R/compute_full_analysis.R
In bcgov/fasstr: Analyze, Summarize, and Visualize Daily Streamflow Data
Defines functions
compute_full_analysis
Documented in
compute_full_analysis
# Copyright 2019 Province of British Columbia
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and limitations under the License.
#' @title Compute a suite of tables and plots from various fasstr functions
#'
#' @description Calculates tables and plots from a suite of statistics from \code{fasstr} functions. Calculates statistics from all
#' values, unless specified.The statistics are grouped into 7 analysis groups (see \code{analyses} argument) which are stored in
#' lists in the object. Due to the number of tables and plots to be made, this function may take several minutes to complete. If
#' \code{ignore_missing = FALSE} (default) and there is missing data, some tables and plots may be empty and produce warnings.
#' Use \code{ignore_missing = TRUE} to ignore the missing values or filter your data to complete years.
#' Returns a list of tibbles and plots.
#'
#' @inheritParams compute_annual_trends
#' @param analyses Numeric vector of analyses to run (default is all (\code{1:7})):
#' \itemize{
#' \item{1: Screening}
#' \item{2: Long-term}
#' \item{3: Annual}
#' \item{4: Monthly}
#' \item{5: Daily}
#' \item{6: Annual Trends}
#' \item{7: Low-flow Frequencies}
#' }
#' @param zyp_method Character string identifying the prewhitened trend method to use from \code{'zyp'}, either \code{'zhang'} or
#' \code{'yuepilon'}. \code{'zhang'} is recommended over \code{'yuepilon'} for hydrologic applications (see
#' \code{compute_annual_trends()}; Bürger 2017; Zhang and Zwiers 2004). Only required if analysis group 6 is included.
#' Default \code{'zhang'}.
#'
#' @return A list of lists of tibble data frames and ggplot2 objects from various fasstr functions
#' organized by the analysis groups as listed above.
#'
#' @seealso \code{\link{plot_flow_data}},
#' \code{\link{screen_flow_data}},
#' \code{\link{plot_data_screening}},
#' \code{\link{plot_missing_dates}},
#' \code{\link{calc_longterm_monthly_stats}},
#' \code{\link{plot_longterm_monthly_stats}},
#' \code{\link{calc_longterm_daily_stats}},
#' \code{\link{plot_longterm_daily_stats}},
#' \code{\link{plot_monthly_means}},
#' \code{\link{plot_flow_duration}},
#' \code{\link{calc_annual_stats}},
#' \code{\link{plot_annual_stats}},
#' \code{\link{calc_annual_cumulative_stats}},
#' \code{\link{plot_annual_cumulative_stats}},
#' \code{\link{calc_annual_flow_timing}},
#' \code{\link{plot_annual_flow_timing}},
#' \code{\link{calc_annual_normal_days}},
#' \code{\link{plot_annual_normal_days}},
#' \code{\link{calc_annual_lowflows}},
#' \code{\link{plot_annual_lowflows}},
#' \code{\link{plot_annual_means}},
#' \code{\link{calc_monthly_stats}},
#' \code{\link{plot_monthly_stats}},
#' \code{\link{calc_monthly_cumulative_stats}},
#' \code{\link{plot_monthly_cumulative_stats}},
#' \code{\link{calc_daily_stats}},
#' \code{\link{plot_daily_stats}},
#' \code{\link{calc_daily_cumulative_stats}},
#' \code{\link{plot_daily_cumulative_stats}},
#' \code{\link{compute_annual_trends}},
#' \code{\link{compute_annual_frequencies}},
#' \code{\link{write_flow_data}},
#' \code{\link{write_plots}}
#'
#' @examples
#' \dontrun{
#'
#' # Working examples:
#'
#' # Compute a full analysis will all the analyses
#' results <- compute_full_analysis(station_number = "08NM116",
#' start_year = 1980,
#' end_year = 2010)
#'
#' # Compute a full analysis with only Annual (3) and Daily (5) analyses
#' results <- compute_full_analysis(station_number = "08NM116",
#' start_year = 1980,
#' end_year = 2010,
#' analyses = c(3,5))
#'
#' }
#' @export
compute_full_analysis <- function(data,
dates = Date,
values = Value,
groups = STATION_NUMBER,
station_number,
analyses = 1:7,
basin_area,
water_year_start = 1,
start_year,
end_year,
exclude_years,
months = 1:12,
complete_years = FALSE,
ignore_missing = FALSE,
allowed_missing_annual = ifelse(ignore_missing,100,0),
allowed_missing_monthly = ifelse(ignore_missing,100,0),
zyp_method = 'zhang',
zyp_alpha){
## ARGUMENT CHECKS
## ---------------
if (missing(data)) {
data <- NULL
}
if (missing(station_number)) {
station_number <- NULL
}
if (missing(start_year)) {
start_year <- 0
}
if (missing(end_year)) {
end_year <- 9999
}
if (missing(exclude_years)) {
exclude_years <- NULL
}
if (missing(basin_area)) {
basin_area <- NA
}
if (missing(zyp_alpha)) {
zyp_alpha <- NA
}
water_year_checks(water_year_start)
years_checks(start_year, end_year, exclude_years)
logical_arg_check(ignore_missing)
allowed_missing_checks(allowed_missing_annual, ignore_missing)
allowed_missing_checks(allowed_missing_monthly, ignore_missing)
months_checks(months)
logical_arg_check(complete_years)
if (complete_years) {
if (ignore_missing | allowed_missing_annual > 0 | allowed_missing_monthly > 0) {
ignore_missing <- FALSE
allowed_missing_annual <- 0
allowed_missing_monthly <- 0
message("complete_years argument overrides ignore_missing and allowed_missing_* arguments.")
