data-raw/NEOTOMA.R
In special-uor/smpds: The SPECIAL Modern Pollen Data Set for Climate Reconstructions
## code to prepare `NEOTOMA` dataset goes here
# Original ----
neotoma_metadata <- readr::read_csv("inst/extdata/neotoma_metadata.csv")
neotoma_count <- readr::read_csv("inst/extdata/neotoma_count.csv")
neotoma_taxa <- readr::read_csv("inst/extdata/neotoma_taxa.csv")
NEOTOMA <- neotoma_metadata %>%
dplyr::left_join(neotoma_count, by = "entity_name") %>%
smpds::parallel_extract_biome(buffer = 12000, cpus = 2) %>%
dplyr::relocate(ID_BIOME, .after = age_BP) %>%
tidyr::pivot_longer(-c(1:13),
names_to = "taxon_name",
values_to = "count") %>%
dplyr::filter(!is.na(count)) %>%
dplyr::left_join(smpds::clean_taxa(), #neotoma_taxa,
by = "taxon_name") %>%
dplyr::filter(action != "delete") %>%
dplyr::select(-action) %>%
dplyr::rename(taxon_name_original = taxon_name,
taxon_name = clean_name) %>%
dplyr::group_by(entity_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(entity_name, taxon_name, .keep_all = TRUE) %>%
dplyr::select(-taxon_name_original) %>%
tidyr::pivot_wider(1:13,
names_from = "taxon_name",
values_from = "count") %>%
smpds::sort_taxa(cols = 1:13) %>%
progressr::with_progress()
aux <- NEOTOMA %>%
dplyr::filter(entity_name %in% EMPDv2$entity_name)
aux_rev <- EMPDv2 %>%
dplyr::filter(entity_name %in% aux$entity_name) %>%
smpds::rm_na_taxa(1:14)
usethis::use_data(NEOTOMA, overwrite = TRUE)
# Export list of taxon names for clean-up
tibble::tibble(taxon_name = colnames(NEOTOMA)[-c(1:13)],
clean_name = taxon_name) %>%
readr::write_excel_csv("~/Downloads/SMPDSv2/NEOTOMA_taxa_2021-08-24.csv", na = "")
# ------------------------------------------------------------------------------
# | Sandbox |
# ------------------------------------------------------------------------------
neotoma_metadata <-
readxl::read_xlsx("~/Downloads/SMPDSv2/NEOTOMA/Modern samples_neotoma update.xlsx",
sheet = 1) %>%
magrittr::set_names(c("source",
"site_name",
"entity_name",
"latitude",
"longitude",
"elevation",
"basin_size",
"site_type",
"entity_type",
"age_BP",
"publication")) %>%
dplyr::group_by(site_name) %>%
dplyr::mutate(publication = publication %>%
stringr::str_c(collapse = ";\n")) %>%
dplyr::ungroup() %>%
dplyr::distinct(site_name, .keep_all = TRUE) %>%
dplyr::mutate(elevation2 = list(latitude, longitude) %>%
purrr::pmap_dbl(function(latitude, longitude) {
rgbif::elevation(latitude = latitude,
longitude = longitude,
username = "villegar",
elevation_model = "srtm1") %>%
.$elevation_geonames
}))
neotoma_metadata %>%
dplyr::mutate(elevation = elevation2) %>%
dplyr::select(-elevation2) %>%
readr::write_excel_csv("inst/extdata/neotoma_metadata.csv", na = "")
neotoma_count <-
readxl::read_xlsx("~/Downloads/SMPDSv2/NEOTOMA/Modern samples_neotoma update.xlsx",
sheet = 2,
col_names = FALSE)
neotoma_count_unused <- neotoma_count[seq(3, nrow(neotoma_count), 3), ]
neotoma_count2 <- neotoma_count %>%
dplyr::slice(-seq(3, nrow(neotoma_count), 3))
neotoma_count_long <- seq(1, nrow(neotoma_count2), 2) %>%
purrr::map_dfr(function(i) {
names <- neotoma_count2[i, ] %>% purrr:::flatten_chr()
values <- neotoma_count2[i + 1, ] %>% purrr:::flatten_chr()
