Package {expoquimR}

Title:	Qualitative and Quantitative Assessment of Occupational Chemical Exposure Risk
Version:	0.1.0
Description:	Provides a unified toolkit for occupational chemical exposure risk assessment, implementing three internationally recognised methods end to end: the qualitative control-banding methods COSHH Essentials (UK Health and Safety Executive) and the method of the French National Research and Safety Institute (INRS), together with the quantitative statistical procedure of the UNE-EN 689 standard for comparing measured exposure levels against occupational exposure limits. Every step of each method, from hazard banding and exposure scoring to lognormal or normal distribution fitting, one-sided tolerance limits, and monitoring-interval recommendations, is implemented as a small, independently callable, and unit-tested function, so assessments are reproducible and auditable without depending on any graphical interface. Optional 'shiny' applications provide a guided, interactive workflow for occupational hygienists and health and safety practitioners who prefer not to write code. References: UK Health and Safety Executive (2003) https://www.hse.gov.uk/coshh/essentials/index.htm; Mallet, Pilorget and Berne (2013, ISBN:978-2-7389-2166-2) "Evaluation du risque chimique" INRS ED 6084; European Committee for Standardisation (2018) https://www.en-standard.eu/bs-en-689-2018-workplace-exposure-measurement-of-exposure-by-inhalation-to-chemical-agents-strategy-for-testing-compliance-with-occupational-exposure-limit-values/.
License:	MIT + file LICENSE
Encoding:	UTF-8
Language:	en-GB
RoxygenNote:	7.3.3
URL:	https://github.com/Aguilar-Elena/expoquimR
BugReports:	https://github.com/Aguilar-Elena/expoquimR/issues
Depends:	R (≥ 4.1.0)
Imports:	stats
Suggests:	DT, ggplot2, kableExtra, knitr, readxl, rmarkdown, shiny, shinyjs, testthat (≥ 3.0.0), tibble, usethis
VignetteBuilder:	knitr
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2026-08-04 18:00:50 UTC; okashi
Author:	Raúl Aguilar Elena [aut, cre], Ana Delgado-Garcia [aut], Alejandro Guillem-Riquelme [aut]
Maintainer:	Raúl Aguilar Elena <raguilar@universidadviu.com>
Repository:	CRAN
Date/Publication:	2026-08-09 07:10:07 UTC

Classify the volatility of a liquid using the COSHH Essentials method

Description

Compares the boiling point of a substance with its process temperature to assign a volatility class ("Low" / "Medium" / "High" in English, "Baja" / "Media" / "Alta" in Spanish), following the thresholds of the COSHH Essentials method.

Usage

coshh_classify_volatility(boiling_point, process_temp)

Arguments

Details

The active language is controlled by expoquimr_lang().

Value

Character scalar: volatility class in the active language.

Examples

coshh_classify_volatility(boiling_point = 111, process_temp = 20)
expoquimr_lang("es")
coshh_classify_volatility(boiling_point = 111, process_temp = 20)
expoquimr_lang("en")

Evaluate a substance using the COSHH Essentials method (high-level wrapper)

Description

Chains coshh_grade(), coshh_classify_volatility() (if applicable), coshh_risk() and coshh_measures() to produce a complete result row from the raw data of a substance. Designed to be called directly from code (scripts, reports, purrr::pmap, vignettes) without going through the Shiny application.

Usage

coshh_evaluate(
  name,
  phrases,
  quantity,
  is_liquid,
  boiling_point = NA_real_,
  process_temp = NA_real_,
  dustiness = NA_character_
)

Arguments

Details

Output labels (volatility class, quantity class, control measures) are returned in the active language; see expoquimr_lang().

Value

A one-row data.frame with columns substance, phrases, grade, volatility, quantity, risk and measures.

Examples

coshh_evaluate(
  name = "Toluene",
  phrases = "H315, H336",
  quantity = "Medium",
  is_liquid = TRUE,
  boiling_point = 111,
  process_temp = 20
)

Evaluate COSHH substances from an Excel file

Description

Reads an Excel sheet with the format of the expoquimR COSHH template (one row per substance) and returns the complete assessment of each one by calling coshh_evaluate().

Usage

coshh_from_excel(path, sheet = "COSHH_datos")

Arguments

Value

A data.frame with one row per substance and the result columns of coshh_evaluate().

