data-raw/APD.R
In special-uor/smpds: The SPECIAL Modern Pollen Data Set for Climate Reconstructions
## code to prepare `APD` dataset goes here
## Original ----
`%>%` <- magrittr::`%>%`
apd_clean_taxon_names <- readr::read_csv("inst/extdata/apd_taxa.csv")
apd_publications <- readr::read_csv("inst/extdata/APD_publications.csv")
APD_SPH <- readxl::read_xlsx("~/Downloads/SMPDSv2/APD-modern-records_diagnosed/APD-modern-records_all_SPH_diagnosed.xlsx",
sheet = 1) %>%
dplyr::rename(SPH_comment = ...14) %>%
dplyr::mutate(ID_APD = seq_along(sigle))
APD_SPH2 <- APD_SPH %>%
dplyr::filter(!is.na(age_BP))
APD_SPH_unused2 <- APD_SPH %>%
dplyr::filter(!(ID_APD %in% APD_SPH2$ID_APD)) %>%
dplyr::arrange(sigle)
APD_all <- APD_SPH2 %>%
dplyr::mutate(taxon_name = taxon_name %>%
stringr::str_squish()) %>%
dplyr::filter(taxon_name != "[ODD taxon]") %>%
dplyr::select(-taxon_name_original,-taxon_name_author, -SPH_comment, -ID_APD) %>%
dplyr::group_by(site_name) %>% # Add suffix based on site_name and depth
# dplyr::mutate(entity_name = paste0(sigle, "_", depth_in_m, "_", age_BP))
dplyr::mutate(n = length(unique(depth_in_m)),
entity_name = paste0(sigle, "_", depth_in_m),
# entity_name = ifelse(n > 1,
# paste0(sigle, "_", depth_in_m), sigle),
.after = site_name) %>%
dplyr::group_by(entity_name) %>% # Add suffix based on entity_name and age_BP
dplyr::mutate(n = length(unique(age_BP)),
entity_name = ifelse(n > 1,
paste0(entity_name,
c("", paste0("_", seq_len(n)[-1]))),
entity_name),
.after = site_name) %>%
dplyr::group_by(entity_name, taxon_name) %>%
dplyr::mutate(count = sum(count, na.rm = TRUE),
n = length(count)) %>%
dplyr::ungroup() %>%
dplyr::distinct(entity_name, taxon_name, .keep_all = TRUE) %>%
dplyr::select(site_name, entity_name, latitude, longitude, elevation, age_BP, publication, taxon_name, count) %>%
dplyr::mutate(ID_APD = seq_along(site_name), .before = 1)
APD_all_wide <- APD_all %>%
dplyr::ungroup() %>%
tidyr::pivot_wider(2:10, names_from = "taxon_name", values_from = "count") %>%
smpds::parallel_extract_biome(buffer = 12000, cpus = 6) %>%
dplyr::relocate(ID_BIOME, .after = age_BP) %>%
progressr::with_progress()
APD_all_wide %>%
dplyr::filter(is.na(ID_BIOME))
APD <- APD_all_wide %>%
dplyr::mutate(ID_BIOME = ifelse(is.na(ID_BIOME),
-888888,
ID_BIOME)) %>%
dplyr::rename(publication_old = publication) %>%
dplyr::left_join(apd_publications,
by = "site_name") %>%
dplyr::relocate(publication, DOI, .after = publication_old) %>%
dplyr::select(-publication_old, -publicationv2)
usethis::use_data(APD, overwrite = TRUE, compress = "xz")
# ------------------------------------------------------------------------------
# | Raw data (.ASCII) |
# ------------------------------------------------------------------------------
output <- smpds::process_apd("~/Downloads/SMPDSv2/APD/",
col_names = c("Taxon Name [APD]",
"Taxon Name [Author]",
"Depth [m]",
"Radiocarbon Chronology [yrs BP]",
"Calendar Chronology [yrs BP]",
"Count"),
col_types = c(readr::col_character(),
readr::col_character(),
readr::col_double(),
readr::col_double(),
readr::col_double(),
readr::col_double()))
APD_old <- output %>%
purrr::map_df(~.x) %>%
dplyr::ungroup() %>%
dplyr::rename(taxon_name = `Taxon Name [APD]`,
taxon_name_author = `Taxon Name [Author]`,
site_name = sitename,
