data-raw/australia_pollen.R
In special-uor/smpds: The SPECIAL Modern Pollen Data Set for Climate Reconstructions
# code to create the australia_pollen dataset
`%>%` <- magrittr::`%>%`
# Load files ----
## File 1 ----
australia_pollen_1_s1 <-
readxl::read_excel("data-raw/GLOBAL/AUSTRALIA/Post 1900_Australia.xlsx",
sheet = 1) %>%
magrittr::set_names(c("ID_SAMPLE",
"clean", "intermediate", "amalgamated",
"taxon_count"))
australia_pollen_1_s2 <-
readxl::read_excel("data-raw/GLOBAL/AUSTRALIA/Post 1900_Australia.xlsx",
sheet = 2) %>%
magrittr::set_names(colnames(.) %>%
stringr::str_replace_all("basin size",
"basin_size")) %>%
dplyr::group_by(entity_name) %>%
dplyr::mutate(sample_name = stringr::str_c(
entity_name,
"_",
seq_along(entity_name)
)) %>%
dplyr::ungroup() %>%
dplyr::mutate(doi = doi %>%
stringr::str_remove_all("https://|http://|www\\.") %>%
stringr::str_remove_all("dx\\.|do\\i.org/"))
australia_pollen_1_s1
australia_pollen_1_s2
# Check the age_BP
australia_pollen_1_s2$age_BP %>% unique() %>% sort()
# Check DOIs
australia_pollen_1_s2$doi %>% unique()
### Missing counts ----
australia_pollen_1_missing_counts <-
readxl::read_excel("data-raw/GLOBAL/AUSTRALIA/Missing counts_Australia.xlsx",
sheet = 1) %>%
dplyr::select(1:5) %>%
magrittr::set_names(c(
"entity_name", "clean", "intermediate", "amalgamated", "counts"
)) %>%
dplyr::left_join(
tibble::tribble(
~entity_name, ~ID_SAMPLE,
"Alexander Morrison NP", 4,
"Cave Bay Cave_1", 133,
"Rotten Swamp_8", 7576,
"Mueller's Rock", 7433
),
by = "entity_name"
) %>%
dplyr::select(-entity_name)
## File 2 ----
australia_pollen_2_s1 <-
readxl::read_excel("data-raw/GLOBAL/AUSTRALIA/Post 1900_Australia2.xlsx",
sheet = 2) %>%
magrittr::set_names(c("ID_SAMPLE",
"clean", "intermediate", "amalgamated",
"taxon_count"))
australia_pollen_2_s2 <-
readxl::read_excel("data-raw/GLOBAL/AUSTRALIA/Post 1900_Australia2.xlsx",
sheet = 1) %>%
magrittr::set_names(colnames(.) %>%
stringr::str_replace_all("basin size",
"basin_size")) %>%
dplyr::group_by(entity_name) %>%
dplyr::mutate(sample_name = stringr::str_c(
entity_name,
"_",
seq_along(entity_name)
)) %>%
dplyr::ungroup() %>%
dplyr::mutate(doi = doi %>%
stringr::str_remove_all("https://|http://|www\\.") %>%
stringr::str_remove_all("dx\\.|do\\i.org/"))
australia_pollen_2_s1
australia_pollen_2_s2
# Check the age_BP
australia_pollen_2_s2$age_BP %>% unique() %>% sort()
# Check DOIs
australia_pollen_2_s2$doi %>% unique()
## File 3 ----
australia_pollen_3_s1 <-
readxl::read_excel("data-raw/GLOBAL/AUSTRALIA/Post 1900_Australia3.xlsx",