}
}
if (!is.numeric(analyses))
stop("analyses argument must be numbers between 1 and 7. See ?compute_full_analysis for analysis group numbers.", call. = FALSE)
if (!all(analyses %in% 1:7))
stop("analyses argument must be numbers between 1 and 7. See ?compute_full_analysis for analysis group numbers.", call. = FALSE)
if (6 %in% analyses) {
zyp_alpha_checks(zyp_alpha)
zyp_method_checks(zyp_method)
}
# Do this for now, until looping of add_year plots is sorted out
if (length(station_number) > 1) stop("Only one station_number can be listed.", call. = FALSE)
message("* this may take a few moments...")
## FLOW DATA CHECKS AND FORMATTING
##########################
# Check if data is provided and import it
flow_data_raw <- flowdata_import(data = data,
station_number = station_number)
# Save the original columns (to check for STATION_NUMBER col at end) and ungroup if necessary
orig_cols <- names(flow_data_raw)
flow_data_raw <- dplyr::ungroup(flow_data_raw)
# Check and rename columns
flow_data_unfiltered <- format_all_cols(data = flow_data_raw,
dates = as.character(substitute(dates)),
values = as.character(substitute(values)),
groups = as.character(substitute(groups)),
rm_other_cols = TRUE)
if (all(flow_data_unfiltered$STATION_NUMBER == "XXXXXXX")) {
flow_data_unfiltered <- dplyr::select(flow_data_unfiltered, -STATION_NUMBER)
}
# Data setup
flow_data_unfiltered <- fill_missing_dates(data = flow_data_unfiltered, water_year_start = water_year_start)
flow_data_unfiltered <- add_date_variables(data = flow_data_unfiltered, water_year_start = water_year_start)
flow_data_unfiltered <- add_rolling_means(data = flow_data_unfiltered)
# Set up basin_area
flow_data_unfiltered <- add_basin_area(flow_data_unfiltered, basin_area = basin_area)
basin_area_stn <- unique(flow_data_unfiltered$Basin_Area_sqkm)[1]
# Get the start and end years of the data to make a list of all included years
flow_data <- flow_data_unfiltered
flow_data <- analysis_prep(data = flow_data,
water_year_start = water_year_start)
if (start_year < min(flow_data$WaterYear)) {
start_year <- min(flow_data$WaterYear)
}
if (end_year > max(flow_data$WaterYear)) {
end_year <- max(flow_data$WaterYear)
}
years_list <- seq(from = start_year, to = end_year, by = 1)[!(seq(from = start_year, to = end_year, by = 1) %in% exclude_years)]
flow_data_plus <- dplyr::filter(flow_data, WaterYear >= start_year - 1 & WaterYear <= end_year + 1)
flow_data <- dplyr::filter(flow_data, WaterYear >= start_year & WaterYear <= end_year)
### Results Setup
##########################
# Create list of all objects
all_objects <- list()
message("** creating data frames and plots")
## Screening
##########################
if (1 %in% analyses) {
# Time series plot
ts_full_plot <- suppressWarnings(plot_flow_data(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start))
# Data screening
flow_screening <- screen_flow_data(data = flow_data,
water_year_start = water_year_start,
months = months)
# Screening plots
ts_screen_plot <- plot_data_screening(data = flow_data,
start_year = start_year, end_year = end_year,
water_year_start = water_year_start,
months = months)
# Missing dates plots
ts_missing_plot <- plot_missing_dates(data = flow_data,
start_year = start_year, end_year = end_year,
water_year_start = water_year_start,
months = months)
# Add to objects list
all_objects <- append(all_objects,
list("Screening" = list("Daily_Flows" = flow_data,
"Daily_Flows_Plot" = ts_full_plot,
"Flow_Screening" = flow_screening,
"Flow_Screening_Plot" = ts_screen_plot,
"Missing_Dates_Plot" = ts_missing_plot)))
}
### Long-term Stats
##########################
if (2 %in% analyses) {
# Long-term stats with percentiles
lt_mon_stats <- calc_longterm_monthly_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
percentiles = seq(5, 95, by = 5),
transpose = TRUE,
ignore_missing = ignore_missing,
complete_years = complete_years)
# Long-term stats plot
lt_mon_stats_plot <- plot_longterm_monthly_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
ignore_missing = ignore_missing,
complete_years = complete_years)
# Monthly-means plot
lt_mon_means_plot <- plot_monthly_means(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
ignore_missing = ignore_missing,
complete_years = complete_years,
percent_MAD = NA)
# Long-term stats with percentiles
lt_stats <- calc_longterm_daily_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
percentiles = 1:99,
transpose = TRUE,
ignore_missing = ignore_missing,
complete_years = complete_years)
# Long-term stats plot
lt_stats_plot <- plot_longterm_daily_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
ignore_missing = ignore_missing,
complete_years = complete_years)
# Flow duration plot
lt_flowduration_plot <- plot_flow_duration(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
ignore_missing = ignore_missing,
complete_years = complete_years)
# Add to objects list
all_objects <- append(all_objects,
list("Longterm" = list("Longterm_Monthly_Summary_Stats_Percentiles" = lt_mon_stats,
"Longterm_Monthly_Summary_Stats_Plot" = lt_mon_stats_plot,
"Longterm_Monthly_Means_Plot" = lt_mon_means_plot,
"Longterm_Daily_Summary_Stats_Percentiles" = lt_stats,
"Longterm_Daily_Summary_Stats_Plot" = lt_stats_plot,
"Flow_Duration_Curves" = lt_flowduration_plot)))