depth_in_m = `Depth [m]`,
age_BP = `Calendar Chronology [yrs BP]`,
count = Count) %>%
dplyr::select(-`Radiocarbon Chronology [yrs BP]`) %>%
dplyr::filter(is.na(age_BP) | age_BP <= 100) %>%
dplyr::mutate(taxon_name = taxon_name %>%
stringr::str_squish()) %>%
dplyr::left_join(apd_clean_taxon_names,
by = "taxon_name") %>%
dplyr::filter(is.na(action) | action != "delete") %>%
dplyr::select(-action) %>%
dplyr::rename(taxon_name_original = taxon_name,
taxon_name = clean_name) %>%
dplyr::relocate(taxon_name, .before = taxon_name_original) %>%
dplyr::relocate(publication, .after = count) %>%
dplyr::mutate(taxon_name = ifelse(is.na(taxon_name),
"[ODD taxon]",
taxon_name)) %>%
# dplyr::group_by(site_name, taxon_name) %>%
# dplyr::mutate(count = sum(as.double(count), na.rm = TRUE)) %>%
# dplyr::ungroup() %>%
# dplyr::mutate(taxon_name = ifelse(is.na(taxon_name),
# taxon_name_original,
# taxon_name)) %>%
# dplyr::distinct(site_name, taxon_name, .keep_all = TRUE) %>%
dplyr::select(-taxon_name_original, -taxon_name_author)
APD_publications <- APD_old %>%
dplyr::filter(site_name %in% APD$site_name) %>%
dplyr::distinct(site_name, publication) %>%
dplyr::arrange(site_name)
APD_publications %>%
readr::write_excel_csv("inst/extdata/APD_publications.csv", na = "")
# ------------------------------------------------------------------------------
# | APD Paradox |
# ------------------------------------------------------------------------------
apd_paradox_files_txt <- list.files("~/Downloads/SMPDSv2/APD_Paradox",
pattern = ".txt", full.names = TRUE)
apd_paradox_files <- apd_paradox_files_txt %>%
purrr::map(~.x %>% readr::read_delim(delim = ";", col_names = FALSE) # %>%
# magrittr::set_attr("name", basename(.x))
)
# ------------------------------------------------------------------------------
# | Sandbox |
# ------------------------------------------------------------------------------
apd_taxa_names <- readxl::read_xlsx("~/Downloads/SMPDSv2/smpdsv2-APD-Herzschuh-taxon-names-2021-08-05_SPH.xlsx",
sheet = 2) %>%
dplyr::slice(-c(1491:1492, 1564:1566)) %>%
dplyr::mutate(action = ifelse(stringr::str_detect(tolower(clean_name),
"exclude"),
"delete", "update"),
clean_name = ifelse(stringr::str_detect(tolower(clean_name),
"exclude"),
NA, clean_name),
clean_name = clean_name %>%
stringr::str_squish()) %>%
dplyr::arrange(dplyr::desc(action), taxon_name) %>%
dplyr::filter(!is.na(taxon_name)) %>%
dplyr::distinct(taxon_name, clean_name, .keep_all = TRUE)
apd_taxa_names %>%
readr::write_excel_csv("inst/extdata/apd_taxa.csv", na = "")
aux <- output %>%
purrr::map_df(~.x) %>%
dplyr::rename(taxon_name = `Taxon Name [APD]`,
taxon_name_author = `Taxon Name [Author]`,
site_name = name,
depth_in_m = `Depth [m]`,
age_BP = `Calendar Chronology [yrs BP]`,
count = Count) %>%
dplyr::select(-`Radiocarbon Chronology [yrs BP]`) %>%
dplyr::mutate(taxon_name = taxon_name %>%
stringr::str_squish())
apd_odd_taxon <- apd_clean_taxon_names %>%
dplyr::filter(is.na(action)) %>%
.$taxon_name
aux2 <- aux %>%
dplyr::filter(taxon_name %in% apd_odd_taxon) %>%
dplyr::filter(is.na(age_BP) | age_BP <= 100) %>%
dplyr::arrange(taxon_name)
APD %>%
dplyr::filter(is.na(age_BP) | age_BP <= 50) %>%
dplyr::arrange(site_name, depth_in_m) %>%
readr::write_excel_csv("~/Downloads/SMPDSv2/APD-modern-records_all.csv", na = "")
