data-raw/japanese_pollen.R
In special-uor/smpds: The SPECIAL Modern Pollen Data Set for Climate Reconstructions
`%>%` <- magrittr::`%>%`
# Load data ----
## Metadata ----
japanese_pollen_metadata <-
"data-raw/GLOBAL/japanese_pollen/Japan sites.xlsx" %>%
readxl::read_excel(sheet = 1, n_max = 94) %>%
janitor::clean_names() %>%
dplyr::rename(age_BP = age_bp) %>%
dplyr::mutate(ID_SAMPLE = seq_along(entity_name), .after = doi)
## Pollen counts ----
### File 1 ----
japanese_pollen_taxa_counts_amalgamation_1 <-
"data-raw/GLOBAL/japanese_pollen/Japan_dat files_clean_SPH.xlsx" %>%
readxl::read_excel(sheet = 1) %>%
magrittr::set_names(
c("entity_name", "clean", "intermediate", "amalgamated", "taxon_count")
) %>%
dplyr::left_join(
japanese_pollen_metadata %>%
dplyr::select(entity_name, ID_SAMPLE)
) %>%
dplyr::relocate(ID_SAMPLE, .before = 1) %>%
dplyr::select(-entity_name) %>%
dplyr::mutate(
clean = clean %>% stringr::str_squish(),
intermediate = intermediate %>% stringr::str_squish(),
amalgamated = amalgamated %>% stringr::str_squish()
)
japanese_pollen_taxa_counts_amalgamation_1 %>%
dplyr::filter(is.na(ID_SAMPLE))
japanese_pollen_metadata %>%
dplyr::filter(ID_SAMPLE %in%
japanese_pollen_taxa_counts_amalgamation_1$ID_SAMPLE)
### File 2 ----
japanese_pollen_taxa_counts_2 <-
"data-raw/GLOBAL/japanese_pollen/Japan0k_Morita_SPH_clean.xlsx" %>%
readxl::read_excel(sheet = 1) %>%
dplyr::rename(taxon_name = Entity) %>%
tidyr::pivot_longer(-taxon_name,
names_to = "entity_name",
values_to = "taxon_count") %>%
dplyr::arrange(entity_name) %>%
dplyr::left_join(
japanese_pollen_metadata %>%
dplyr::select(entity_name, ID_SAMPLE)
) %>%
dplyr::relocate(ID_SAMPLE, .before = 1) %>%
dplyr::select(-entity_name) %>%
dplyr::filter(!is.na(taxon_count))
japanese_pollen_taxa_counts_2 %>%
dplyr::filter(is.na(ID_SAMPLE))
japanese_pollen_taxa_amalgamations_2 <-
"data-raw/GLOBAL/japanese_pollen/Japan0k_Morita_SPH_clean.xlsx" %>%
readxl::read_excel(sheet = 2) %>%
magrittr::set_names(
c("taxon_name", "clean", "intermediate", "amalgamated")
) %>%
purrr::map_df(stringr::str_squish)
japanese_pollen_taxa_counts_amalgamation_2 <-
japanese_pollen_taxa_counts_2 %>%
dplyr::left_join(japanese_pollen_taxa_amalgamations_2) %>%
dplyr::select(-taxon_name) %>%
dplyr::relocate(taxon_count, .after = amalgamated)
japanese_pollen_taxa_counts_amalgamation_2 %>%
dplyr::filter(is.na(clean) | is.na(intermediate) | is.na(amalgamated)) %>%
dplyr::distinct(clean, intermediate, amalgamated)
japanese_pollen_metadata %>%
dplyr::filter(ID_SAMPLE %in%
japanese_pollen_taxa_counts_amalgamation_2$ID_SAMPLE)
### File 3 ----
japanese_pollen_taxa_counts_3 <-
"data-raw/GLOBAL/japanese_pollen/JPND01_clean_SPH.xlsx" %>%
readxl::read_excel(sheet = 1) %>%
dplyr::rename(taxon_name = Entity) %>%
tidyr::pivot_longer(-taxon_name,
names_to = "entity_name",
values_to = "taxon_count") %>%
dplyr::arrange(entity_name) %>%
dplyr::left_join(
japanese_pollen_metadata %>%
dplyr::select(entity_name, ID_SAMPLE)
) %>%
dplyr::relocate(ID_SAMPLE, .before = 1) %>%
dplyr::select(-entity_name) %>%
dplyr::filter(!is.na(taxon_count))
japanese_pollen_taxa_counts_3 %>%
dplyr::filter(is.na(ID_SAMPLE))
japanese_pollen_taxa_amalgamations_3 <-
"data-raw/GLOBAL/japanese_pollen/JPND01_clean_SPH.xlsx" %>%
