r - mutate 找不到函数

Question

我正在通过这个网页处理 eBird 代码:
https://github.com/CornellLabofOrnithology/ebird-best-practices/blob/master/03_covariates.Rmd
除了使用我自己的数据。我有一个来自澳大利亚 gadm.org 的 .gpkg，以及我自己为澳大利亚选择的电子鸟数据。我完全遵循了代码，除了不使用“bcr”，因为我的数据集没有 bcr 代码，同时删除了 st_buffer(dist = 10000)来自 rgdal 代码，因为这使我由于某种原因无法实际下载 MODIS 数据。
编辑:我也使用了网站提供的数据，但仍然收到相同的错误
我被这个代码困住了:

lc_extract <- ebird_buff %>% 
mutate(pland = map2(year_lc, data, calculate_pland, lc = landcover)) %>% 
select(pland) %>% 
unnest(cols = pland)

它返回此错误:

Error: Problem with `mutate()` input `pland`.
x error in evaluating the argument 'x' in selecting a method for function 'exact_extract': invalid layer names
i Input `pland` is `map2(year_lc, data, calculate_pland, lc = landcover)`.)`

我似乎无法弄清楚如何纠正它，我对这样的密集地理空间代码很陌生。
链接里有免费的数据集，但是我还没试过，所以可能是我的数据与代码不兼容？但是，我查看了提供的 Gis-data.gpkg，我从 gadm 获得的数据似乎很好。
上面的前两个代码是:

neighborhood_radius <- 5 * ceiling(max(res(landcover))) / 2
 ebird_buff <- red_knot %>% 
     distinct(year = format(observation_date, "%Y"),
              locality_id, latitude, longitude) %>% 
     # for 2019 use 2018 landcover data
     mutate(year_lc = if_else(as.integer(year) > max_lc_year, 
                              as.character(max_lc_year), year),
            year_lc = paste0("y", year_lc)) %>% 
     # convert to spatial features
     st_as_sf(coords = c("longitude", "latitude"), crs = 4326) %>% 
     # transform to modis projection
     st_transform(crs = projection(landcover)) %>% 
     # buffer to create neighborhood around each point
     st_buffer(dist = neighborhood_radius) %>% 
     # nest by year
     nest(data = c(year, locality_id, geometry))
 calculate_pland <- function(yr, regions, lc) {
     locs <- st_set_geometry(regions, NULL)
     exact_extract(lc[[yr]], regions, progress = FALSE) %>% 
         map(~ count(., landcover = value)) %>% 
         tibble(locs, data = .) %>% 
         unnest(data)
 }

Answer 1

网页的作者已经回答了这个问题。
解决方案是这样的代码:

lc_extract <- NULL
for (yr in names(landcover)) {
  # get the buffered checklists for a given year
  regions <- ebird_buff$data[[which(yr == ebird_buff$year_lc)]]
  # get landcover values within each buffered checklist area
  ee <- exact_extract(landcover[[yr]], regions, progress = FALSE)
  # count the number of each landcover class for each checklist buffer
  ee_count <- map(ee, ~ count(., landcover = value))
  # attach the year and locality id back to the checklists
  ee_summ <- tibble(st_drop_geometry(regions), data = ee_count) %>% 
    unnest(data)
  # bind to results
  lc_extract <- bind_rows(lc_extract, ee_summ)
}

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