`%>%` <- magrittr::`%>%`
# Load data ----
## Metadata ----
additional_european_pollen_metadata <-
"data-raw/GLOBAL/E_additional_Europe_clean.xlsx" %>%
readxl::read_excel(sheet = 1) %>%
dplyr::relocate(source, .before = 1) %>%
dplyr::mutate(ID_SAMPLE = seq_along(entity_name), .after = age_BP)
## Pollen counts ----
additional_european_pollen_taxa_counts_amalgamation <-
"data-raw/GLOBAL/E_additional_Europe_clean.xlsx" %>%
readxl::read_excel(sheet = 2) %>%
magrittr::set_names(
c("entity_name", "clean", "intermediate", "amalgamated", "taxon_count")
) %>%
dplyr::left_join(
additional_european_pollen_metadata %>%
dplyr::select(entity_name, ID_SAMPLE)
) %>%
dplyr::relocate(ID_SAMPLE, .before = 1) %>%
dplyr::select(-entity_name)
### Additional taxonomic corrections (SPH - May 20th) ----
taxonomic_corrections <- "data-raw/GLOBAL/taxonomic_corrections.xlsx" %>%
readxl::read_excel(sheet = 1) %>%
purrr::map_df(stringr::str_squish)
additional_european_pollen_taxa_counts_amalgamation_rev <-
additional_european_pollen_taxa_counts_amalgamation %>%
dplyr::mutate(ID_COUNT = seq_along(ID_SAMPLE)) %>%
dplyr::left_join(taxonomic_corrections %>%
dplyr::filter(level %in% c("clean", "all")),
by = c("clean" = "original_taxon")) %>%
dplyr::mutate(clean = dplyr::coalesce(corrected_taxon_name,
clean)) %>%
dplyr::select(-corrected_taxon_name, -level) %>%
dplyr::left_join(taxonomic_corrections %>%
dplyr::filter(level %in% c("intermediate", "all")),
by = c("clean" = "original_taxon")) %>%
dplyr::mutate(intermediate = dplyr::coalesce(corrected_taxon_name,
intermediate)) %>%
dplyr::select(-corrected_taxon_name, -level) %>%
dplyr::left_join(taxonomic_corrections %>%
dplyr::filter(level %in% c("amalgamated", "all")),
by = c("clean" = "original_taxon")) %>%
dplyr::mutate(amalgamated = dplyr::coalesce(corrected_taxon_name,
amalgamated)) %>%
dplyr::select(-corrected_taxon_name, -level) %>%
dplyr::left_join(taxonomic_corrections %>%
dplyr::filter(level %in% c("all")),
by = c("clean" = "original_taxon")) %>%
dplyr::mutate(clean = dplyr::coalesce(corrected_taxon_name,
clean)) %>%
dplyr::select(-corrected_taxon_name, -level) %>%
dplyr::left_join(taxonomic_corrections %>%
dplyr::filter(level %in% c("all")),
by = c("intermediate" = "original_taxon")) %>%
dplyr::mutate(intermediate = dplyr::coalesce(corrected_taxon_name,
intermediate)) %>%
dplyr::select(-corrected_taxon_name, -level) %>%
dplyr::left_join(taxonomic_corrections %>%
dplyr::filter(level %in% c("all")),
by = c("amalgamated" = "original_taxon")) %>%
dplyr::mutate(amalgamated = dplyr::coalesce(corrected_taxon_name,
amalgamated)) %>%
dplyr::select(-corrected_taxon_name, -level)
additional_european_pollen_taxa_counts_amalgamation_rev %>%
dplyr::group_by(ID_COUNT) %>%
dplyr::mutate(n = dplyr::n()) %>%
dplyr::filter(n > 1)
waldo::compare(additional_european_pollen_taxa_counts_amalgamation,
additional_european_pollen_taxa_counts_amalgamation_rev)
waldo::compare(additional_european_pollen_taxa_counts_amalgamation %>%
dplyr::distinct(clean, intermediate, amalgamated),
additional_european_pollen_taxa_counts_amalgamation_rev %>%
dplyr::distinct(clean, intermediate, amalgamated),
max_diffs = Inf)
additional_european_pollen_taxa_counts_amalgamation <-
additional_european_pollen_taxa_counts_amalgamation_rev %>%