readxl::read_excel(sheet = 2) %>%
magrittr::set_names(
c("taxon_name", "clean", "intermediate", "amalgamated")
) %>%
purrr::map_df(stringr::str_squish)
japanese_pollen_taxa_counts_amalgamation_3 <-
japanese_pollen_taxa_counts_3 %>%
dplyr::left_join(japanese_pollen_taxa_amalgamations_3) %>%
dplyr::select(-taxon_name) %>%
dplyr::relocate(taxon_count, .after = amalgamated)
japanese_pollen_taxa_counts_amalgamation_3 %>%
dplyr::filter(is.na(clean) | is.na(intermediate) | is.na(amalgamated)) %>%
dplyr::distinct(clean, intermediate, amalgamated)
japanese_pollen_metadata %>%
dplyr::filter(ID_SAMPLE %in%
japanese_pollen_taxa_counts_amalgamation_3$ID_SAMPLE)
### File 4 ----
japanese_pollen_taxa_counts_4 <-
"data-raw/GLOBAL/japanese_pollen/JPND02_clean_SPH.xlsx" %>%
readxl::read_excel(sheet = 1) %>%
dplyr::rename(taxon_name = Entity) %>%
tidyr::pivot_longer(-taxon_name,
names_to = "entity_name",
values_to = "taxon_count") %>%
dplyr::arrange(entity_name) %>%
dplyr::left_join(
japanese_pollen_metadata %>%
dplyr::select(entity_name, ID_SAMPLE)
) %>%
dplyr::relocate(ID_SAMPLE, .before = 1) %>%
dplyr::select(-entity_name) %>%
dplyr::filter(!is.na(taxon_count))
japanese_pollen_taxa_counts_4 %>%
dplyr::filter(is.na(ID_SAMPLE))
japanese_pollen_taxa_amalgamations_4 <-
"data-raw/GLOBAL/japanese_pollen/JPND02_clean_SPH.xlsx" %>%
readxl::read_excel(sheet = 2) %>%
magrittr::set_names(
c("taxon_name", "clean", "intermediate", "amalgamated")
) %>%
purrr::map_df(stringr::str_squish)
japanese_pollen_taxa_counts_amalgamation_4 <-
japanese_pollen_taxa_counts_4 %>%
dplyr::left_join(japanese_pollen_taxa_amalgamations_4) %>%
dplyr::select(-taxon_name) %>%
dplyr::relocate(taxon_count, .after = amalgamated)
japanese_pollen_taxa_counts_amalgamation_4 %>%
dplyr::filter(is.na(clean) | is.na(intermediate) | is.na(amalgamated)) %>%
dplyr::distinct(clean, intermediate, amalgamated)
japanese_pollen_metadata %>%
dplyr::filter(ID_SAMPLE %in%
japanese_pollen_taxa_counts_amalgamation_4$ID_SAMPLE)
### File 5 ----
japanese_pollen_taxa_counts_5 <-
"data-raw/GLOBAL/japanese_pollen/JPND03_clean_SPH.xlsx" %>%
readxl::read_excel(sheet = 1) %>%
dplyr::rename(taxon_name = Site) %>%
tidyr::pivot_longer(-taxon_name,
names_to = "entity_name",
values_to = "taxon_count") %>%
dplyr::arrange(entity_name) %>%
dplyr::left_join(
japanese_pollen_metadata %>%
dplyr::select(entity_name, ID_SAMPLE)
) %>%
dplyr::relocate(ID_SAMPLE, .before = 1) %>%
dplyr::select(-entity_name) %>%
dplyr::filter(!is.na(taxon_count))
japanese_pollen_taxa_counts_5 %>%
dplyr::filter(is.na(ID_SAMPLE))
japanese_pollen_taxa_amalgamations_5 <-
"data-raw/GLOBAL/japanese_pollen/JPND03_clean_SPH.xlsx" %>%
readxl::read_excel(sheet = 2) %>%
magrittr::set_names(
c("taxon_name", "clean", "intermediate", "amalgamated")
) %>%
purrr::map_df(stringr::str_squish)
japanese_pollen_taxa_counts_amalgamation_5 <-
japanese_pollen_taxa_counts_5 %>%
dplyr::left_join(japanese_pollen_taxa_amalgamations_5) %>%
dplyr::select(-taxon_name) %>%
dplyr::relocate(taxon_count, .after = amalgamated)
japanese_pollen_taxa_counts_amalgamation_5 %>%
dplyr::filter(is.na(clean) | is.na(intermediate) | is.na(amalgamated)) %>%
dplyr::distinct(clean, intermediate, amalgamated)
japanese_pollen_metadata %>%
dplyr::filter(ID_SAMPLE %in%
