Help Needed with R Error: "unable to find an inherited method for function ‘coordinates’ for signature ‘sf’"

General
,
1

Hello,

I'm working on spatial data processing in R and encountered this error:

r

Error

Error in h(simpleError(msg, call)) : 
  error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': unable to find an inherited method for function ‘coordinates’ for signature ‘sf’

Here’s the full code:

r

Copy code

# Extract number of POIs and distance to nearest POIs for each survey
# Extract number of POIs and distance to nearest POIs for each survey
# location and type of POI

# Extract number of POIs and distance to nearest POIs for each survey
# location and type of POI
rm(list = ls())



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)


data_dir <- "E:/Big Data Poverty Estimation/Data"
osm_dir          <- file.path(data_dir, "OSM")
cntry_dtls_dir   <- file.path(data_dir, "Country Details")
# Options:
# -- DHS
# -- DHS_nga_policy_experiment
# -- LSMS
SURVEY_NAME <- "DHS"

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")


REPLACE_IF_EXTRACTED <- TRUE  # or FALSE, depending on your preference
# Parameters -------------------------------------------------------------------
BUFFER_OSM       <- 5000
BUFFER_SATELLITE <- 2500
# Load country_code to OSM dir data --------------------------------------------
# Make dataset that has [country_code] and [osm_root_name] (root name of OSM dir)

## Survey Details
survey_details_df <- read_xlsx(file.path(cntry_dtls_dir, "survey_details.xlsx"))
survey_details_df <- survey_details_df %>%
  dplyr::select(country_code, osm_root_name)


## OSM directories
# If multiple, choose latest
osm_dirs <- list.files(file.path(osm_dir, "FinalData"))

osm_dir_df <- data.frame(osm_dirs = osm_dirs)
osm_dir_df <- osm_dir_df %>%
  dplyr::mutate(osm_root_name = osm_dirs %>%
                  str_replace_all("2.*", "") %>%
                  str_replace_all("1.*", "") %>%
                  str_replace_all("-$", ""),
                osm_dirs = osm_dirs %>% as.character()) %>%
  arrange(desc(osm_dirs)) %>%
  distinct(osm_root_name, .keep_all = T) %>%
  left_join(survey_details_df, by = "osm_root_name") %>%
  dplyr::filter(!is.na(country_code))




country_code <- survey_details_df$country_code

# Define functions -------------------------------------------------------------
load_osm_poi <- function(country_code, osm_dir_df){
  
  ### A. Define directory
  osm_country_dir <- osm_dir_df$osm_dirs[osm_dir_df$country_code %in% country_code]
  
  ### B. Load data
  osm1_df <- readRDS(file.path(osm_dir, "FinalData", osm_country_dir, "gis_osm_pois_free_1.Rds"))
  osm2_df <- readRDS(file.path(osm_dir, "FinalData", osm_country_dir, "gis_osm_pois_a_free_1.Rds"))
  
  ### C. Prep data and spatially define
  osm1_coords_df <- osm1_df %>% 
    coordinates() %>% 
    as.data.frame() %>%
    dplyr::rename(longitude = 1,
                  latitude = 2) %>%
    bind_cols(osm1_df@data)
  
  osm2_coords_df <- osm2_df %>% 
    coordinates() %>% 
    as.data.frame() %>%
    dplyr::rename(longitude = 1,
                  latitude = 2) %>%
    bind_cols(osm2_df@data)
  
  osm_df <- bind_rows(osm1_coords_df,
                      osm2_coords_df) %>%
    distinct(osm_id, .keep_all = T)
  
  coordinates(osm_df) <- ~longitude+latitude
  crs(osm_df) <- CRS("+init=epsg:4326")
  
  osm_df$one <- 1
  
  return(osm_df)
}

extract_n_poi <- function(buffer_m, country_code, survey_df, osm_dir_df){
  
  # 1. Prep survey data --------------------------------------------------------
  # Subset and buffer
  survey_df <- survey_df[survey_df$country_code %in% country_code,]
  
  survey_df <- survey_df %>%
    dplyr::select(uid, latitude, longitude)
  
  coordinates(survey_df) <- ~longitude+latitude
  crs(survey_df) <- "+init=epsg:4326"
  
  survey_buff_df <- geo.buffer_chunks(survey_df, r = buffer_m, chunk_size = 100)
  survey_buff_df$one <- 1
  
  # 2. Load and prep OSM data --------------------------------------------------
  osm_df <- load_osm_poi(country_code, osm_dir_df)
  