Examples

# With the template included in the package:
path <- system.file("plantillas", "plantilla_coshh.xlsx",
                    package = "expoquimR")
coshh_from_excel(path)

# With your own file, simply pass its path:
# coshh_from_excel("my_coshh_data.xlsx")

Determine the COSHH hazard group from R/H phrases

Description

Looks up each risk phrase (R) or hazard phrase (H) in the COSHH Essentials hazard group assignment table (groups A to E) and returns the most unfavourable group found. Any phrase not listed explicitly in groups B-E is assigned to group A, following the default rule of the original method.

Usage

coshh_grade(phrases)

Arguments

Value

Character scalar with the group ("A" to "E"), or NA_character_ if phrases is empty or NA.

Examples

coshh_grade("H315, H319")
coshh_grade("R23/24/25")

Get the recommended control measures for a COSHH risk level

Description

Get the recommended control measures for a COSHH risk level

Usage

coshh_measures(risk_level)

Arguments

Value

Character scalar with the recommended control measures in the active language (see expoquimr_lang()), or NA_character_ if the level is not defined.

Examples

coshh_measures(3)
expoquimr_lang("es")
coshh_measures(3)
expoquimr_lang("en")

Calculate the COSHH risk level

Description

Queries the hazard x quantity x volatility matrix of the COSHH Essentials method to obtain the potential risk level (1 to 4). Substances of grade "E" (carcinogenic, mutagenic or similar) always receive the maximum level, regardless of quantity or volatility.

Usage

coshh_risk(grade, quantity, volatility)

Arguments

Value

Integer with the risk level (1 to 4), or NA_integer_ if the combination is not defined in the table or grade is NA.

Examples

coshh_risk(grade = "C", quantity = "Medium", volatility = "High")
coshh_risk(grade = "E", quantity = "Small",  volatility = "Low")

Get or set the language used by expoquimR

Description

expoquimR supports English ("en", default) and Spanish ("es"). The active language controls the language of function output messages, column labels returned by high-level wrapper functions, and error/warning messages. It does not affect the Shiny apps, which have their own in-app language selector.

Usage

expoquimr_lang(lang = NULL)

Arguments

Details

The active language is stored in a private package environment, not in options(), so calling this function never alters the user's global R session settings.

Value

Invisibly returns the previously active language.

Examples

expoquimr_lang()          # query current language
old <- expoquimr_lang("es")  # switch to Spanish, saving the previous value
expoquimr_lang(old)          # restore it

Generate an occupational chemical exposure risk assessment report

Description

Renders a self-contained HTML report from the results of one or more assessment methods implemented in expoquimR. Each method section is included only when the corresponding argument is supplied. The report includes input data, result tables, density plots (UNE-EN 689), additive effect groups, and a citation block for the package.

Usage

expoquimr_report(
  coshh = NULL,
  inrs = NULL,
  une689 = NULL,
  evaluator = "",
  workplace = "",
  output = "expoquimr_report.html",
  lang = .expoquimR_state$lang,
  open = TRUE
)

Arguments

Value

Invisibly returns the path to the generated HTML file.

Examples

# COSHH only
res <- coshh_evaluate(
  name = "Toluene", phrases = "H315, H336",
  quantity = "Medium", is_liquid = TRUE,
  boiling_point = 111, process_temp = 20
)
out1 <- tempfile(fileext = ".html")
expoquimr_report(
  coshh     = res,
  evaluator = "Dr. Jane Smith",
  workplace = "Printing workshop A",
  output    = out1,
  open      = FALSE
)

# All three methods
path <- system.file("plantillas", "plantilla_une689.xlsx",
                    package = "expoquimR")
res_une <- une689_from_excel(path)

out2 <- tempfile(fileext = ".html")
expoquimr_report(
  coshh     = res,
  une689    = res_une,
  evaluator = "Dr. Jane Smith",
  workplace = "Printing workshop A",
  output    = out2,
  lang      = "en",
  open      = FALSE
)

Collective protection class and score (INRS method)

Description

Collective protection class and score (INRS method)

Usage

inrs_collective_protection(situation)

Arguments

Value

A one-row data.frame with columns class and score.

Examples

inrs_collective_protection("Enclosing hood / full enclosure")

Evaluate a chemical product with the INRS method (high-level wrapper)

Description

Chains all the steps of the INRS method (hazard class, quantity, frequency, potential exposure, potential risk, volatility or dustiness, process and collective protection) from the raw data of a product, and returns a complete result row. Designed to be used directly from code, without going through the Shiny application.