japanese_pollen_taxa_counts_amalgamation_5$ID_SAMPLE)
## Combine pollen counts and amalgamations ----
"Check that all the entities in the metadata have pollen counts"
samples_with_counts <-
dplyr::bind_rows(
japanese_pollen_taxa_counts_amalgamation_1 %>%
dplyr::distinct(ID_SAMPLE) %>%
dplyr::mutate(group = 1),
japanese_pollen_taxa_counts_amalgamation_2 %>%
dplyr::distinct(ID_SAMPLE) %>%
dplyr::mutate(group = 2),
japanese_pollen_taxa_counts_amalgamation_3 %>%
dplyr::distinct(ID_SAMPLE) %>%
dplyr::mutate(group = 3),
japanese_pollen_taxa_counts_amalgamation_4 %>%
dplyr::distinct(ID_SAMPLE) %>%
dplyr::mutate(group = 4),
japanese_pollen_taxa_counts_amalgamation_5 %>%
dplyr::distinct(ID_SAMPLE) %>%
dplyr::mutate(group = 5)
) %>%
dplyr::group_by(ID_SAMPLE) %>%
dplyr::mutate(n = dplyr::n()) %>%
dplyr::arrange(dplyr::desc(n), ID_SAMPLE) %>%
dplyr::ungroup()
samples_with_counts$ID_SAMPLE %>% range()
all(japanese_pollen_metadata$ID_SAMPLE %in% samples_with_counts$ID_SAMPLE)
japanese_pollen_taxa_counts_amalgamation_all <-
dplyr::bind_rows(
japanese_pollen_taxa_counts_amalgamation_1,
japanese_pollen_taxa_counts_amalgamation_2,
japanese_pollen_taxa_counts_amalgamation_3,
japanese_pollen_taxa_counts_amalgamation_4,
japanese_pollen_taxa_counts_amalgamation_5
) %>%
dplyr::arrange(ID_SAMPLE, clean)
### 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)
japanese_pollen_taxa_counts_amalgamation_all_rev <-
japanese_pollen_taxa_counts_amalgamation_all %>%
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)
japanese_pollen_taxa_counts_amalgamation_all_rev %>%
dplyr::group_by(ID_COUNT) %>%
dplyr::mutate(n = dplyr::n()) %>%
dplyr::filter(n > 1)
waldo::compare(japanese_pollen_taxa_counts_amalgamation_all %>%
dplyr::distinct(clean, intermediate, amalgamated),
japanese_pollen_taxa_counts_amalgamation_all_rev %>%
dplyr::distinct(clean, intermediate, amalgamated),
max_diffs = Inf)
japanese_pollen_taxa_counts_amalgamation_all <-
japanese_pollen_taxa_counts_amalgamation_all_rev %>%
dplyr::filter(!is.na(taxon_count), taxon_count > 0) %>%
dplyr::select(-ID_COUNT)
# Find DOIs ----
japanese_pollen_metadata_pubs <-
japanese_pollen_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("\\]$") %>%
stringr::str_remove_all("doi:") %>%
stringr::str_squish() %>%
stringr::str_c(collapse = ";\n"))
) %>%
dplyr::mutate(ID_PUB = seq_along(publication)) %>%
dplyr::mutate(updated_publication = NA, .before = publication) %>%
dplyr::mutate(updated_DOI = NA, .before = DOI)
# japanese_pollen_metadata_pubs %>%
# readr::write_excel_csv("data-raw/GLOBAL/japanese_pollen_modern-references.csv")
## Load cleaned publications list ----
japanese_pollen_clean_publications <-
"data-raw/GLOBAL/japanese_pollen_modern-references_clean.csv" %>%
readr::read_csv() %>%
dplyr::select(-DOI)
# Append clean publications ----
japanese_pollen_metadata_2 <-
japanese_pollen_metadata %>%
dplyr::left_join(japanese_pollen_metadata_pubs %>%
dplyr::select(-DOI, -doi, -dplyr::contains("updated")),
by = "publication") %>%
dplyr::left_join(japanese_pollen_clean_publications,
by = "ID_PUB") %>%
dplyr::select(-publication.x, -publication.y, -doi, -ID_PUB) %>%
dplyr::rename(doi = updated_DOI,
publication = updated_publication)
# Extract PNV/BIOME ----
japanese_pollen_metadata_3 <-
japanese_pollen_metadata_2 %>%