APD %>%
dplyr::filter(!is.na(age_BP) & (age_BP >= -72 & age_BP <= 50)) %>%
dplyr::arrange(site_name, depth_in_m) %>%
readr::write_excel_csv("~/Downloads/SMPDSv2/APD-modern-records_50_-72.csv", na = "")
APD %>%
dplyr::filter(!is.na(age_BP) & (age_BP < -72)) %>%
dplyr::arrange(site_name, depth_in_m) %>%
readr::write_excel_csv("~/Downloads/SMPDSv2/APD-modern-records_over_-72.csv")
# SPH revisions ----
"The entities and their counts were manually inspected by SPH"
`%>%` <- magrittr::`%>%`
## Load data ----
APD_all <-
"data-raw/GLOBAL/APD_clean_SPH.xlsx" %>%
readxl::read_excel(sheet = 1) %>%
dplyr::mutate(ID_SAMPLE = seq_along(entity_name), .after = doi)
### Metadata ----
APD_metadata <-
APD_all %>%
dplyr::select(site_name:ID_SAMPLE) %>%
dplyr::rename(basin_size = `basin_size (km2)`)
### Pollen counts ----
APD_counts <-
APD_all %>%
dplyr::select(ID_SAMPLE) %>%
dplyr::bind_cols(
APD_all %>% # Convert columns with counts to numeric type
dplyr::select(-c(site_name:ID_SAMPLE)) %>%
purrr::map_dfc(~.x %>% as.numeric)
) %>%
magrittr::set_names(
colnames(.) %>%
stringr::str_replace_all("\\.\\.\\.", "#")
) %>%
tidyr::pivot_longer(-ID_SAMPLE,
names_to = "clean",
values_to = "taxon_count") %>%
dplyr::mutate(clean = clean %>%
stringr::str_remove_all("\\#[0-9]+$") %>%
stringr::str_squish()) %>%
dplyr::group_by(ID_SAMPLE, clean) %>%
dplyr::mutate(taxon_count = sum(taxon_count, na.rm = TRUE)) %>%
dplyr::distinct() %>%
dplyr::ungroup()
### Amalgamations ----
APD_taxa_amalgamation <-
"data-raw/GLOBAL/APD_clean_SPH.xlsx" %>%
readxl::read_excel(sheet = 2) %>%
magrittr::set_names(c(
"clean", "intermediate", "amalgamated"
)) %>%
dplyr::distinct() %>%
dplyr::mutate(clean = clean %>% stringr::str_squish(),
intermediate = intermediate %>% stringr::str_squish(),
amalgamated = amalgamated %>% stringr::str_squish())
### Combine counts and amalgamation ----
APD_taxa_counts_amalgamation <-
APD_counts %>%
dplyr::left_join(APD_taxa_amalgamation,
by = c("clean")) %>%
dplyr::relocate(taxon_count, .after = amalgamated) %>%
dplyr::relocate(ID_SAMPLE, .before = 1)
### 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)
APD_taxa_counts_amalgamation_rev <-
APD_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)
APD_taxa_counts_amalgamation_rev %>%
dplyr::group_by(ID_COUNT) %>%
dplyr::mutate(n = dplyr::n()) %>%
dplyr::filter(n > 1)
waldo::compare(APD_taxa_counts_amalgamation,
APD_taxa_counts_amalgamation_rev)
waldo::compare(APD_taxa_counts_amalgamation %>%
dplyr::distinct(clean, intermediate, amalgamated),
APD_taxa_counts_amalgamation_rev %>%
dplyr::distinct(clean, intermediate, amalgamated),
max_diffs = Inf)
APD_taxa_counts_amalgamation <- APD_taxa_counts_amalgamation_rev %>%
dplyr::filter(!is.na(taxon_count), taxon_count > 0) %>%
dplyr::select(-ID_COUNT)
APD_taxa_counts_amalgamation %>%
dplyr::filter(is.na(clean) | is.na(intermediate) | is.na(amalgamated))
## Find DOIs ----
APD_metadata_pubs <-
APD_metadata %>%
dplyr::distinct(publication) %>%
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_squish() %>%
stringr::str_c(collapse = ";\n"))
) %>%
dplyr::mutate(ID_PUB = seq_along(publication))
# APD_metadata_pubs %>%
# readr::write_excel_csv("data-raw/GLOBAL/APD_modern-references.csv")
### Load cleaned publications list ----
APD_clean_publications <-