tibble::tibble(entity_name = values[1],
taxon_name = names[-1],
count = values[-1]) %>%
dplyr::filter(!is.na(count))
}) %>%
dplyr::group_by(entity_name, taxon_name) %>%
dplyr::mutate(count = as.double(count) %>%
sum(na.rm = FALSE)) %>%
dplyr::ungroup() %>%
dplyr::distinct(entity_name, taxon_name, .keep_all = TRUE)
neotoma_count_wide <- neotoma_count_long %>%
tidyr::pivot_wider(entity_name,
names_from = "taxon_name",
values_from = "count") %>%
smpds::sort_taxa() %>%
smpds::rm_na_taxa()
neotoma_count_wide %>%
readr::write_excel_csv("inst/extdata/neotoma_count.csv", na = "")
neotoma_taxa <- readr::read_csv("~/Downloads/SMPDSv2/NEOTOMA_taxa_2021-08-24_SPH.csv") %>%
dplyr::distinct() %>%
dplyr::mutate(action = ifelse(clean_name %>%
stringr::str_detect("EXCLUDE"),
"delete", "update"),
clean_name = ifelse(clean_name %>%
stringr::str_detect("EXCLUDE"),
NA, clean_name)) %>%
dplyr::arrange(dplyr::desc(action), taxon_name) %>%
dplyr::filter(!is.na(taxon_name))
neotoma_taxa %>%
readr::write_excel_csv("inst/extdata/neotoma_taxa.csv", na = "")
# SPH revisions ----
"The entities and their counts were manually inspected by SPH"
`%>%` <- magrittr::`%>%`
## Load data ----
### Metadata ----
NEOTOMA_metadata <-
"data-raw/GLOBAL/Neotoma_extras_SPH.xlsx" %>%
readxl::read_excel(sheet = 1) %>%
dplyr::rename(doi = DOI) %>%
dplyr::mutate(ID_SAMPLE = seq_along(entity_name), .after = doi)
### Pollen counts ----
NEOTOMA_counts <-
"data-raw/GLOBAL/Neotoma_extras_SPH.xlsx" %>%
readxl::read_excel(sheet = 2) %>%
dplyr::left_join(NEOTOMA_metadata %>%
dplyr::select(entity_name, ID_SAMPLE),
by = "entity_name") %>%
dplyr::relocate(ID_SAMPLE, .before = 1)
NEOTOMA_counts_2 <- NEOTOMA_counts %>%
dplyr::select(ID_SAMPLE) %>%
dplyr::bind_cols(
NEOTOMA_counts %>% # Convert columns with counts to numeric type
dplyr::select(-c(ID_SAMPLE:entity_name)) %>%
purrr::map_dfc(~.x %>% as.numeric)
) %>%
magrittr::set_names(
colnames(.) %>%
stringr::str_replace_all("\\.\\.\\.", "#")
) %>%
tidyr::pivot_longer(-ID_SAMPLE,
names_to = "taxon_name",
values_to = "taxon_count") %>%
dplyr::mutate(taxon_name = taxon_name %>%
stringr::str_remove_all("\\#[0-9]+$") %>%
stringr::str_squish()) %>%
dplyr::group_by(ID_SAMPLE, taxon_name) %>%
dplyr::mutate(taxon_count = sum(taxon_count, na.rm = TRUE)) %>%
dplyr::distinct() %>%
dplyr::ungroup()
### Amalgamations ----
NEOTOMA_taxa_amalgamation <-
"data-raw/GLOBAL/Neotoma_extras_SPH.xlsx" %>%
readxl::read_excel(sheet = 3) %>%
magrittr::set_names(c(
"taxon_name", "clean", "intermediate", "amalgamated"
)) %>%
dplyr::distinct() %>%
dplyr::mutate(taxon_name = taxon_name %>% stringr::str_squish(),
clean = clean %>% stringr::str_squish(),
intermediate = intermediate %>% stringr::str_squish(),
amalgamated = amalgamated %>% stringr::str_squish())
### Combine counts and amalgamation ----
NEOTOMA_taxa_counts_amalgamation <-
NEOTOMA_counts_2 %>%
dplyr::left_join(NEOTOMA_taxa_amalgamation,
by = c("taxon_name")) %>%
dplyr::relocate(taxon_count, .after = amalgamated) %>%
dplyr::relocate(ID_SAMPLE, .before = 1) %>%
dplyr::select(-taxon_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)
NEOTOMA_taxa_counts_amalgamation_rev <-
NEOTOMA_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)
