Package index • HydroBOT

Primary functions

prep_run_save_ewrs(): Set up, run, and (possibly) save EWR outputs

read_and_agg(): Read in data and aggregate along theme and spatial dimension

run_hydrobot_params(): Run HydroBOT through the aggregation step with a file of yaml parameters or passing in yaml as characters.

plot_outcomes(): Standardized plot functions for HydroBOT.

Aggregator

Multi-dimensional

read_and_agg(): Read in data and aggregate along theme and spatial dimension

multi_aggregate(): Iterative aggregation along theme and spatial dimensions

Dimension handling

general_aggregate(): Aggregate data along theme dimension

spatial_aggregate(): Aggregate along spatial dimension

temporal_aggregate(): Aggregate along the time dimension

theme_aggregate(): Aggregate along theme dimension

Built-in summary functions

ArithmeticMean(): Arithmetic mean aggregation

CompensatingFactor(): Compensating aggregation

GeometricMean(): Geometric mean aggregation

HarmonicMean(): Harmonic mean aggregation

LimitingFactor(): Limiting aggregation

Max(): Max aggregation with na.rm = TRUE by default except that all-NA vectors still return NA, not 0.

Median(): Median

Min(): Min aggregation with na.rm = TRUE by default except that all-NA vectors still return NA, not 0.

NumberOfValues(): Find number of items being aggregated

SpatialWeightedMean(): Weighted mean by area column

Sum(): Sum aggregation

Variance(): Variance aggregation

maxInterevent(): Get maximum interevent duration of a vector

Helpers

agg_names_to_cols(): Change aggregation history in col names to stepwise columns

identify_dimension(): Helper to identify the dimension of each step in aggsequence

is_notpoint(): Test whether an object is an sf other than a point

is_point(): Test whether an object is a spatial point sf

is_sf(): Test whether an object is an sf

Plotting and comparer

plot_outcomes(): Standardized plot functions for HydroBOT.

baseline_compare(): Compare values to a baseline

theme_hydrobot(): A standard ggplot theme for HydroBOT

create_base(): Creates a baseline column in several different ways. Helper for baseline_compare() but useful elsewhere too

difference(): Difference

relative(): Division (relative comparison)

oddsratio(): Odds ratio

grouped_colors(): Sets up colors within groups according to separate palettes

handle_palettes(): Makes the calls to scale_fill_* and scale_color_*, depending on whether color/fill and the palette type needed

make_pal(): Function to ensure colors match across plots

make_plot_table(): Make a table of the aggregations from a plot

plot_data_prep(): Some standard data preparation for plotting

plot_style_prep(): Some data preparation, checking, and type-finding based on the type of data and the way we want plots to look

Causal networks

causal_colors_general(): Set colors of nodes or edges

causal_ewr: Causal relationships for the EWRs

extract_vals_causal(): Extracts and arranges the aggregated values for use in causal network plots.

get_causal_ewr(): Extract causal network from EWR tool

make_causal_plot(): Builds a causal network plot

make_edges(): Create edges dataframe for causal network or theme aggregation

make_nodes(): Make a node df from the edges df

find_related_nodes(): Finds all nodes upstream and downstream of a set of focal nodes

Provided Data

basin: Murray-Darling Basin Polygon

basin_rivers: River lines in the basin

bom_basin_gauges: Gauge locations

causal_ewr: Causal relationships for the EWRs

cewo_valleys: Catchments within the Murray-Darling Basin

planning_units: Planning Units for long-term watering plans

resource_plan_areas: Resource plan areas

sdl_units: Sustainable diversion limits units

Module helpers

read_hydro(): Wrapper to read hydrographs into R

assess_ewr_achievement(): EWR logic test on incoming Annual EWRs

assess_ewr_interevents(): Some calculations needed for aggregation of interevent statistics

causal_ewr: Causal relationships for the EWRs

clean_ewr_obj(): Pair EWR indicators to environmental objectives

clean_ewr_requirements(): Cleans the EWR table to use for assessing target requirements by the outputs

clean_ewr_yearly(): Additional cleanup specific to yearly EWR outputs.

cleanewrs(): Clean up and standardise names and column types

get_causal_ewr(): Extract causal network from EWR tool

get_ewr_gauges(): get the gauges from the EWR tool

get_ewr_table(): Get the table of EWRs from the EWR tool

get_ewr_version(): Extract the package version. Does not get complications like git branches.

make_ewrformat(): Convert pulled gauge data into the EWR 'Standard time-series' format

prep_ewr_output(): Make EWR results with achievement for ongoing use

prep_run_save_ewrs(): Set up, run, and (possibly) save EWR outputs

separate_ewr_codes(): Parser for EWR codes into the main code and the extra bits (called 'timing' for some reason)

get_iqqm_gauges(): Get the mapping of gauges to IQQM nodes for netcdf

get_module_output(): Get the outputs from modules and do minor cleaning

make_output_dir(): Set up the output directory structure based on parent directory and module at parent_dir/module_output/MODULE_NAME/subdir

read_and_geo(): Prepare ewr results from the EWR tool for use in aggregation

Sometimes helpful extras

check_missing_runs(): Similar to find_missing_runs, but excised from prep_run_save_ewrs to take the same arguments

find_expected_files(): Figure out the expected outputs

find_missing_runs(): Figure out missing expected outputs

find_scenario_paths(): get the paths to scenarios

safe_imap(): A wrapper for purrr::imap() and furrr::future_imap() that bypasses errors and potentially retries them