Error in .local(x, y, ...) : extents do not overlap

[GIS243]

Hi
I am trying to run the following code please

but getting this error Error in .local(x, y, ...) : extents do not overlap

how to resolve this issue

Thanks

# Extract Black Marble Data
# Extract Black Marble Data
# Download Monthly Black Marble
rm(list = ls())

renv::restore() 
pacman::p_load(tidyverse,
               rgdal,
               viridis,
               readstata13,
               dplyr,
               data.table,
               raster,
               stargazer,
               stringdist,
               tmaptools,
               stringr,
               geosphere,
               rgeos,
               haven,
               ggmap,
               sf,
               sp,
               glmnet,
               rgeos,
               caret,
               mltest,
               RANN,
               lubridate,
               jsonlite,
               httr,
               curl,
               ggpmisc,
               haven,
               sjmisc,
               dbscan,
               ggplot2,
               spatialEco,
               geosphere,
               radiant.data,
               readxl,
               mclust,
               missMDA,
               DescTools,
               furrr,
               countrycode,
               FactoMineR,
               progressr,
               ggmap,
               ggridges,
               ggpubr,
               xgboost,
               WDI,
               scales,
               ggExtra,
               ggrepel,
               ggcorrplot,
               rnaturalearth,
               ggthemes,
               gghalves,
               ggtext,
               ggsignif,
               LiblineaR,
               caret,
               exactextractr)

github_dir = "E:/Big Data Poverty Estimation/"
source(file.path(github_dir, "Functions", "functions.R"))


source("https://raw.githubusercontent.com/ramarty/download_blackmarble/main/R/download_blackmarble.R")
source("https://raw.githubusercontent.com/ramarty/fast-functions/master/R/functions_in_chunks.R")
source("https://raw.githubusercontent.com/ramarty/rSocialWatcher/52eede6cf561a74584503846eb78ee8bc8fa780b/R/main.R")



BEARER <- "eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJlbWFpbF9hZGRyZXNzIjoibXJhbXphbkBjbGFya3UuZWR1IiwiaXNzIjoiQVBTIE9BdXRoMiBBdXRoZW50aWNhdG9yIiwiaWF0IjoxNzEzMTA3MDk1LCJuYmYiOjE3MTMxMDcwOTUsImV4cCI6MTg3MDc4NzA5NSwidWlkIjoicmFtemFuIiwidG9rZW5DcmVhdG9yIjoicmFtemFuIn0.-dPqpjfSYzUT27FOjjSkXBziFvqAncus8Y8E_2GXLtA"

data_dir   <- "E:/Big Data Poverty Estimation/Data"
ntl_bm_dir <- file.path(data_dir, "NTL Black Marble")

# Parameters -------------------------------------------------------------------
BUFFER_OSM       <- 5000
BUFFER_SATELLITE <- 2500


# Options:
# -- DHS
# -- DHS_nga_policy_experiment
# -- LSMS
SURVEY_NAME <- "DHS"
REPLACE_IF_EXTRACTED <- F
# Load data --------------------------------------------------------------------
df <- readRDS(file.path(data_dir, SURVEY_NAME, "FinalData", "Individual Datasets", 
                        "survey_socioeconomic.Rds"))

# Delete existing files --------------------------------------------------------
if(F){
  to_rm <- file.path(data_dir, SURVEY_NAME, "FinalData", "Individual Datasets", 
                     "blackmarble") %>%
    list.files(full.names = T) %>%
    str_subset("bm_")
  
  for(to_rm_i in to_rm) file.remove(to_rm_i)
}

# Function to Extract Black Marble Data ----------------------------------------
#country_code_i <- "IA"
#buffer_m <- 5000
extract_bm <- function(df_country, 
                       year_i,
                       buffer_m){
  
  ## Project, buffer, then back to WGS
  # Go back to WGS so don't have to project larger raster
  coordinates(df_country) <- ~longitude+latitude
  crs(df_country) <- CRS("+init=epsg:4326")
  
  df_country <- geo.buffer_chunks(df_country, r = buffer_m, chunk_size = 100)
  
  ## Load globcover
  if(year_i < 2012) year_i <- 2012
  if(year_i > 2021) year_i <- 2021
  
  r <- raster(file.path(ntl_bm_dir, "FinalData", "annual_rasters", paste0("bm_vnp46A4_",
                                                                          year_i,
                                                                          ".tif")))
  
  ## Crop globcover
  r_crop <- crop(r, bbox(df_country))
  
  df_country$viirs_bm_mean <- exact_extract(r_crop, df_country, 'mean')
  
  df_out <- df_country@data %>%
    dplyr::select(uid, year, viirs_bm_mean)
  
  return(df_out)
}

# Implement Function and Export ------------------------------------------------
for(buffer_i in c(BUFFER_SATELLITE, 1120, 3360)){
  for(country_i in unique(df$country_code)){
    
    df_country <- df[df$country_code %in% country_i,]
    
    for(year_i in unique(df_country$year)){
      print(paste0(country_i, " - ", buffer_i, " - ", year_i))
      
      OUT_PATH <- file.path(data_dir, SURVEY_NAME, "FinalData", "Individual Datasets", 
                            "blackmarble", 
                            paste0("bm_", country_i, "_", buffer_i, "m_",year_i,".Rds"))
      
      if(REPLACE_IF_EXTRACTED | !file.exists(OUT_PATH)){
        df_bm_i <- extract_bm(df_country[df_country$year %in% year_i,], 
                              year_i,
                              buffer_i)
        saveRDS(df_bm_i, OUT_PATH)
      }
      
    }
  }
}