"data-raw/GLOBAL/APD_modern-references_clean.csv" %>%
readr::read_csv() %>%
dplyr::select(-DOI)
## Append clean publications ----
APD_metadata_2 <-
APD_metadata %>%
dplyr::left_join(APD_metadata_pubs %>%
dplyr::select(-DOI),
by = "publication") %>%
dplyr::left_join(APD_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 ----
APD_metadata_3 <-
APD_metadata_2 %>%
dplyr::select(-dplyr::starts_with("ID_BIOME")) %>%
smpds::parallel_extract_biome(cpus = 8) %>%
# smpds::biome_name() %>%
dplyr::relocate(ID_BIOME, .after = doi) %>%
smpds::pb()
APD_metadata_3 %>%
smpds::plot_biome(xlim = range(.$longitude, na.rm = TRUE) * 1.1,
ylim = range(.$latitude, na.rm = TRUE) * 1.1)
## Create count tables ----
### Clean ----
APD_clean <-
APD_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 ----
APD_intermediate <-
APD_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 ----
APD_amalgamated <-
APD_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 ----
APD <-
APD_metadata_3 %>%
dplyr::mutate(
clean = APD_clean %>%
dplyr::select(-c(ID_SAMPLE)),
intermediate = APD_intermediate %>%
dplyr::select(-c(ID_SAMPLE)),
amalgamated = APD_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("unknown", "not known"),
site_type = site_type %>%
stringr::str_replace_all("unknown", "not known")
) %>%
dplyr::relocate(ID_SAMPLE, .before = clean) %>%
dplyr::mutate(source = "APD", .before = 1) %>%
dplyr::select(-basin_size_num)
usethis::use_data(APD, overwrite = TRUE, compress = "xz")
# Inspect enumerates ----
### basin_size -----
APD$basin_size %>%
unique() %>% sort()
### site_type ----
APD$site_type %>%
unique() %>% sort()
### entity_type ----
APD$entity_type %>%
unique() %>% sort()
# Export Excel workbook ----
wb <- openxlsx::createWorkbook()
openxlsx::addWorksheet(wb, "metadata")
openxlsx::writeData(wb, "metadata",
APD %>%
dplyr::select(source:ID_SAMPLE))
openxlsx::addWorksheet(wb, "clean")
openxlsx::writeData(wb, "clean",
APD %>%
dplyr::select(ID_SAMPLE, clean) %>%
tidyr::unnest(clean))
openxlsx::addWorksheet(wb, "intermediate")
openxlsx::writeData(wb, "intermediate",
APD %>%
dplyr::select(ID_SAMPLE, intermediate) %>%
tidyr::unnest(intermediate))
openxlsx::addWorksheet(wb, "amalgamated")
openxlsx::writeData(wb, "amalgamated",
APD %>%
dplyr::select(ID_SAMPLE, amalgamated) %>%
tidyr::unnest(amalgamated))
openxlsx::saveWorkbook(wb,
paste0("data-raw/GLOBAL/APD_",
Sys.Date(),
".xlsx"))
# Load climate reconstructions ----
climate_reconstructions <-
"data-raw/reconstructions/APD_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/APD_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 <-
APD %>%
# smpds::APD %>%
# 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::APD,
climate_reconstructions_with_counts %>%
dplyr::select(-c(mi:map, sn, en, new_elevation))
)
APD <- climate_reconstructions_with_counts %>%
dplyr::select(-sn, -en, -new_elevation)
usethis::use_data(APD, overwrite = TRUE, compress = "xz")
waldo::compare(smpds::APD,
APD,
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(.$latitude, na.rm = TRUE)
)
rm(climate_reconstructions,
climate_reconstructions_2,
climate_reconstructions_pre,
climate_reconstructions_with_counts)
special-uor/smpds documentation built on July 9, 2024, 5:39 p.m.