sheet = 2) %>%
magrittr::set_names(c("entity_name", "ID_SAMPLE",
"clean", "intermediate", "amalgamated",
"taxon_count")) %>%
dplyr::select(-ID_SAMPLE)
australia_pollen_3_s2 <-
readxl::read_excel("data-raw/GLOBAL/AUSTRALIA/Post 1900_Australia3.xlsx",
sheet = 1) %>%
magrittr::set_names(colnames(.) %>%
stringr::str_replace_all("basin size",
"basin_size")) %>%
dplyr::select(-`...13`) %>%
dplyr::rename(notes = `...14`) %>%
dplyr::mutate(notes = notes %>%
stringr::str_replace_all("samplles", "samples"),
age_BP = as.character(age_BP)) %>%
dplyr::group_by(entity_name) %>%
dplyr::mutate(publication = publication %>%
stringr::str_c(collapse = ";\n")) %>%
dplyr::distinct() %>%
dplyr::mutate(sample_name = stringr::str_c(
entity_name,
"_",
seq_along(entity_name)
)) %>%
dplyr::ungroup() %>%
dplyr::mutate(doi = doi %>%
stringr::str_remove_all("https://|http://|www\\.") %>%
stringr::str_remove_all("dx\\.|do\\i.org/"))
australia_pollen_3_s1
australia_pollen_3_s2
# Check the age_BP
australia_pollen_2_s2$age_BP %>% unique() %>% sort()
# Check DOIs
australia_pollen_3_s2$doi %>% unique()
# Combine metadata with counts ----
## File 1 ----
australia_pollen_1_all <- australia_pollen_1_s2 %>%
dplyr::full_join(
australia_pollen_1_s1 %>%
dplyr::bind_rows(australia_pollen_1_missing_counts), # Add missing counts
by = "ID_SAMPLE")
australia_pollen_1_all %>%
dplyr::filter(is.na(clean) | is.na(intermediate) | is.na(amalgamated))
australia_pollen_1_all %>%
dplyr::filter(is.na(entity_name))
australia_pollen_1_all %>%
dplyr::filter(ID_SAMPLE %in% c(133, 7433, 7576))
## File 2 ----
australia_pollen_2_all <- australia_pollen_2_s2 %>%
dplyr::mutate(sample_name = ifelse(stringr::str_detect(site_name,
"Fitzerald"),
sample_name %>%
stringr::str_replace_all("_1", "_2"),
sample_name)) %>%
dplyr::full_join(australia_pollen_2_s1,
by = "ID_SAMPLE")
australia_pollen_2_all %>%
dplyr::filter(is.na(clean) | is.na(intermediate) | is.na(amalgamated))
australia_pollen_2_all %>%
dplyr::filter(is.na(entity_name))
## File 3 ----
australia_pollen_3_all <- australia_pollen_3_s2 %>%
dplyr::full_join(australia_pollen_3_s1,
by = "entity_name")
australia_pollen_3_all %>%
dplyr::filter(is.na(clean) | is.na(intermediate) | is.na(amalgamated))
australia_pollen_3_all %>%
dplyr::filter(is.na(entity_name))
dplyr::bind_rows(
australia_pollen_1_s2,
australia_pollen_2_s2,
australia_pollen_3_s2
) %>%
dplyr::arrange(site_name, entity_name) %>%
dplyr::group_by(entity_name) %>%
dplyr::mutate(n = dplyr::n()) %>%
dplyr::filter(n > 1) %>%
View()