dplyr::select(-dplyr::starts_with("ID_BIOME")) %>%
smpds::parallel_extract_biome(cpus = 2) %>%
# smpds::biome_name() %>%
dplyr::relocate(ID_BIOME, .after = doi) %>%
smpds::pb()
japanese_pollen_metadata_3 %>%
smpds::plot_biome(xlim = range(.$longitude, na.rm = TRUE) * c(0.9, 1.1),
ylim = range(.$latitude, na.rm = TRUE) * c(0.9, 1.1))
# Create count tables ----
## Clean ----
japanese_pollen_clean <-
japanese_pollen_taxa_counts_amalgamation_all %>%
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 ----
japanese_pollen_intermediate <-
japanese_pollen_taxa_counts_amalgamation_all %>%
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 ----
japanese_pollen_amalgamated <-
japanese_pollen_taxa_counts_amalgamation_all %>%
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 ----
japanese_pollen <-
japanese_pollen_metadata_3 %>%
dplyr::mutate(
clean = japanese_pollen_clean %>%
dplyr::select(-c(ID_SAMPLE)),
intermediate = japanese_pollen_intermediate %>%
dplyr::select(-c(ID_SAMPLE)),
amalgamated = japanese_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::relocate(basin_size_old, .after = basin_size) %>%
dplyr::select(-basin_size_num)
usethis::use_data(japanese_pollen, overwrite = TRUE, compress = "xz")
# Inspect enumerates ----
## basin_size -----
japanese_pollen$basin_size %>%
unique() %>%
sort()
## site_type ----
japanese_pollen$site_type %>%
unique() %>%
sort()
## entity_type ----
japanese_pollen$entity_type %>%
unique() %>%
sort()
# Export Excel workbook ----
wb <- openxlsx::createWorkbook()
openxlsx::addWorksheet(wb, "metadata")
openxlsx::writeData(wb, "metadata",
japanese_pollen %>%
dplyr::select(source:ID_SAMPLE))
openxlsx::addWorksheet(wb, "clean")
openxlsx::writeData(wb, "clean",
japanese_pollen %>%
dplyr::select(ID_SAMPLE, clean) %>%
tidyr::unnest(clean))
openxlsx::addWorksheet(wb, "intermediate")
openxlsx::writeData(wb, "intermediate",
japanese_pollen %>%
dplyr::select(ID_SAMPLE, intermediate) %>%
tidyr::unnest(intermediate))
openxlsx::addWorksheet(wb, "amalgamated")
openxlsx::writeData(wb, "amalgamated",
japanese_pollen %>%
dplyr::select(ID_SAMPLE, amalgamated) %>%
tidyr::unnest(amalgamated))
openxlsx::saveWorkbook(wb,
paste0("data-raw/GLOBAL/japanese_pollen_",
Sys.Date(),
".xlsx"))
# Load climate reconstructions ----
climate_reconstructions <-
"data-raw/reconstructions/japanese_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/japanese_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 <-
japanese_pollen %>%
# smpds::japanese_pollen %>%
# 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::japanese_pollen,
climate_reconstructions_with_counts %>%
dplyr::select(-c(mi:map, sn, en, new_elevation))
)
japanese_pollen <- climate_reconstructions_with_counts %>%
dplyr::select(-sn, -en, -new_elevation)
usethis::use_data(japanese_pollen, overwrite = TRUE, compress = "xz")
waldo::compare(smpds::japanese_pollen,
japanese_pollen,
max_diffs = Inf)
climate_reconstructions_2 %>%
smpds::plot_climate_countour(
var = "mat",
xlim = range(.$longitude, na.rm = TRUE),
ylim = range(.$latitude, na.rm = TRUE)
)
climate_reconstructions_2 %>%
smpds::plot_climate(
var = "map",
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)
special-uor/smpds documentation built on July 9, 2024, 5:39 p.m.
