'Agricultural productivity',
uni_vars[i] == "Drought" ~ 'Drought',
uni_vars[i] == 'Heat' ~ 'Heat Stress',
uni_vars[i] == 'Waterlogging' ~ 'Waterlogging / flooding')
unv <- all_Hz %>% dplyr::select(x, y, time1, uni_vars[i]) %>%
dplyr::rename(ind = uni_vars[i]) %>% dplyr::mutate(ind = as.factor(ind))
gg <- ggplot2::ggplot() +
ggplot2::geom_tile(data = unv, aes(x = x, y = y, fill = ind) ) +
geom_sf(data = glwd1, fill = 'lightblue', color = 'lightblue') +
geom_sf(data = glwd2, fill = 'lightblue', color = 'lightblue') +
geom_sf(data = ctn, fill = NA, color = gray(.5)) +
# geom_sf(data = shp_sf, fill = NA, color = gray(.1)) +
geom_sf(data = zone, fill = NA, color = 'black') +
ggplot2::scale_fill_brewer(palette = 'PuBuGn') +
coord_sf(xlim = xlims, ylim = ylims) +
ggplot2::theme_bw() +
ggplot2::xlab('') +
ggplot2::ylab('') +
ggplot2::labs(fill = pattern) +
ggplot2::facet_wrap(~time1, labeller = as_labeller( c('1' = "Historic", '2' = "2021-2040", '3' = '2041-2060')) ) +
ggplot2::theme(legend.position = 'bottom',
axis.text = element_blank(),
text = element_text(size=35),
strip.text.x = element_text(size = 35),
strip.background = element_rect(colour = "black", fill = "white"))
ggplot2::ggsave(filename = glue::glue('{path}/Uni_{uni_vars[i]}.png'), width = 15, height = 10, dpi = 300, device = 'jpeg', units = 'in')
}
# =-------------------------------
# This funcion is built a bibariate scale colours.
colmat <- function(nquantiles=10, upperleft=rgb(0,150,235, maxColorValue=255), upperright=rgb(130,0,80, maxColorValue=255), bottomleft="grey", bottomright=rgb(255,230,15, maxColorValue=255), xlab="x label", ylab="y label"){
my.data<-seq(0,1,.01)
my.class<-classInt::classIntervals(my.data,n=nquantiles,style="quantile")
my.pal.1<-classInt::findColours(my.class,c(upperleft,bottomleft))
my.pal.2<-classInt::findColours(my.class,c(upperright, bottomright))
col.matrix<-matrix(nrow = 101, ncol = 101, NA)
for(i in 1:101){
my.col<-c(paste(my.pal.1[i]),paste(my.pal.2[i]))
col.matrix[102-i,]<-classInt::findColours(my.class,my.col)}
plot(c(1,1),pch=19,col=my.pal.1, cex=0.5,xlim=c(0,1),ylim=c(0,1),frame.plot=F, xlab=xlab, ylab=ylab,cex.lab=1.3)
for(i in 1:101){
col.temp<-col.matrix[i-1,]
points(my.data,rep((i-1)/100,101),pch=15,col=col.temp, cex=1)}
seqs<-seq(0,100,(100/nquantiles))
seqs[1]<-1
col.matrix<-col.matrix[c(seqs), c(seqs)]}
# =-------------------------------
# # =----------------
# # Bivariate maps.
# final_map <- function(lab_t, all_Hz){
#
# st_hz <- dplyr::select(all_Hz, lab_t) %>% dplyr::pull(.)
# tbl <- all_Hz %>% dplyr::select(id, x, y, time1, lab_t) %>%
# setNames(c('id', 'x', 'y', 'time1', 'var'))
#
# # This built a bivariate legend and colours (please don't modify!!!!!!!!!).
# tst <- data.frame(var = sort(unique(st_hz) ))
# mx_lvl <- base::strsplit(tst$var, '-') %>% unlist %>% as.numeric() %>% max()
#
# col.matrix<- colmat(nquantiles = mx_lvl +1 , upperleft="#64ACBE", upperright="#574249", bottomleft="#F5EEF8", bottomright="#C85A5A", xlab='', ylab = '')
# col.matrix <- col.matrix[-1, ][, -1]
# colnames(col.matrix) = glue::glue('{0:mx_lvl}')
# rownames(col.matrix) = glue::glue('{0:mx_lvl}')
#
#
# col.matrix <- col.matrix %>% as.table() %>% as.data.frame() %>%
# dplyr::mutate(var = glue::glue('{Var1}-{Var2}')) %>%
# dplyr::rename(col = 'Freq')
#
# tst <- dplyr::inner_join(tst, dplyr::select(col.matrix, var, col) )
# tbl <- dplyr::left_join(x = tbl, y = tst, by = 'var')
#
# dat <- ggplot2::ggplot() +
# ggplot2::geom_tile(data = tbl, aes(x = x, y = y), fill = tbl$col, alpha = 0.8) +
# geom_sf(data = glwd1, fill = 'lightblue', color = 'lightblue') +
# geom_sf(data = glwd2, fill = 'lightblue', color = 'lightblue') +
# geom_sf(data = ctn, fill = NA, color = gray(.5)) +
# # geom_sf(data = shp_sf, fill = NA, color = gray(.1)) +
# geom_sf(data = zone, fill = NA, color = 'black') +
# ggplot2::scale_fill_brewer(palette = 'PuRd') +
# coord_sf(xlim = xlims, ylim = ylims) +
# ggplot2::theme_bw() +