# Note: there are entities for the site 'Fitzerald River National Park FRNP'
# in two separate files, all have age_BP = 'modern'. To distinguish across them,
# the sample_name values in australia_pollen_2_s2 were updated (see above).
# Combine all the files ----
australia_pollen <- australia_pollen_1_all %>%
dplyr::filter(!is.na(clean), !is.na(entity_name)) %>%
dplyr::bind_rows(australia_pollen_2_all, australia_pollen_3_all) %>%
dplyr::mutate(site_name = site_name %>%
stringr::str_squish(),
entity_name = entity_name %>%
stringr::str_squish()) %>%
dplyr::arrange(site_name, entity_name) %>%
dplyr::select(-ID_SAMPLE)
# australia_pollen %>%
# dplyr::distinct(sample_name, .keep_all = TRUE) %>%
# smpds::plot_climate(var = "elevation")
### 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)
australia_pollen_rev <-
australia_pollen %>%
dplyr::mutate(ID_COUNT = seq_along(sample_name)) %>%
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)
australia_pollen_rev %>%
dplyr::group_by(ID_COUNT) %>%
dplyr::mutate(n = dplyr::n()) %>%
dplyr::filter(n > 1)
waldo::compare(australia_pollen %>%
dplyr::distinct(clean, intermediate, amalgamated),
australia_pollen_rev %>%
dplyr::distinct(clean, intermediate, amalgamated),
max_diffs = Inf)
australia_pollen <- australia_pollen_rev %>%
dplyr::filter(!is.na(taxon_count), taxon_count > 0) %>%
dplyr::select(-ID_COUNT)
# Extract PNV/BIOME ----
australia_pollen_biomes <- australia_pollen %>%
dplyr::distinct(sample_name, latitude, longitude) %>%
smpds::parallel_extract_biome(cpus = 12) %>%
# smpds::biome_name() %>%
smpds::pb()
australia_pollen_biomes %>%
smpds::plot_biome()
australia_pollen_with_pnv <- australia_pollen %>%
dplyr::left_join(australia_pollen_biomes %>%
dplyr::select(sample_name, ID_BIOME),
by = c("sample_name")) %>%
dplyr::relocate(notes, ID_BIOME, .after = doi) #%>%
# dplyr::group_by(sample_name) %>%
# dplyr::mutate(ID_SAMPLE = seq_along(sample_name)) %>%
# dplyr::relocate(ID_SAMPLE, .before = sample_name) %>%
# dplyr::ungroup()
# Create count tables ----
## Clean ----
australia_pollen_clean <- australia_pollen_with_pnv %>%
dplyr::select(-intermediate, -amalgamated) %>%
dplyr::rename(taxon_name = clean) %>%
dplyr::group_by(sample_name, taxon_name) %>%
dplyr::mutate(taxon_count = sum(taxon_count, na.rm = TRUE)) %>%
dplyr::ungroup() %>%
dplyr::distinct() %>%
tidyr::pivot_wider(site_name:sample_name,
names_from = taxon_name,
values_from = taxon_count,
values_fill = 0,
names_sort = TRUE)
## Intermediate ----
australia_pollen_intermediate <- australia_pollen_with_pnv %>%
dplyr::select(-clean, -amalgamated) %>%
dplyr::rename(taxon_name = intermediate) %>%
dplyr::group_by(sample_name, taxon_name) %>%
dplyr::mutate(taxon_count = sum(taxon_count, na.rm = TRUE)) %>%
dplyr::ungroup() %>%
dplyr::distinct() %>%
tidyr::pivot_wider(site_name:sample_name,
names_from = taxon_name,
values_from = taxon_count,
values_fill = 0,
names_sort = TRUE)
## Amalgamated ----
australia_pollen_amalgamated <- australia_pollen_with_pnv %>%
dplyr::select(-clean, -intermediate) %>%
dplyr::rename(taxon_name = amalgamated) %>%
dplyr::group_by(sample_name, taxon_name) %>%
dplyr::mutate(taxon_count = sum(taxon_count, na.rm = TRUE)) %>%