Usage

inrs_evaluate(
  name,
  r_phrases = character(0),
  h_phrases = character(0),
  process = NULL,
  vla = NA_real_,
  quantity_value = NA_real_,
  quantity_unit = c("g", "ml", "kg", "l"),
  frequency_value = NA_real_,
  frequency_unit = c("minutes", "hours", "days", "months", "not_used"),
  substance_type = c("liquid", "solid"),
  liquid_method = c("graph", "pressure"),
  use_temperature = NA_real_,
  boiling_point = NA_real_,
  vapour_pressure = NA_real_,
  solid_description = NA_character_,
  procedure,
  protection
)

Arguments

Value

A one-row data.frame with all the intermediate classes and scores, the final inhalation risk score and its characterisation.

Examples

inrs_evaluate(
  name = "Solvent X",
  h_phrases = "H336",
  vla = 50,
  quantity_value = 5, quantity_unit = "l",
  frequency_value = 3, frequency_unit = "hours",
  substance_type = "liquid",
  liquid_method = "graph",
  use_temperature = 40, boiling_point = 80,
  procedure = "Open",
  protection = "Moderate dispersion conditions"
)

Frequency of use class (INRS method)

Description

Converts the given frequency of use to the reference units of Table 3 of the INRS method (hours/day or days/month or days/year, depending on the input unit) and returns the corresponding class (0 to 4).

Usage

inrs_frequency_class(
  value = NA_real_,
  unit = c("minutes", "hours", "days", "months", "not_used")
)

Arguments

Value

Character scalar ("0" to "4"), or NA_character_ if there is no match in the reference table.

Examples

inrs_frequency_class(3, "hours")
inrs_frequency_class(unit = "not_used")

Evaluate INRS chemical products from an Excel file

Description

Reads an Excel sheet with the format of the expoquimR INRS template (one row per product) and returns the complete assessment by calling inrs_evaluate().

Usage

inrs_from_excel(path, sheet = "INRS_datos")

Arguments

Value

A data.frame with one row per product and all the result columns of inrs_evaluate().

Examples

path <- system.file("plantillas", "plantilla_inrs.xlsx",
                    package = "expoquimR")
inrs_from_excel(path)

Hazard class of a substance (INRS method)

Description

Determines the hazard class (1 to 5) of a substance from its R phrases, its H phrases, its VLA, or the material/process it belongs to, by consulting Table 1 of the INRS method. It is explored from the most hazardous class (5) down to the least hazardous (1), and the first class with a match is returned. If no phrase, VLA or process matches classes 2 to 5, class 1 is assigned by default (catch-all: "has phrases but none of the above"), as established by the INRS methodology.

Usage

inrs_hazard_class(
  r_phrases = character(0),
  h_phrases = character(0),
  process = NULL,
  vla = NA_real_
)

Arguments

Value

Character scalar ("1" to "5"). Only returns NA_character_ if no criterion at all was supplied (r_phrases, h_phrases, process and vla all empty/NA), in which case there is not enough information to classify.

Examples

inrs_hazard_class(h_phrases = "H335")
inrs_hazard_class(vla = 0.05)
inrs_hazard_class(vla = 200) # falls into class 1 by default

Final inhalation risk score (INRS method)

Description

Product of the five partial scores of the INRS method.

Usage

inrs_inhalation_risk(
  potential_risk_score,
  volatility_score,
  procedure_score,
  protection_score,
  vla_correction_factor
)

Arguments

Value

Numeric, or NA_real_ if any component is missing.

Examples

inrs_inhalation_risk(100, 10, 0.5, 0.7, 10)

Volatility class of a liquid from use temperature and boiling point

Description

Classifies the volatility of a liquid by comparing its boiling point with the two class-separating lines of the INRS method graph (Figure 2, use temperature on the X axis, boiling point on the Y axis).

Usage

inrs_liquid_volatility_graph(use_temperature, boiling_point)

Arguments

Value

Character scalar ("1" low, "2" medium, "3" high).

Examples

inrs_liquid_volatility_graph(use_temperature = 20, boiling_point = 200)
inrs_liquid_volatility_graph(use_temperature = 20, boiling_point = 80)

Volatility class of a liquid from vapour pressure

Description

Classifies the volatility of a liquid according to the official thresholds of Table 8 of the INRS method.