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## code to prepare `APD` dataset goes here
## Original ----
`%>%` <- magrittr::`%>%`
apd_clean_taxon_names <- readr::read_csv("inst/extdata/apd_taxa.csv")
apd_publications <- readr::read_csv("inst/extdata/APD_publications.csv")
APD_SPH <- readxl::read_xlsx("~/Downloads/SMPDSv2/APD-modern-records_diagnosed/APD-modern-records_all_SPH_diagnosed.xlsx",
sheet = 1) %>%
dplyr::rename(SPH_comment = ...14) %>%
dplyr::mutate(ID_APD = seq_along(sigle))
APD_SPH2 <- APD_SPH %>%
dplyr::filter(!is.na(age_BP))
APD_SPH_unused2 <- APD_SPH %>%
dplyr::filter(!(ID_APD %in% APD_SPH2$ID_APD)) %>%
dplyr::arrange(sigle)
APD_all <- APD_SPH2 %>%
dplyr::mutate(taxon_name = taxon_name %>%
stringr::str_squish()) %>%
dplyr::filter(taxon_name != "[ODD taxon]") %>%
dplyr::select(-taxon_name_original,-taxon_name_author, -SPH_comment, -ID_APD) %>%
dplyr::group_by(site_name) %>% # Add suffix based on site_name and depth
# dplyr::mutate(entity_name = paste0(sigle, "_", depth_in_m, "_", age_BP))
dplyr::mutate(n = length(unique(depth_in_m)),
entity_name = paste0(sigle, "_", depth_in_m),
# entity_name = ifelse(n > 1,
# paste0(sigle, "_", depth_in_m), sigle),
.after = site_name) %>%
dplyr::group_by(entity_name) %>% # Add suffix based on entity_name and age_BP
dplyr::mutate(n = length(unique(age_BP)),
entity_name = ifelse(n > 1,
paste0(entity_name,
c("", paste0("_", seq_len(n)[-1]))),
entity_name),
.after = site_name) %>%
dplyr::group_by(entity_name, taxon_name) %>%
dplyr::mutate(count = sum(count, na.rm = TRUE),
n = length(count)) %>%
dplyr::ungroup() %>%
dplyr::distinct(entity_name, taxon_name, .keep_all = TRUE) %>%
dplyr::select(site_name, entity_name, latitude, longitude, elevation, age_BP, publication, taxon_name, count) %>%
dplyr::mutate(ID_APD = seq_along(site_name), .before = 1)
APD_all_wide <- APD_all %>%
dplyr::ungroup() %>%
tidyr::pivot_wider(2:10, names_from = "taxon_name", values_from = "count") %>%
smpds::parallel_extract_biome(buffer = 12000, cpus = 6) %>%
dplyr::relocate(ID_BIOME, .after = age_BP) %>%
progressr::with_progress()
APD_all_wide %>%
dplyr::filter(is.na(ID_BIOME))
APD <- APD_all_wide %>%
dplyr::mutate(ID_BIOME = ifelse(is.na(ID_BIOME),
-888888,
ID_BIOME)) %>%
dplyr::rename(publication_old = publication) %>%
dplyr::left_join(apd_publications,
by = "site_name") %>%
dplyr::relocate(publication, DOI, .after = publication_old) %>%
dplyr::select(-publication_old, -publicationv2)
usethis::use_data(APD, overwrite = TRUE, compress = "xz")
# ------------------------------------------------------------------------------
# | Raw data (.ASCII) |
# ------------------------------------------------------------------------------
output <- smpds::process_apd("~/Downloads/SMPDSv2/APD/",
col_names = c("Taxon Name [APD]",
"Taxon Name [Author]",
"Depth [m]",
"Radiocarbon Chronology [yrs BP]",
"Calendar Chronology [yrs BP]",
"Count"),
col_types = c(readr::col_character(),
readr::col_character(),
readr::col_double(),
readr::col_double(),
readr::col_double(),
readr::col_double()))
APD_old <- output %>%
purrr::map_df(~.x) %>%
dplyr::ungroup() %>%
dplyr::rename(taxon_name = `Taxon Name [APD]`,
taxon_name_author = `Taxon Name [Author]`,
site_name = sitename,
depth_in_m = `Depth [m]`,
age_BP = `Calendar Chronology [yrs BP]`,