dplyr::filter(!is.na(taxon_count), taxon_count > 0) %>%
dplyr::select(-ID_COUNT)
# Extract PNV/BIOME ----
additional_european_pollen_metadata_3 <-
# additional_european_pollen_metadata_2 %>%
additional_european_pollen_metadata %>%
dplyr::select(-dplyr::starts_with("ID_BIOME")) %>%
smpds::parallel_extract_biome(cpus = 1) %>%
# smpds::biome_name() %>%
# dplyr::relocate(ID_BIOME, .after = doi) %>%
dplyr::relocate(ID_BIOME, .after = ID_SAMPLE) %>%
smpds::pb()
additional_european_pollen_metadata_3 %>%
smpds::plot_biome(xlim = range(.$longitude, na.rm = TRUE) * c(0.9, 1.1),
ylim = range(.$latitude, na.rm = TRUE) * c(0.9, 1.1))
# Create count tables ----
## Clean ----
additional_european_pollen_clean <-
additional_european_pollen_taxa_counts_amalgamation %>%
dplyr::select(-intermediate, -amalgamated) %>%
dplyr::rename(taxon_name = clean) %>%
dplyr::group_by(ID_SAMPLE, taxon_name) %>%
dplyr::mutate(taxon_count = sum(taxon_count, na.rm = TRUE)) %>%
dplyr::ungroup() %>%
dplyr::distinct() %>%
tidyr::pivot_wider(ID_SAMPLE,
names_from = taxon_name,
values_from = taxon_count,
values_fill = 0,
names_sort = TRUE) %>%
dplyr::arrange(ID_SAMPLE)
## Intermediate ----
additional_european_pollen_intermediate <-
additional_european_pollen_taxa_counts_amalgamation %>%
dplyr::select(-clean, -amalgamated) %>%
dplyr::rename(taxon_name = intermediate) %>%
dplyr::group_by(ID_SAMPLE, taxon_name) %>%
dplyr::mutate(taxon_count = sum(taxon_count, na.rm = TRUE)) %>%
dplyr::ungroup() %>%
dplyr::distinct() %>%
tidyr::pivot_wider(ID_SAMPLE,
names_from = taxon_name,
values_from = taxon_count,
values_fill = 0,
names_sort = TRUE) %>%
dplyr::arrange(ID_SAMPLE)
## Amalgamated ----
additional_european_pollen_amalgamated <-
additional_european_pollen_taxa_counts_amalgamation %>%
dplyr::select(-clean, -intermediate) %>%
dplyr::rename(taxon_name = amalgamated) %>%
dplyr::group_by(ID_SAMPLE, taxon_name) %>%
dplyr::mutate(taxon_count = sum(taxon_count, na.rm = TRUE)) %>%
dplyr::ungroup() %>%
dplyr::distinct() %>%
tidyr::pivot_wider(ID_SAMPLE,
names_from = taxon_name,
values_from = taxon_count,
values_fill = 0,
names_sort = TRUE) %>%
dplyr::arrange(ID_SAMPLE)
# Store subsets ----
additional_european_pollen <-
additional_european_pollen_metadata_3 %>%
dplyr::mutate(
clean = additional_european_pollen_clean %>%
dplyr::select(-c(ID_SAMPLE)),
intermediate = additional_european_pollen_intermediate %>%
dplyr::select(-c(ID_SAMPLE)),
amalgamated = additional_european_pollen_amalgamated %>%
dplyr::select(-c(ID_SAMPLE))
) %>%
dplyr::mutate(
basin_size_num = basin_size %>%
as.numeric() %>%
round(digits = 6) %>%
as.character(),
basin_size = dplyr::coalesce(
basin_size_num,
basin_size
),
basin_size = basin_size %>%
stringr::str_to_lower() %>%
stringr::str_squish() %>%
stringr::str_replace_all("unknown|Unknown", "not known"),
entity_type = entity_type %>%
stringr::str_to_lower() %>%
stringr::str_squish() %>%
stringr::str_replace_all("unknown|Unknown", "not known") %>%
stringr::str_to_lower(),
site_type = site_type %>%
stringr::str_to_lower() %>%
stringr::str_squish() %>%
stringr::str_replace_all("estuarine", "coastal, estuarine") %>%
stringr::str_replace_all("drained/dry lake", "lacustrine, drained lake") %>%