Usage

inrs_liquid_volatility_pressure(vapour_pressure)

Arguments

Value

Character scalar ("1" if Pv < 0.5 kPa, "2" if 0.5 <= Pv < 25 kPa, "3" if Pv >= 25 kPa).

Examples

inrs_liquid_volatility_pressure(15)

VLA correction factor (INRS method)

Description

VLA correction factor (INRS method)

Usage

inrs_oel_correction_factor(vla)

Arguments

Value

Numeric (1, 10, 30 or 100), or NA_real_ if vla is NA.

Examples

inrs_oel_correction_factor(0.05)

Potential exposure class (INRS method)

Description

Consults Table 4 of the INRS method (quantity class x frequency class) to obtain the potential exposure class.

Usage

inrs_potential_exposure_class(quantity_class, frequency_class)

Arguments

Value

Character scalar ("0" to "5"), or NA_character_ if the combination is not defined.

Examples

inrs_potential_exposure_class("3", "2")

Potential risk class (INRS method)

Description

Consults Table 5 of the INRS method (potential exposure class x hazard class) to obtain the potential risk class.

Usage

inrs_potential_risk_class(potential_exposure_class, hazard_class)

Arguments

Value

Character scalar ("1" to "5"), or NA_character_ if the combination is not defined.

Examples

inrs_potential_risk_class("4", "3")

Potential risk score (INRS method)

Description

Potential risk score (INRS method)

Usage

inrs_potential_risk_score(potential_risk_class)

Arguments

Value

Numeric (1, 10, 100, 1000 or 10000), or NA_real_.

Examples

inrs_potential_risk_score("3")

Process class and score (INRS method)

Description

Process class and score (INRS method)

Usage

inrs_process_type(type)

Arguments

Value

A one-row data.frame with columns class and score.

Examples

inrs_process_type("Open")

Daily handled quantity class (INRS method)

Description

Classifies the daily quantity of substance handled into one of the 5 classes of the INRS method, according to its unit.

Usage

inrs_quantity_class(value, unit = c("g", "ml", "kg", "l"))

Arguments

Value

Character scalar ("1" to "5"), or NA_character_ if value is NA.

Examples

inrs_quantity_class(50, "g")
inrs_quantity_class(500, "kg")

Inhalation risk characterisation (INRS method)

Description

Inhalation risk characterisation (INRS method)

Usage

inrs_risk_characterisation(inhalation_risk)

Arguments

Value

Character scalar describing the action priority, or NA_character_ if inhalation_risk is NA.

Examples

inrs_risk_characterisation(2500)

Dustiness class of a solid (INRS method)

Description

Dustiness class of a solid (INRS method)

Usage

inrs_solid_dustiness(description)

Arguments

Value

Character scalar ("1" to "3"), or NA_character_ if description does not match any valid option.

Examples

inrs_solid_dustiness("Fine dust with little visible dispersion")

Volatility or dustiness score (INRS method)

Description

Volatility or dustiness score (INRS method)

Usage

inrs_volatility_score(volatility_class)

Arguments

Value

Numeric (1, 10 or 100), or NA_real_.

Examples

inrs_volatility_score("2")

Launch the COSHH Essentials Shiny application

Description

Launch the COSHH Essentials Shiny application

Usage

run_coshh(...)

Arguments

Value

No return value, called for side effects. Launches a Shiny application in the default browser.

Examples

run_coshh()

Launch the INRS method Shiny application

Description

Launch the INRS method Shiny application

Usage

run_inrs(...)

Arguments

Value

No return value, called for side effects. Launches a Shiny application in the default browser.

Examples

run_inrs()

Launch the UNE-EN 689 Shiny application (preliminary, statistical and periodic assessment)

Description

Launch the UNE-EN 689 Shiny application (preliminary, statistical and periodic assessment)

Usage

run_une689(...)

Arguments

Value

No return value, called for side effects. Launches a Shiny application in the default browser.

Examples

run_une689()

Classify the conformity of the UNE-EN 689 preliminary assessment

Description

From the exposure indices (IE) of all the days evaluated, determines whether exposure is conforming, non-conforming, or whether no decision can be made without further measurements, following the criteria of the UNE-EN 689 preliminary assessment.