}
### Annual Stats
##########################
if (3 %in% analyses) {
# Annual stats
ann_stats <- calc_annual_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
ignore_missing = ignore_missing,
allowed_missing = allowed_missing_annual,
complete_years = complete_years)
# Annual volume
ann_vol <- calc_annual_cumulative_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
include_seasons = all(1:12 %in% months),
complete_years = complete_years)
# Annual yield
ann_yield <- calc_annual_cumulative_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
include_seasons = all(1:12 %in% months),
use_yield = TRUE,
basin_area = basin_area_stn,
complete_years = complete_years)
# Annual flow timing
ann_timing <- calc_annual_flow_timing(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months)
# Annual days outside normal
ann_norm <- calc_annual_normal_days(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months)
# Annual lowflows
ann_lowflow <- calc_annual_lowflows(data = flow_data_plus,
start_year = start_year,
end_year = end_year,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
ignore_missing = ignore_missing,
allowed_missing = allowed_missing_annual,
complete_years = complete_years)
# Create each of the annual stats plots
ann_stats_plot <- plot_annual_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
complete_years = complete_years,
ignore_missing = ignore_missing,
allowed_missing = allowed_missing_annual,
log_discharge = TRUE)
ann_vol_plot <- plot_annual_cumulative_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
include_seasons = all(1:12 %in% months),
complete_years = complete_years)
ann_yield_plot <- plot_annual_cumulative_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
include_seasons = all(1:12 %in% months),
use_yield = TRUE,
basin_area = basin_area_stn,
complete_years = complete_years)
ann_timing_plot <- plot_annual_flow_timing(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months)
ann_norm_plot <- plot_annual_normal_days(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months)
ann_lowflow_plot <- plot_annual_lowflows(data = flow_data_plus,
start_year = start_year,
end_year = end_year,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
complete_years = complete_years,
ignore_missing = ignore_missing,
allowed_missing = allowed_missing_annual)
ann_means_plot <- plot_annual_means(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
ignore_missing = ignore_missing,
allowed_missing = allowed_missing_annual,
complete_years = complete_years)
# Add to objects list
all_objects <- append(all_objects,
list("Annual" = list("Annual_Summary_Stats" = ann_stats,
"Annual_Summary_Stats_Plot" = ann_stats_plot,
"Annual_Cumul_Volume_Stats_m3" = ann_vol,
"Annual_Cumul_Volume_Stats_m3_Plot" = ann_vol_plot,
"Annual_Cumul_Yield_Stats_mm" = ann_yield,
"Annual_Cumul_Yield_Stats_mm_Plot" = ann_yield_plot,
"Annual_Flow_Timing" = ann_timing,
"Annual_Flow_Timing_Plot" = ann_timing_plot,
"Annual_Normal_Days" = ann_norm,
"Annual_Normal_Days_Plot" = ann_norm_plot,
"Annual_Low_Flows" = ann_lowflow,
"Annual_Low_Flows_Plot" = ann_lowflow_plot,
"Annual_Means_Plot" = ann_means_plot)))
}
### Monthly Stats
##########################
if (4 %in% analyses) {
# Create all the monthly stats
mon_stats <- calc_monthly_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
ignore_missing = ignore_missing,
allowed_missing = allowed_missing_monthly,
complete_years = complete_years)
mon_stats_spread <- calc_monthly_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
ignore_missing = ignore_missing,
allowed_missing = allowed_missing_monthly,
complete_years = complete_years,
spread = TRUE)
mon_vol <- calc_monthly_cumulative_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months)
mon_yield <- calc_monthly_cumulative_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
use_yield = TRUE,
basin_area = basin_area_stn)
# Monthly stats plots
mon_stats_plot <- plot_monthly_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
ignore_missing = ignore_missing,
allowed_missing = allowed_missing_monthly,
complete_years = complete_years)
# Monthly cumulative plots
mon_vol_plot <- plot_monthly_cumulative_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months)
mon_yield_plot <- plot_monthly_cumulative_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
use_yield = TRUE,
basin_area = basin_area_stn)
# Add to objects list
all_objects <- append(all_objects,
list("Monthly" = list("Monthly_Summary_Stats" = mon_stats,
"Monthly_Summary_Stats_Plot" = mon_stats_plot,
"Monthly_Total_Cumul_Volume_m3" = mon_vol,
"Monthly_Total_Cumul_Volume_m3_Plot" = mon_vol_plot,
"Monthly_Total_Cumul_Yield_mm" = mon_yield,
"Monthly_Total_Cumul_Yield_mm_Plot" = mon_yield_plot)))
}
### Daily Stats
##########################
if (5 %in% analyses) {
# Daily stats
day_stats <- calc_daily_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
ignore_missing = ignore_missing,
complete_years = complete_years)
day_vol <- calc_daily_cumulative_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months)
day_yield <- calc_daily_cumulative_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
use_yield = TRUE,
basin_area = basin_area_stn)
# Daily stats plots
day_stats_plot <- plot_daily_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