count = Count) %>%
dplyr::select(-`Radiocarbon Chronology [yrs BP]`) %>%
dplyr::filter(is.na(age_BP) | age_BP <= 100) %>%
dplyr::mutate(taxon_name = taxon_name %>%
stringr::str_squish()) %>%
dplyr::left_join(apd_clean_taxon_names,
by = "taxon_name") %>%
dplyr::filter(is.na(action) | action != "delete") %>%
dplyr::select(-action) %>%
dplyr::rename(taxon_name_original = taxon_name,
taxon_name = clean_name) %>%
dplyr::relocate(taxon_name, .before = taxon_name_original) %>%
dplyr::relocate(publication, .after = count) %>%
dplyr::mutate(taxon_name = ifelse(is.na(taxon_name),
"[ODD taxon]",
taxon_name)) %>%
# dplyr::group_by(site_name, taxon_name) %>%
# dplyr::mutate(count = sum(as.double(count), na.rm = TRUE)) %>%
# dplyr::ungroup() %>%
# dplyr::mutate(taxon_name = ifelse(is.na(taxon_name),
# taxon_name_original,
# taxon_name)) %>%
# dplyr::distinct(site_name, taxon_name, .keep_all = TRUE) %>%
dplyr::select(-taxon_name_original, -taxon_name_author)
APD_publications <- APD_old %>%
dplyr::filter(site_name %in% APD$site_name) %>%
dplyr::distinct(site_name, publication) %>%
dplyr::arrange(site_name)
APD_publications %>%
readr::write_excel_csv("inst/extdata/APD_publications.csv", na = "")
# ------------------------------------------------------------------------------
# | APD Paradox |
# ------------------------------------------------------------------------------
apd_paradox_files_txt <- list.files("~/Downloads/SMPDSv2/APD_Paradox",
pattern = ".txt", full.names = TRUE)
apd_paradox_files <- apd_paradox_files_txt %>%
purrr::map(~.x %>% readr::read_delim(delim = ";", col_names = FALSE) # %>%
# magrittr::set_attr("name", basename(.x))
)
# ------------------------------------------------------------------------------
# | Sandbox |
# ------------------------------------------------------------------------------
apd_taxa_names <- readxl::read_xlsx("~/Downloads/SMPDSv2/smpdsv2-APD-Herzschuh-taxon-names-2021-08-05_SPH.xlsx",
sheet = 2) %>%
dplyr::slice(-c(1491:1492, 1564:1566)) %>%
dplyr::mutate(action = ifelse(stringr::str_detect(tolower(clean_name),
"exclude"),
"delete", "update"),
clean_name = ifelse(stringr::str_detect(tolower(clean_name),
"exclude"),
NA, clean_name),
clean_name = clean_name %>%
stringr::str_squish()) %>%
dplyr::arrange(dplyr::desc(action), taxon_name) %>%
dplyr::filter(!is.na(taxon_name)) %>%
dplyr::distinct(taxon_name, clean_name, .keep_all = TRUE)
apd_taxa_names %>%
readr::write_excel_csv("inst/extdata/apd_taxa.csv", na = "")
aux <- output %>%
purrr::map_df(~.x) %>%
dplyr::rename(taxon_name = `Taxon Name [APD]`,
taxon_name_author = `Taxon Name [Author]`,
site_name = name,
depth_in_m = `Depth [m]`,
age_BP = `Calendar Chronology [yrs BP]`,
count = Count) %>%
dplyr::select(-`Radiocarbon Chronology [yrs BP]`) %>%
dplyr::mutate(taxon_name = taxon_name %>%
stringr::str_squish())
apd_odd_taxon <- apd_clean_taxon_names %>%
dplyr::filter(is.na(action)) %>%
.$taxon_name
aux2 <- aux %>%
dplyr::filter(taxon_name %in% apd_odd_taxon) %>%
dplyr::filter(is.na(age_BP) | age_BP <= 100) %>%
dplyr::arrange(taxon_name)
APD %>%
dplyr::filter(is.na(age_BP) | age_BP <= 50) %>%
dplyr::arrange(site_name, depth_in_m) %>%
readr::write_excel_csv("~/Downloads/SMPDSv2/APD-modern-records_all.csv", na = "")
APD %>%
dplyr::filter(!is.na(age_BP) & (age_BP >= -72 & age_BP <= 50)) %>%