dplyr::ungroup() %>%
dplyr::distinct() %>%
tidyr::pivot_wider(site_name:sample_name,
names_from = taxon_name,
values_from = taxon_count,
values_fill = 0,
names_sort = TRUE)
# Find missing elevations ----
australia_pollen_2022_04_11 <-
"~/Downloads/ready to upload/australia_pollen_2022-04-11.xlsx" %>%
readxl::read_excel()
australia_pollen_2022_04_11_missing_elevations <-
australia_pollen_2022_04_11 %>%
dplyr::filter(is.na(elevation)) %>%
smpds:::get_elevation(cpus = 2)
australia_pollen_2022_04_11 %>%
dplyr::left_join(australia_pollen_2022_04_11_missing_elevations %>%
dplyr::select(ID_SAMPLE,
new_elevation = elevation),
by = "ID_SAMPLE") %>%
# dplyr::filter(elevation != new_elevation | is.na(elevation)) %>%
# dplyr::select(site_name:elevation, new_elevation)
dplyr::mutate(elevation = dplyr::coalesce(elevation, new_elevation)) %>%
dplyr::select(-new_elevation) %>%
readr::write_csv(
"~/Downloads/ready to upload/australia_pollen_2022-04-11_with_elevations.csv",
na = ""
)
australia_pollen_base <- australia_pollen_clean %>%
dplyr::select(site_name:sample_name) %>%
dplyr::mutate(ID_SAMPLE = seq_along(sample_name), .before = sample_name)
# Store subsets ----
australia_pollen <-
australia_pollen_base %>%
# australia_pollen_clean %>%
# dplyr::select(site_name:sample_name) %>%
dplyr::mutate(
clean = australia_pollen_clean %>%
dplyr::select(-c(site_name:sample_name)),
intermediate = australia_pollen_intermediate %>%
dplyr::select(-c(site_name:sample_name)),
amalgamated = australia_pollen_amalgamated %>%
dplyr::select(-c(site_name:sample_name))
) %>%
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("Cave", "cave") %>%
stringr::str_replace_all("drained/dry lake|Drained/dry lake",
"lacustrine, drained lake") %>%
stringr::str_replace_all("estuarine|Estuarine",
"coastal, estuarine") %>%
stringr::str_replace_all("Cave", "cave") %>%
stringr::str_replace_all("terrestrial, other sediments",
"terrestrial") %>%
stringr::str_replace_all("terrestrial, soil", "soil") %>%
stringr::str_replace_all("unknown", "not known")
) %>%
dplyr::mutate(source = "Australian pollen", .before = 1) %>%
dplyr::select(-dplyr::contains("notes")) %>%
dplyr::select(-basin_size_num)
usethis::use_data(australia_pollen, overwrite = TRUE, compress = "xz")
# Check enumerates ----
## basin_size -----
australia_pollen$basin_size %>%
unique() %>%
sort()
## site_type -----
australia_pollen$site_type %>%
unique() %>%
sort()
## entity_type -----
australia_pollen$entity_type %>%
unique() %>%
sort()
# Export Excel workbook ----
wb <- openxlsx::createWorkbook()
openxlsx::addWorksheet(wb, "metadata")
openxlsx::writeData(wb, "metadata",
australia_pollen %>%
dplyr::select(site_name:sample_name))
openxlsx::addWorksheet(wb, "clean")
openxlsx::writeData(wb, "clean",
australia_pollen %>%
dplyr::select(ID_SAMPLE, clean) %>%
tidyr::unnest(clean))
openxlsx::addWorksheet(wb, "intermediate")
openxlsx::writeData(wb, "intermediate",
australia_pollen %>%
dplyr::select(ID_SAMPLE, intermediate) %>%
tidyr::unnest(intermediate))
openxlsx::addWorksheet(wb, "amalgamated")
openxlsx::writeData(wb, "amalgamated",
australia_pollen %>%
dplyr::select(ID_SAMPLE, amalgamated) %>%
tidyr::unnest(amalgamated))
