R/compute_annual_frequencies.R
compute_annual_frequencies.Rd
Performs a flow volume frequency analysis on annual statistics from a daily streamflow data set. Defaults to a low
flow frequency analysis using annual minimums. Set use_max = TRUE
for annual high flow frequency analyses. Calculates
statistics from all values, unless specified. Function will calculate using all values in 'Values' column (no grouped analysis).
Analysis methodology replicates that from HECSSP. Returns a list of
tibbles and plots.
compute_annual_frequencies( data, dates = Date, values = Value, station_number, roll_days = c(1, 3, 7, 30), roll_align = "right", use_max = FALSE, use_log = FALSE, prob_plot_position = c("weibull", "median", "hazen"), prob_scale_points = c(0.9999, 0.999, 0.99, 0.9, 0.5, 0.2, 0.1, 0.02, 0.01, 0.001, 1e04), fit_distr = c("PIII", "weibull"), fit_distr_method = ifelse(fit_distr == "PIII", "MOM", "MLE"), fit_quantiles = c(0.975, 0.99, 0.98, 0.95, 0.9, 0.8, 0.5, 0.2, 0.1, 0.05, 0.01), plot_curve = TRUE, water_year_start = 1, start_year, end_year, exclude_years, months = 1:12, ignore_missing = FALSE )
data  A data frame of daily data that contains columns of dates and flow values. Groupings and the 

dates  Name of column in 
values  Name of column in 
station_number  Character string vector of seven digit Water Survey of Canada station numbers (e.g. 
roll_days  Numeric value of the number of days to apply a rolling mean. Default 
roll_align  Character string identifying the direction of the rolling mean from the specified date, either by the first
( 
use_max  Logical value to indicate using maximums rather than the minimums for analysis. Default 
use_log  Logical value to indicate logscale transforming of flow data before analysis. Default 
prob_plot_position  Character string indicating the plotting positions used in the frequency plots, one of 
prob_scale_points  Numeric vector of probabilities to be plotted along the X axis in the frequency plot. Inverse of
return period. Default 
fit_distr  Character string identifying the distribution to fit annual data, one of 
fit_distr_method  Character string identifying the method used to fit the distribution, one of 
fit_quantiles  Numeric vector of quantiles to be estimated from the fitted distribution.
Default 
plot_curve  Logical value to indicate plotting the computed curve on the probability plot. Default 
water_year_start  Numeric value indicating the month ( 
start_year  Numeric value of the first year to consider for analysis. Leave blank to use the first year of the source data. 
end_year  Numeric value of the last year to consider for analysis. Leave blank to use the last year of the source data. 
exclude_years  Numeric vector of years to exclude from analysis. Leave blank to include all years. 
months  Numeric vector of months to include in analysis (e.g. 
ignore_missing  Logical value indicating whether dates with missing values should be included in the calculation. If

A list with the following elements:
Data frame with computed annual summary statistics used in analysis.
Data frame with coordinates used in frequency plot.
ggplot2 object with frequency plot.
List of fitted objects from fitdistrplus.
Data frame with fitted quantiles.
if (FALSE) { # Working examples (see arguments for further analysis options): # Compute an annual frequency analysis using default arguments results < compute_annual_frequencies(station_number = "08NM116", start_year = 1980, end_year = 2010) # Compute an annual frequency analysis using default arguments (as listed) results < compute_annual_frequencies(station_number = "08NM116", roll_days = c(1,3,7,30), start_year = 1980, end_year = 2010, prob_plot_position = "weibull", prob_scale_points = c(.9999, .999, .99, .9, .5, .2, .1, .02, .01, .001, .0001), fit_distr = "PIII", fit_distr_method = "MOM") # Compute a 7day annual frequency analysis with "median" plotting positions # and fitting the data to a weibull distribution (not default PIII) results < compute_annual_frequencies(station_number = "08NM116", roll_days = 7, start_year = 1980, end_year = 2010, prob_plot_position = "median", fit_distr = "weibull") }