dplyr::arrange(site_name, depth_in_m) %>%
readr::write_excel_csv("~/Downloads/SMPDSv2/APD-modern-records_50_-72.csv", na = "")
APD %>%
dplyr::filter(!is.na(age_BP) & (age_BP < -72)) %>%
dplyr::arrange(site_name, depth_in_m) %>%
readr::write_excel_csv("~/Downloads/SMPDSv2/APD-modern-records_over_-72.csv")
# SPH revisions ----
"The entities and their counts were manually inspected by SPH"
`%>%` <- magrittr::`%>%`
## Load data ----
APD_all <-
"data-raw/GLOBAL/APD_clean_SPH.xlsx" %>%
readxl::read_excel(sheet = 1) %>%
dplyr::mutate(ID_SAMPLE = seq_along(entity_name), .after = doi)
### Metadata ----
APD_metadata <-
APD_all %>%
dplyr::select(site_name:ID_SAMPLE) %>%
dplyr::rename(basin_size = `basin_size (km2)`)
### Pollen counts ----
APD_counts <-
APD_all %>%
dplyr::select(ID_SAMPLE) %>%
dplyr::bind_cols(
APD_all %>% # Convert columns with counts to numeric type
dplyr::select(-c(site_name:ID_SAMPLE)) %>%
purrr::map_dfc(~.x %>% as.numeric)
) %>%
magrittr::set_names(
colnames(.) %>%
stringr::str_replace_all("\\.\\.\\.", "#")
) %>%
tidyr::pivot_longer(-ID_SAMPLE,
names_to = "clean",
values_to = "taxon_count") %>%
dplyr::mutate(clean = clean %>%
stringr::str_remove_all("\\#[0-9]+$") %>%
stringr::str_squish()) %>%
dplyr::group_by(ID_SAMPLE, clean) %>%
dplyr::mutate(taxon_count = sum(taxon_count, na.rm = TRUE)) %>%
dplyr::distinct() %>%
dplyr::ungroup()
### Amalgamations ----
APD_taxa_amalgamation <-
"data-raw/GLOBAL/APD_clean_SPH.xlsx" %>%
readxl::read_excel(sheet = 2) %>%
magrittr::set_names(c(
"clean", "intermediate", "amalgamated"
)) %>%
dplyr::distinct() %>%
dplyr::mutate(clean = clean %>% stringr::str_squish(),
intermediate = intermediate %>% stringr::str_squish(),
amalgamated = amalgamated %>% stringr::str_squish())
### Combine counts and amalgamation ----
APD_taxa_counts_amalgamation <-
APD_counts %>%
dplyr::left_join(APD_taxa_amalgamation,
by = c("clean")) %>%
dplyr::relocate(taxon_count, .after = amalgamated) %>%
dplyr::relocate(ID_SAMPLE, .before = 1)
### 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)
APD_taxa_counts_amalgamation_rev <-
APD_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)
APD_taxa_counts_amalgamation_rev %>%
dplyr::group_by(ID_COUNT) %>%
dplyr::mutate(n = dplyr::n()) %>%
dplyr::filter(n > 1)
waldo::compare(APD_taxa_counts_amalgamation,
APD_taxa_counts_amalgamation_rev)
waldo::compare(APD_taxa_counts_amalgamation %>%
dplyr::distinct(clean, intermediate, amalgamated),
APD_taxa_counts_amalgamation_rev %>%
dplyr::distinct(clean, intermediate, amalgamated),
max_diffs = Inf)
APD_taxa_counts_amalgamation <- APD_taxa_counts_amalgamation_rev %>%
dplyr::filter(!is.na(taxon_count), taxon_count > 0) %>%
dplyr::select(-ID_COUNT)
APD_taxa_counts_amalgamation %>%
dplyr::filter(is.na(clean) | is.na(intermediate) | is.na(amalgamated))
## Find DOIs ----
APD_metadata_pubs <-
APD_metadata %>%
dplyr::distinct(publication) %>%
dplyr::arrange(publication) %>%
dplyr::mutate(DOI = publication %>%
stringr::str_extract_all("\\[DOI\\s*(.*?)\\s*\\](;|$)") %>%
purrr::map_chr(~.x %>%
stringr::str_remove_all("^\\[DOI:|\\]$") %>%
stringr::str_squish() %>%
stringr::str_c(collapse = ";\n"))
) %>%
dplyr::mutate(ID_PUB = seq_along(publication))