openxlsx::saveWorkbook(wb,
paste0("data-raw/GLOBAL/AUSTRALIA/australia_pollen_",
Sys.Date(),
".xlsx"))
# Load climate reconstructions ----
climate_reconstructions <-
"data-raw/reconstructions/australia_pollen_climate_reconstructions_2022-04-30.csv" %>%
readr::read_csv()
# Load daily values for precipitation to compute MAP (mean annual precipitation)
climate_reconstructions_pre <-
"data-raw/reconstructions/australia_pollen_climate_reconstructions_pre_2022-04-30.csv" %>%
readr::read_csv() %>%
dplyr::rowwise() %>%
dplyr::mutate(map = sum(dplyr::c_across(T1:T365), na.rm = TRUE), .before = T1)
climate_reconstructions_2 <- climate_reconstructions %>%
dplyr::bind_cols(climate_reconstructions_pre %>%
dplyr::select(map)) %>%
dplyr::filter(entity_name %in% australia_pollen$entity_name)
climate_reconstructions_with_counts <-
australia_pollen %>%
# smpds::australia_pollen %>%
# 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::australia_pollen,
climate_reconstructions_with_counts %>%
dplyr::select(-c(mi:map, sn, en, new_elevation))
)
australia_pollen <- climate_reconstructions_with_counts %>%
dplyr::select(-sn, -en, -new_elevation) %>%
dplyr::select(-dplyr::starts_with("notes"))
usethis::use_data(australia_pollen, overwrite = TRUE, compress = "xz")
waldo::compare(smpds::australia_pollen,
australia_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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# code to create the australia_pollen dataset
`%>%` <- magrittr::`%>%`
# Load files ----
## File 1 ----
australia_pollen_1_s1 <-
readxl::read_excel("data-raw/GLOBAL/AUSTRALIA/Post 1900_Australia.xlsx",
sheet = 1) %>%
magrittr::set_names(c("ID_SAMPLE",
"clean", "intermediate", "amalgamated",
"taxon_count"))
australia_pollen_1_s2 <-
readxl::read_excel("data-raw/GLOBAL/AUSTRALIA/Post 1900_Australia.xlsx",
sheet = 2) %>%
magrittr::set_names(colnames(.) %>%
stringr::str_replace_all("basin size",
"basin_size")) %>%
dplyr::group_by(entity_name) %>%
dplyr::mutate(sample_name = stringr::str_c(
entity_name,
"_",
seq_along(entity_name)
)) %>%
dplyr::ungroup() %>%
dplyr::mutate(doi = doi %>%
stringr::str_remove_all("https://|http://|www\\.") %>%
stringr::str_remove_all("dx\\.|do\\i.org/"))
australia_pollen_1_s1
australia_pollen_1_s2
# Check the age_BP
australia_pollen_1_s2$age_BP %>% unique() %>% sort()
# Check DOIs
australia_pollen_1_s2$doi %>% unique()
### Missing counts ----
australia_pollen_1_missing_counts <-
readxl::read_excel("data-raw/GLOBAL/AUSTRALIA/Missing counts_Australia.xlsx",
sheet = 1) %>%
dplyr::select(1:5) %>%
magrittr::set_names(c(
"entity_name", "clean", "intermediate", "amalgamated", "counts"
)) %>%
dplyr::left_join(
tibble::tribble(
~entity_name, ~ID_SAMPLE,
"Alexander Morrison NP", 4,
"Cave Bay Cave_1", 133,
"Rotten Swamp_8", 7576,
"Mueller's Rock", 7433
),
by = "entity_name"
) %>%
dplyr::select(-entity_name)
## File 2 ----
australia_pollen_2_s1 <-
readxl::read_excel("data-raw/GLOBAL/AUSTRALIA/Post 1900_Australia2.xlsx",
sheet = 2) %>%
magrittr::set_names(c("ID_SAMPLE",
"clean", "intermediate", "amalgamated",
"taxon_count"))
australia_pollen_2_s2 <-
readxl::read_excel("data-raw/GLOBAL/AUSTRALIA/Post 1900_Australia2.xlsx",