Usage

une689_classify_conformity(ie)

Arguments

Value

Character scalar: .t("une689_conformity") if all IE values are below 0.1; .t("une689_no_conformity") if any IE is above 1; .t("une689_no_decision") if any IE is between 0.1 and 1 (both inclusive) and none exceeds 1. Returns NA_character_ if there is no valid IE at all (not enough data to classify).

Correction relative to the original app

In the original Shiny app, if all days had IE = NA (due to missing data), the check all(IEs < 0.1, na.rm = TRUE) returned TRUE (because all() over an empty vector is TRUE in R), and the result was incorrectly reported as .t("une689_conformity") with no actual data. This function fixes that case by returning NA_character_ (not enough data) instead of a false conformity. This change is flagged here because, unlike the INRS points, it was applied without prior confirmation: reporting "conformity" with no data is a safety flaw, not a methodological judgement call.

Examples

une689_classify_conformity(c(0.02))
une689_classify_conformity(c(1, 0.9, 0.56))
une689_classify_conformity(c(1.2, 0.05))

Daily exposure (ED) for a measurement day, per UNE-EN 689

Description

Calculates the daily exposure from the concentrations and times of the valid samples of a measurement day. If there is a single valid sample taken over the complete 8-hour working day, the ED is directly that concentration. Otherwise, it is calculated as the time-weighted average over an 8-hour day (sum(concentration * time) / 8).

Usage

une689_daily_exposure(concentration, time)

Arguments

Value

Numeric scalar with the ED, or NA_real_ if there is no valid (concentration, time) pair.

Examples

une689_daily_exposure(concentration = c(12, 8), time = c(4, 4))
une689_daily_exposure(concentration = 9, time = 8)

Determine the distribution type (UNE-EN 689)

Description

Decides whether the data best fits a lognormal, normal, or neither distribution, from the Shapiro-Wilk p-values. Priority is given to the lognormal fit, following common practice in industrial hygiene (exposure is usually lognormal).

Usage

une689_distribution_type(pval_normal, pval_lognormal, alpha = 0.05)

Arguments

Value

Character scalar: one of "Lognormal", "Normal" or "Neither" (in English, the default), or the equivalent Spanish labels when expoquimr_lang("es") is active.

Examples

une689_distribution_type(pval_normal = 0.03, pval_lognormal = 0.20)

Complete UNE-EN 689 preliminary assessment (high-level wrapper)

Description

Calculates the ED and IE for each day and classifies the overall conformity, from a set of samples organised by measurement day. Designed to be used directly from code, without going through the Shiny application.

Usage

une689_evaluate_preliminary(data, vla)

Arguments

Value

A list with two elements:

days_table

A data.frame with columns day, ED and IE, one row per day.

result

Character scalar with the overall classification, see une689_classify_conformity().

Examples

data <- data.frame(
  day = c(1, 1, 2, 3, 3),
  concentration = c(12, 8, 9, 5, 6),
  time = c(4, 4, 8, 3, 5)
)
une689_evaluate_preliminary(data, vla = 10)

Complete UNE-EN 689 statistical assessment (high-level wrapper)

Description

Chains the distribution fit, the calculation of UT, LSC(95,70), UR and the statistical conformity from a set of daily exposure (ED) values. Designed to be used directly from code, without going through the Shiny application.

Usage

une689_evaluate_statistical(ed, vla)

Arguments

Value

A list with elements n, distribution_type, MA, DS, MG, DSG, W_normal, pval_normal, W_lognormal, pval_lognormal, ut, lsc, ur, conformity.

Examples

une689_evaluate_statistical(c(5, 6, 7, 8, 9, 10), vla = 10)

Exposure index (IE) for a measurement day, per UNE-EN 689

Description

Exposure index (IE) for a measurement day, per UNE-EN 689

Usage

une689_exposure_index(ed, vla)

Arguments

Value

Numeric scalar (ed / vla), or NA_real_ if ed or vla are not valid (vla must be ⁠> 0⁠).

Examples

une689_exposure_index(ed = 9, vla = 10)

Evaluate UNE-EN 689 chemical exposure from an Excel file

Description

Reads the three sheets of the expoquimR UNE-EN 689 template (Agents, Measurements and optionally Additive_effects) and returns a list with the preliminary assessment of each agent, and if applicable, the additive effects calculation by group.

Usage

une689_from_excel(path)

Arguments

Value

A list with the elements:

preliminary

A list with one element per agent, each with name, vla, days_table and result.

additive

data.frame with columns group, agent, mean_ie and combined_ie, or NULL if there is no additive effects sheet.

