A_05_Predictors_avonet.R

A - 05: Prepare AVONET predictors

Source 00_Configuration.R

Code

source(here::here("Code/00_Configuration.R"))
x <- lapply(package_list, require, character = TRUE)
sf_use_s2(TRUE)
rm(x)


tax_path <-
  here("Data/input/Tax_lookup.csv")

Load data & avonet

Code

sci_name <-
  readRDS(here::here("Data/output/1_data_filtered.rds")) %>%
  distinct(verbatimIdentification, scientificName) # BirdLife 2024 taxonomy

Tax <- read.csv(tax_path)

traits <-
  read_excel(here::here("Data/input/AVONET Supplementary dataset 1.xlsx"),
              sheet = "AVONET1_BirdLife")  %>%
  dplyr::select(
    Species1, Mass,
    Habitat, Migration, Primary.Lifestyle) %>%
  mutate(
    across(c(Habitat, Migration, Primary.Lifestyle),
           as.factor))

traits_merged <- traits %>%
  rename("ScientificName2018" = "Species1") %>%
  right_join(Tax %>%
               select(verbatimIdentification, scientificName, ScientificName2018)) %>%
  select(-ScientificName2018)

traits_merged %>% skimr::skim()

Data summary

Name	Piped data
Number of rows	890
Number of columns	6
_______________________
Column type frequency:
character	2
factor	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
verbatimIdentification	0	1	9	32	0	762	0
scientificName	0	1	9	39	0	726	0

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
Habitat	9	0.99	FALSE	11	For: 263, Wet: 171, Gra: 110, Shr: 84
Migration	9	0.99	FALSE	3	3: 486, 1: 202, 2: 193, NA: 0
Primary.Lifestyle	9	0.99	FALSE	5	Ins: 318, Ter: 252, Gen: 130, Aer: 103

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
Mass	9	0.99	412.26	1066.86	3.09	19.44	65.49	339.63	10682.04	▇▁▁▁▁

Save to .rds

Code

#| eval: false
saveRDS(traits_merged, here::here("Data/output/A_predictors/Avonet.rds"))