sheet = 1) %>%
magrittr::set_names(colnames(.) %>%
stringr::str_replace_all("basin size",
"basin_size")) %>%
dplyr::group_by(entity_name) %>%
dplyr::mutate(sample_name = stringr::str_c(
entity_name,
"_",
seq_along(entity_name)
)) %>%
dplyr::ungroup() %>%
dplyr::mutate(doi = doi %>%
stringr::str_remove_all("https://|http://|www\\.") %>%
stringr::str_remove_all("dx\\.|do\\i.org/"))
australia_pollen_2_s1
australia_pollen_2_s2
# Check the age_BP
australia_pollen_2_s2$age_BP %>% unique() %>% sort()
# Check DOIs
australia_pollen_2_s2$doi %>% unique()
## File 3 ----
australia_pollen_3_s1 <-
readxl::read_excel("data-raw/GLOBAL/AUSTRALIA/Post 1900_Australia3.xlsx",
sheet = 2) %>%
magrittr::set_names(c("entity_name", "ID_SAMPLE",
"clean", "intermediate", "amalgamated",
"taxon_count")) %>%
dplyr::select(-ID_SAMPLE)
australia_pollen_3_s2 <-
readxl::read_excel("data-raw/GLOBAL/AUSTRALIA/Post 1900_Australia3.xlsx",
sheet = 1) %>%
magrittr::set_names(colnames(.) %>%
stringr::str_replace_all("basin size",
"basin_size")) %>%
dplyr::select(-`...13`) %>%
dplyr::rename(notes = `...14`) %>%
dplyr::mutate(notes = notes %>%
stringr::str_replace_all("samplles", "samples"),
age_BP = as.character(age_BP)) %>%
dplyr::group_by(entity_name) %>%
dplyr::mutate(publication = publication %>%
stringr::str_c(collapse = ";\n")) %>%
dplyr::distinct() %>%
dplyr::mutate(sample_name = stringr::str_c(
entity_name,
"_",
seq_along(entity_name)
)) %>%
dplyr::ungroup() %>%
dplyr::mutate(doi = doi %>%
stringr::str_remove_all("https://|http://|www\\.") %>%
stringr::str_remove_all("dx\\.|do\\i.org/"))
australia_pollen_3_s1
australia_pollen_3_s2
# Check the age_BP
australia_pollen_2_s2$age_BP %>% unique() %>% sort()
# Check DOIs
australia_pollen_3_s2$doi %>% unique()
# Combine metadata with counts ----
## File 1 ----
australia_pollen_1_all <- australia_pollen_1_s2 %>%
dplyr::full_join(
australia_pollen_1_s1 %>%
dplyr::bind_rows(australia_pollen_1_missing_counts), # Add missing counts
by = "ID_SAMPLE")
australia_pollen_1_all %>%
dplyr::filter(is.na(clean) | is.na(intermediate) | is.na(amalgamated))
australia_pollen_1_all %>%
dplyr::filter(is.na(entity_name))
australia_pollen_1_all %>%
dplyr::filter(ID_SAMPLE %in% c(133, 7433, 7576))
## File 2 ----
australia_pollen_2_all <- australia_pollen_2_s2 %>%
dplyr::mutate(sample_name = ifelse(stringr::str_detect(site_name,
"Fitzerald"),
sample_name %>%
stringr::str_replace_all("_1", "_2"),
sample_name)) %>%
dplyr::full_join(australia_pollen_2_s1,
by = "ID_SAMPLE")
australia_pollen_2_all %>%
dplyr::filter(is.na(clean) | is.na(intermediate) | is.na(amalgamated))
australia_pollen_2_all %>%
dplyr::filter(is.na(entity_name))
## File 3 ----
australia_pollen_3_all <- australia_pollen_3_s2 %>%
dplyr::full_join(australia_pollen_3_s1,
by = "entity_name")
australia_pollen_3_all %>%
dplyr::filter(is.na(clean) | is.na(intermediate) | is.na(amalgamated))
australia_pollen_3_all %>%
dplyr::filter(is.na(entity_name))
dplyr::bind_rows(
australia_pollen_1_s2,
australia_pollen_2_s2,
australia_pollen_3_s2
) %>%
dplyr::arrange(site_name, entity_name) %>%
dplyr::group_by(entity_name) %>%
dplyr::mutate(n = dplyr::n()) %>%
dplyr::filter(n > 1) %>%
View()