NEOTOMA_taxa_counts_amalgamation_rev %>%
dplyr::group_by(ID_COUNT) %>%
dplyr::mutate(n = dplyr::n()) %>%
dplyr::filter(n > 1)
waldo::compare(NEOTOMA_taxa_counts_amalgamation %>%
dplyr::distinct(clean, intermediate, amalgamated),
NEOTOMA_taxa_counts_amalgamation_rev %>%
dplyr::distinct(clean, intermediate, amalgamated),
max_diffs = Inf)
NEOTOMA_taxa_counts_amalgamation <- NEOTOMA_taxa_counts_amalgamation_rev %>%
dplyr::filter(!is.na(taxon_count), taxon_count > 0) %>%
dplyr::select(-ID_COUNT)
NEOTOMA_taxa_counts_amalgamation %>%
dplyr::filter(is.na(clean) | is.na(intermediate) | is.na(amalgamated)) %>%
dplyr::distinct(clean, intermediate, amalgamated)
## Find DOIs ----
NEOTOMA_metadata_pubs <-
NEOTOMA_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("\\]$") %>%
stringr::str_remove_all("doi:") %>%
stringr::str_squish() %>%
stringr::str_c(collapse = ";\n"))
) %>%
dplyr::mutate(ID_PUB = seq_along(publication)) %>%
dplyr::mutate(updated_publication = NA, .before = publication) %>%
dplyr::mutate(updated_DOI = NA, .before = DOI)
# NEOTOMA_metadata_pubs %>%
# readr::write_excel_csv("data-raw/GLOBAL/NEOTOMA_modern-references.csv")
### Load cleaned publications list ----
NEOTOMA_clean_publications <-
"data-raw/GLOBAL/NEOTOMA_modern-references_clean.csv" %>%
readr::read_csv() %>%
dplyr::select(-DOI)
## Append clean publications ----
NEOTOMA_metadata_2 <-
NEOTOMA_metadata %>%
dplyr::left_join(NEOTOMA_metadata_pubs %>%
dplyr::select(-DOI, -doi, -dplyr::contains("updated")),
by = "publication") %>%
dplyr::left_join(NEOTOMA_clean_publications,
by = "ID_PUB") %>%
dplyr::select(-publication.x, -publication.y, -doi, -ID_PUB) %>%
dplyr::rename(doi = updated_DOI,
publication = updated_publication)
## Extract PNV/BIOME ----
NEOTOMA_metadata_3 <-
NEOTOMA_metadata_2 %>%
dplyr::select(-dplyr::starts_with("ID_BIOME")) %>%
smpds::parallel_extract_biome(cpus = 6) %>%
# smpds::biome_name() %>%
dplyr::relocate(ID_BIOME, .after = doi) %>%
smpds::pb()
NEOTOMA_metadata_3 %>%
smpds::plot_biome(xlim = range(.$longitude, na.rm = TRUE) * c(0.9, 1.1),
ylim = range(.$latitude, na.rm = TRUE) * c(0.9, 1.1))
## Create count tables ----
### Clean ----
NEOTOMA_clean <-
NEOTOMA_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 ----
NEOTOMA_intermediate <-
NEOTOMA_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 ----
NEOTOMA_amalgamated <-
NEOTOMA_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 ----
NEOTOMA <-
NEOTOMA_metadata_3 %>%
dplyr::mutate(
clean = NEOTOMA_clean %>%
dplyr::select(-c(ID_SAMPLE)),
intermediate = NEOTOMA_intermediate %>%
dplyr::select(-c(ID_SAMPLE)),
amalgamated = NEOTOMA_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("ccore top", "core top") %>%
stringr::str_replace_all("unknown", "not known") %>%
stringr::str_to_lower(),
site_type = site_type %>%
stringr::str_replace_all("unknown", "not known")
) %>%
dplyr::relocate(ID_SAMPLE, .before = clean) %>%
dplyr::select(-basin_size_num)
usethis::use_data(NEOTOMA, overwrite = TRUE, compress = "xz")
# Inspect enumerates ----
### basin_size -----
NEOTOMA$basin_size %>%
unique() %>% sort()
### site_type ----
NEOTOMA$site_type %>%
unique() %>% sort()
### entity_type ----
NEOTOMA$entity_type %>%
