# Modern data ----
`%>%` <- magrittr::`%>%`
## Load data ----
### Metadata ----
african_modern_samples_metadata <-
"data-raw/GLOBAL/African modern surface samples_SPH.xlsx" %>%
readxl::read_excel(sheet = 1) %>%
janitor::clean_names() %>%
dplyr::rename(age_BP = age_bp) %>%
dplyr::mutate(ID_SAMPLE = seq_along(entity_name), .after = doi)
### Pollen counts ----
african_modern_samples_counts <-
"data-raw/GLOBAL/African modern surface samples_SPH.xlsx" %>%
readxl::read_excel(sheet = 2) %>%
janitor::clean_names() %>%
dplyr::left_join(african_modern_samples_metadata %>%
dplyr::select(entity_name, ID_SAMPLE),
by = "entity_name") %>%
dplyr::relocate(ID_SAMPLE, .before = 1)
### Amalgamations ----
african_modern_samples_taxa_amalgamation <-
african_modern_samples_counts %>%
dplyr::select(-entity_name, -original_taxon_name) %>%
dplyr::rename(taxon_count = counts) %>%
dplyr::distinct() %>%
dplyr::mutate(clean = clean %>% stringr::str_squish(),
intermediate = intermediate %>% stringr::str_squish(),
amalgamated = amalgamated %>% stringr::str_squish())
### 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)
african_modern_samples_taxa_amalgamation_rev <-
african_modern_samples_taxa_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)
african_modern_samples_taxa_amalgamation_rev %>%
dplyr::group_by(ID_COUNT) %>%
dplyr::mutate(n = dplyr::n()) %>%
dplyr::filter(n > 1)
waldo::compare(african_modern_samples_taxa_amalgamation,
african_modern_samples_taxa_amalgamation_rev)
african_modern_samples_taxa_amalgamation <-
african_modern_samples_taxa_amalgamation_rev %>%
dplyr::filter(!is.na(taxon_count), taxon_count > 0) %>%
dplyr::select(-ID_COUNT)
african_modern_samples_taxa_amalgamation %>%
dplyr::filter(is.na(clean) | is.na(intermediate) | is.na(amalgamated)) %>%
dplyr::distinct(clean, intermediate, amalgamated)
## Find DOIs ----
african_modern_samples_metadata_pubs <-
african_modern_samples_metadata %>%
dplyr::distinct(publication, doi) %>%
dplyr::arrange(publication) %>%
dplyr::mutate(DOI = publication %>%
stringr::str_extract_all("\\[DOI\\s*(.*?)\\s*\\](;|$)") %>%
purrr::map_chr(~.x %>%
stringr::str_remove_all("^\\[DOI:") %>%
stringr::str_remove_all("\\]\\s*;\\s*$") %>%
stringr::str_remove_all("\\]$") %>%
stringr::str_remove_all("doi:") %>%
stringr::str_squish() %>%
stringr::str_c(collapse = ";\n"))
) %>%
dplyr::mutate(ID_PUB = seq_along(publication)) %>%
dplyr::mutate(updated_publication = NA, .before = publication) %>%
dplyr::mutate(updated_DOI = NA, .before = DOI)
# african_modern_samples_metadata_pubs %>%
# readr::write_excel_csv("data-raw/GLOBAL/african_modern_samples_modern-references.csv")
### Load cleaned publications list ----
african_modern_samples_clean_publications <-
"data-raw/GLOBAL/african_modern_samples_modern-references_clean.csv" %>%
readr::read_csv() %>%
dplyr::select(-DOI)
## Append clean publications ----
african_modern_samples_metadata_2 <-
african_modern_samples_metadata %>%
dplyr::left_join(african_modern_samples_metadata_pubs %>%
dplyr::select(-DOI, -doi, -dplyr::contains("updated")),
by = "publication") %>%
dplyr::left_join(african_modern_samples_clean_publications,
by = "ID_PUB") %>%
dplyr::select(-publication.x, -publication.y, -doi, -ID_PUB) %>%
dplyr::rename(doi = updated_DOI,
publication = updated_publication)
## Extract PNV/BIOME ----
african_modern_samples_metadata_3 <-
african_modern_samples_metadata_2 %>%