ignore_missing = ignore_missing,
complete_years = complete_years)
day_vol_plot <- plot_daily_cumulative_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months)
day_yield_plot <- plot_daily_cumulative_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
use_yield = TRUE,
basin_area = basin_area_stn)
# Create the daily stats plots
daily_plots_table <- dplyr::group_by(flow_data, WaterYear)
daily_plots_table <- tidyr::nest(daily_plots_table)
daily_plots_table <- dplyr::filter(daily_plots_table, !(WaterYear %in% exclude_years))
daily_plots_table <- dplyr::mutate(daily_plots_table,
plot = purrr::map(WaterYear,
~suppressWarnings(
suppressMessages(
plot_daily_stats(
data = flow_data,
start_year = start_year,
end_year = end_year,
water_year_start = water_year_start,
exclude_years = exclude_years,
months = months,
ignore_missing = ignore_missing,
add_year = .)[[1]]))))
day_stats_year_plots <- daily_plots_table$plot
names(day_stats_year_plots) <- paste0(daily_plots_table$WaterYear, "_Daily_Statistics")
daily_vol_table <- dplyr::group_by(flow_data, WaterYear)
daily_vol_table <- tidyr::nest(daily_vol_table)
daily_vol_table <- dplyr::filter(daily_vol_table, !(WaterYear %in% exclude_years))
daily_vol_table <- dplyr::mutate(daily_vol_table,
plot = purrr::map(WaterYear,
~suppressWarnings(
suppressMessages(
plot_daily_cumulative_stats(
data = flow_data,
start_year = start_year,
end_year = end_year,
water_year_start = water_year_start,
exclude_years = exclude_years,
months = months,
add_year = .)[[1]]))))
day_vol_year_plots <- daily_vol_table$plot
names(day_vol_year_plots) <- paste0(daily_vol_table$WaterYear, "_Daily_Cumulative_Volumetric_Stats")
daily_yield_table <- dplyr::group_by(flow_data, WaterYear)
daily_yield_table <- tidyr::nest(daily_yield_table)
daily_yield_table <- dplyr::filter(daily_yield_table, !(WaterYear %in% exclude_years))
daily_yield_table <- dplyr::mutate(daily_yield_table,
plot = purrr::map(WaterYear,
~suppressWarnings(
suppressMessages(
plot_daily_cumulative_stats(
data = flow_data,
start_year = start_year,
end_year = end_year,
water_year_start = water_year_start,
exclude_years = exclude_years,
months = months,
use_yield = TRUE,
basin_area = basin_area_stn,
add_year = .)[[1]]))))
day_yield_year_plots <- daily_yield_table$plot
names(day_yield_year_plots) <- paste0(daily_yield_table$WaterYear, "_Daily_Cumulative_Yield_Stats")
# Add to objects list
all_objects <- append(all_objects,
list("Daily" = list("Daily_Summary_Stats" = day_stats,
"Daily_Summary_Stats_Plot" = day_stats_plot,
"Daily_Summary_Stats_with_Years" = day_stats_year_plots,
"Daily_Total_Cumul_Volume_m3" = day_vol,
"Daily_Total_Cumul_Volume_m3_Plot" = day_vol_plot,
"Daily_Total_Cumul_Yield_mm" = day_yield,
"Daily_Total_Cumul_Yield_mm_Plot" = day_yield_plot,
"Daily_Total_Cumul_Volume_m3_with_Years" = day_vol_year_plots,
"Daily_Total_Cumul_Yield_mm_with_Years" = day_yield_year_plots
)))
}
# ### Annual Trending
##########################
if (6 %in% analyses) {
# Trends results
ann_trends <- compute_annual_trends(data = flow_data_plus,
start_year = start_year, end_year = end_year,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
ignore_missing = ignore_missing,
complete_years = complete_years,
allowed_missing_annual = allowed_missing_annual,
allowed_missing_monthly = allowed_missing_monthly,
basin_area = basin_area_stn,
zyp_method = zyp_method,
zyp_alpha = zyp_alpha)
ann_data <- ann_trends$Annual_Trends_Data
ann_results <- ann_trends$Annual_Trends_Results
ann_trends_plots <- ann_trends[c(3:length(names(ann_trends)))]
# Add to objects list
all_objects <- append(all_objects,
list("Trending" = list("Annual_Trends_Data" = ann_data,
"Annual_Trends_Results" = ann_results,
"Annual_Trends_Plots" = ann_trends_plots)))
}
### Low Flow Frequency
##########################
if (7 %in% analyses) {
data_check <- calc_annual_lowflows(data = flow_data,
start_year = start_year, end_year = end_year, exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
ignore_missing = ignore_missing,
complete_years = complete_years)
data_check <- dplyr::select(data_check, Min_1_Day, Min_3_Day, Min_7_Day, Min_30_Day)
if (any(as.numeric(colSums(!is.na(data_check))) < 3)) {
warning("Not enough annual data (3 years) for frequency analysis. Consider filtering for appropriate years or use ignore_missing = TRUE,",
call. = FALSE)
} else {
freq_results <- compute_annual_frequencies(data = flow_data_plus,
start_year = start_year,
end_year = end_year,
exclude_years = exclude_years,
water_year_start = water_year_start,
complete_years = complete_years,
ignore_missing = ignore_missing,
allowed_missing = allowed_missing_annual,
months = months,
roll_days = c(1, 3, 7, 30, 60))
# freq_ann_data <- tidyr::spread(freq_results$Freq_Analysis_Data, Measure, value)
freq_ann_data <- freq_results$Freq_Analysis_Data
freq_plot_data <- freq_results$Freq_Plot_Data
freq_plots <- list("Frequency_Plot" = freq_results$Freq_Plot)
#freq_fitting <- freq_results$fit
freq_quantiles <- freq_results$Freq_Fitted_Quantiles
# Add to objects list
all_objects <- append(all_objects,
list("Lowflow_Frequencies" = freq_results))
}
}
message("* DONE")
return(all_objects)
}
bcgov/fasstr documentation built on Oct. 1, 2024, 4:14 p.m.