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`%>%` <- magrittr::`%>%`
# Load data ----
## Metadata ----
japanese_pollen_metadata <-
"data-raw/GLOBAL/japanese_pollen/Japan sites.xlsx" %>%
readxl::read_excel(sheet = 1, n_max = 94) %>%
janitor::clean_names() %>%
dplyr::rename(age_BP = age_bp) %>%
dplyr::mutate(ID_SAMPLE = seq_along(entity_name), .after = doi)
## Pollen counts ----
### File 1 ----
japanese_pollen_taxa_counts_amalgamation_1 <-
"data-raw/GLOBAL/japanese_pollen/Japan_dat files_clean_SPH.xlsx" %>%
readxl::read_excel(sheet = 1) %>%
magrittr::set_names(
c("entity_name", "clean", "intermediate", "amalgamated", "taxon_count")
) %>%
dplyr::left_join(
japanese_pollen_metadata %>%
dplyr::select(entity_name, ID_SAMPLE)
) %>%
dplyr::relocate(ID_SAMPLE, .before = 1) %>%
dplyr::select(-entity_name) %>%
dplyr::mutate(
clean = clean %>% stringr::str_squish(),
intermediate = intermediate %>% stringr::str_squish(),
amalgamated = amalgamated %>% stringr::str_squish()
)
japanese_pollen_taxa_counts_amalgamation_1 %>%
dplyr::filter(is.na(ID_SAMPLE))
japanese_pollen_metadata %>%
dplyr::filter(ID_SAMPLE %in%
japanese_pollen_taxa_counts_amalgamation_1$ID_SAMPLE)
### File 2 ----
japanese_pollen_taxa_counts_2 <-
"data-raw/GLOBAL/japanese_pollen/Japan0k_Morita_SPH_clean.xlsx" %>%
readxl::read_excel(sheet = 1) %>%
dplyr::rename(taxon_name = Entity) %>%
tidyr::pivot_longer(-taxon_name,
names_to = "entity_name",
values_to = "taxon_count") %>%
dplyr::arrange(entity_name) %>%
dplyr::left_join(
japanese_pollen_metadata %>%
dplyr::select(entity_name, ID_SAMPLE)
) %>%
dplyr::relocate(ID_SAMPLE, .before = 1) %>%
dplyr::select(-entity_name) %>%
dplyr::filter(!is.na(taxon_count))
japanese_pollen_taxa_counts_2 %>%
dplyr::filter(is.na(ID_SAMPLE))
japanese_pollen_taxa_amalgamations_2 <-
"data-raw/GLOBAL/japanese_pollen/Japan0k_Morita_SPH_clean.xlsx" %>%
readxl::read_excel(sheet = 2) %>%
magrittr::set_names(
c("taxon_name", "clean", "intermediate", "amalgamated")
) %>%
purrr::map_df(stringr::str_squish)
japanese_pollen_taxa_counts_amalgamation_2 <-
japanese_pollen_taxa_counts_2 %>%
dplyr::left_join(japanese_pollen_taxa_amalgamations_2) %>%
dplyr::select(-taxon_name) %>%
dplyr::relocate(taxon_count, .after = amalgamated)
japanese_pollen_taxa_counts_amalgamation_2 %>%
dplyr::filter(is.na(clean) | is.na(intermediate) | is.na(amalgamated)) %>%
dplyr::distinct(clean, intermediate, amalgamated)
japanese_pollen_metadata %>%
dplyr::filter(ID_SAMPLE %in%
japanese_pollen_taxa_counts_amalgamation_2$ID_SAMPLE)
### File 3 ----
japanese_pollen_taxa_counts_3 <-
"data-raw/GLOBAL/japanese_pollen/JPND01_clean_SPH.xlsx" %>%
readxl::read_excel(sheet = 1) %>%
dplyr::rename(taxon_name = Entity) %>%
tidyr::pivot_longer(-taxon_name,
names_to = "entity_name",
values_to = "taxon_count") %>%
dplyr::arrange(entity_name) %>%
dplyr::left_join(
japanese_pollen_metadata %>%
dplyr::select(entity_name, ID_SAMPLE)
) %>%
dplyr::relocate(ID_SAMPLE, .before = 1) %>%
dplyr::select(-entity_name) %>%
dplyr::filter(!is.na(taxon_count))
japanese_pollen_taxa_counts_3 %>%
dplyr::filter(is.na(ID_SAMPLE))
japanese_pollen_taxa_amalgamations_3 <-
"data-raw/GLOBAL/japanese_pollen/JPND01_clean_SPH.xlsx" %>%
readxl::read_excel(sheet = 2) %>%
magrittr::set_names(
