R/measurement_merge.R

In neonSoilFlux: Compute Soil Carbon Fluxes for the National Ecological Observatory Network Sites

#' @title Internal function that filters environmental data for easier processing of fluxes.

#' @author
#' John Zobitz \email{zobitz@augsburg.edu}

#' @description
#' Internal function. Given a set of environmental measurements, create a merged data frame of measurements and positions where the QF_flag exists (either observed or a smoothed mean measurement).

#' @param neon_data Required. A list of NEON data downloaded from the utilities
#' @param data_code Required. Names of data product we are interpolating. (SWS = soil water, ST = soil temperature, SCO2C = soil CO2)
#' @param data_product_id Name of the data product 00094 = soil water, 00041 = soil temperature, 00095 = soil CO2
#' @param measurement_name Required. Names of column we are grabbing. (VSWCMean = soil water, soilTempMean = soil temperature, soilCO2concentrationMean = soil CO2)
#' @param qf_name Required. Names of qf column we are grabbing. (VSWCFinalQF = soil water, finalQF = soil temperature, finalQF = soil CO2)
#'
#' @return A data frame of the requested data.


measurement_merge <- function(neon_data,data_code,data_product_id,measurement_name,qf_name) {


  # changelog and author contributions / copyrights
  #   John Zobitz (2021-07-19)
  #     original creation
  #     2024-04-08: update to get namespaces correct



  .data = NULL  # Appease R CMD Check
  # Create data frame for soil temperature data after interpolating across depths with columns from 0 m to 2 m


  # Determine a data frame of the different horizontal and vertial positions
  positions_name <- paste0("sensor_positions_",data_product_id)
  positions <- neon_data[[positions_name]] |>
    tidyr::separate(.data[["HOR.VER"]],into=c("HOR","VER")) |>
    dplyr::select(.data[["siteID"]],.data[["HOR"]],.data[["VER"]],.data[["zOffset"]])


  # Identify good (i.e., unflagged) soil temperature data, join the positions, and group by and nest

  data_name <- paste0(data_code,"_30_minute")

  good_data <- neon_data[[data_name]] |>
    dplyr::select(c("domainID","siteID","horizontalPosition","verticalPosition","startDateTime","endDateTime") | tidyselect::matches(paste0("^",measurement_name,"$")) | tidyselect::matches(paste0("^",qf_name,"$")) ) |>
    dplyr::left_join(positions,by=c("siteID"="siteID","horizontalPosition"="HOR","verticalPosition"="VER"))

  return(good_data)

}