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Defines functions compute_full_analysis
Documented in compute_full_analysis
# Copyright 2019 Province of British Columbia
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and limitations under the License.
#' @title Compute a suite of tables and plots from various fasstr functions
#'
#' @description Calculates tables and plots from a suite of statistics from \code{fasstr} functions. Calculates statistics from all
#' values, unless specified.The statistics are grouped into 7 analysis groups (see \code{analyses} argument) which are stored in
#' lists in the object. Due to the number of tables and plots to be made, this function may take several minutes to complete. If
#' \code{ignore_missing = FALSE} (default) and there is missing data, some tables and plots may be empty and produce warnings.
#' Use \code{ignore_missing = TRUE} to ignore the missing values or filter your data to complete years.
#' Returns a list of tibbles and plots.
#'
#' @inheritParams compute_annual_trends
#' @param analyses Numeric vector of analyses to run (default is all (\code{1:7})):
#' \itemize{
#' \item{1: Screening}
#' \item{2: Long-term}
#' \item{3: Annual}
#' \item{4: Monthly}
#' \item{5: Daily}
#' \item{6: Annual Trends}
#' \item{7: Low-flow Frequencies}
#' }
#' @param zyp_method Character string identifying the prewhitened trend method to use from \code{'zyp'}, either \code{'zhang'} or
#' \code{'yuepilon'}. \code{'zhang'} is recommended over \code{'yuepilon'} for hydrologic applications (see
#' \code{compute_annual_trends()}; Bürger 2017; Zhang and Zwiers 2004). Only required if analysis group 6 is included.
#' Default \code{'zhang'}.
#'
#' @return A list of lists of tibble data frames and ggplot2 objects from various fasstr functions
#' organized by the analysis groups as listed above.
#'
#' @seealso \code{\link{plot_flow_data}},
#' \code{\link{screen_flow_data}},
#' \code{\link{plot_data_screening}},
#' \code{\link{plot_missing_dates}},
#' \code{\link{calc_longterm_monthly_stats}},
#' \code{\link{plot_longterm_monthly_stats}},
#' \code{\link{calc_longterm_daily_stats}},
#' \code{\link{plot_longterm_daily_stats}},
#' \code{\link{plot_monthly_means}},
#' \code{\link{plot_flow_duration}},
#' \code{\link{calc_annual_stats}},
#' \code{\link{plot_annual_stats}},
#' \code{\link{calc_annual_cumulative_stats}},
#' \code{\link{plot_annual_cumulative_stats}},
#' \code{\link{calc_annual_flow_timing}},
#' \code{\link{plot_annual_flow_timing}},
#' \code{\link{calc_annual_normal_days}},
#' \code{\link{plot_annual_normal_days}},
#' \code{\link{calc_annual_lowflows}},
#' \code{\link{plot_annual_lowflows}},
#' \code{\link{plot_annual_means}},
#' \code{\link{calc_monthly_stats}},
#' \code{\link{plot_monthly_stats}},
#' \code{\link{calc_monthly_cumulative_stats}},
#' \code{\link{plot_monthly_cumulative_stats}},
#' \code{\link{calc_daily_stats}},
#' \code{\link{plot_daily_stats}},
#' \code{\link{calc_daily_cumulative_stats}},
#' \code{\link{plot_daily_cumulative_stats}},
#' \code{\link{compute_annual_trends}},
#' \code{\link{compute_annual_frequencies}},
#' \code{\link{write_flow_data}},
#' \code{\link{write_plots}}
#'
#' @examples
#' \dontrun{
#'
#' # Working examples:
#'
#' # Compute a full analysis will all the analyses
#' results <- compute_full_analysis(station_number = "08NM116",
#' start_year = 1980,
#' end_year = 2010)
#'
#' # Compute a full analysis with only Annual (3) and Daily (5) analyses
#' results <- compute_full_analysis(station_number = "08NM116",
#' start_year = 1980,
#' end_year = 2010,
#' analyses = c(3,5))
#'
#' }
#' @export
compute_full_analysis <- function(data,
dates = Date,
values = Value,
groups = STATION_NUMBER,
station_number,
analyses = 1:7,
basin_area,
water_year_start = 1,
start_year,
end_year,
exclude_years,
months = 1:12,
complete_years = FALSE,
ignore_missing = FALSE,
allowed_missing_annual = ifelse(ignore_missing,100,0),
allowed_missing_monthly = ifelse(ignore_missing,100,0),
zyp_method = 'zhang',
zyp_alpha){
## ARGUMENT CHECKS
## ---------------
if (missing(data)) {
data <- NULL
}
if (missing(station_number)) {
station_number <- NULL
}
if (missing(start_year)) {
start_year <- 0
}
if (missing(end_year)) {
end_year <- 9999
}
if (missing(exclude_years)) {
exclude_years <- NULL
}
if (missing(basin_area)) {
basin_area <- NA
}
if (missing(zyp_alpha)) {
zyp_alpha <- NA
}
water_year_checks(water_year_start)
years_checks(start_year, end_year, exclude_years)
logical_arg_check(ignore_missing)
allowed_missing_checks(allowed_missing_annual, ignore_missing)
allowed_missing_checks(allowed_missing_monthly, ignore_missing)
months_checks(months)
logical_arg_check(complete_years)
if (complete_years) {
if (ignore_missing | allowed_missing_annual > 0 | allowed_missing_monthly > 0) {
ignore_missing <- FALSE
allowed_missing_annual <- 0
allowed_missing_monthly <- 0
message("complete_years argument overrides ignore_missing and allowed_missing_* arguments.")
