# code to create the `latin_america_pollen` dataset
# Load raw data ----
latin_america_pollen <-
"data-raw/GLOBAL/Latin America/Latin American modern 2008_SPH_cleaned.xls" %>%
readxl::read_excel(sheet = 2) %>%
dplyr::mutate(A135 = A135 %>%
stringr::str_replace_all("6.888.765", "6.888"))
## Metadata ----
latin_america_pollen_metadata <- latin_america_pollen %>%
dplyr::slice(1:3) %>%
dplyr::select(-c(2:4)) %>%
dplyr::rowwise() %>%
purrr::map_dfr(~suppressWarnings({
.x %>%
as.numeric() %>%
t() %>%
tibble::as_tibble()
})) %>%
dplyr::slice(-1) %>%
magrittr::set_names(c("longitude", "latitude", "elevation")) %>%
dplyr::filter(!is.na(longitude), !is.na(latitude)) %>%
dplyr::mutate(site_id =
stringr::str_c("A",
ifelse(seq_along(longitude) < 10, "0", ""),
seq_along(longitude)),
.before = 1) %>%
dplyr::mutate(longitude = ifelse(abs(longitude) > 180, NA, longitude),
publication = "Marchant, R., Cleef, A., Harrison, S.P., Hooghiemstra, H., Markgraf, V., Van Boxel, J., Ager, T., Almeida, L., Anderson, R., Baied, C. and Behling, H., 2009. Pollen-based biome reconstructions for Latin America at 0, 6000 and 18 000 radiocarbon years ago. Climate of the Past, 5(4), pp.725-767.",
doi = "10.5194/cp-5-725-2009")
### Plot ----
latin_america_pollen_metadata %>%
smpds::plot_climate("elevation",
xlim = range(latin_america_pollen_metadata$longitude,
na.rm = TRUE),
contour = TRUE)
## Counts ----
latin_america_pollen_counts <- latin_america_pollen %>%
dplyr::slice(-c(1:4)) %>%
magrittr::set_names(c(
"clean", "intermediate", "amalgamated", "smpdsv1",
colnames(.)[-c(1:4)]
)) %>%
dplyr::mutate(A135 = as.numeric(A135)) %>%
dplyr::select(clean:A219) %>%
tidyr::pivot_longer(cols = -c(clean:smpdsv1),
names_to = "site_id",
values_to = "taxon_count")
latin_america_pollen_counts_clean <-
latin_america_pollen_counts %>%
dplyr::select(-c(intermediate, amalgamated, smpdsv1)) %>%
dplyr::rename(taxon_name = clean) %>%
dplyr::group_by(site_id, taxon_name) %>%
dplyr::mutate(taxon_count = sum(taxon_count, na.rm = TRUE)) %>%
dplyr::distinct() %>%
dplyr::ungroup() %>%
tidyr::pivot_wider(site_id,
names_from = "taxon_name",
values_from = "taxon_count", names_sort = TRUE)
sa_la_matches <- smpds::compare_latlon(south_america_pollen,
latin_america_pollen_metadata)
sa_la_matches_comparison <- sa_la_matches %>%
purrr::pmap_df(function(entity_name,
site_id,
latitude.x,
longitude.x,
elevation.x,
latitude.y,
longitude.y,
elevation.y,
...) {
# browser()
sa_counts <- south_america_pollen %>%
dplyr::filter(entity_name == !!entity_name) %>%
dplyr::select(clean) %>%
tidyr::unnest(clean) %>%
dplyr::mutate(ID_SAMPLE_SA = seq_len(nrow(.)), .before = 1) %>%
tidyr::pivot_longer(-1) %>%
dplyr::filter(!is.na(value), value != 0) %>%
dplyr::rename(count_SA = value)
la_counts <- latin_america_pollen_counts_clean %>%
dplyr::filter(site_id == !!site_id) %>%
dplyr::select(-site_id) %>%
dplyr::mutate(ID_SAMPLE_LA = seq_len(nrow(.)), .before = 1) %>%
tidyr::pivot_longer(-1) %>%
dplyr::filter(!is.na(value), value != 0) %>%
dplyr::rename(count_LA = value)
aux <- sa_counts %>%
dplyr::full_join(la_counts,
by = "name") %>%
dplyr::rename(taxon_name = name)
tibble::tibble(
sa_name = entity_name,
la_name = site_id,
sa_lat = latitude.x,
sa_lon = longitude.x,
sa_elv = elevation.x,
la_lat = latitude.y,
la_lon = longitude.y,
la_elv = elevation.y,
same_num_counts = nrow(sa_counts) == nrow(la_counts),
counts = aux
)
})
sa_la_matches_comparison %>%
tidyr::unnest(counts) %>%
magrittr::set_names(colnames(.) %>%
stringr::str_replace_all("la_", "LA_") %>%
stringr::str_replace_all("sa_", "SA_")) # %>%
# readr::write_excel_csv("~/Downloads/sa_la_pollen_comparison.csv", na = "0")
south_america_pollen %>%
dplyr::filter(entity_name == "AJATA") %>%
dplyr::select(clean) %>%
tidyr::unnest(clean) %>%
dplyr::mutate(ID_SAMPLE_SA = seq_len(nrow(.)), .before = 1) %>%
tidyr::pivot_longer(-1) %>%
dplyr::filter(!is.na(value), value != 0)
latin_america_pollen_counts_clean %>%
dplyr::filter(site_id == "A120") %>%
dplyr::select(-site_id) %>%
dplyr::mutate(ID_SAMPLE_LA = seq_len(nrow(.)), .before = 1) %>%
tidyr::pivot_longer(-1) %>%
dplyr::filter(!is.na(value), value != 0) %>%
dplyr::rename(count_LA = value)
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