c("taxon_name", "clean", "intermediate", "amalgamated")
) %>%
purrr::map_df(stringr::str_squish)
japanese_pollen_taxa_counts_amalgamation_3 <-
japanese_pollen_taxa_counts_3 %>%
dplyr::left_join(japanese_pollen_taxa_amalgamations_3) %>%
dplyr::select(-taxon_name) %>%
dplyr::relocate(taxon_count, .after = amalgamated)
japanese_pollen_taxa_counts_amalgamation_3 %>%
dplyr::filter(is.na(clean) | is.na(intermediate) | is.na(amalgamated)) %>%
dplyr::distinct(clean, intermediate, amalgamated)
japanese_pollen_metadata %>%
dplyr::filter(ID_SAMPLE %in%
japanese_pollen_taxa_counts_amalgamation_3$ID_SAMPLE)
### File 4 ----
japanese_pollen_taxa_counts_4 <-
"data-raw/GLOBAL/japanese_pollen/JPND02_clean_SPH.xlsx" %>%
readxl::read_excel(sheet = 1) %>%
dplyr::rename(taxon_name = Entity) %>%
tidyr::pivot_longer(-taxon_name,
names_to = "entity_name",
values_to = "taxon_count") %>%
dplyr::arrange(entity_name) %>%
dplyr::left_join(
japanese_pollen_metadata %>%
dplyr::select(entity_name, ID_SAMPLE)
) %>%
dplyr::relocate(ID_SAMPLE, .before = 1) %>%
dplyr::select(-entity_name) %>%
dplyr::filter(!is.na(taxon_count))
japanese_pollen_taxa_counts_4 %>%
dplyr::filter(is.na(ID_SAMPLE))
japanese_pollen_taxa_amalgamations_4 <-
"data-raw/GLOBAL/japanese_pollen/JPND02_clean_SPH.xlsx" %>%
readxl::read_excel(sheet = 2) %>%
magrittr::set_names(
c("taxon_name", "clean", "intermediate", "amalgamated")
) %>%
purrr::map_df(stringr::str_squish)
japanese_pollen_taxa_counts_amalgamation_4 <-
japanese_pollen_taxa_counts_4 %>%
dplyr::left_join(japanese_pollen_taxa_amalgamations_4) %>%
dplyr::select(-taxon_name) %>%
dplyr::relocate(taxon_count, .after = amalgamated)
japanese_pollen_taxa_counts_amalgamation_4 %>%
dplyr::filter(is.na(clean) | is.na(intermediate) | is.na(amalgamated)) %>%
dplyr::distinct(clean, intermediate, amalgamated)
japanese_pollen_metadata %>%
dplyr::filter(ID_SAMPLE %in%
japanese_pollen_taxa_counts_amalgamation_4$ID_SAMPLE)
### File 5 ----
japanese_pollen_taxa_counts_5 <-
"data-raw/GLOBAL/japanese_pollen/JPND03_clean_SPH.xlsx" %>%
readxl::read_excel(sheet = 1) %>%
dplyr::rename(taxon_name = Site) %>%
tidyr::pivot_longer(-taxon_name,
names_to = "entity_name",
values_to = "taxon_count") %>%
dplyr::arrange(entity_name) %>%
dplyr::left_join(
japanese_pollen_metadata %>%
dplyr::select(entity_name, ID_SAMPLE)
) %>%
dplyr::relocate(ID_SAMPLE, .before = 1) %>%
dplyr::select(-entity_name) %>%
dplyr::filter(!is.na(taxon_count))
japanese_pollen_taxa_counts_5 %>%
dplyr::filter(is.na(ID_SAMPLE))
japanese_pollen_taxa_amalgamations_5 <-
"data-raw/GLOBAL/japanese_pollen/JPND03_clean_SPH.xlsx" %>%
readxl::read_excel(sheet = 2) %>%
magrittr::set_names(
c("taxon_name", "clean", "intermediate", "amalgamated")
) %>%
purrr::map_df(stringr::str_squish)
japanese_pollen_taxa_counts_amalgamation_5 <-
japanese_pollen_taxa_counts_5 %>%
dplyr::left_join(japanese_pollen_taxa_amalgamations_5) %>%
dplyr::select(-taxon_name) %>%
dplyr::relocate(taxon_count, .after = amalgamated)
japanese_pollen_taxa_counts_amalgamation_5 %>%
dplyr::filter(is.na(clean) | is.na(intermediate) | is.na(amalgamated)) %>%
dplyr::distinct(clean, intermediate, amalgamated)
japanese_pollen_metadata %>%
dplyr::filter(ID_SAMPLE %in%
japanese_pollen_taxa_counts_amalgamation_5$ID_SAMPLE)
