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Merge pull request #18 from KWB-R/dev
Release v0.1.0
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@@ -3,3 +3,4 @@ sensitivity_analysis_models/tmp.out | |
| docs | ||
| inst/doc | ||
| .Rhistory | ||
| swmm_scenarios | ||
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| #' Title | ||
| #' | ||
| #' @param export_dir default: tempdir() | ||
| #' | ||
| #' @return write "performances" to "swmm_lid-performances.xlsx" in directory | ||
| #' "export_dir" and return path to fike | ||
| #' @export | ||
| #' | ||
| #' @importFrom stats setNames | ||
| #' @importFrom tidyselect all_of | ||
| #' @importFrom openxlsx write.xlsx | ||
| #' @importFrom dplyr select | ||
| #' @importFrom tidyr nest | ||
| export_performances <- function(export_dir = tempdir()) { | ||
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| path <- file.path(export_dir, "swmm_lid-performances.xlsx") | ||
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| list_elements <- names(performances)[sapply(performances, is.list)] | ||
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| unnest_list_col <- function(list_element) { | ||
| list_elements_to_remove <- list_elements[! list_elements %in% list_element] | ||
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| performances %>% | ||
| dplyr::select(!tidyselect::all_of(list_elements_to_remove)) %>% | ||
| tidyr::unnest(tidyselect::all_of(list_element)) | ||
| } | ||
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| export <- stats::setNames(lapply(list_elements, function(list_element) { | ||
| unnest_list_col(list_element)}), | ||
| list_elements) | ||
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| openxlsx::write.xlsx(x = export, file = path) | ||
| path | ||
| } |
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| @@ -0,0 +1,86 @@ | ||
| #' Get Percentiles for Events | ||
| #' | ||
| #' @param performances nested tibble (default: \code{\link{performances}}) | ||
| #' @return list with percentiles for "event_sum" and "event_max" | ||
| #' @export | ||
| #' @importFrom tidyselect all_of | ||
| #' @importFrom tidyr unnest | ||
| #' @importFrom dplyr select group_by mutate summarise | ||
| get_event_percentiles <- function(performances = keys.lid::performances) { | ||
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| volume <- performances %>% | ||
| tidyr::unnest(.data$events_sum) | ||
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| sel_cols <- c("zone_id", | ||
| "lid_name_tidy", | ||
| "scenario_name", | ||
| "lid_area_fraction", | ||
| "runoff_cbm", | ||
| "tBeg", | ||
| "tEnd") | ||
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| volume_stats <- volume %>% | ||
| dplyr::select(tidyselect::all_of(sel_cols)) %>% | ||
| dplyr::group_by(.data$zone_id, | ||
| .data$lid_name_tidy, | ||
| .data$scenario_name, | ||
| .data$lid_area_fraction) %>% | ||
| dplyr::mutate(runoff_LitrePerSqm = 1000 * .data$runoff_cbm) %>% | ||
| dplyr::summarise(datetime_min = min(.data$tBeg), | ||
| datetime_max = max(.data$tEnd), | ||
| timeperiod_days = as.numeric(diff(c(datetime_min, datetime_max))), | ||
| timeperiod_years = timeperiod_days/365, | ||
| number_of_events = dplyr::n(), | ||
| events_per_year = number_of_events / timeperiod_years, | ||
| runoff_LitrePerSqm_q00 = quantile(.data$runoff_LitrePerSqm, probs = 0), | ||
| runoff_LitrePerSqm_q01 = quantile(.data$runoff_LitrePerSqm, probs = 0.01), | ||
| runoff_LitrePerSqm_q05 = quantile(.data$runoff_LitrePerSqm, probs = 0.05), | ||
| runoff_LitrePerSqm_q10 = quantile(.data$runoff_LitrePerSqm, probs = 0.10), | ||
| runoff_LitrePerSqm_q25 = quantile(.data$runoff_LitrePerSqm, probs = 0.25), | ||
| runoff_LitrePerSqm_q50 = quantile(.data$runoff_LitrePerSqm, probs = 0.5), | ||
| runoff_LitrePerSqm_q75 = quantile(.data$runoff_LitrePerSqm, probs = 0.75), | ||
| runoff_LitrePerSqm_q90 = quantile(.data$runoff_LitrePerSqm, probs = 0.9), | ||
| runoff_LitrePerSqm_q95 = quantile(.data$runoff_LitrePerSqm, probs = 0.95), | ||
| runoff_LitrePerSqm_q99 = quantile(.data$runoff_LitrePerSqm, probs = 0.99), | ||
| runoff_LitrePerSqm_q100 = quantile(.data$runoff_LitrePerSqm, probs = 1)) | ||
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| peak <- performances %>% | ||
| tidyr::unnest(.data$events_max) | ||
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| sel_cols <- c("zone_id", | ||
| "lid_name_tidy", | ||
| "scenario_name", | ||
| "lid_area_fraction", | ||
| "max_total_runoff_mmPerHour", | ||
| "tBeg", | ||
| "tEnd") | ||
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| peak_stats <- peak %>% | ||
| dplyr::select(tidyselect::all_of(sel_cols)) %>% | ||
| dplyr::group_by(.data$zone_id, | ||
| .data$lid_name_tidy, | ||
| .data$scenario_name, | ||
| .data$lid_area_fraction) %>% | ||
| dplyr::summarise(datetime_min = min(.data$tBeg), | ||
| datetime_max = max(.data$tEnd), | ||
| timeperiod_days = as.numeric(diff(c(datetime_min, datetime_max))), | ||
| timeperiod_years = timeperiod_days/365, | ||
| number_of_events = dplyr::n(), | ||
| events_per_year = number_of_events / timeperiod_years, | ||
| runoff_max_mmPerHour_q00 = quantile(.data$max_total_runoff_mmPerHour, probs = 0), | ||
| runoff_max_mmPerHour_q01 = quantile(.data$max_total_runoff_mmPerHour, probs = 0.01), | ||
| runoff_max_mmPerHour_q05 = quantile(.data$max_total_runoff_mmPerHour, probs = 0.05), | ||
| runoff_max_mmPerHour_q10 = quantile(.data$max_total_runoff_mmPerHour, probs = 0.10), | ||
| runoff_max_mmPerHour_q25 = quantile(.data$max_total_runoff_mmPerHour, probs = 0.25), | ||
| runoff_max_mmPerHour_q50 = quantile(.data$max_total_runoff_mmPerHour, probs = 0.5), | ||
| runoff_max_mmPerHour_q75 = quantile(.data$max_total_runoff_mmPerHour, probs = 0.75), | ||
| runoff_max_mmPerHour_q90 = quantile(.data$max_total_runoff_mmPerHour, probs = 0.9), | ||
| runoff_max_mmPerHour_q95 = quantile(.data$max_total_runoff_mmPerHour, probs = 0.95), | ||
| runoff_max_mmPerHour_q99 = quantile(.data$max_total_runoff_mmPerHour, probs = 0.99), | ||
| runoff_max_mmPerHour_q100 = quantile(.data$max_total_runoff_mmPerHour, probs = 1)) | ||
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| list(event_max_percentiles = peak_stats, | ||
| event_sum_percentiles = volume_stats | ||
| ) | ||
| } |
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