## code to prepare `DATASET` dataset goes here
baude_supp <- readr::read_csv("./inst/extdata/baude_ST11.csv") %>%
dplyr::mutate(bloom.start = lubridate::yday(lubridate::mdy(bloom.start)),
bloom.end = lubridate::yday(lubridate::mdy(bloom.end)),
bloom.peak = lubridate::yday(lubridate::mdy(bloom.peak))) %>%
dplyr::select(-flower.density.mean) %>%
dplyr::filter(!is.na(flower.density.peak)) %>%
dplyr::filter(!is.na(sugar.per.flower)) %>%
# Add a class with zero floral resources; this can represent impervious surface, water, turfgrass, etc.
tibble::add_row(name.latin = "null", name.common = "null", family = "null",
breeding.system = "null", life.span = "null", dicliny = "null",
height = "null", bloom.start = 1, bloom.end = 365, bloom.peak = 180,
bloom.days = 365, flower.density.peak = 0, sugar.per.flower = 0)
ktype <- readr::read_csv("./inst/extdata/baude_ktypes.csv")
baude_traits <- dplyr::left_join(baude_supp, ktype) %>%
dplyr::mutate(k.type.s = stringr::str_extract(ktype, "[-+]?[0-9]*\\.?[0-9]+"), # make simplified (and more simplified) k types
k.type.ss = stringr::str_extract(ktype, "[-+]?[0-9]*"))
usethis::use_data(baude_traits, overwrite = TRUE)
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