## Combine pollen counts and amalgamations ----
"Check that all the entities in the metadata have pollen counts"
samples_with_counts <-
dplyr::bind_rows(
japanese_pollen_taxa_counts_amalgamation_1 %>%
dplyr::distinct(ID_SAMPLE) %>%
dplyr::mutate(group = 1),
japanese_pollen_taxa_counts_amalgamation_2 %>%
dplyr::distinct(ID_SAMPLE) %>%
dplyr::mutate(group = 2),
japanese_pollen_taxa_counts_amalgamation_3 %>%
dplyr::distinct(ID_SAMPLE) %>%
dplyr::mutate(group = 3),
japanese_pollen_taxa_counts_amalgamation_4 %>%
dplyr::distinct(ID_SAMPLE) %>%
dplyr::mutate(group = 4),
japanese_pollen_taxa_counts_amalgamation_5 %>%
dplyr::distinct(ID_SAMPLE) %>%
dplyr::mutate(group = 5)
) %>%
dplyr::group_by(ID_SAMPLE) %>%
dplyr::mutate(n = dplyr::n()) %>%
dplyr::arrange(dplyr::desc(n), ID_SAMPLE) %>%
dplyr::ungroup()
samples_with_counts$ID_SAMPLE %>% range()
all(japanese_pollen_metadata$ID_SAMPLE %in% samples_with_counts$ID_SAMPLE)
japanese_pollen_taxa_counts_amalgamation_all <-
dplyr::bind_rows(
japanese_pollen_taxa_counts_amalgamation_1,
japanese_pollen_taxa_counts_amalgamation_2,
japanese_pollen_taxa_counts_amalgamation_3,
japanese_pollen_taxa_counts_amalgamation_4,
japanese_pollen_taxa_counts_amalgamation_5
) %>%
dplyr::arrange(ID_SAMPLE, clean)
### 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)
japanese_pollen_taxa_counts_amalgamation_all_rev <-
japanese_pollen_taxa_counts_amalgamation_all %>%
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)
japanese_pollen_taxa_counts_amalgamation_all_rev %>%
dplyr::group_by(ID_COUNT) %>%
dplyr::mutate(n = dplyr::n()) %>%
dplyr::filter(n > 1)
waldo::compare(japanese_pollen_taxa_counts_amalgamation_all %>%
dplyr::distinct(clean, intermediate, amalgamated),
japanese_pollen_taxa_counts_amalgamation_all_rev %>%
dplyr::distinct(clean, intermediate, amalgamated),
max_diffs = Inf)
japanese_pollen_taxa_counts_amalgamation_all <-
japanese_pollen_taxa_counts_amalgamation_all_rev %>%
dplyr::filter(!is.na(taxon_count), taxon_count > 0) %>%
dplyr::select(-ID_COUNT)
# Find DOIs ----
japanese_pollen_metadata_pubs <-
japanese_pollen_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("\\]$") %>%
stringr::str_remove_all("doi:") %>%
stringr::str_squish() %>%
stringr::str_c(collapse = ";\n"))
) %>%
dplyr::mutate(ID_PUB = seq_along(publication)) %>%
dplyr::mutate(updated_publication = NA, .before = publication) %>%
dplyr::mutate(updated_DOI = NA, .before = DOI)
# japanese_pollen_metadata_pubs %>%
# readr::write_excel_csv("data-raw/GLOBAL/japanese_pollen_modern-references.csv")
## Load cleaned publications list ----
japanese_pollen_clean_publications <-
"data-raw/GLOBAL/japanese_pollen_modern-references_clean.csv" %>%
readr::read_csv() %>%
dplyr::select(-DOI)
# Append clean publications ----
japanese_pollen_metadata_2 <-
japanese_pollen_metadata %>%
dplyr::left_join(japanese_pollen_metadata_pubs %>%
dplyr::select(-DOI, -doi, -dplyr::contains("updated")),
by = "publication") %>%
dplyr::left_join(japanese_pollen_clean_publications,
by = "ID_PUB") %>%
dplyr::select(-publication.x, -publication.y, -doi, -ID_PUB) %>%
dplyr::rename(doi = updated_DOI,
publication = updated_publication)
# Extract PNV/BIOME ----
japanese_pollen_metadata_3 <-
japanese_pollen_metadata_2 %>%