}
}
if (!is.numeric(analyses))
stop("analyses argument must be numbers between 1 and 7. See ?compute_full_analysis for analysis group numbers.", call. = FALSE)
if (!all(analyses %in% 1:7))
stop("analyses argument must be numbers between 1 and 7. See ?compute_full_analysis for analysis group numbers.", call. = FALSE)
if (6 %in% analyses) {
zyp_alpha_checks(zyp_alpha)
zyp_method_checks(zyp_method)
}
# Do this for now, until looping of add_year plots is sorted out
if (length(station_number) > 1) stop("Only one station_number can be listed.", call. = FALSE)
message("* this may take a few moments...")
## FLOW DATA CHECKS AND FORMATTING
##########################
# Check if data is provided and import it
flow_data_raw <- flowdata_import(data = data,
station_number = station_number)
# Save the original columns (to check for STATION_NUMBER col at end) and ungroup if necessary
orig_cols <- names(flow_data_raw)
flow_data_raw <- dplyr::ungroup(flow_data_raw)
# Check and rename columns
flow_data_unfiltered <- format_all_cols(data = flow_data_raw,
dates = as.character(substitute(dates)),
values = as.character(substitute(values)),
groups = as.character(substitute(groups)),
rm_other_cols = TRUE)
if (all(flow_data_unfiltered$STATION_NUMBER == "XXXXXXX")) {
flow_data_unfiltered <- dplyr::select(flow_data_unfiltered, -STATION_NUMBER)
}
# Data setup
flow_data_unfiltered <- fill_missing_dates(data = flow_data_unfiltered, water_year_start = water_year_start)
flow_data_unfiltered <- add_date_variables(data = flow_data_unfiltered, water_year_start = water_year_start)
flow_data_unfiltered <- add_rolling_means(data = flow_data_unfiltered)
# Set up basin_area
flow_data_unfiltered <- add_basin_area(flow_data_unfiltered, basin_area = basin_area)
basin_area_stn <- unique(flow_data_unfiltered$Basin_Area_sqkm)[1]
# Get the start and end years of the data to make a list of all included years
flow_data <- flow_data_unfiltered
flow_data <- analysis_prep(data = flow_data,
water_year_start = water_year_start)
if (start_year < min(flow_data$WaterYear)) {
start_year <- min(flow_data$WaterYear)
}
if (end_year > max(flow_data$WaterYear)) {
end_year <- max(flow_data$WaterYear)
}
years_list <- seq(from = start_year, to = end_year, by = 1)[!(seq(from = start_year, to = end_year, by = 1) %in% exclude_years)]
flow_data_plus <- dplyr::filter(flow_data, WaterYear >= start_year - 1 & WaterYear <= end_year + 1)
flow_data <- dplyr::filter(flow_data, WaterYear >= start_year & WaterYear <= end_year)
### Results Setup
##########################
# Create list of all objects
all_objects <- list()
message("** creating data frames and plots")
## Screening
##########################
if (1 %in% analyses) {
# Time series plot
ts_full_plot <- suppressWarnings(plot_flow_data(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start))
# Data screening
flow_screening <- screen_flow_data(data = flow_data,
water_year_start = water_year_start,
months = months)
# Screening plots
ts_screen_plot <- plot_data_screening(data = flow_data,
start_year = start_year, end_year = end_year,
water_year_start = water_year_start,
months = months)
# Missing dates plots
ts_missing_plot <- plot_missing_dates(data = flow_data,
start_year = start_year, end_year = end_year,
water_year_start = water_year_start,
months = months)
# Add to objects list
all_objects <- append(all_objects,
list("Screening" = list("Daily_Flows" = flow_data,
"Daily_Flows_Plot" = ts_full_plot,
"Flow_Screening" = flow_screening,
"Flow_Screening_Plot" = ts_screen_plot,
"Missing_Dates_Plot" = ts_missing_plot)))
}
### Long-term Stats
##########################
if (2 %in% analyses) {
# Long-term stats with percentiles
lt_mon_stats <- calc_longterm_monthly_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
percentiles = seq(5, 95, by = 5),
transpose = TRUE,
ignore_missing = ignore_missing,
complete_years = complete_years)
# Long-term stats plot
lt_mon_stats_plot <- plot_longterm_monthly_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
ignore_missing = ignore_missing,
complete_years = complete_years)
# Monthly-means plot
lt_mon_means_plot <- plot_monthly_means(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
ignore_missing = ignore_missing,
complete_years = complete_years,
percent_MAD = NA)
# Long-term stats with percentiles
lt_stats <- calc_longterm_daily_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
percentiles = 1:99,
transpose = TRUE,
ignore_missing = ignore_missing,
complete_years = complete_years)
# Long-term stats plot
lt_stats_plot <- plot_longterm_daily_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
ignore_missing = ignore_missing,
complete_years = complete_years)
# Flow duration plot
lt_flowduration_plot <- plot_flow_duration(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
ignore_missing = ignore_missing,
complete_years = complete_years)
# Add to objects list
all_objects <- append(all_objects,
list("Longterm" = list("Longterm_Monthly_Summary_Stats_Percentiles" = lt_mon_stats,
"Longterm_Monthly_Summary_Stats_Plot" = lt_mon_stats_plot,
"Longterm_Monthly_Means_Plot" = lt_mon_means_plot,
"Longterm_Daily_Summary_Stats_Percentiles" = lt_stats,
"Longterm_Daily_Summary_Stats_Plot" = lt_stats_plot,
"Flow_Duration_Curves" = lt_flowduration_plot)))
}