  # 3. N Poi Nearby ------------------------------------------------------------
  for(class_i in unique(osm_df$fclass[!is.na(osm_df$fclass)])){
    print(class_i)
    
    osm_df_classi <- osm_df[osm_df$fclass %in% class_i,]
    
    class_df <- over(osm_df_classi, survey_buff_df)$uid %>%
      as.character() %>%
      as.data.frame() %>%
      dplyr::rename(uid = ".") %>%
      dplyr::filter(!is.na(uid)) %>%
      group_by(uid) %>%
      dplyr::summarise(N = n()) %>%
      ungroup()
    
    names(class_df)[names(class_df) %in% "N"] <- paste0("osm_n_poi_", class_i)
    
    survey_df <- merge(survey_df, class_df, by = "uid", all.x=T)
    survey_df@data[paste0("osm_n_poi_", class_i)][is.na(survey_df@data[paste0("osm_n_poi_", class_i)])] <- 0
  }
  
  # 4. Cleanup -----------------------------------------------------------------
  survey_df <- survey_df@data
  
  survey_df <- survey_df %>%
    dplyr::rename_at(vars(-uid), ~ paste0(., "_",buffer_m,"m_buff"))
  
  return(survey_df)
}

extract_dist_poi <- function(country_code, survey_df, osm_dir_df){
  
  # 1. Prep survey data --------------------------------------------------------
  # Subset and buffer
  survey_df <- survey_df[survey_df$country_code %in% country_code,]
  
  survey_df <- survey_df %>%
    dplyr::select(uid, latitude, longitude)
  
  coordinates(survey_df) <- ~longitude+latitude
  crs(survey_df) <- "+init=epsg:4326"
  
  survey_sf <- survey_df %>% st_as_sf()
  
  # 2. Load and prep OSM data --------------------------------------------------
  osm_df <- load_osm_poi(country_code, osm_dir_df)
  
  # 3. Distance to Class --------------------------------------------------------
  for(class_i in unique(osm_df$fclass[!is.na(osm_df$fclass)])){
    
    osm_df_classi <- osm_df[osm_df$fclass %in% class_i,]
    
    print(paste0(class_i, " - ", nrow(osm_df_classi)))
    
    ## Grab one observation; replace geometry in next step
    osm_df_classi_agg <- osm_df_classi[1,] %>% st_as_sf()
    
    osm_df_classi_combine <- osm_df_classi %>%
      st_as_sf() %>%
      st_combine()
    
    osm_df_classi_agg$geometry <- st_geometry(osm_df_classi_combine)
    
    #osm_df_classi_agg <- osm_df_classi %>%
    #  gBuffer_chunks(width = 0.0001, chunk_size = 2000) %>%
    #  raster::aggregate(by = "one") %>%
    #  st_as_sf()
    
    
    if(nrow(osm_df_classi) <= 50000){
      buffer_chunk_n <- 3000
    } else{
      buffer_chunk_n <- 1000
    }
    
    survey_df[[paste0("osm_distmeters_poi_", class_i)]] <- st_distance_chunks(survey_sf, osm_df_classi_agg, buffer_chunk_n)
  } 
  
  return(survey_df@data)
}

# Load survey data -------------------------------------------------------------
survey_df <- readRDS(file.path(data_dir, SURVEY_NAME, "FinalData", "Individual Datasets", 
                               "survey_socioeconomic.Rds"))

if(SURVEY_NAME %in% "DHS"){
  survey_df <- survey_df %>%
    dplyr::filter(most_recent_survey %in% T)
}

survey_df <- survey_df %>%
  dplyr::select(uid, country_code, year, latitude, longitude) %>%
  dplyr::filter(!is.na(latitude))

if(SURVEY_NAME %in% "OPM"){
  survey_df <- survey_df %>%
    distinct(uid, .keep_all = T)
}

country_codes_all <- survey_df$country_code %>% unique()
#country_codes_all <- country_codes_all[country_codes_all != "GY"]

# Load country_code to OSM dir data --------------------------------------------
# Make dataset that has [country_code] and [osm_root_name] (root name of OSM dir)

## Survey Details
survey_details_df <- read_xlsx(file.path(cntry_dtls_dir, "survey_details.xlsx"))
survey_details_df <- survey_details_df %>%
  dplyr::select(country_code, osm_root_name)

## OSM directories
# If multiple, choose latest
osm_dirs <- list.files(file.path(osm_dir, "FinalData"))

osm_dir_df <- data.frame(osm_dirs = osm_dirs)
osm_dir_df <- osm_dir_df %>%
  dplyr::mutate(osm_root_name = osm_dirs %>%
                  str_replace_all("2.*", "") %>%
                  str_replace_all("1.*", "") %>%
                  str_replace_all("-$", ""),
                osm_dirs = osm_dirs %>% as.character()) %>%
  arrange(desc(osm_dirs)) %>%
  distinct(osm_root_name, .keep_all = T) %>%
  left_join(survey_details_df, by = "osm_root_name") %>%
  dplyr::filter(!is.na(country_code))

# Implement Functions ----------------------------------------------------------
#### N POI
for(buffer_i in BUFFER_OSM){
  for(country_code_i in country_codes_all){
    print(paste0("N POI: ", country_code_i, " - ", buffer_i, " =============="))
    