# Note: there are entities for the site 'Fitzerald River National Park FRNP'
# in two separate files, all have age_BP = 'modern'. To distinguish across them,
# the sample_name values in australia_pollen_2_s2 were updated (see above).
# Combine all the files ----
australia_pollen <- australia_pollen_1_all %>%
dplyr::filter(!is.na(clean), !is.na(entity_name)) %>%
dplyr::bind_rows(australia_pollen_2_all, australia_pollen_3_all) %>%
dplyr::mutate(site_name = site_name %>%
stringr::str_squish(),
entity_name = entity_name %>%
stringr::str_squish()) %>%
dplyr::arrange(site_name, entity_name) %>%
dplyr::select(-ID_SAMPLE)
# australia_pollen %>%
# dplyr::distinct(sample_name, .keep_all = TRUE) %>%
# smpds::plot_climate(var = "elevation")
### 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)
australia_pollen_rev <-
australia_pollen %>%
dplyr::mutate(ID_COUNT = seq_along(sample_name)) %>%
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)
australia_pollen_rev %>%
dplyr::group_by(ID_COUNT) %>%
dplyr::mutate(n = dplyr::n()) %>%
dplyr::filter(n > 1)
waldo::compare(australia_pollen %>%
dplyr::distinct(clean, intermediate, amalgamated),
australia_pollen_rev %>%
dplyr::distinct(clean, intermediate, amalgamated),
max_diffs = Inf)
australia_pollen <- australia_pollen_rev %>%
dplyr::filter(!is.na(taxon_count), taxon_count > 0) %>%
dplyr::select(-ID_COUNT)
# Extract PNV/BIOME ----
australia_pollen_biomes <- australia_pollen %>%
dplyr::distinct(sample_name, latitude, longitude) %>%
smpds::parallel_extract_biome(cpus = 12) %>%
# smpds::biome_name() %>%
smpds::pb()
australia_pollen_biomes %>%
smpds::plot_biome()
australia_pollen_with_pnv <- australia_pollen %>%
dplyr::left_join(australia_pollen_biomes %>%
dplyr::select(sample_name, ID_BIOME),
by = c("sample_name")) %>%
dplyr::relocate(notes, ID_BIOME, .after = doi) #%>%
# dplyr::group_by(sample_name) %>%
# dplyr::mutate(ID_SAMPLE = seq_along(sample_name)) %>%
# dplyr::relocate(ID_SAMPLE, .before = sample_name) %>%
# dplyr::ungroup()
# Create count tables ----
## Clean ----
australia_pollen_clean <- australia_pollen_with_pnv %>%
dplyr::select(-intermediate, -amalgamated) %>%
dplyr::rename(taxon_name = clean) %>%
dplyr::group_by(sample_name, taxon_name) %>%
dplyr::mutate(taxon_count = sum(taxon_count, na.rm = TRUE)) %>%
dplyr::ungroup() %>%
dplyr::distinct() %>%
tidyr::pivot_wider(site_name:sample_name,
names_from = taxon_name,
values_from = taxon_count,
values_fill = 0,
names_sort = TRUE)
## Intermediate ----
australia_pollen_intermediate <- australia_pollen_with_pnv %>%
dplyr::select(-clean, -amalgamated) %>%
dplyr::rename(taxon_name = intermediate) %>%
dplyr::group_by(sample_name, taxon_name) %>%
dplyr::mutate(taxon_count = sum(taxon_count, na.rm = TRUE)) %>%
dplyr::ungroup() %>%
dplyr::distinct() %>%
tidyr::pivot_wider(site_name:sample_name,
names_from = taxon_name,
values_from = taxon_count,
values_fill = 0,
names_sort = TRUE)
## Amalgamated ----
australia_pollen_amalgamated <- australia_pollen_with_pnv %>%
dplyr::select(-clean, -intermediate) %>%
dplyr::rename(taxon_name = amalgamated) %>%
dplyr::group_by(sample_name, taxon_name) %>%
dplyr::mutate(taxon_count = sum(taxon_count, na.rm = TRUE)) %>%
dplyr::ungroup() %>%
dplyr::distinct() %>%
tidyr::pivot_wider(site_name:sample_name,
names_from = taxon_name,
values_from = taxon_count,
values_fill = 0,
names_sort = TRUE)
# Find missing elevations ----
australia_pollen_2022_04_11 <-
"~/Downloads/ready to upload/australia_pollen_2022-04-11.xlsx" %>%
readxl::read_excel()
australia_pollen_2022_04_11_missing_elevations <-
australia_pollen_2022_04_11 %>%
dplyr::filter(is.na(elevation)) %>%
smpds:::get_elevation(cpus = 2)
australia_pollen_2022_04_11 %>%
dplyr::left_join(australia_pollen_2022_04_11_missing_elevations %>%
dplyr::select(ID_SAMPLE,
new_elevation = elevation),
by = "ID_SAMPLE") %>%