### Annual Stats
##########################
if (3 %in% analyses) {
# Annual stats
ann_stats <- calc_annual_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
ignore_missing = ignore_missing,
allowed_missing = allowed_missing_annual,
complete_years = complete_years)
# Annual volume
ann_vol <- calc_annual_cumulative_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
include_seasons = all(1:12 %in% months),
complete_years = complete_years)
# Annual yield
ann_yield <- calc_annual_cumulative_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
include_seasons = all(1:12 %in% months),
use_yield = TRUE,
basin_area = basin_area_stn,
complete_years = complete_years)
# Annual flow timing
ann_timing <- calc_annual_flow_timing(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months)
# Annual days outside normal
ann_norm <- calc_annual_normal_days(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months)
# Annual lowflows
ann_lowflow <- calc_annual_lowflows(data = flow_data_plus,
start_year = start_year,
end_year = end_year,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
ignore_missing = ignore_missing,
allowed_missing = allowed_missing_annual,
complete_years = complete_years)
# Create each of the annual stats plots
ann_stats_plot <- plot_annual_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
complete_years = complete_years,
ignore_missing = ignore_missing,
allowed_missing = allowed_missing_annual,
log_discharge = TRUE)
ann_vol_plot <- plot_annual_cumulative_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
include_seasons = all(1:12 %in% months),
complete_years = complete_years)
ann_yield_plot <- plot_annual_cumulative_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
include_seasons = all(1:12 %in% months),
use_yield = TRUE,
basin_area = basin_area_stn,
complete_years = complete_years)
ann_timing_plot <- plot_annual_flow_timing(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months)
ann_norm_plot <- plot_annual_normal_days(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months)
ann_lowflow_plot <- plot_annual_lowflows(data = flow_data_plus,
start_year = start_year,
end_year = end_year,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
complete_years = complete_years,
ignore_missing = ignore_missing,
allowed_missing = allowed_missing_annual)
ann_means_plot <- plot_annual_means(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
ignore_missing = ignore_missing,
allowed_missing = allowed_missing_annual,
complete_years = complete_years)
# Add to objects list
all_objects <- append(all_objects,
list("Annual" = list("Annual_Summary_Stats" = ann_stats,
"Annual_Summary_Stats_Plot" = ann_stats_plot,
"Annual_Cumul_Volume_Stats_m3" = ann_vol,
"Annual_Cumul_Volume_Stats_m3_Plot" = ann_vol_plot,
"Annual_Cumul_Yield_Stats_mm" = ann_yield,
"Annual_Cumul_Yield_Stats_mm_Plot" = ann_yield_plot,
"Annual_Flow_Timing" = ann_timing,
"Annual_Flow_Timing_Plot" = ann_timing_plot,
"Annual_Normal_Days" = ann_norm,
"Annual_Normal_Days_Plot" = ann_norm_plot,
"Annual_Low_Flows" = ann_lowflow,
"Annual_Low_Flows_Plot" = ann_lowflow_plot,
"Annual_Means_Plot" = ann_means_plot)))
}
### Monthly Stats
##########################
if (4 %in% analyses) {
# Create all the monthly stats
mon_stats <- calc_monthly_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
ignore_missing = ignore_missing,
allowed_missing = allowed_missing_monthly,
complete_years = complete_years)
mon_stats_spread <- calc_monthly_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
ignore_missing = ignore_missing,
allowed_missing = allowed_missing_monthly,
complete_years = complete_years,
spread = TRUE)
mon_vol <- calc_monthly_cumulative_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months)
mon_yield <- calc_monthly_cumulative_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
use_yield = TRUE,
basin_area = basin_area_stn)
# Monthly stats plots
mon_stats_plot <- plot_monthly_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
ignore_missing = ignore_missing,
allowed_missing = allowed_missing_monthly,
complete_years = complete_years)
# Monthly cumulative plots
mon_vol_plot <- plot_monthly_cumulative_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months)
mon_yield_plot <- plot_monthly_cumulative_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
use_yield = TRUE,
basin_area = basin_area_stn)
# Add to objects list
all_objects <- append(all_objects,
list("Monthly" = list("Monthly_Summary_Stats" = mon_stats,
"Monthly_Summary_Stats_Plot" = mon_stats_plot,
"Monthly_Total_Cumul_Volume_m3" = mon_vol,
"Monthly_Total_Cumul_Volume_m3_Plot" = mon_vol_plot,
"Monthly_Total_Cumul_Yield_mm" = mon_yield,
"Monthly_Total_Cumul_Yield_mm_Plot" = mon_yield_plot)))
}
### Daily Stats
##########################
if (5 %in% analyses) {
# Daily stats
day_stats <- calc_daily_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
ignore_missing = ignore_missing,
complete_years = complete_years)
day_vol <- calc_daily_cumulative_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months)
day_yield <- calc_daily_cumulative_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
use_yield = TRUE,
basin_area = basin_area_stn)
# Daily stats plots
day_stats_plot <- plot_daily_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