# APD_metadata_pubs %>%
# readr::write_excel_csv("data-raw/GLOBAL/APD_modern-references.csv")
### Load cleaned publications list ----
APD_clean_publications <-
"data-raw/GLOBAL/APD_modern-references_clean.csv" %>%
readr::read_csv() %>%
dplyr::select(-DOI)
## Append clean publications ----
APD_metadata_2 <-
APD_metadata %>%
dplyr::left_join(APD_metadata_pubs %>%
dplyr::select(-DOI),
by = "publication") %>%
dplyr::left_join(APD_clean_publications,
by = "ID_PUB") %>%
dplyr::select(-publication.x, -publication.y, -doi, -ID_PUB) %>%
dplyr::rename(doi = updated_DOI,
publication = updated_publication)
## Extract PNV/BIOME ----
APD_metadata_3 <-
APD_metadata_2 %>%
dplyr::select(-dplyr::starts_with("ID_BIOME")) %>%
smpds::parallel_extract_biome(cpus = 8) %>%
# smpds::biome_name() %>%
dplyr::relocate(ID_BIOME, .after = doi) %>%
smpds::pb()
APD_metadata_3 %>%
smpds::plot_biome(xlim = range(.$longitude, na.rm = TRUE) * 1.1,
ylim = range(.$latitude, na.rm = TRUE) * 1.1)
## Create count tables ----
### Clean ----
APD_clean <-
APD_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 ----
APD_intermediate <-
APD_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 ----
APD_amalgamated <-
APD_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 ----
APD <-
APD_metadata_3 %>%
dplyr::mutate(
clean = APD_clean %>%
dplyr::select(-c(ID_SAMPLE)),
intermediate = APD_intermediate %>%
dplyr::select(-c(ID_SAMPLE)),
amalgamated = APD_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("unknown", "not known"),
site_type = site_type %>%
stringr::str_replace_all("unknown", "not known")
) %>%
dplyr::relocate(ID_SAMPLE, .before = clean) %>%
dplyr::mutate(source = "APD", .before = 1) %>%
dplyr::select(-basin_size_num)
usethis::use_data(APD, overwrite = TRUE, compress = "xz")
# Inspect enumerates ----
### basin_size -----
APD$basin_size %>%
unique() %>% sort()
### site_type ----
APD$site_type %>%
unique() %>% sort()
### entity_type ----
APD$entity_type %>%
unique() %>% sort()
# Export Excel workbook ----
wb <- openxlsx::createWorkbook()
openxlsx::addWorksheet(wb, "metadata")
openxlsx::writeData(wb, "metadata",
APD %>%
dplyr::select(source:ID_SAMPLE))
openxlsx::addWorksheet(wb, "clean")
openxlsx::writeData(wb, "clean",
APD %>%
dplyr::select(ID_SAMPLE, clean) %>%
tidyr::unnest(clean))
openxlsx::addWorksheet(wb, "intermediate")
openxlsx::writeData(wb, "intermediate",
APD %>%
dplyr::select(ID_SAMPLE, intermediate) %>%
tidyr::unnest(intermediate))
openxlsx::addWorksheet(wb, "amalgamated")
openxlsx::writeData(wb, "amalgamated",
APD %>%
dplyr::select(ID_SAMPLE, amalgamated) %>%
tidyr::unnest(amalgamated))
openxlsx::saveWorkbook(wb,
paste0("data-raw/GLOBAL/APD_",
Sys.Date(),
".xlsx"))
# Load climate reconstructions ----
climate_reconstructions <-
"data-raw/reconstructions/APD_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/APD_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 <-
APD %>%
# smpds::APD %>%
# 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::APD,
climate_reconstructions_with_counts %>%
dplyr::select(-c(mi:map, sn, en, new_elevation))
)
APD <- climate_reconstructions_with_counts %>%
dplyr::select(-sn, -en, -new_elevation)
usethis::use_data(APD, overwrite = TRUE, compress = "xz")
waldo::compare(smpds::APD,
APD,
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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