unique() %>% sort()
# Export Excel workbook ----
wb <- openxlsx::createWorkbook()
openxlsx::addWorksheet(wb, "metadata")
openxlsx::writeData(wb, "metadata",
NEOTOMA %>%
dplyr::select(source:ID_SAMPLE))
openxlsx::addWorksheet(wb, "clean")
openxlsx::writeData(wb, "clean",
NEOTOMA %>%
dplyr::select(ID_SAMPLE, clean) %>%
tidyr::unnest(clean))
openxlsx::addWorksheet(wb, "intermediate")
openxlsx::writeData(wb, "intermediate",
NEOTOMA %>%
dplyr::select(ID_SAMPLE, intermediate) %>%
tidyr::unnest(intermediate))
openxlsx::addWorksheet(wb, "amalgamated")
openxlsx::writeData(wb, "amalgamated",
NEOTOMA %>%
dplyr::select(ID_SAMPLE, amalgamated) %>%
tidyr::unnest(amalgamated))
openxlsx::saveWorkbook(wb,
paste0("data-raw/GLOBAL/NEOTOMA_",
Sys.Date(),
".xlsx"))
# Load climate reconstructions ----
climate_reconstructions <-
"data-raw/reconstructions/neotoma_climate_reconstructions_2022-04-29.csv" %>%
readr::read_csv()
# Load daily values for precipitation to compute MAP (mean annual precipitation)
climate_reconstructions_pre <-
"data-raw/reconstructions/neotoma_climate_reconstructions_pre_2022-04-29.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 <-
NEOTOMA %>%
# smpds::NEOTOMA %>%
# dplyr::select(-c(mi:map)) %>%
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::NEOTOMA,
climate_reconstructions_with_counts %>%
dplyr::select(-c(mi:map, sn, en, new_elevation))
)
NEOTOMA <- climate_reconstructions_with_counts %>%
dplyr::select(-sn, -en, -new_elevation)
usethis::use_data(NEOTOMA, overwrite = TRUE, compress = "xz")
waldo::compare(smpds::NEOTOMA,
NEOTOMA,
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 `NEOTOMA` dataset goes here
# Original ----
neotoma_metadata <- readr::read_csv("inst/extdata/neotoma_metadata.csv")
neotoma_count <- readr::read_csv("inst/extdata/neotoma_count.csv")
neotoma_taxa <- readr::read_csv("inst/extdata/neotoma_taxa.csv")
NEOTOMA <- neotoma_metadata %>%
dplyr::left_join(neotoma_count, by = "entity_name") %>%
smpds::parallel_extract_biome(buffer = 12000, cpus = 2) %>%
dplyr::relocate(ID_BIOME, .after = age_BP) %>%
tidyr::pivot_longer(-c(1:13),
names_to = "taxon_name",
values_to = "count") %>%
dplyr::filter(!is.na(count)) %>%
dplyr::left_join(smpds::clean_taxa(), #neotoma_taxa,
by = "taxon_name") %>%
dplyr::filter(action != "delete") %>%
dplyr::select(-action) %>%
dplyr::rename(taxon_name_original = taxon_name,
taxon_name = clean_name) %>%
dplyr::group_by(entity_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(entity_name, taxon_name, .keep_all = TRUE) %>%
dplyr::select(-taxon_name_original) %>%
tidyr::pivot_wider(1:13,
names_from = "taxon_name",
values_from = "count") %>%
smpds::sort_taxa(cols = 1:13) %>%
progressr::with_progress()
aux <- NEOTOMA %>%
dplyr::filter(entity_name %in% EMPDv2$entity_name)
aux_rev <- EMPDv2 %>%
dplyr::filter(entity_name %in% aux$entity_name) %>%
smpds::rm_na_taxa(1:14)
usethis::use_data(NEOTOMA, overwrite = TRUE)
# Export list of taxon names for clean-up
tibble::tibble(taxon_name = colnames(NEOTOMA)[-c(1:13)],
clean_name = taxon_name) %>%
readr::write_excel_csv("~/Downloads/SMPDSv2/NEOTOMA_taxa_2021-08-24.csv", na = "")
