R/report_basic.R

In markhogue/AeroSampleR: Estimate Aerosol Particle Collection Through Sample Lines

#' report on transport efficiency
#'
#' In order to run a report, first produce a model of each individual
#' element. Start with producing a particle distribution
#' with the `particle_dist` function, then produce a parameter set with
#' the `set_params` function. Both of these results must be stored as
#' per examples described in the help set with each. Next, add elements
#' in the sample system until all are complete.
#'
#' @param df is the particle data set (data frame) established with the
#' `particle_dist` function
#' @param params is the parameter data set for parameters that are not
#' particle size-dependent
#' @param dist selects the distribution for the report. Options are
#' 'discrete' for discrete particle sizes or 'log' for the log-normal
#' distribution of particles that were started with the `particle_dist`
#' function.
#'
#' @returns report of system efficiency
#'
#' @examples
#' df <- particle_dist() # set up particle distribution
#' params <- set_params_1("D_tube" = 2.54, "Q_lpm" = 100,
#' "T_C" = 25, "P_kPa" = 101.325) #example system parameters
#' df <- set_params_2(df, params) #particle size-dependent parameters
#' df <- probe_eff(df, params, orient = 'h') #probe orientation - horizontal
#' df <- bend_eff(df, params, method='Zhang', bend_angle=90,
#' bend_radius=0.1, elnum=3)
#' df <- tube_eff(df, params, L = 100,
#' angle_to_horiz = 90, elnum = 3)
#' report_basic(df, params, dist = 'discrete')
#'
#' @export
report_basic <- function(df, params, dist) {

    # # housekeeping to avoid no visible binding warnings #. = NULL -
    # try this if below doesn't work
    D_p = microns = sys_eff = dens = ambient = bin_eff = sampled = . = starts_with = NULL
    # provide parameter details
    cat("System Parameters")
    cat("\n")
    cat("All values in MKS units, except noted")
    cat("\n")
    cat("Notes: D_tube is in m.")
    cat("\n")
    cat("Q_lpm is system flow in liters per minute.")
    cat("\n")
    cat("velocity_air is the derived air flow velocity in meters per second.")
    cat("T_K is system temperature in Kelvin.")
    cat("\n")
    cat("P_kPa is system pressure in kiloPascals.")
    cat("\n")
    cat("Re is the system Reynolds number, a measure of turbulence.")
    cat("\n")
    #utils::str(params[c(1, 2, 3, 4, 5, 10)])
    data.frame(t(params))
    cat("\n")
    eff_cols <- tidyselect::starts_with("eff_", vars = names(df))

    if (dist == "discrete") {
# make data frame with just the discrete data
            df_disc <- df |>
        dplyr::filter(dist == "discrete")
# compute system efficiency and add this column
      df_disc$sys_eff <- apply(df_disc[, eff_cols], 1, prod)


# select columns for the report
      discrete_report <- df_disc |> dplyr::select(D_p, sys_eff)
      return(discrete_report)
    }

    if (dist == "log") {
# make data frame of just the log data
            df_log <- df |>
        dplyr::filter(dist == "log_norm")

 # compute efficiency for each particle size (bin) and add this column

df_log$bin_eff <- apply(df_log[, eff_cols], 1, prod)
# compute ambient mass-based quantity for each bin

df_log$ambient <- df_log$dens * 4/3 *
                   pi * (df_log$D_p/2)^3 * diff(c(0, df_log$D_p))

df_log$sampled <- df_log$ambient * df_log$bin_eff

data.frame("activity fraction sampled" = sum(df_log$sampled)/
             sum(df_log$ambient))
    }
}