stringr::str_replace_all("terrestrial, other sediments", "terrestrial") %>%
stringr::str_replace_all("terrestrial, soil", "soil") %>%
stringr::str_replace_all("unknown", "not known")
) %>%
dplyr::relocate(ID_SAMPLE, .before = clean) %>%
dplyr::select(-basin_size_num)
usethis::use_data(additional_european_pollen, overwrite = TRUE, compress = "xz")
# Inspect enumerates ----
## basin_size -----
additional_european_pollen$basin_size %>%
unique() %>% sort()
## site_type ----
additional_european_pollen$site_type %>%
unique() %>% sort()
## entity_type ----
additional_european_pollen$entity_type %>%
unique() %>% sort()
# Export Excel workbook ----
wb <- openxlsx::createWorkbook()
openxlsx::addWorksheet(wb, "metadata")
openxlsx::writeData(wb, "metadata",
additional_european_pollen %>%
dplyr::select(source:ID_SAMPLE))
openxlsx::addWorksheet(wb, "clean")
openxlsx::writeData(wb, "clean",
additional_european_pollen %>%
dplyr::select(ID_SAMPLE, clean) %>%
tidyr::unnest(clean))
openxlsx::addWorksheet(wb, "intermediate")
openxlsx::writeData(wb, "intermediate",
additional_european_pollen %>%
dplyr::select(ID_SAMPLE, intermediate) %>%
tidyr::unnest(intermediate))
openxlsx::addWorksheet(wb, "amalgamated")
openxlsx::writeData(wb, "amalgamated",
additional_european_pollen %>%
dplyr::select(ID_SAMPLE, amalgamated) %>%
tidyr::unnest(amalgamated))
openxlsx::saveWorkbook(wb,
paste0("data-raw/GLOBAL/additional_european_pollen_",
Sys.Date(),
".xlsx"))
# Load climate reconstructions ----
climate_reconstructions <-
"data-raw/reconstructions/additional_european_pollen_climate_reconstructions_2022-05-12.csv" %>%
readr::read_csv()
# Load daily values for precipitation to compute MAP (mean annual precipitation)
climate_reconstructions_pre <-
"data-raw/reconstructions/additional_european_pollen_climate_reconstructions_pre_2022-05-12.csv" %>%
readr::read_csv() %>%
dplyr::rowwise() %>%
dplyr::mutate(map = sum(dplyr::c_across(T1:T365), na.rm = TRUE), .before = T1)
climate_reconstructions_2 <- climate_reconstructions %>%
dplyr::bind_cols(climate_reconstructions_pre %>%
dplyr::select(map))
climate_reconstructions_with_counts <-
additional_european_pollen %>%
# smpds::additional_european_pollen %>%
dplyr::bind_cols(
climate_reconstructions_2 %>%
dplyr::select(sn = site_name,
en = entity_name,
new_elevation = elevation,
mi:map)
) %>%
dplyr::relocate(mi:map, .before = clean) %>%
dplyr::mutate(elevation = dplyr::coalesce(elevation, new_elevation))
climate_reconstructions_with_counts %>%
dplyr::filter(site_name != sn | entity_name != en)
waldo::compare(smpds::additional_european_pollen,
climate_reconstructions_with_counts %>%
dplyr::select(-c(mi:map, sn, en, new_elevation))
)
additional_european_pollen <- climate_reconstructions_with_counts %>%
dplyr::select(-sn, -en, -new_elevation)
usethis::use_data(additional_european_pollen, overwrite = TRUE, compress = "xz")
waldo::compare(smpds::additional_european_pollen,
additional_european_pollen,
max_diffs = Inf)
climate_reconstructions %>%
smpds::plot_climate_countour(
var = "mat",
xlim = range(.$longitude, na.rm = TRUE),
ylim = range(.$latitude, na.rm = TRUE)
)
rm(climate_reconstructions,
climate_reconstructions_2,
climate_reconstructions_pre,
climate_reconstructions_with_counts)
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