dplyr::select(-dplyr::starts_with("ID_BIOME")) %>%
smpds::parallel_extract_biome(cpus = 2) %>%
# smpds::biome_name() %>%
dplyr::relocate(ID_BIOME, .after = doi) %>%
smpds::pb()
japanese_pollen_metadata_3 %>%
smpds::plot_biome(xlim = range(.$longitude, na.rm = TRUE) * c(0.9, 1.1),
ylim = range(.$latitude, na.rm = TRUE) * c(0.9, 1.1))
# Create count tables ----
## Clean ----
japanese_pollen_clean <-
japanese_pollen_taxa_counts_amalgamation_all %>%
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 ----
japanese_pollen_intermediate <-
japanese_pollen_taxa_counts_amalgamation_all %>%
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 ----
japanese_pollen_amalgamated <-
japanese_pollen_taxa_counts_amalgamation_all %>%
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 ----
japanese_pollen <-
japanese_pollen_metadata_3 %>%
dplyr::mutate(
clean = japanese_pollen_clean %>%
dplyr::select(-c(ID_SAMPLE)),
intermediate = japanese_pollen_intermediate %>%
dplyr::select(-c(ID_SAMPLE)),
amalgamated = japanese_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::relocate(basin_size_old, .after = basin_size) %>%
dplyr::select(-basin_size_num)
usethis::use_data(japanese_pollen, overwrite = TRUE, compress = "xz")
# Inspect enumerates ----
## basin_size -----
japanese_pollen$basin_size %>%
unique() %>%
sort()
## site_type ----
japanese_pollen$site_type %>%
unique() %>%
sort()
## entity_type ----
japanese_pollen$entity_type %>%
unique() %>%
sort()
# Export Excel workbook ----
wb <- openxlsx::createWorkbook()
openxlsx::addWorksheet(wb, "metadata")
openxlsx::writeData(wb, "metadata",
japanese_pollen %>%
dplyr::select(source:ID_SAMPLE))
openxlsx::addWorksheet(wb, "clean")
openxlsx::writeData(wb, "clean",
japanese_pollen %>%
dplyr::select(ID_SAMPLE, clean) %>%
tidyr::unnest(clean))
openxlsx::addWorksheet(wb, "intermediate")
openxlsx::writeData(wb, "intermediate",
japanese_pollen %>%
dplyr::select(ID_SAMPLE, intermediate) %>%
tidyr::unnest(intermediate))
openxlsx::addWorksheet(wb, "amalgamated")
openxlsx::writeData(wb, "amalgamated",
japanese_pollen %>%
dplyr::select(ID_SAMPLE, amalgamated) %>%
tidyr::unnest(amalgamated))
openxlsx::saveWorkbook(wb,
paste0("data-raw/GLOBAL/japanese_pollen_",
Sys.Date(),
".xlsx"))
# Load climate reconstructions ----
climate_reconstructions <-
"data-raw/reconstructions/japanese_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/japanese_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 <-
japanese_pollen %>%
# smpds::japanese_pollen %>%
# 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::japanese_pollen,
climate_reconstructions_with_counts %>%
dplyr::select(-c(mi:map, sn, en, new_elevation))
)
japanese_pollen <- climate_reconstructions_with_counts %>%
dplyr::select(-sn, -en, -new_elevation)
usethis::use_data(japanese_pollen, overwrite = TRUE, compress = "xz")
waldo::compare(smpds::japanese_pollen,
japanese_pollen,
max_diffs = Inf)
climate_reconstructions_2 %>%
smpds::plot_climate_countour(
var = "mat",
xlim = range(.$longitude, na.rm = TRUE),
ylim = range(.$latitude, na.rm = TRUE)
)
climate_reconstructions_2 %>%
smpds::plot_climate(
var = "map",
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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