# dplyr::filter(elevation != new_elevation | is.na(elevation)) %>%
# dplyr::select(site_name:elevation, new_elevation)
dplyr::mutate(elevation = dplyr::coalesce(elevation, new_elevation)) %>%
dplyr::select(-new_elevation) %>%
readr::write_csv(
"~/Downloads/ready to upload/australia_pollen_2022-04-11_with_elevations.csv",
na = ""
)
australia_pollen_base <- australia_pollen_clean %>%
dplyr::select(site_name:sample_name) %>%
dplyr::mutate(ID_SAMPLE = seq_along(sample_name), .before = sample_name)
# Store subsets ----
australia_pollen <-
australia_pollen_base %>%
# australia_pollen_clean %>%
# dplyr::select(site_name:sample_name) %>%
dplyr::mutate(
clean = australia_pollen_clean %>%
dplyr::select(-c(site_name:sample_name)),
intermediate = australia_pollen_intermediate %>%
dplyr::select(-c(site_name:sample_name)),
amalgamated = australia_pollen_amalgamated %>%
dplyr::select(-c(site_name:sample_name))
) %>%
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("Cave", "cave") %>%
stringr::str_replace_all("drained/dry lake|Drained/dry lake",
"lacustrine, drained lake") %>%
stringr::str_replace_all("estuarine|Estuarine",
"coastal, estuarine") %>%
stringr::str_replace_all("Cave", "cave") %>%
stringr::str_replace_all("terrestrial, other sediments",
"terrestrial") %>%
stringr::str_replace_all("terrestrial, soil", "soil") %>%
stringr::str_replace_all("unknown", "not known")
) %>%
dplyr::mutate(source = "Australian pollen", .before = 1) %>%
dplyr::select(-dplyr::contains("notes")) %>%
dplyr::select(-basin_size_num)
usethis::use_data(australia_pollen, overwrite = TRUE, compress = "xz")
# Check enumerates ----
## basin_size -----
australia_pollen$basin_size %>%
unique() %>%
sort()
## site_type -----
australia_pollen$site_type %>%
unique() %>%
sort()
## entity_type -----
australia_pollen$entity_type %>%
unique() %>%
sort()
# Export Excel workbook ----
wb <- openxlsx::createWorkbook()
openxlsx::addWorksheet(wb, "metadata")
openxlsx::writeData(wb, "metadata",
australia_pollen %>%
dplyr::select(site_name:sample_name))
openxlsx::addWorksheet(wb, "clean")
openxlsx::writeData(wb, "clean",
australia_pollen %>%
dplyr::select(ID_SAMPLE, clean) %>%
tidyr::unnest(clean))
openxlsx::addWorksheet(wb, "intermediate")
openxlsx::writeData(wb, "intermediate",
australia_pollen %>%
dplyr::select(ID_SAMPLE, intermediate) %>%
tidyr::unnest(intermediate))
openxlsx::addWorksheet(wb, "amalgamated")
openxlsx::writeData(wb, "amalgamated",
australia_pollen %>%
dplyr::select(ID_SAMPLE, amalgamated) %>%
tidyr::unnest(amalgamated))
openxlsx::saveWorkbook(wb,
paste0("data-raw/GLOBAL/AUSTRALIA/australia_pollen_",
Sys.Date(),
".xlsx"))
# Load climate reconstructions ----
climate_reconstructions <-
"data-raw/reconstructions/australia_pollen_climate_reconstructions_2022-04-30.csv" %>%
readr::read_csv()
# Load daily values for precipitation to compute MAP (mean annual precipitation)
climate_reconstructions_pre <-
"data-raw/reconstructions/australia_pollen_climate_reconstructions_pre_2022-04-30.csv" %>%
readr::read_csv() %>%
dplyr::rowwise() %>%
dplyr::mutate(map = sum(dplyr::c_across(T1:T365), na.rm = TRUE), .before = T1)
climate_reconstructions_2 <- climate_reconstructions %>%
dplyr::bind_cols(climate_reconstructions_pre %>%
dplyr::select(map)) %>%
dplyr::filter(entity_name %in% australia_pollen$entity_name)
climate_reconstructions_with_counts <-
australia_pollen %>%
# smpds::australia_pollen %>%
# 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::australia_pollen,
climate_reconstructions_with_counts %>%
dplyr::select(-c(mi:map, sn, en, new_elevation))
)
australia_pollen <- climate_reconstructions_with_counts %>%
dplyr::select(-sn, -en, -new_elevation) %>%
dplyr::select(-dplyr::starts_with("notes"))
usethis::use_data(australia_pollen, overwrite = TRUE, compress = "xz")
waldo::compare(smpds::australia_pollen,
australia_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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