# ------------------------------------------------------------------------------
# | Sandbox |
# ------------------------------------------------------------------------------
neotoma_metadata <-
readxl::read_xlsx("~/Downloads/SMPDSv2/NEOTOMA/Modern samples_neotoma update.xlsx",
sheet = 1) %>%
magrittr::set_names(c("source",
"site_name",
"entity_name",
"latitude",
"longitude",
"elevation",
"basin_size",
"site_type",
"entity_type",
"age_BP",
"publication")) %>%
dplyr::group_by(site_name) %>%
dplyr::mutate(publication = publication %>%
stringr::str_c(collapse = ";\n")) %>%
dplyr::ungroup() %>%
dplyr::distinct(site_name, .keep_all = TRUE) %>%
dplyr::mutate(elevation2 = list(latitude, longitude) %>%
purrr::pmap_dbl(function(latitude, longitude) {
rgbif::elevation(latitude = latitude,
longitude = longitude,
username = "villegar",
elevation_model = "srtm1") %>%
.$elevation_geonames
}))
neotoma_metadata %>%
dplyr::mutate(elevation = elevation2) %>%
dplyr::select(-elevation2) %>%
readr::write_excel_csv("inst/extdata/neotoma_metadata.csv", na = "")
neotoma_count <-
readxl::read_xlsx("~/Downloads/SMPDSv2/NEOTOMA/Modern samples_neotoma update.xlsx",
sheet = 2,
col_names = FALSE)
neotoma_count_unused <- neotoma_count[seq(3, nrow(neotoma_count), 3), ]
neotoma_count2 <- neotoma_count %>%
dplyr::slice(-seq(3, nrow(neotoma_count), 3))
neotoma_count_long <- seq(1, nrow(neotoma_count2), 2) %>%
purrr::map_dfr(function(i) {
names <- neotoma_count2[i, ] %>% purrr:::flatten_chr()
values <- neotoma_count2[i + 1, ] %>% purrr:::flatten_chr()
tibble::tibble(entity_name = values[1],
taxon_name = names[-1],
count = values[-1]) %>%
dplyr::filter(!is.na(count))
}) %>%
dplyr::group_by(entity_name, taxon_name) %>%
dplyr::mutate(count = as.double(count) %>%
sum(na.rm = FALSE)) %>%
dplyr::ungroup() %>%
dplyr::distinct(entity_name, taxon_name, .keep_all = TRUE)
neotoma_count_wide <- neotoma_count_long %>%
tidyr::pivot_wider(entity_name,
names_from = "taxon_name",
values_from = "count") %>%
smpds::sort_taxa() %>%
smpds::rm_na_taxa()
neotoma_count_wide %>%
readr::write_excel_csv("inst/extdata/neotoma_count.csv", na = "")
neotoma_taxa <- readr::read_csv("~/Downloads/SMPDSv2/NEOTOMA_taxa_2021-08-24_SPH.csv") %>%
dplyr::distinct() %>%
dplyr::mutate(action = ifelse(clean_name %>%
stringr::str_detect("EXCLUDE"),
"delete", "update"),
clean_name = ifelse(clean_name %>%
stringr::str_detect("EXCLUDE"),
NA, clean_name)) %>%
dplyr::arrange(dplyr::desc(action), taxon_name) %>%
dplyr::filter(!is.na(taxon_name))
neotoma_taxa %>%
readr::write_excel_csv("inst/extdata/neotoma_taxa.csv", na = "")
# SPH revisions ----
"The entities and their counts were manually inspected by SPH"
`%>%` <- magrittr::`%>%`
## Load data ----
### Metadata ----
NEOTOMA_metadata <-
"data-raw/GLOBAL/Neotoma_extras_SPH.xlsx" %>%
readxl::read_excel(sheet = 1) %>%
dplyr::rename(doi = DOI) %>%
dplyr::mutate(ID_SAMPLE = seq_along(entity_name), .after = doi)
### Pollen counts ----
NEOTOMA_counts <-
"data-raw/GLOBAL/Neotoma_extras_SPH.xlsx" %>%
readxl::read_excel(sheet = 2) %>%
dplyr::left_join(NEOTOMA_metadata %>%
dplyr::select(entity_name, ID_SAMPLE),
by = "entity_name") %>%
dplyr::relocate(ID_SAMPLE, .before = 1)
NEOTOMA_counts_2 <- NEOTOMA_counts %>%
dplyr::select(ID_SAMPLE) %>%
