mutate cannot find function - r

Question

I'm working through eBird code from this webpage:
https://github.com/CornellLabofOrnithology/ebird-best-practices/blob/master/03_covariates.Rmd
with the exception of using my own data. I have a .gpkg from gadm.org of Australia, and my own ebird data selected for Australia. I have followed out the code exactly with the exception of not using "bcr" as my dataset has no bcr codes, along with removing st_buffer(dist = 10000) from the rgdal code because this prevented me from actually downloading the MODIS data for some reason.
EDIT:I have also used the provided data from the site and still received the same error
I got stuck at this code:
lc_extract <- ebird_buff %>%
mutate(pland = map2(year_lc, data, calculate_pland, lc = landcover)) %>%
select(pland) %>%
unnest(cols = pland)
It returns this error:
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)`.)`
I can not seem to figure out how to correct it, I'm rather new to dense geo-spatial code like this.
There is a free dataset in the link, but I haven't yet tried it out, so it may be that my data is incompatible with the code? however, I have had a look at the Gis-data.gpkg provided, and my data from gadm seems fine.
The previous two codes to the one above were:
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

This has been answered by the author of the webpage.
The solution was this code:
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)
}
credits go to:
Matt Strimas-Mackey