    OUT_PATH <- file.path(data_dir, SURVEY_NAME, 
                          "FinalData", "Individual Datasets", "osm", "poi", 
                          paste0("osm_",country_code_i,"_n_poi_",buffer_i,"m_buff.Rds"))
    
    if(!file.exists(OUT_PATH) | REPLACE_IF_EXTRACTED){
      survey_df_i <- extract_n_poi(buffer_i, country_code_i, survey_df, osm_dir_df)
      saveRDS(survey_df_i, OUT_PATH)
    }
  }
}

buffer_m = buffer_i
country_code = country_code_i
survey_df = survey_df
osm_dir_df = osm_dir_df

#### Dist POI
for(country_code_i in country_codes_all){
  print(paste0("DIST POI: ", country_code_i, " =============================="))
  
  OUT_PATH <- file.path(data_dir, SURVEY_NAME, 
                        "FinalData", "Individual Datasets", "osm", "poi", 
                        paste0("osm_",country_code_i,"_dist_poi_buff.Rds"))
  
  if(!file.exists(OUT_PATH) | REPLACE_IF_EXTRACTED){
    survey_df_i <- extract_dist_poi(country_code_i, survey_df, osm_dir_df)
    saveRDS(survey_df_i, OUT_PATH)
  }
}

Screen shot of the error please

Error_03_extract_poi_0
Error_03_extract_poi_01920×1080 271 KB

Any insights on resolving this error would be greatly appreciated!

Thanks

2

Your screenshot suggest you attempted to run all the code at once; if so, I would recommend running one statement at a time, so that you can share a screenshot of the statement being run along with the error it produces.

You screenshot also seems to me to suggest that your code is failing in this loop:

so you might want to add some commands to help with debugging, sort of like this:

  loop_index <- 0
  for(class_i in unique(osm_df$fclass[!is.na(osm_df$fclass)])){
    loop_index <- loop_index + 1
    print("iteration:" |> paste(loop_index))
    print("class_i:" |> paste(class_i))
    
    print("attempting to create osm_df_classi")
    osm_df_classi <- osm_df[osm_df$fclass %in% class_i,]
    
    print("attempting to create class_df")
    class_df <- over(osm_df_classi, survey_buff_df)$uid %>%
      as.character() %>%
      as.data.frame() %>%
      dplyr::rename(uid = ".") %>%
      dplyr::filter(!is.na(uid)) %>%
      group_by(uid) %>%
      dplyr::summarise(N = n()) %>%
      ungroup()
    
    print("attempting to modify names of class_df")
    names(class_df)[names(class_df) %in% "N"] <- paste0("osm_n_poi_", class_i)
    
    print("attempting to merge survey_df and class_df")
    survey_df <- merge(survey_df, class_df, by = "uid", all.x=T)
    print("attempting to replace NA with 0")
    survey_df@data[paste0("osm_n_poi_", class_i)][is.na(survey_df@data[paste0("osm_n_poi_", class_i)])] <- 0
  }
3

As @dromano mentions, it's a good idea to add something to your code to try and track where things go off the rails.
That being said, I am 99% sure your custom function load_osm_poi() is giving you problems when it tries to use the function coordinates(). This is a function from the package sp, which at this point is "old" and I would not recommend using it anymore. Try to completely switch to using sf, terra and stars. That's why the error mentions an "inherited method" for "signature sf" (I am assuming that the .Rds objects that are being loaded are spatial (sf) dataframes -> that is also something you should double-check).
I tried using coordinates() on an sf dataframe and it gave me the same error. This then leads to the error in evaluating the argument 'x' in selecting a method for function 'as.data.frame', because that's the next step in the piping operation after coordinates(). That function is complaining it is not getting something in a format it can work with (which is correct).
To fix this, turn coordinates() into sf_coordinates(). But, @data, in the last line of the piping operation also will not work on sf objects. You will have to use st_drop_geometry() - assuming you do not want to keep the geometry columns. Hence, the code becomes:

osm1_coords_df <- osm1_df %>% 
    st_coordinates() %>% 
    as.data.frame() %>%
    dplyr::rename(longitude = 1,
                  latitude = 2) %>%
    bind_cols(st_drop_geometry(osm1_df))

You will have to change similar code throughout the function. However, this code seems to use other sp functions later on as well, in coordinates(osm_df) <- and crs(osm_df) <- CRS("+init=epsg:4326") to name two locations. You will run into many more errors.
Assuming this is "inherited" code, I very strongly suggest to spend some time making sure it only uses sf and no sp anywhere. An advantage is that you will probably be able to streamline a lot of the code, since sf and tidyverse play nice(r) with each other.
Also see the github repo for sp (GitHub - edzer/sp: Classes and methods for spatial data), very first line of the readme: "R Classes and Methods for Spatial Data. Note that this is package will (slowly) be deprecated, in favor of package sf . It is in maintenance mode, and currently requires sf for coordinate reference system validation and coordinate transformation, conversion and (re)projection."