ignore_missing = ignore_missing,
complete_years = complete_years)
day_vol_plot <- plot_daily_cumulative_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months)
day_yield_plot <- plot_daily_cumulative_stats(data = flow_data,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
use_yield = TRUE,
basin_area = basin_area_stn)
# Create the daily stats plots
daily_plots_table <- dplyr::group_by(flow_data, WaterYear)
daily_plots_table <- tidyr::nest(daily_plots_table)
daily_plots_table <- dplyr::filter(daily_plots_table, !(WaterYear %in% exclude_years))
daily_plots_table <- dplyr::mutate(daily_plots_table,
plot = purrr::map(WaterYear,
~suppressWarnings(
suppressMessages(
plot_daily_stats(
data = flow_data,
start_year = start_year,
end_year = end_year,
water_year_start = water_year_start,
exclude_years = exclude_years,
months = months,
ignore_missing = ignore_missing,
add_year = .)[[1]]))))
day_stats_year_plots <- daily_plots_table$plot
names(day_stats_year_plots) <- paste0(daily_plots_table$WaterYear, "_Daily_Statistics")
daily_vol_table <- dplyr::group_by(flow_data, WaterYear)
daily_vol_table <- tidyr::nest(daily_vol_table)
daily_vol_table <- dplyr::filter(daily_vol_table, !(WaterYear %in% exclude_years))
daily_vol_table <- dplyr::mutate(daily_vol_table,
plot = purrr::map(WaterYear,
~suppressWarnings(
suppressMessages(
plot_daily_cumulative_stats(
data = flow_data,
start_year = start_year,
end_year = end_year,
water_year_start = water_year_start,
exclude_years = exclude_years,
months = months,
add_year = .)[[1]]))))
day_vol_year_plots <- daily_vol_table$plot
names(day_vol_year_plots) <- paste0(daily_vol_table$WaterYear, "_Daily_Cumulative_Volumetric_Stats")
daily_yield_table <- dplyr::group_by(flow_data, WaterYear)
daily_yield_table <- tidyr::nest(daily_yield_table)
daily_yield_table <- dplyr::filter(daily_yield_table, !(WaterYear %in% exclude_years))
daily_yield_table <- dplyr::mutate(daily_yield_table,
plot = purrr::map(WaterYear,
~suppressWarnings(
suppressMessages(
plot_daily_cumulative_stats(
data = flow_data,
start_year = start_year,
end_year = end_year,
water_year_start = water_year_start,
exclude_years = exclude_years,
months = months,
use_yield = TRUE,
basin_area = basin_area_stn,
add_year = .)[[1]]))))
day_yield_year_plots <- daily_yield_table$plot
names(day_yield_year_plots) <- paste0(daily_yield_table$WaterYear, "_Daily_Cumulative_Yield_Stats")
# Add to objects list
all_objects <- append(all_objects,
list("Daily" = list("Daily_Summary_Stats" = day_stats,
"Daily_Summary_Stats_Plot" = day_stats_plot,
"Daily_Summary_Stats_with_Years" = day_stats_year_plots,
"Daily_Total_Cumul_Volume_m3" = day_vol,
"Daily_Total_Cumul_Volume_m3_Plot" = day_vol_plot,
"Daily_Total_Cumul_Yield_mm" = day_yield,
"Daily_Total_Cumul_Yield_mm_Plot" = day_yield_plot,
"Daily_Total_Cumul_Volume_m3_with_Years" = day_vol_year_plots,
"Daily_Total_Cumul_Yield_mm_with_Years" = day_yield_year_plots
)))
}
# ### Annual Trending
##########################
if (6 %in% analyses) {
# Trends results
ann_trends <- compute_annual_trends(data = flow_data_plus,
start_year = start_year, end_year = end_year,
exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
ignore_missing = ignore_missing,
complete_years = complete_years,
allowed_missing_annual = allowed_missing_annual,
allowed_missing_monthly = allowed_missing_monthly,
basin_area = basin_area_stn,
zyp_method = zyp_method,
zyp_alpha = zyp_alpha)
ann_data <- ann_trends$Annual_Trends_Data
ann_results <- ann_trends$Annual_Trends_Results
ann_trends_plots <- ann_trends[c(3:length(names(ann_trends)))]
# Add to objects list
all_objects <- append(all_objects,
list("Trending" = list("Annual_Trends_Data" = ann_data,
"Annual_Trends_Results" = ann_results,
"Annual_Trends_Plots" = ann_trends_plots)))
}
### Low Flow Frequency
##########################
if (7 %in% analyses) {
data_check <- calc_annual_lowflows(data = flow_data,
start_year = start_year, end_year = end_year, exclude_years = exclude_years,
water_year_start = water_year_start,
months = months,
ignore_missing = ignore_missing,
complete_years = complete_years)
data_check <- dplyr::select(data_check, Min_1_Day, Min_3_Day, Min_7_Day, Min_30_Day)
if (any(as.numeric(colSums(!is.na(data_check))) < 3)) {
warning("Not enough annual data (3 years) for frequency analysis. Consider filtering for appropriate years or use ignore_missing = TRUE,",
call. = FALSE)
} else {
freq_results <- compute_annual_frequencies(data = flow_data_plus,
start_year = start_year,
end_year = end_year,
exclude_years = exclude_years,
water_year_start = water_year_start,
complete_years = complete_years,
ignore_missing = ignore_missing,
allowed_missing = allowed_missing_annual,
months = months,
roll_days = c(1, 3, 7, 30, 60))
# freq_ann_data <- tidyr::spread(freq_results$Freq_Analysis_Data, Measure, value)
freq_ann_data <- freq_results$Freq_Analysis_Data
freq_plot_data <- freq_results$Freq_Plot_Data
freq_plots <- list("Frequency_Plot" = freq_results$Freq_Plot)
#freq_fitting <- freq_results$fit
freq_quantiles <- freq_results$Freq_Fitted_Quantiles
# Add to objects list
all_objects <- append(all_objects,
list("Lowflow_Frequencies" = freq_results))
}
}
message("* DONE")
return(all_objects)
}
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