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Build Species Time Series Data

Source: R/functions.R
prep_ts_sp.Rd

Aggregates species abundance data by temporal resolution, computing mean and standard error for visualization in time series plots. Uses dbplyr lazy evaluation for efficient database queries.

Usage

prep_ts_sp(df_sp, ts_res)

Arguments

df_sp

dbplyr lazy table or data.frame with columns: time_start, name, std_tally

ts_res

Character string specifying temporal resolution: "year", "quarter", "month", "day", "year_quarter", "year_month", or "year_day"

Value

data.frame with columns:

  • time - aggregated time value

  • name - species name

  • avg - mean standardized tally

  • std - standard error (sd/n)

  • n - number of observations

  • upr - upper confidence bound (avg + std)

  • lwr - lower confidence bound (avg - std)

Details

For seasonal plots (ts_res = "quarter"), the function adds a wrapping row to ensure visual continuity across the year boundary. Data is collected from database before aggregation.

See also

expr_time_sp for temporal transformation logic

plot_ts for visualization

Examples

if (FALSE) { # \dontrun{
df_sp <- get_sp("Anchovy (Engraulis mordax)", qtr = 1:4, date_range = c("2000-01-01", "2020-12-31"))
sp_ts <- prep_ts_sp(df_sp, ts_res = "year")
} # }