dplyr::bind_cols(
NEOTOMA_counts %>% # Convert columns with counts to numeric type
dplyr::select(-c(ID_SAMPLE:entity_name)) %>%
purrr::map_dfc(~.x %>% as.numeric)
) %>%
magrittr::set_names(
colnames(.) %>%
stringr::str_replace_all("\\.\\.\\.", "#")
) %>%
tidyr::pivot_longer(-ID_SAMPLE,
names_to = "taxon_name",
values_to = "taxon_count") %>%
dplyr::mutate(taxon_name = taxon_name %>%
stringr::str_remove_all("\\#[0-9]+$") %>%
stringr::str_squish()) %>%
dplyr::group_by(ID_SAMPLE, taxon_name) %>%
dplyr::mutate(taxon_count = sum(taxon_count, na.rm = TRUE)) %>%
dplyr::distinct() %>%
dplyr::ungroup()
### Amalgamations ----
NEOTOMA_taxa_amalgamation <-
"data-raw/GLOBAL/Neotoma_extras_SPH.xlsx" %>%
readxl::read_excel(sheet = 3) %>%
magrittr::set_names(c(
"taxon_name", "clean", "intermediate", "amalgamated"
)) %>%
dplyr::distinct() %>%
dplyr::mutate(taxon_name = taxon_name %>% stringr::str_squish(),
clean = clean %>% stringr::str_squish(),
intermediate = intermediate %>% stringr::str_squish(),
amalgamated = amalgamated %>% stringr::str_squish())
### Combine counts and amalgamation ----
NEOTOMA_taxa_counts_amalgamation <-
NEOTOMA_counts_2 %>%
dplyr::left_join(NEOTOMA_taxa_amalgamation,
by = c("taxon_name")) %>%
dplyr::relocate(taxon_count, .after = amalgamated) %>%
dplyr::relocate(ID_SAMPLE, .before = 1) %>%
dplyr::select(-taxon_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)
NEOTOMA_taxa_counts_amalgamation_rev <-
NEOTOMA_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)
NEOTOMA_taxa_counts_amalgamation_rev %>%
dplyr::group_by(ID_COUNT) %>%
dplyr::mutate(n = dplyr::n()) %>%
dplyr::filter(n > 1)
waldo::compare(NEOTOMA_taxa_counts_amalgamation %>%
dplyr::distinct(clean, intermediate, amalgamated),
NEOTOMA_taxa_counts_amalgamation_rev %>%
dplyr::distinct(clean, intermediate, amalgamated),
max_diffs = Inf)
NEOTOMA_taxa_counts_amalgamation <- NEOTOMA_taxa_counts_amalgamation_rev %>%
dplyr::filter(!is.na(taxon_count), taxon_count > 0) %>%
dplyr::select(-ID_COUNT)
NEOTOMA_taxa_counts_amalgamation %>%
dplyr::filter(is.na(clean) | is.na(intermediate) | is.na(amalgamated)) %>%
dplyr::distinct(clean, intermediate, amalgamated)
## Find DOIs ----
NEOTOMA_metadata_pubs <-
NEOTOMA_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("\\]$") %>%
stringr::str_remove_all("doi:") %>%
stringr::str_squish() %>%
stringr::str_c(collapse = ";\n"))
) %>%
dplyr::mutate(ID_PUB = seq_along(publication)) %>%
dplyr::mutate(updated_publication = NA, .before = publication) %>%
dplyr::mutate(updated_DOI = NA, .before = DOI)
# NEOTOMA_metadata_pubs %>%
# readr::write_excel_csv("data-raw/GLOBAL/NEOTOMA_modern-references.csv")
### Load cleaned publications list ----
NEOTOMA_clean_publications <-
"data-raw/GLOBAL/NEOTOMA_modern-references_clean.csv" %>%
readr::read_csv() %>%
dplyr::select(-DOI)
## Append clean publications ----
NEOTOMA_metadata_2 <-
NEOTOMA_metadata %>%
dplyr::left_join(NEOTOMA_metadata_pubs %>%
dplyr::select(-DOI, -doi, -dplyr::contains("updated")),
by = "publication") %>%
dplyr::left_join(NEOTOMA_clean_publications,
by = "ID_PUB") %>%
dplyr::select(-publication.x, -publication.y, -doi, -ID_PUB) %>%
dplyr::rename(doi = updated_DOI,