smpds::parallel_extract_biome(cpus = 8) %>%
# smpds::biome_name() %>%
dplyr::relocate(ID_BIOME, .after = doi) %>%
smpds::pb()
african_modern_samples_metadata_3 %>%
smpds::plot_biome(xlim = range(.$longitude, na.rm = TRUE) * c(0.9, 1.1),
ylim = range(.$latitude, na.rm = TRUE) * c(0.9, 1.1))
## Create count tables ----
### Clean ----
african_modern_samples_clean <-
african_modern_samples_taxa_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 ----
african_modern_samples_intermediate <-
african_modern_samples_taxa_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 ----
african_modern_samples_amalgamated <-
african_modern_samples_taxa_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 ----
AMSS <-
african_modern_samples_metadata_3 %>%
dplyr::mutate(
clean = african_modern_samples_clean %>%
dplyr::select(-c(ID_SAMPLE)),
intermediate = african_modern_samples_intermediate %>%
dplyr::select(-c(ID_SAMPLE)),
amalgamated = african_modern_samples_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_replace_all("unknown", "not known"),
entity_type = entity_type %>%
stringr::str_replace_all("terrestrial, soil", "soil") %>%
stringr::str_replace_all("unknown", "not known"),
site_type = site_type %>%
stringr::str_replace_all("unknown", "not known")
) %>%
dplyr::relocate(ID_SAMPLE, .before = clean) %>%
dplyr::select(-dplyr::starts_with("ID_PUB")) %>%
dplyr::mutate(source = "AMSS", .before = 1) %>%
dplyr::mutate(age_BP = as.character(age_BP)) %>%
dplyr::select(-basin_size_num)
usethis::use_data(AMSS, overwrite = TRUE, compress = "xz")
# Inspect enumerates ----
### basin_size -----
AMSS$basin_size %>%
unique() %>% sort()
### site_type ----
AMSS$site_type %>%
unique() %>% sort()
### entity_type ----
AMSS$entity_type %>%
unique() %>% sort()
# Export Excel workbook ----
wb <- openxlsx::createWorkbook()
openxlsx::addWorksheet(wb, "metadata")
openxlsx::writeData(wb, "metadata",
AMSS %>%
dplyr::select(source:ID_SAMPLE))
openxlsx::addWorksheet(wb, "clean")
openxlsx::writeData(wb, "clean",
AMSS %>%
dplyr::select(ID_SAMPLE, clean) %>%
tidyr::unnest(clean))
openxlsx::addWorksheet(wb, "intermediate")
openxlsx::writeData(wb, "intermediate",
AMSS %>%
dplyr::select(ID_SAMPLE, intermediate) %>%
tidyr::unnest(intermediate))
openxlsx::addWorksheet(wb, "amalgamated")
openxlsx::writeData(wb, "amalgamated",
AMSS %>%
dplyr::select(ID_SAMPLE, amalgamated) %>%
tidyr::unnest(amalgamated))
openxlsx::saveWorkbook(wb,
paste0("data-raw/GLOBAL/AMSS_",
Sys.Date(),
".xlsx"))
# Load climate reconstructions ----
climate_reconstructions <-
"data-raw/reconstructions/AMSS_climate_reconstructions_2022-04-28.csv" %>%
readr::read_csv()
# Load daily values for precipitation to compute MAP (mean annual precipitation)
climate_reconstructions_pre <-
"data-raw/reconstructions/AMSS_climate_reconstructions_pre_2022-04-28.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 <-
AMSS %>%
# smpds::AMSS %>%
# dplyr::select(-c(mi:mtwa)) %>%
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::AMSS,
climate_reconstructions_with_counts %>%
dplyr::select(-c(mi:map, sn, en, new_elevation))
)
AMSS <- climate_reconstructions_with_counts %>%
dplyr::select(-sn, -en, -new_elevation)
usethis::use_data(AMSS, overwrite = TRUE, compress = "xz")
waldo::compare(smpds::AMSS,
AMSS,
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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