# combine_pump_test_and_Q_monitoring_data --------------------------------------
#' Combined Pumptest and Q Monitoring Dataset
#'
#' @param df_pump_tests_tidy df_pump_tests_tidy
#' @param df_Q_monitoring df_Q_monitoring
#' @param pump_test_vars default: \code{\link{get_pump_test_vars}}
#' @return combined pumptest and Q monitoring dataset
#' @export
#'
combine_pump_test_and_Q_monitoring_data <- function(df_pump_tests_tidy,
df_Q_monitoring,
pump_test_vars) {
df_Qs_all <- df_pump_tests_tidy %>%
dplyr::bind_rows(df_Q_monitoring) %>%
dplyr::arrange(.data$well_id, .data$date) %>%
dplyr::select(c(dplyr::all_of(pump_test_vars), "operational_start.date", "W_static.origin")) %>%
dplyr::filter(!is.na(.data$date)) %>%
dplyr::ungroup()
# complete data
df_Qs_all <- df_Qs_all %>%
tidyr::fill(.data$n_rehab) %>%
tidyr::fill(.data$last_rehab.date) %>%
dplyr::mutate(key = dplyr::if_else(!is.na(.data$W_static.origin),
"quantity measurements",
.data$key),
key2 = forcats::fct_collapse(
.data$key,
'pump tests' = c("operational_start", "pump_test_1", "pump_test_2")
),
W_static.origin = tidyr::replace_na(.data$W_static.origin, "measured"),
days_since_operational_start = as.integer(
difftime(.data$date, .data$operational_start.date, units = "days")
)) %>%
dplyr::group_by(.data$well_id, .data$n_rehab) %>%
dplyr::mutate(time_since_rehab_days = dplyr::if_else(
is.na(.data$time_since_rehab_days),
as.integer(.data$date - min(.data$last_rehab.date)),
.data$time_since_rehab_days
)) %>%
dplyr::ungroup()
df_Qs_all
}
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