rm(list = ls())
library(rspecan)
sethere()
data_name <- "ecosis_milkweed_stress"
data_longname <- "Common Milkweed Leaf Responses to Water Stress and Elevated Temperature"
ecosis_id <- "9425d5b2-7633-45b5-9c07-6ec3323499a0"
ecosis_file <- sprintf(
"https://ecosis.org/package/export?package_id=%s&metadata=true",
ecosis_id
)
message("Downloading data...")
dat_raw <- read_csv(ecosis_file)
message("Download complete!")
dat_full <- dat_raw %>%
mutate(spectra_id = sprintf("%s_%03d_%02d", data_name, ID, rep_ID))
############################################################
# Process spectra
############################################################
wave_rxp <- "^[[:digit:]]+$"
spectra_colname <- "spectra_id"
spectra <- dat2specmat(dat_full, spectra_colname, wave_rxp)
str(spectra)
wl <- getwl(spectra)
if (FALSE) {
matplot(wl, spectra, type = "l")
}
wl_prospect <- wl >= 400 & wl <= 2500
wl_bad <- FALSE
wl_keep <- wl_prospect & !wl_bad
data_wl_inds <- which(wl_keep)
wl_kept <- wl[wl_keep]
prospect_wl_inds <- which(prospect_wl %in% wl_kept)
############################################################
# Process metadata
############################################################
dat_sub <- dat_full %>%
select(-matches(wave_rxp))
dat <- dat_sub %>%
transmute(
data_name = !!data_name,
spectra_id = spectra_id,
spectra_type = "reflectance",
USDA_code = "ASSY",
CN_ratio = `C:N`,
fiber = `Fiber (% dm)`,
fiber_unit = "%",
Cmass = `Leaf carbon content per leaf area`,
Cmass_unit = "%",
LMA = `Leaf mass per area`,
LMA_unit = "g m-2",
Nmass = `Leaf nitrogen content per leaf area (% dm)`,
Nmass_unit = "%",
lignin = `Lignin (% dm)`,
lignin_unit = "%",
Vcmax_area = `Vcmax`,
Vcmax_area_unit = "umol m-2 s-1",
treatment_water = recode(`Water treatment`, ww = "Well-watered", ws = "Water stressed")
)
############################################################
# Store results
############################################################
store_path <- file.path(processed_dir, paste0(data_name, ".rds"))
datalist <- list(
data_name = data_name,
data_longname = data_longname,
data_filename = ecosis_file,
self_filename = store_path,
metadata = dat,
spectra = spectra,
data_wl_inds = data_wl_inds,
prospect_wl_inds = prospect_wl_inds
)
check_datalist(datalist)
submit_df <- dat %>%
filter(spectra_type == "reflectance") %>%
select(data_name, spectra_id)
saveRDS(datalist, store_path)
write_submit_file(submit_df, data_name)
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