publication = updated_publication)
## Extract PNV/BIOME ----
NEOTOMA_metadata_3 <-
NEOTOMA_metadata_2 %>%
dplyr::select(-dplyr::starts_with("ID_BIOME")) %>%
smpds::parallel_extract_biome(cpus = 6) %>%
# smpds::biome_name() %>%
dplyr::relocate(ID_BIOME, .after = doi) %>%
smpds::pb()
NEOTOMA_metadata_3 %>%
smpds::plot_biome(xlim = range(.$longitude, na.rm = TRUE) * c(0.9, 1.1),
ylim = range(.$latitude, na.rm = TRUE) * c(0.9, 1.1))
## Create count tables ----
### Clean ----
NEOTOMA_clean <-
NEOTOMA_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 ----
NEOTOMA_intermediate <-
NEOTOMA_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 ----
NEOTOMA_amalgamated <-
NEOTOMA_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 ----
NEOTOMA <-
NEOTOMA_metadata_3 %>%
dplyr::mutate(
clean = NEOTOMA_clean %>%
dplyr::select(-c(ID_SAMPLE)),
intermediate = NEOTOMA_intermediate %>%
dplyr::select(-c(ID_SAMPLE)),
amalgamated = NEOTOMA_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("ccore top", "core top") %>%
stringr::str_replace_all("unknown", "not known") %>%
stringr::str_to_lower(),
site_type = site_type %>%
stringr::str_replace_all("unknown", "not known")
) %>%
dplyr::relocate(ID_SAMPLE, .before = clean) %>%
dplyr::select(-basin_size_num)
usethis::use_data(NEOTOMA, overwrite = TRUE, compress = "xz")
# Inspect enumerates ----
### basin_size -----
NEOTOMA$basin_size %>%
unique() %>% sort()
### site_type ----
NEOTOMA$site_type %>%
unique() %>% sort()
### entity_type ----
NEOTOMA$entity_type %>%
unique() %>% sort()
# Export Excel workbook ----
wb <- openxlsx::createWorkbook()
openxlsx::addWorksheet(wb, "metadata")
openxlsx::writeData(wb, "metadata",
NEOTOMA %>%
dplyr::select(source:ID_SAMPLE))
openxlsx::addWorksheet(wb, "clean")
openxlsx::writeData(wb, "clean",
NEOTOMA %>%
dplyr::select(ID_SAMPLE, clean) %>%
tidyr::unnest(clean))
openxlsx::addWorksheet(wb, "intermediate")
openxlsx::writeData(wb, "intermediate",
NEOTOMA %>%
dplyr::select(ID_SAMPLE, intermediate) %>%
tidyr::unnest(intermediate))
openxlsx::addWorksheet(wb, "amalgamated")
openxlsx::writeData(wb, "amalgamated",
NEOTOMA %>%
dplyr::select(ID_SAMPLE, amalgamated) %>%
tidyr::unnest(amalgamated))
openxlsx::saveWorkbook(wb,
paste0("data-raw/GLOBAL/NEOTOMA_",
Sys.Date(),
".xlsx"))
# Load climate reconstructions ----
climate_reconstructions <-
"data-raw/reconstructions/neotoma_climate_reconstructions_2022-04-29.csv" %>%
readr::read_csv()
# Load daily values for precipitation to compute MAP (mean annual precipitation)
climate_reconstructions_pre <-
"data-raw/reconstructions/neotoma_climate_reconstructions_pre_2022-04-29.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 <-
NEOTOMA %>%
# smpds::NEOTOMA %>%
# dplyr::select(-c(mi:map)) %>%
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::NEOTOMA,
climate_reconstructions_with_counts %>%
dplyr::select(-c(mi:map, sn, en, new_elevation))
)
NEOTOMA <- climate_reconstructions_with_counts %>%
dplyr::select(-sn, -en, -new_elevation)
usethis::use_data(NEOTOMA, overwrite = TRUE, compress = "xz")
waldo::compare(smpds::NEOTOMA,
NEOTOMA,
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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