data-raw/individual.R

The data-raw/individual.R script contains the code required to prepare the raw data for analysis.

Your script should contain:

data-raw/individual.R

## code to prepare `individual` dataset goes here
## Setup
library(dplyr)
source(here::here("R", "geolocate.R"))

## Combine individual tables ----
# Create paths to inputs
raw_data_path <- here::here("data-raw", "wood-survey-data-master")
individual_paths <- fs::dir_ls(fs::path(raw_data_path, "individual"))

# read in all individual tables into one
individual <- purrr::map(
  individual_paths,
  ~ readr::read_csv(
    file = .x,
    col_types = readr::cols(.default = "c"),
    show_col_types = FALSE
  )
) %>%
  purrr::list_rbind() %>%
  readr::type_convert()

individual %>%
  readr::write_csv(file = fs::path(raw_data_path, "vst_individuals.csv"))

# Combine NEON data tables ----
# read in additional table
maptag <- readr::read_csv(
  fs::path(
    raw_data_path,
    "vst_mappingandtagging.csv"
  ),
  show_col_types = FALSE
) %>%
  select(-eventID)

perplot <- readr::read_csv(
  fs::path(
    raw_data_path,
    "vst_perplotperyear.csv"
  ),
  show_col_types = FALSE
) %>%
  select(-eventID)

# Left join tables to individual
individual %<>%
  left_join(maptag,
    by = "individualID",
    suffix = c("", "_map")
  ) %>%
  left_join(perplot,
    by = "plotID",
    suffix = c("", "_ppl")
  ) %>%
  assertr::assert(
    assertr::not_na, stemDistance, stemAzimuth, pointID,
    decimalLongitude, decimalLatitude, plotID
  )

# ---- Geolocate individuals_functions ----
individual <- individual %>%
  mutate(
    stemLat = get_stem_location(
      decimalLongitude = decimalLongitude,
      decimalLatitude = decimalLatitude,
      stemAzimuth = stemAzimuth,
      stemDistance = stemDistance
    )$lat,
    stemLon = get_stem_location(
      decimalLongitude = decimalLongitude,
      decimalLatitude = decimalLatitude,
      stemAzimuth = stemAzimuth,
      stemDistance = stemDistance
    )$lon
  )

# create data directory
fs::dir_create(here::here("data"))

# write out analytic file
individual %>%
  janitor::clean_names() %>%
  readr::write_csv(here::here("data", "individual.csv"))