Prepare Data for Species-Environment Scatterplot

Joins species and environmental data by matching observations that are close in time and space, enabling correlation analysis between abundance and environmental variables.

Usage

prep_splot(
  df_sp,
  df_env,
  env_stat,
  method = "nearest_time",
  max_hours_diff = 72,
  max_meters_diff = 1000
)

Arguments

df_sp: dbplyr lazy table or data.frame with species data
df_env: dbplyr lazy table or data.frame with environmental data
env_stat: Character string specifying aggregation function (e.g., "mean", "median")
max_hours_diff: Numeric maximum time difference (in hours) for matching observations (default: 72)
max_meters_diff: Numeric maximum spatial distance (in meters) for matching observations (default: 1000)

Value

data.frame with matched species-environment observations

Details

This function performs a fuzzy join based on temporal proximity using fuzzyjoin::difference_inner_join(). For each species observation, the closest environmental measurement (within max_hours_diff) is selected. Data is collected from database before joining.

Examples

if (FALSE) { # \dontrun{
df_sp <- get_sp("Anchovy (Engraulis mordax)", qtr = 1:4, date_range = c("2000-01-01", "2020-12-31"))
df_env <- get_env("t_deg_c", qtr = 1:4, date_range = c("2000-01-01", "2020-12-31"), min_depth = 0, max_depth = 100)
df_splot <- prep_splot(df_sp, df_env, env_stat = "mean")
} # }