Examples

path <- system.file("plantillas", "plantilla_une689.xlsx",
                    package = "expoquimR")
res <- une689_from_excel(path)
res$preliminary
res$additive

Upper confidence limit LSC(95,70) (UNE-EN 689)

Description

Upper confidence limit LSC(95,70) (UNE-EN 689)

Usage

une689_lsc(
  distribution_type,
  ut,
  MA = NA_real_,
  DS = NA_real_,
  MG = NA_real_,
  DSG = NA_real_
)

Arguments

Value

Numeric scalar with the LSC(95,70), or NA_real_ if distribution_type = "Neither" (or "Ninguna" in Spanish).

Examples

une689_lsc("Normal", ut = 2.005, MA = 7.5, DS = 1.87)

Recommended monitoring interval, option 1 (MG or MA vs VLA)

Description

Recommended monitoring interval, option 1 (MG or MA vs VLA)

Usage

une689_monitoring_interval_opt1(reference_value, vla)

Arguments

Value

Character scalar describing the recommended monitoring interval, in months.

Examples

une689_monitoring_interval_opt1(reference_value = 0.8, vla = 10)

Recommended monitoring interval, option 2 (LSC95,70 vs VLA)

Description

Recommended monitoring interval, option 2 (LSC95,70 vs VLA)

Usage

une689_monitoring_interval_opt2(lsc, vla)

Arguments

Value

Character scalar describing the recommended monitoring interval, in months, or a warning that exposure must be reviewed.

Examples

une689_monitoring_interval_opt2(lsc = 4, vla = 10)

Normality and lognormality tests (UNE-EN 689)

Description

Applies the Shapiro-Wilk test to the ED values (to test normality) and to their logarithm (to test lognormality).

Usage

une689_normality_test(ed)

Arguments

Value

A list with elements W_normal, pval_normal, W_lognormal, pval_lognormal.

Examples

une689_normality_test(c(5, 6, 7, 8, 9, 10))

Conformity of the statistical assessment (UNE-EN 689)

Description

Conformity of the statistical assessment (UNE-EN 689)

Usage

une689_statistical_conformity(ur, ut)

Arguments

Value

Character scalar: "CONFORMITY" if ur >= ut, "NON-CONFORMITY" if ur < ut, or NA_character_ if ur is NA (e.g. when neither the normal nor the lognormal fit is adequate). Labels are returned in the active language; see expoquimr_lang().

Examples

une689_statistical_conformity(ur = 2.1, ut = 2.005)

Descriptive statistics of daily exposure (UNE-EN 689)

Description

Calculates the arithmetic mean and standard deviation (MA, DS) and the geometric mean and standard deviation (MG, DSG) of a set of daily exposure (ED) values, needed to test the normal and lognormal fits.

Usage

une689_statistics(ed)

Arguments

Value

A list with elements MA, DS, MG, DSG.

Examples

une689_statistics(c(5, 6, 7, 8, 9, 10))

One-sided risk index UR (UNE-EN 689)

Description

One-sided risk index UR (UNE-EN 689)

Usage

une689_ur(
  distribution_type,
  vla,
  MA = NA_real_,
  DS = NA_real_,
  MG = NA_real_,
  DSG = NA_real_
)

Arguments

Value

Numeric scalar, or NA_real_ if distribution_type = "Neither".

Examples

une689_ur("Normal", vla = 10, MA = 7.5, DS = 1.87)

UNE-EN 689 UT factor from the sample size

Description

Looks up the one-sided tolerance factor (UT) tabulated by UNE-EN 689 for a number of days n between 6 and 30. For n > 30 the limit value 1.820 is used, as established by the standard.

Usage

une689_ut(n)

Arguments

Value

Numeric scalar with the UT value, or NA_real_ if n < 6.

Examples

une689_ut(6)
une689_ut(50)

Check the minimum number of days for the preliminary assessment

Description

The UNE-EN 689 preliminary assessment requires a minimum number of evaluated days (usually 3). Helper function to validate this before calculating, both from code and from the Shiny app.

Usage

une689_validate_min_days(n_days, minimum = 3L)

Arguments

Value

Logical: TRUE if n_days >= minimum.

Examples

une689_validate_min_days(2)
une689_validate_min_days(3)