Build Environmental Time Series Data

Source: R/functions.R

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

prep_ts_env(df_env, ts_res)

Arguments

df_env

dbplyr lazy table with columns: dtime, qty

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

  • avg - mean of qty

  • std - standard error of qty (sd/n)

  • 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 only at the end of aggregation.

See also

expr_time_env for temporal transformation logic

plot_ts for visualization

Examples

if (FALSE) { # \dontrun{
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)
env_ts <- prep_ts_env(df_env, ts_res = "year")
} # }