Package 'piamValidation'

Validation Tools for PIK-PIAM

Description

The piamValidation package provides validation tools for the Potsdam Integrated Assessment Modelling environment.

Author(s)

Maintainer: Pascal Weigmann [email protected]

Authors:

• Oliver Richters

• Fabrice Lécuyer

See Also

Useful links:

• https://github.com/pik-piam/piamValidation

• https://pik-piam.github.io/piamValidation

---

construct tooltips for interactive plots

Description

construct tooltips for interactive plots

Usage

appendTooltips(df)

Arguments

df	data.frame as returned from 'validateScenarios()'

---

Check variable consistency

Description

Test whether unit of on row of config and data for this variable match.

Usage

checkUnits(data, cfgRow)

Arguments

data	scenario or reference data for one variable
cfgRow	one row of a config file containing the same variable as the data object

---

Combine scenario and reference data with thresholds

Description

for one row of cfg: filter and merge relevant scenario data with cfg results in one df that contains scenario data, reference data and thresholds

Usage

combineData(scenData, cfgRow, histData = NULL)

Arguments

scenData	scenario data for one variable
cfgRow	one row of a config file
histData	reference data

---

takes the output of "validateScenarios()" and plots heat maps per variable

Description

takes the output of "validateScenarios()" and plots heat maps per variable

Usage

linePlotThresholds(
  valiData,
  scenData = NULL,
  refData = NULL,
  xlim = c(2010, 2030)
)

Arguments

valiData	data to be plotted, as returned by “validateScenarios()“ and after filtering for one variable and one region.
scenData	hand over additional scenario data to be plotted alongside the validation data. Will use the same variable and region, otherwise all available data.
refData	hand over additional reference data to be plotted alongside the validation data. Will use the same variable and region, otherwise all available data.
xlim	set limits for the x axis

---

evaluate the content of the "ref_<type>" column and filter reference data accordingly. cases: - mode chosen - range - mean - no mode chosen - use mean if multiple references

Description

returns df without variable, unit and <type> columns (see below) returns df with ref_value_min/max, ref_model, ref_scenario, ref_period

Usage

refineRefData(ref_data, cfgRow, ref_type = "ref_model")

Arguments

ref_data	pre-filtered reference data
cfgRow	row of validation config used for this data slice
ref_type	historical, model, scenario, period

---

performs the validation checks from a config on a scenario data set

Description

performs the validation checks from a config on a scenario data set

Usage

validateScenarios(dataPath, config, outputFile = NULL, extraColors = TRUE)

Arguments

dataPath	one or multiple path(s) to scenario data in .mif or .csv format, in case of historic comparison, also path to reference data
config	select config from inst/config or give a full path to a config file on your computer
outputFile	give name of output file in case results should be exported; include file extension
extraColors	if TRUE, use cyan and blue for violation of min thresholds instead of using the same colors as for max thresholds (yel and red)

---

takes the output of "validateScenarios()" and plots heat maps per variable

Description

takes the output of "validateScenarios()" and plots heat maps per variable

Usage

validationHeatmap(
  valiData,
  main_dim = "variable",
  x_plot = NULL,
  y_plot = NULL,
  x_facet = NULL,
  y_facet = NULL,
  interactive = TRUE
)

Arguments

valiData	data to be plotted, as returned by “validateScenarios()“ (and “appendTooltips()“ if interactive), plus optional filtering. Needs to have at least one dimension with only one unique element.
main_dim	out of the 5-dim df, 1 dim has to contain only on element, this is the main dimension of the plot, default: variable
x_plot	choose dimension to display on x-axis of plot, if any is NULL, arrangement is chosen automatically based on data dimensions
y_plot	choose dimension to display on y-axis of plot
x_facet	choose dimension to display on x-dim of facets
y_facet	choose dimension to display on x-dim of facets
interactive	return plots as interactive plotly plots by default

---

returns information on whether scenarios passed critical validation checks

Description

returns information on whether scenarios passed critical validation checks

Usage

validationPass(data, yellowFail = FALSE)

Arguments

data	data.frame as returned from “validateScenarios()“
yellowFail	if set to TRUE a yellow check result of a critical variable will lead to the scenario not passing as validated

---

perform validateScenarios and create an .html report using .Rmd templates

Description

perform validateScenarios and create an .html report using .Rmd templates

Usage

validationReport(
  dataPath,
  config,
  report = "default",
  outputDir = "output",
  extraColors = TRUE
)

Arguments

dataPath	one or multiple path(s) to scenario data in .mif or .csv format
config	name a config from inst/config ("validationConfig_<name>.csv") or give a full path to a separate configuration file
report	name a .Rmd from inst/markdown ("validationReport_<name>.Rmd") to be rendered or give a full path to a separate .Rmd file
outputDir	choose a directory to save validation reports to
extraColors	if TRUE, use cyan and blue for violation of min thresholds instead of using the same colors as for max thresholds (yel and red)

---