geo.buffer_chunks Error

[GIS243]

Title: Need help resolving "could not find function" error in R code

Question:
I've been working on a script in R for extracting data and encountered an error that I'm having trouble resolving. Here's the code snippet where the error occurs:

...
df <- readRDS(file.path(data_dir, SURVEY_NAME, "FinalData", "Individual Datasets",
                        "survey_socioeconomic.Rds"))

coordinates(df) <- ~longitude.x+latitude.x
crs(df) <- CRS("+init=epsg:4326")

df <- geo.buffer_chunks(df, r = buffer_i, chunk_size = 100)
...

The error message I'm getting is: "Error in geo.buffer_chunks(df, r = buffer_i, chunk_size = 100) : could not find function "geo.buffer_chunks"."

I've checked my loaded packages, but it seems that geo.buffer_chunks isn't recognized. I've also looked through the code, but couldn't find where this function is defined.

Can anyone help me understand why this function isn't recognized and how I can resolve this error? Any working alternative to this please Thanks in advance!

# Extract Black Marble Data


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"))

data_dir <- "E:/Big Data Poverty Estimation/Data"

ntl_harmon_dir   <- file.path(data_dir, "DMSPOLS_VIIRS_Harmonized")
# Options:
# -- DHS
# -- DHS_nga_policy_experiment
# -- LSMS
SURVEY_NAME <- "DHS"


# Delete existing files --------------------------------------------------------
if(F){
  to_rm <- file.path(data_dir, SURVEY_NAME, "FinalData", "Individual Datasets") %>%
    list.files(full.names = T) %>%
    str_subset("ntl_harmonized181920_")
  
  for(to_rm_i in to_rm) file.remove(to_rm_i)
}

# Prep NTL Data ----------------------------------------------------------------
r18 <- raster(file.path(ntl_harmon_dir, "RawData", paste0("Harmonized_DN_NTL_",2018,"_simVIIRS.tif")))
r19 <- raster(file.path(ntl_harmon_dir, "RawData", paste0("Harmonized_DN_NTL_",2019,"_simVIIRS.tif")))
r20 <- raster(file.path(ntl_harmon_dir, "RawData", paste0("Harmonized_DN_NTL_",2020,"_simVIIRS.tif")))

r_stack <- stack(r18, r19, r20)

r <- calc(r_stack, fun = mean, na.rm = T)

# Extract Data -----------------------------------------------------------------
buffer_i <- 1120

for(buffer_i in c(1120, 3360)){
  print(buffer_i)
  
  OUT_PATH <- file.path(data_dir, SURVEY_NAME, "FinalData", "Individual Datasets",
                        paste0("ntl_harmonized181920_",buffer_i,".Rds"))
  
  if(!file.exists(OUT_PATH)){
    
    #### Prep Survey Data
    df <- readRDS(file.path(data_dir, SURVEY_NAME, "FinalData", "Individual Datasets",
                            "survey_socioeconomic.Rds"))
    
    coordinates(df) <- ~longitude.x+latitude.x
    crs(df) <- CRS("+init=epsg:4326")
    
    df <- geo.buffer_chunks(df, r = buffer_i, chunk_size = 100)
    
    #### Extract values
    df$ntlharmon_avg <- exact_extract(r, df, 'mean')
    
    df_out <- df@data %>%
      dplyr::select(uid, year, ntlharmon_avg)
    
    saveRDS(df_out, OUT_PATH)
    
  }
}